essay on trading strategy

Essays On Trading Strategy : A Review

• November 14, 2023

Trading and Investing

I recently had the pleasure of diving into the latest book by Graham Giller, an engaging and innovative work that blends advanced financial concepts with accessible writing. This monograph-style book delves into the complexities of identifying optimal trading strategies in markets characterized by significant tail risk. It navigates through the Markowitz approach and utility-based theories with a keen analytical eye.

What stands out in Giller’s work is his comprehensive and detailed exploration of portfolio selection problems, particularly focusing on exponential utility and the Laplace return distribution. While the book notably omits an alpha-model, this seems a deliberate choice, inviting readers to engage with the material actively. This aspect turns the book into not just a source of information but a platform for practical application, encouraging readers to develop their alpha-signals and construct robust, scientifically sound trading strategies.

Another aspect of the book that resonated with me was the level of mathematical content!

Giller, a physicist by training, employs a presentation style that is both rigorous yet accessible, mirroring the clarity and approachability found in physics literature. This style particularly reminded me of Lev Okun’s textbook on quarks and leptons, where Okun aimed to create a book that could be easily read on a tram. While trams aren’t common in my area, I’m confident that Giller’s book is equally suitable for a train journey. The number of equations per page strikes the perfect balance for those familiar with elementary particle physics, aligning closely with what I consider an ideal ratio.

In conclusion, Graham Giller’s book is not just an academic achievement but a testament to his ability to make complex theories understandable and engaging. It’s a commendable work that stands as a significant contribution to the field. Congratulations to Graham Giller on this exceptional accomplishment!

-Review written by Dr. Igor Halperin

Verdict? Buy The Book! A must read!

Amazon.com: Essays On Trading Strategy (World Scientific Series In Finance): 9789811273810: Giller, Graham L

Essays on Trading Strategy : Author Statement : Graham Giller, November, 2023 

When working in PDT, at Morgan Stanley in the 1990’s, I tried, many >mes, to use mean variance op>miza>on (M.V.O.) when backtes>ng trading strategies. It never worked, and was  always out-performed by much simpler rules. When I set up our family office in the 2000’s, I  wanted to understand why this was so. I had seen many quants, essen>ally, abandon science in  favour of ad hoc rules and strategies in response to such an observa>on, but I decided that I  should try to understand what was wrong about the model and not simply declare that “science  works everywhere, other than in finance,” as many do. Keynes is well known for saying: “when  the facts change, I change my opinions” and I interpret this to mean that when a model doesn’t  match what we see in Nature the correct conclusion is that the model is wrong, not that Nature  is. This was the origin of the research agenda that resulted in Essays on Trading Strategy . For  many years I simply sat on these results, but more recently I decided that there was an  opportunity to provide more rigorous support for the part of the investment process where an  alpha becomes a posi>on, which is why I wrote the book. 

One of the earliest theore>cal results was around the unreliability of the Sharpe Ra>o as  a sta:s:c with which to grade the performance of both backtests and live trading. Later on I  realized just how much of the “hyperparameter tuning” that Quants engage in when bringing a  strategy to market is essen>ally worthless. That “improvements” to a performance of up to 0.5 (or more) Sharpe Ra>o points discovered in backtest are not meaningful at all, and this is why  excessive backtes>ng is not a produc>ve ac>vity. Looking back, I realized I had probably spent  many man-years of my career engaged in this fu>le endeavour. 

Empirically, I discovered how remarkably stable the descrip>ons of the market provided  by asymmetric GARCH with non-Normally distributed innova>ons was. 1

The Generalized Error  distribu>on 2 seem to give an empirically accurate distribu>on and my thoughts on poraolio  selec>on via U>lity Theory had me thinking about the moment genera>ng func>on of the  distribu>on of returns and the impact of the higher moments on the final value of the expected  U>lity integral. I saw then that such fat tailed distribu>ons were not only accurate descrip>ons of the markets but might also be the factor that explained the prior empirical result that M.V.O.  doesn’t work but more “binary” discrete posi>on func>ons did. At the >me I was trading  strongly correlated futures strips and my focus was on poraolio investment strategies and I was  able to obtain a mul>variate decision rule that, numerically, delivered everything I wanted, but  that could only be expressed in analy>cal terms more generally. It was only when actually  wri>ng the book a few years ago that the closed form analy>c solu>on for a holding func>on  

1 This is discussed extensively in my first book, Adventures in Financial Data Science. 

2 Also frequently called the Generalized Normal distribu>on or the Exponen>al Power distribu>on.

under the Laplace distribu>on dropped out in front of me and I was so astonished that I had to  work it out several >mes before I believed it was true. 

Moving on from U>lity Theory to a more general stochas:c programming framework  took years of thought about what the key assump>ons about alphas and posi>ons needed to be  to establish a framework under which the massively mul>-horizon integrals needed to compute  the expected score of a set of future behaviours could be reduced to a simpler, but s>ll  challenging, homogenous policy solu>on. Those assump>ons, including that future returns,  alphas and, cri>cally, posi>ons, all were stochas>c processes came slowly over >me but finally  dropped into place when I was dining alone in New York ader work one day. 

It seems quite remarkable to me that such complex, and in >mes challenging,  mathema>cs is necessary to get to the solu>on that simple trading rules, when opera>ng in the  real markets, are a beeer strategy than mean-variance op>miza>on and that this is not an ad  hoc, or heuris>c, solu>on but, in fact, well supported by a theory that makes sense and that the  basic answer, which is when an alpha is “large” don’t trust it as much as M.V.O. would have you  do, seems completely sensible to an experienced trader.

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World Scientific Series In Finance

Graham L Giller

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• Published: 25 March 2020

Research on a stock-matching trading strategy based on bi-objective optimization

• Haican Diao 1 ,
• Guoshan Liu 1 &
• Zhuangming Zhu 1  

Frontiers of Business Research in China volume  14 , Article number:  8 ( 2020 ) Cite this article

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In recent years, with strict domestic financial supervision and other policy-oriented factors, some products are becoming increasingly restricted, including nonstandard products, bank-guaranteed wealth management products, and other products that can provide investors with a more stable income. Pairs trading, a type of stable strategy that has proved efficient in many financial markets worldwide, has become the focus of investors. Based on the traditional Gatev–Goetzmann–Rouwenhorst (GGR, Gatev et al., 2006) strategy, this paper proposes a stock-matching strategy based on bi-objective quadratic programming with quadratic constraints (BQQ) model. Under the condition of ensuring a long-term equilibrium between paired-stock prices, the volatility of stock spreads is increased as much as possible, improving the profitability of the strategy. To verify the effectiveness of the strategy, we use the natural logs of the daily stock market indices in Shanghai. The GGR model and the BQQ model proposed in this paper are back-tested and compared. The results show that the BQQ model can achieve a higher rate of returns.

Introduction

Since the A-share margin trading system opened in 2010, there has been a gradual improvement in short sales of stock index futures (Wang and Wang 2013 ) and investors are again favoring prudent investment strategies, which include pairs-trading strategies. As a kind of statistical arbitrage strategy (Bondarenko 2003 ), the essence of pairs trading (Gatev et al. 2006 ) is to discover wrongly priced securities in the market, and to correct the pricing through trading means to earn a profit from the spreads. However, with the increase in statistical trading strategies and the gradual improvement of market efficiency (Hu et al. 2017 ), profit opportunities using existing trading strategies have become more scarce, driving investors to seek new trading strategies. At present, academic research on pairs trading has mainly concentrated on the construction of pairing models and the optimization design of trading parameters, with a greater focus on the latter. However, merely improving trading parameters does not guarantee a high return for the strategy, and this drives researchers back to the foundations of the pairs-trading model.

There are three main methods for screening stocks: the minimum distance method, the cointegration pairing method, and the stochastic spread method. The minimum distance method was proposed by Gatev et al. ( 2006 )—hence its common name, the GGR model. Gatev et al. ( 2006 ) used the distance of a price series to measure the correlation between the price movements of two stocks. When making a specific transaction, the strategy user determines the trading signal by observing the magnitude of the change in the Euclidean distance between the normalized price series of two stocks (the sum of the squared deviations, or SSD). Perlin ( 2007 ) promoted GGR as a unitary method rather than a pluralistic one; testing it in the Brazilian financial market, he found that risk can be lessened by increasing the number of pairs and stock. Do and Faff ( 2010 ) found that the length of a trading period can affect strategy returns; their study laid the foundation for later research. Jacobs and Weber ( 2011 ) found that the GGR model’s revenue comes from the difference in the speed of paired-stock information diffusion. Chen et al. ( 2017 ) revised the measurement method of the GGR model, changing the original measure (SSD) to the correlation coefficient, and increased the reliability of the multi-pairing strategy. Wu and Cui ( 2011 ) first applied the GGR model to the A-share market; conducting a back-test on the stock markets in Shanghai, they found that the GGR model can generate considerable returns, and its profits come from a market’s non-validity. Wang and Mai ( 2014 ) measured the return on stock markets in Shanghai, Shenzhen, and Hong Kong respectively, and found that improvements to the original approach can bring portfolio construction strategic benefits but can also increase the risk of exploitation of the GGR model.

The cointegration pairing method was first used by Vidyamurthy ( 2004 ) to find stock pairs with a cointegral relationship. He used cointegrating vectors as the weight of pairs when trading. To solve the problem of single-stock pairing risks, Dunis and Ho ( 2005 ) extended the cointegration method from unitarism to pluralism and proposed an enhanced index strategy based on cointegration. By extracting sparse mean–return portfolios from multiple time series, D’Aspremont ( 2007 ) found that small portfolios had lower transaction costs and higher portfolio interpretability than the original intensive portfolios. Peters et al. ( 2010 ) and Gatarek et al. ( 2014 ) applied the Bayesian process to the cointegration test and found that the pairing method can be applied to high-frequency data.

The stochastic spread method first appeared in a paper by Elliott et al. ( 2005 ), who used the continuous Gauss–Markov model to describe the mean return process of paired-stock spreads, thus theoretically predicting stock spreads. Based on the research by Elliott et al. ( 2005 ) . Do et al. ( 2006 ) first linked the capital asset pricing model (CAPM) with the pairs-trading strategy and achieved a higher strategic benefit than when using the traditional random spread method. Bertram ( 2010 ), assuming that the price differences of stock obey the Ornstein–Uhlenbeck process, derived the expression of the mean and variance of the strategic return on the position and found the parameter value when the expected return was maximized.

Based on above approaches, many scholars have begun to study mixed multistage pairing-trading strategies. Miao ( 2014 ) added a correlation test to the traditional cointegration method and found that screening stock-correlation analysis improved the profitability of the strategy. Xu et al. ( 2012 ) combined cointegration pairing with the stochastic spread model and conducted a back-test on the stock markets in both Shanghai and Shenzhen; they found that higher returns could be obtained. Following Bertram’s ( 2010 ) research, Zhang and Liu ( 2017 ) examined a pairs-trading strategy based on cointegration and the Ornstein–Uhlenbeck process and found the strategy to be robust and profitable.

In recent years, most scholars have focused on improving the long-term equilibrium of paired-stock prices in the stock-matching process continuously. Few studies have considered the short-term fluctuations of paired-stock spreads, which has led to poor profitability of the strategy. Therefore, this paper focuses on the stock matching of pairs trading and constructs a bi-objective optimized stock-matching strategy based on the traditional GGR model. The strategy introduces weight parameters, conducts long-term stock price volatility spreads, and adjusts the equalizer to match investors’ preferences, enhancing the flexibility and practicality of the strategy.

The remainder of this paper is organized as follows. Basic theory and model section provides the basic theories and models of pairs-trading strategies and double bi-objective optimization. Optimized pairing model section establishes an optimized pairing model. Pairing strategy empirical analysis section provides an empirical analysis of the optimal matching strategy proposed in this paper. Finally, Conclusions section presents conclusions and suggests future research direction.

Basic theory and model

Based on theories of pairs trading, stock-pairing rules in the minimum distance method, and multi-objective programming, we propose a strategy to improve profits based on the minimum distance method.

• Pairs trading

Pairs-trading parameters

Using a pairs-trading strategy requires a focus on the following trading parameters:

Formation period : the time interval for stock-pair screening using the stock-matching strategy.

Trading period : the time interval in which selected stock pairs are used for actual trading.

Configuration of opening : the value of the portfolio construction triggered. For example, we can start a transaction by satisfying the following conditions: (1) The user is in the short position state; (2) the degree to which the paired-stock spread deviates from the mean changes; and (3) the degree changes from less than a given standard deviation to more than a given standard deviation.

Closing threshold : the value of the position closing triggered. For example, when the strategy user is in position and the paired-stock spread hits the mean.

Stop-loss threshold : the value of the stop-loss triggered; that is, when the rules are engaged for exiting an investment after reaching a maximum acceptable threshold of loss or for re-entering after achieving a specified level of gains.

• Minimum distance method

When using the minimum distance method to screen stocks, it is necessary to standardize the stock price series first. Suppose the price sequence of stock A in period T is \( {P}_i^A\left(i=1,2,3,\dots, T\right) \) ; \( {r}_t^A \) is the daily rate of return of stock A . By compounding r , we can get the cumulative rate of return of stock A in period T , which is recorded as:

where t  = 1, 2, 3, …, T . When we record the standardized stock price series as \( S{P}_t^A \) , the distance SSD of each two-stock normalized price series can be calculated as follows (Krauss 2016 ):

Multi-objective programming

The multi-objective optimization problem was first proposed by economist Vilfredo Pareto (Deb and Sundar 2006 ). It means that in an actual problem, there are several objective functions that need to be optimized, and they often conflict with each other. In general, the multi-objective optimization problem can be written as a plurality of objective functions, and the constraint equation and the inequality can be expressed as follows:

where, x   ∈   R u , f i  :  R n  →  R ( i  = 1, 2, ...,  n ) is the objective function; and g i  :  R n  →  R and h i  :  R n  →  R are constraint functions. The feasible domain is given as follows:

If there is not an x   ∈   X , such that

then x ∗   ∈   X is called an effective solution (Bazaraa et al. 2008 ) to the multi-objective optimization problem.

Optimized pairing model

Previous studies on the GGR model have mostly focused on similarities in stock trends and have cared less about the volatility of stock spreads. Such studies could not present ways to achieve higher returns. This paper, however, is based on the traditional GGR model, and can thus propose a new pairs-trading model, namely bi-objective quadratic programming with quadratic constraints (BQQ) model. By adjusting the weights between maintaining a long-term equilibrium of paired-stock prices and increasing the volatility of stock spreads (Whistler 2004 ), we can achieve equilibrium.

Mean-variance minimization distance model

Assume that there are m stocks in the alternative stock pool, and the formation period of the stock pairing is n days. Take the daily closing price of the stock as the original price series, recorded as P 1 , P 2 , ⋯ , P m . To make the price sequence smoother, we use the average price series over the past 30 days: \( \overline{P_1},\overline{P_2},\cdots, \overline{P_m} \) (instead of the original price series), to eliminate short-term fluctuations in stock prices. Then, in the moment, t can be expressed as follows:

First we consider \( \sum {\alpha}_i\overline{P_i} \) .

Let α be the weight of the stock in the stock pool, and then let

Then, we divide the stock into two groups according to the positive and negative weights. The stock combination with a positive weight is called \( {P}_t^{+} \) , while the stock combination with a negative weight is called \( {P}_t^{-} \) , so

According to the GGR method, as long as we are in the formation period n , we can consider that the groups’ prices have to represent a long-term equilibrium relationship. Therefore, we get the bi-objective optimization model as follows:

The volatility of the paired-stock spread is a source of revenue for the pairs-trading strategy. Variances are used to describe the volatility of a time series. Therefore, we use the formula below to measure the stock spread:

Avoiding the case that α  = 0, we increase the regularity constraint; that is, the second-order modulus is 1, so we can obtain the BQQ model as:

This paper uses a linear weighting method by introducing weight λ ( λ  > 0), transforming the bi-objective optimization problem into a single-objective optimization problem. The model is denoted as revised quadratic programming with quadratic constraints (RQQ):

Since users of the matching strategy have different risk preferences, λ can be seen as an important indicator of strategic risk. When λ is large, the model magnifies the volatility of the paired-stock spread sequence, and the strategy may obtain higher returns, but it also raises the risk of divergence in the stock spread. Therefore, users can adjust λ to match their risk preferences, which increases the usefulness of the pairing strategy.

Let \( \overline{p}=\frac{1}{n}\sum \limits_{t=1}^n\overline{p_t} \) .

To facilitate the model solution, we perform matrix transformation as follows:

For a given α k , we get the sub-problem of the model as this:

The sequential quadratic programming algorithm

Since the objective and constraints of RQQ are quadratic functions, these are typical nonlinear programming problems. Therefore, the sequential quadratic programming algorithm can solve the original problem by solving a series of quadratic programming sub-problems (Jacobs and Weber 2011 ; Zhang and Liu 2017 ). The solution process is as follows:

Step 1 : Give α 1   ∈   R m , ε  > 0, μ  > 0, δ  > 0, k  = 1, B 1   ∈   R m  ×  m .

Step 2 : Solve sub-question sub ( α k ), and we get its solution d k and the Lagrange multiplier μ k in the case of ∣ d k   ∣   ≤  ε , terminating the iteration; therefore, let s k   ∈  [0,  δ ] and μ  =   max  ( μ ,  μ k ). By solving this:

we get s k , where ε k ( k  = 1, 2,  ⋯ ) satisfies the non-negative condition and

Equation ( 21 ) is the exact penalty function.

Step 3 : Let α k  + 1  =  α k  +  s k d k , and use the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS, Zhu et al. 1997 ) to find B k  + 1 , then let k  =  k  + 1 and go back to Step 2 .

Thus, we find the optimal sub-solution d k . Make d k the search direction and perform a one-dimensional search in direction d k on the exact penalty function of the original problem; we get the next iteration point of the original problem as α k  + 1 . The iteration is terminated when the iteration point satisfies the given accuracy, obtaining the optimal solution of the original problem.

Pairing strategy empirical analysis

To verify the profitability of the BQQ strategy, this paper compares the empirical investment effects of the BQQ strategy and the GGR strategy with the same transaction parameters and applies a profit-risk test for the arbitrage results of the two strategies.

Data selection and preprocessing

We use SSE 50 Index constituent stocks in the Shanghai stock market as the sample set for this study. We choose this sample set for its high circulation market value and large market capitalizations. Since the stock-pairing method proposed in this paper is based on an improvement of the traditional minimum distance method, this is consistent with the GGR model in the time interval selection of the sample: The paired stocks for trading are selected during the formation period of 12 months, and the stocks are traded in the next 6 months. To verify the effectiveness of the strategy, the paper conducts a strategic back-test from January 2016 to December 2018. Within the period, the broader market experienced a complete set of ups and downs.

Due to the existence of share allotments and share issues by listed companies, and because the suspension of stocks will also lead to a lack of market data, the raw data needs to be preprocessed. By reversing the stock price forward, the stock price changes caused by the allotments and stock offerings are eliminated. In addition, we exclude stocks that have been suspended for more than 10 days in the formation period. These missing data are replaced by the closing price of the nearest trading day.

Parameters settings

Transaction parameters setting.

The implementation of a pairs-trading strategy relies on setting trading parameters. To compare this strategy with the traditional minimum distance method and verify the validity of the BQQ strategy, this paper uses the same parameters used in the GGR model for setting the trading parameters. We set the stop-loss threshold to 3 to prevent excessive losses due to excessive strategy losses and transaction costs. We set the number of paired shares to 10. For convenience, we divide the stocks into groups according to their weights, positive and negative.

Portfolio construction

After determining the trading parameters and cost parameters, we also need to determine the stock opening method; assuming that the final selected pair of stocks is \( \left\{{S}_1^{+},{S}_2^{+},\cdots, {S}_5^{+}\right\},\left\{{S}_1^{-},{S}_2^{-},\cdots, {S}_5^{-}\right\} \) (corresponding to two sets of paired stocks), and the corresponding weight is \( \left\{{\alpha}_1^{+},{\alpha}_2^{+},...,{\alpha}_3^{+}\right\} \) and \( \left\{{\alpha}_1^{-},{\alpha}_2^{-},...,{\alpha}_3^{-}\right\} \) . When the trading strategy issues a trading signal for opening, closing, or stop-loss, the trading begins. The user needs to trade α i / α 1 ( i  = 2, 3, 4, …, 10) units of stock \( \left\{{S}_1^{-},{S}_2^{-},\cdots, {S}_5^{-}\right\} \) for each unit of \( \left\{{S}_1^{+},{S}_2^{+},\cdots, {S}_5^{+}\right\} \) . Then, the strategy user has a net position, which is the paired-stock spread.

Performance evaluation

To compare the effects of the GGR model and the proposed BQQ model, we verify the effectiveness of the proposed optimization pairing strategy. This paper selects the income coefficient α , risk coefficient β , and the Sharpe ratio as evaluation indicators, and the two strategies are back-tested and compared on the JoinQuant platform.

Stock-matching stage

When adopting the GGR model, we select five groups of stocks with the smallest SSD (two in each group) from each formation period. There is a small distance between these stocks. The stocks are selected from 50 constituent stocks. The matching results are shown in Table  1 . When adopting the BQQ model, since the trend of the stock was screened beforehand, we select two sets of stocks (five in each group) for pairing. To explore the impact of λ on strategy performance, we perform a back-test on the optimal matching strategy under different values (when λ is greater than 0.7, the paired-stock spread is relatively poor, resulting in a strategy failure). Therefore, this paper is limited to a λ range from 0 to 0.7. The pairing results are shown in Table  2 .

As can be seen in Table 2 , when λ changes from 0 to 0.4, the selected stock pairs show a very dramatic change; when λ changes from 0.4 to 0.6, the selected stock pairs are almost identical. At that time, the change of λ cannot significantly affect the return; when λ changes from 0.6 to 0.7, the selected stock pairs change less. However, the positives and negatives of the paired-stock weights have changed. Therefore, compared with the GGR model, the optimized pairing strategy makes better use of stock price information and is more flexible.

Stock trading stage

The GGR model and the BQQ model use the same parameters set in the back-test. The trading period is 2016.01–2016.12. The results obtained are shown in Table  3 . By comparing the back-test performance of the BQQ strategy with the GGR model, we arrive at five findings:

The ability of the BQQ strategy to obtain revenue is significantly stronger than of the GGR model, which shows that the BQQ strategy is effective in increasing the volatility of the spread to improve the profitability of the pairs-trading strategy.

Figure  1 shows the average annualized rate of return of the BQQ strategy and the GGR strategy for different λ values (both in-sample data and out-of-sample data, respectively). For the in-sample rate of return, both strategies were carried out for a total of 32 back-tests, with a total of 31 positive gains. The return of the BQQ strategy is better than that of the GGR strategy in 87.5% of the cases. For the out-of-sample rate of return, the return of the BQQ strategy is better than that of the GGR strategy in 68.8% of the cases. To rule out the deviation of income caused by the different ways of opening a position, we also need to examine the coefficient of the two strategies and the Sharpe ratio.

Average annualized rate of return of the two strategies

As shown in Figs.  2 and 3 , the BQQ model performs significantly better than the GGR model, both in terms of the coefficient α and the Sharpe ratio. This result indicates that the BQQ model bears the average return of nonmarket risk during the four trading periods, and the average return on unit risk is higher than with the GGR model. Therefore, the better perfomance of the BQQ strategy is not from the strategy taking more market risk; rather, it is independent of the way the strategy is opened.

Coefficient α of the two strategies

Sharpe ratio of the two strategies

The BQQ strategy has a strong ability to hedge the market. Table  4 shows the average value of the coefficient β of the BQQ strategy under different values of λ . It can be seen that the absolute value of β is below 0.1, which indicates and proves that the performance of the strategy is not affected by market fluctuations, which in turn proves that the pairs-trading strategy based on the minimum distance method can hedge market risk well. Compared with the GGR model, the coefficient β of the BQQ strategy is magnified because the GGR model uses a capital-neutral approach when in the opening position, while the BQQ strategy uses a coefficient-neutral approach. Due to the existence of the spread, the BQQ strategy cannot guarantee that the market value of the bought stock will be equal to the market value of the sold stock when the position is opened, which is equivalent to the fact that some net positions follow market ups and downs and the coefficient will increase.

Similar to the GGR model, the BQQ strategy performs poorly in out-of-sample data. In the 32 out-of-sample back-tests, the annualized return of the BQQ strategy was positive only six times, and the coefficient α was positive only eight times. The main reason for this phenomenon is the lack of rationality in the length of the formation period used at the stock-matching stage and the trading parameters used in the stock-trading stage. The yield of the GGR model is affected by trading parameters in many cases, such as the formation period, trading period, and opening threshold. Since this article presents only a methodological improvement for the stock-pairing trading model, it does not provide a more in-depth study of trading parameters.

Performance of the BQQ strategy is very sensitive to the value of λ . Adjustable λ enhances the practicality of the strategy. In the same trading period, the return of the BQQ strategy does not show a monotonous change with λ . When the value of λ is too large, the stock-matching strategy is invalid because when λ increases, the volatility of the paired-stock spread is increasing, which means that the strategy is likely to obtain higher returns. Conversely, the increase of λ raises the risk of divergence in the spread, making it easier for the strategy to trigger a stop-loss signal and cause losses. Therefore, λ is a significant parameter to adjust the risk of the strategy, and the strategy user can adjust λ to match risk preferences, which enhances the usefulness of the strategy.

The optimal λ value is time dependent. The benefit of the BQQ strategy are non-monotonic changes in λ . Excessive λ assembly leads to the invalidation of the stock-matching strategy, which means that for a specific trading period, there is an optimized λ that maximizes the strategy’s return. From the perspective of revenue indicators and risk indicators, there are no obvious rules about the performance of the strategy and the change of λ . That is, the optimal λ value varies with the trading period and is time dependent.

Table  5 shows the values of coefficient α and the Sharpe ratio from four out-of-sample back-tests. When λ is 0.5, coefficient α and the Sharpe ratio take the maximum value at the same time.

The results show that when λ is 0.5, the average matching revenue of the optimized matching strategy for non-market risk in the four trading periods and the average return from unit risk are the largest, but the value needs to be verified by large-scale data.

Conclusions

By introducing multi-objective optimization to the GGR model, this paper considers the long-term equilibrium of stock prices and the volatility of spreads and establishes a BQQ model. This novel pairs-trading model provides a new perspective for pairs-trading strategy research. At the same time, it provides investors with a stock-matching method that effectively improves the profitability of the trading strategy. This paper introduces the weight λ when solving bi-objective optimization problems, and these problems are transformed into single-objective optimization problems and solved by a sequential quadratic programming algorithm. To verify the effectiveness of the optimized pairing strategy, this paper selects the traditional GGR model as model for comparison and conducts back-testing on multiple time intervals on the SSE 50 constituents. We find that the BQQ strategy was able to obtain significantly higher revenue than the GGR model, and the adjustment of the weight λ increases the flexibility and practicality of the strategy.

This paper has some limitations. We used the SSE 50 Index as the research target in our empirical analysis. However, this was subject to the limitation of financing and securities lending; the small number of stocks may have affected the performance of the trading strategy. Additionally, when we performed the validity check of the optimized pairing strategy, there was scarce in-depth research available on the trading parameters and optimal values of the strategy, and this may have affected the profitability of the strategy to some extent. Therefore, subsequent research work should include these aspects. In the future, we will expand the number of stock share pools. In addition, the screening method for the transaction parameter of pairs-trading strategy requires in-depth research to find the right trading parameters for the BQQ strategy. Finally, we will try to establish an optimized pairing strategy by attaining the function of risk indicator λ through extended empirical analysis.

Availability of data and materials

Shanghai Composite Index

Please contact authors for data requests.

Abbreviations

The Broyden–Fletcher–Goldfarb–Shanno algorithm

Bi-objective quadratic programming with quadratic constraints

Capital asset pricing model

The distance approach proposed by Gatev, Goetzmann and Rouwenhorst in 2006

Revised quadratic programming with quadratic constraints

Sum of squared deviations

Shanghai Stock Exchange

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The research is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 19XNH089).

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Diao, H., Liu, G. & Zhu, Z. Research on a stock-matching trading strategy based on bi-objective optimization. Front. Bus. Res. China 14 , 8 (2020). https://doi.org/10.1186/s11782-020-00076-4

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• Bi-objective optimization
• Quadratic programming

Quantified Trader

• HIGH FREQUENCY TRADING
• Trading with Machine Learning

Step 3: Perform a T-Test on Trading Strategy Returns

Now, assess the significance of any performance differences between the strategies using a T-test. The T-test helps determine if the differences in returns are statistically significant or merely due to chance.

Step 4: Monte Carlo Simulation to evaluate performance

For a more robust assessment, apply a Monte Carlo simulation. This technique involves generating numerous random alternative scenarios based on historical data. By comparing the actual strategy performance with these simulations, you gain insights into the strategy’s robustness.

Step 5: Bootstrap Analysis for Trading Strategy Returns

Bootstrap analysis complements the Monte Carlo simulation by resampling data with replacement to estimate the distribution of returns. This helps evaluate the probability of achieving the observed strategy performance by chance.

For more such trading Strategies, visit us at Quantified Trader/Trading Strategies

What is t – test?

A t-test, also known as a Student’s t-test, is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups or sets of data. It helps in comparing the averages (means) of two samples to assess whether any observed differences are statistically significant or simply due to random chance.

There are different types of t-tests, but the most common ones include:

• Independent Samples t-test: This type of t-test is used when you want to compare the means of two separate and unrelated groups or populations. For example, you might use it to compare the average test scores of two different classes of students to see if one class performed significantly better than the other.
• Paired Samples t-test: This t-test is used when you want to compare the means of two related groups, often before and after an intervention or treatment. For instance, you might use it to determine if a new teaching method significantly improved students’ test scores compared to their scores before the new method was implemented.

The t-test generates a t-statistic and a p-value as part of its output. The t-statistic quantifies the difference between the sample means and takes into account the sample sizes and standard deviations. The p-value indicates the probability of obtaining such results by random chance if there is no real difference between the groups.

In hypothesis testing, you typically set a significance level (alpha), often at 0.05 (5%), which represents the threshold below which you consider the results to be statistically significant. If the p-value calculated by the t-test is less than the significance level (usually 0.05), you reject the null hypothesis (which assumes no difference) in favor of the alternative hypothesis, suggesting that there is a significant difference between the groups.

In summary, a t-test is a statistical tool used to determine if the differences between two sets of data are likely due to a real effect or if they could have occurred by random chance. It is widely used in various fields, including science, medicine, economics, and social sciences, for comparing means and drawing conclusions about the significance of observed differences.

Evaluating and comparing trading strategies is a critical aspect of successful trading and investment. It ensures that strategies are not only profitable but also resilient to market variations. By following a systematic approach that includes data analysis, statistical tests like the T-test, and simulation techniques like Monte Carlo and bootstrap analysis, traders and investors can make informed decisions and enhance their chances of success in the dynamic world of financial markets.

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Reading Lists +

The review +, 46 possible stock market strategies from academics get a retest.

9 March 2022

Research by

• Athanasse Zafirov
• Corporate Investment
• Stock Market
• Corporate Finance
• Stock Returns

We won’t call it debunking, but not all investing tips hold up

For nearly 3,000 years, bloodletting was an accepted medical practice for all types of maladies. It was only in the early 1800s when some doctors carefully reviewed data on the practice that they realized bloodletting didn’t improve patients’ health, and may sometimes be harmful.

Such review of accepted theories is currently a growing field among social and natural scientists. Peer-reviewed research is increasingly being thrown back into the review process to see if stands up.

Opt In to the Review Monthly Email Update.

A working paper by the University of Lausanne’s Amit Goyal, UCLA Anderson’s Ivo Welch and Athanasse Zafirov, a Ph.D. student, seeks to prevent the financial equivalent of bloodletting. Their meta-research — the term given for research on research — on papers published in top academic journals finds that many investing factors don’t hold up. To be precise, the 46 variables aren’t full-blown market strategies, but rather observed correlations that could form the basis for a strategy.

Past Performance May Not Be Indicative of Future Results

Building on Goyal and Welch’s 2008 paper that studied the predictive success of 17 variables, the researchers survey 26 papers identifying 29 variables considered useful in predicting the equity premium — the total rate of return on the stock market minus the prevailing short-term interest rate. The 17 variables from the 2008 paper are also reexamined. The researchers’ findings suggest that most of the variables have lost their predictive ability when tested on datasets extended to the end of 2020. A few variables do show flickers of promise but not overwhelming success across the researchers’ evaluation metrics.

The researchers’ first goal was to replicate the original findings of the papers’ authors. This involved recreating the variables and recalculating the reported statistics on the variables’ ability to predict the equity premium. Goyal, Welch and Zafirov were able to confirm the papers’ original findings, using the original dataset, on all but two of the papers. (The two remaining papers had data issues.)

The datasets to create the variables were then extended through December 2020, and the predictions for each of the 29 variables from the papers and the original 17 variables from the 2008 paper were retested.

The datasets in the papers ended between 2000 and 2017 and began as early as 1926. When building a predictive model, a researcher will typically split a dataset into at least two samples —one sample to train the model and another sample, typically the data from the latest years, to test the model. By extending the original datasets with data to the end of 2020 and starting the test sample 20 years after the start of the training sample, the components of these samples were slightly different than the samples used in the papers. It’s worth noting that the new data only made up a small percentage of the overall datasets.

“Because our paper reuses the data that the authors themselves had originally used to discover and validate their variables and theories, all that the predictors had to do in the few added years was not to ‘screw up’ badly.”

Nonetheless, of the 46 variables, only five managed to predict at a statistically significant level on the samples in the extended dataset.

But statistics are one thing, and investment performance is another. As a second test, Goyal, Welch and Zafirov devised simple investment strategies using the variables’ predictions to time investments by determining whether to go long or short the market and weighting the investments. The results of the investment strategies were compared with a buy-and-hold strategy. None of the five variables was able to significantly outperform the buy-and-hold approach in any of the investment strategies. Across all of the variable predictors, half lost money in the simplest investment strategy that used the variable to determine whether to go long or short.

Why Does the Performance Degrade?

The researchers suggest that the deterioration in predictive performance is at least partly explained by the fact that the market has shown greater variety in regimes over the last 20 years with many steep downturns. Campbell R. Harvey of Duke University and Yan Liu of Purdue University have performed similar meta-research and suggest that over-adapting the model to a particular data set may also be a factor due to authors running numerous backtests (simulations over historical data); they further suggest increasing necessary performance thresholds (raising the bar) as the number of backtests increase. Finally, a more generous explanation may be that as the predictive variables become well known by market practitioners, they lose their edge, just like a stock tip — when those tipped off start buying, the stock price rises and the tip loses its value.

Looking at the table below, the variables that were found to remain statistically significant on the extended dataset were those with the fewest citations and likely less well known among market participants.

The Five Best Variables on a Statistical Basis

Fourth-Quarter Growth Rate in Personal Consumption Expenditures ( gpce) : This macroeconomic variable from researchers Møller and Rangvid posits that high personal consumption growth rates at the end of the year predicts poor stock-market gains in the following year. The researchers found it to be the best, and most consistent, variable in the investment strategies. It outperformed a buy-and-hold approach with three of the four strategies tested. However, the outperformance was only marginal.

Aggregate Accruals (accru) : This is a sentiment-based variable introduced by Hirshleifer, Hou and Teoh and uses aggressive corporate accounting to predict future stock returns — more aggressive accruals lead to lower future returns. The variable also marginally beat buy-and-hold returns in three out of four approaches. Most of its performance came from its prediction of the post-tech market crash in 2000-2002.

Credit Standards (crdstd) : This is another macroeconomic variable and was introduced by Chava, Gallmeyer and Park. It finds that optimistic (loose) credit standards predict poor market returns and comes from survey data by the Fed. This variable did well in the researchers’ investment strategies and had good performance on test sample data, but statistical measures of the variable on the training sample data were not as convincing and much of its performance comes from the first four years in that sample.

The Investment Capital Ratio (i/k) : This a financial ratio introduced by Cochrane all the way back in 1991 and was also included in the 2008 paper from Goyal and Welch. It posits that high capital investment in the current quarter predicts poor stock-market returns in the next quarter. While it was a poor predictor from 1975 to 1998, it has since improved performance yet was not able to outperform a buy-and-hold strategy in three of four of the researchers’ timing strategies.

Treasury-bill Rates (tbl ): This is another variable examined in the 2008 paper. It does well statistically but had poor performance in the investment strategies.

Oft-Cited Papers With Poor-Performing Variables

Variance Risk Premium (vrp) : This variable was introduced by Bollerslev, Tauchen and Zhou and has the most citations. The variable had poor statistical performance, as well as poor performance in all four of the investment strategies.

Share of Housing Consumption (house) : This macroeconomic variable introduced by Piazzesi, Schneider and Tuzel has the second-highest number of citations. It uses housing share of consumer spending to forecast the excess return of stocks. (The higher the spending on housing, the higher the excess returns in the stock market.) The variable had poor statistical performance on the extended dataset and poor performance in the investment strategies.

The Price of West-Texas Intermediate Crude Oil (wtexas) : This was the only commodity-based variable and was introduced by Driesprong, Jacobsen and Maat. The paper posits that changes in the price of oil predict stock returns — higher oil prices lead to lower stock returns — with lags. The variable had poor statistical performance for the extended dataset and inconsistent performance in the investment strategies.

The First Principal Component of 14 Technical Indicators ( tchi ): This variable was introduced by Neely, Rapach, Tu and Zhou and is a linear combination of technical indicators including moving price averages, momentum and volume. It only had marginal statistical performance and inconsistent performance in the trading strategies.

Featured Faculty

Distinguished Professor of Finance; J. Fred Weston Chair in Finance

About the Research

Goyal, A., Welch, I., & Zafirov, A. (2021). A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II . http://dx.doi.org/10.2139/ssrn.3929119

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Automation of Trading Machine for Traders pp 105–117 Cite as

Conclusion: End of Course and Beginning of Trading

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The conclusion is never to trade without a statistically proven trading model. This last section lists out the checklist for the trader to complete the final check before he begins trading successfully. He drafts his own trading plan including many markets. To find the best trading instrument, specific research data analysis on times series and tests on the most profitable ones are done. The most suitable technical indicator, like adjustable moving average, is selected, further innovated and tested to make it work for him. He writes his own formulae into his own trading program, including the stop loss for risk management. He manages his capital well. Auditing and periodic checks on the trading journal keep the trading system updated. Completing the checklist, he starts trading.

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Abstract/Summary

This thesis focuses on three research questions in the areas of empirical asset pricing and corporate finance. I introduce the overview in the first chapter and conclude in the final chapter. In the second chapter, we investigate the impact of the beta’s statistical significance on the performance of the betting against beta (BAB) portfolios in the U.S. and major international markets. After dropping stocks with statistically insignificant betas, we find that a betting against statistically significant beta strategy reduces the monthly alphas of BAB portfolios by 20% – 50%, depending on beta estimation methods. If we replace the value of statistically insignificant beta by zero, a refined BAB strategy can generate a higher alpha than the original BAB strategy. In the third chapter, we find a negative relationship between abnormal investment and future stock performance in the U.S. market. This negative relation is mainly driven by firm under-investment, not over-investment. Our explanations can be that market investors may not react promptly to the fundamental information contained in under-investment about a firm’s future profitability, asset growth, and financial distress probability. Alternatively, the negative relation between underinvestment and future stock returns is more pronounced for firms with lower investor monitoring and higher agency costs. In the fourth chapter, we identify a positive link between peer firms’ investment and focal firm’s value of cash holding in the U.S. market. This effect is likely to result from the positive externalities brought by peer investment which are reflected in young and growing industries with ample investment opportunities that are shared by the focal firms. We find little evidence to support either the precautionary hypothesis or the learning hypothesis. Our further analyses show that firms increase their level of cash holdings while peer investment increases. Meanwhile, firms are less willing to use cash for dividend payments while they are more willing to use it for capital expenditure and R&D investment.

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Theoretical background

This section gives a summary of the concepts that will be used during this paper. Firstly, the general concept of trading strategy analysis is discussed. Afterwards, more in-depth information on the concept of technical strategy analysis is given. Since this is an empirical research, several technical trading strategies will be used for analysis. The most common groups of technical analysis are discussed on both theoretical background and earlier findings. Lastly, different types of pricing models are elaborated upon.

2.1 General concept of trading strategies

Trading strategies that appear to “beat the market” go back to the beginning of trading in financial assets. It was believed that returning pattern in stock returns could lead to “abnormal” profits to trading strategies. Ever since, there has been an increasing interest in the predictability of asset returns based on their past history or fundamental values. Many of the techniques used today have been utilized for over 60 years. These techniques for discovering hidden relations in stock returns can range from extremely simple to quite elaborate (Brock, Lakonishok, & LeBaron, 1992). Most of these trading strategies are based on the concept of an anomaly. This is a term describing the phenomenon when the there is a structural, replicable pattern, that cannot be explained in the framework of existing financial theory, but can be economically. An anomaly provides evidence that a given assumption or model does not hold in practice. In reality it is often the case that trading restrictions or trading fees eliminate the possibility of making money from possible anomalies. If no money can be made from it, the anomaly is not anomalous after all. Overall, anomalies often occur with respect to asset pricing models, in particular the capital asset pricing model (CAPM). Although the CAPM was derived by using innovative assumptions and theories, it often does a poor job in predicting stock returns. The numerous market anomalies that were observed after the formation of the CAPM helped form the basis for those wishing to disprove the model. Although the model may not hold up in empirical and practical tests, that is not to say that the model does not hold some utility.

Based on the possibility of making abnormal returns, investors have developed trading strategies over time, which are supposed to profit from this mispricing in the markets. In general, there are five major styles of equity trading: scalping, momentum trading, technical trading, fundamental trading and swing trading (Van Bergen, 2016). For a detailed breakdown of these types of traders and their description, have a look at appendix A. When looking at academic literature, most articles focused on strategy analysis on either fundamental or technical analyses. The focus of this research lies within technical analysis, but in order to give a complete view of trading strategies, fundamental analysis is shortly discussed below.

Investors who focus on fundamental strategies, trade stocks based on fundamental analysis, which examines things like corporate events such as actual or expected earnings reports, stock splits, reorganizations or acquisitions. Some of the most common financial data used in this type of analysis is earnings per share, revenue and cash flow. Two famous anomalies found on a fundamental basis, are the SMB (small minus big) and HML (high minus low) factors, found by Fama & French in 1996. These anomalies focus on size and book-to-market (BM) ratios respectively, in which it is anticipated that smaller companies are more profitable relative to bigger companies and that companies with high BM ratios are more profitable compared to low ratios. These anomalies have become so well known, that they are now incorporated in the Fama & French three-factor model , which is an addition to the CAPM. More on this is discussed in section 2.4. pricing models.

2.2 Technical strategy analysis

Technical analysis is considered by many to be the original form of investment analysis, dating back to the 1800s (Brock, Lakonishok, & LeBaron, 1992). In general, technical analysis studies the historical price patterns or trends or any other clues that are indicative of future price movements (Chong & Ng, 2008). One of the greatest gulfs between academic finance and industry practice is the separation that exists between technical analysts and their academic critics. In contrast to fundamental analysis, which was quick to be adopted by the scholars of modern quantitative finance, technical analysis has been an orphan from the very start (Lo, Mamaysky, & Wang, 2000).

However, several academic studies suggest that despite its jargon and methods, technical analysis may well be an effective means for extracting useful information from market prices. The attitude of academics towards technical analysis, until recently, is well described by Malkiel (1981): “Obviously, I am biased against the chartist. This is not only a personal predilection, but a professional one as well. Technical analysis is anathema to the academic world. We love to pick on it. Our bullying tactics are prompted by two considerations: (1) the method is patently false; and (2) it’s easy to pick on. And while it may seem a bit unfair to pick on such a sorry target, just remember: it is your money we are trying to save”. Nonetheless, technical analysis has been enjoying a renaissance on Wall Street. All major brokerage firms publish technical commentary on the market and individual securities, and many of the newsletters published by various “experts” are based on technical analysis.

An import difference between technical analysis and quantitative finance is that technical analysis is primarily visual, whereas quantitative finance is primarily algebraic and numerical.

Therefore, technical analysis employs the tools of geometry and pattern recognition, and quantitative finance employs the tools of mathematical analysis and probability and statistics. In the wake of recent breakthroughs in financial engineering, computer technology, and numerical algorithms, it is no wonder that quantitative finance has overtaken technical analysis in popularity-the principles of portfolio optimization are far easier to program into a computer than the basic tenets of technical analysis. Nevertheless, technical analysis has survived through the years, perhaps because its visual mode of analysis is more conducive to human cognition, and because pattern recognition is one of the few repetitive activities for which computers do not have an absolute advantage (Lo, Mamaysky, & Wang, 2000).

The main challenge of technical analysis is that there are literally hundreds of technical indicators available – enough to make even the most advanced statistician’s or mathematician’s eyes bug out. Next to that, there is also no single indicator that can be considered as the best, since each indicator might be applicable only to specific circumstances. Because of the unique patterns that highly traded stocks might exhibit throughout history, some indicators may be relevant only to certain individual stocks. Technical indicators are not to be used as a silver bullet solution for when to buy or sell. They are poor predictors of exact timing, but they are good at indicating which stocks are candidates for further analysis. As such, technical analysis can be viewed as a starting point – the historical patterns do not necessarily translate into an exact picture of future performance.

2.3 Different types of technical strategy analysis

As mentioned earlier, there are several hundreds of indicators used in technical analysis. In this section some of the most common groupings are discussed. To be noted, these groupings are limited to indicators applicable to individual stocks – there are many indicators that might be useful to predict an index or industry group, but that is not what this paper is concerned about.

2.3.1. Relative Strength Index – RSI

The relative strength index (RSI) is a momentum indicator developed by noted technical analyst Welles Wilder, that compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. It is primarily used to attempt to identify overbought or oversold conditions in the trading of an asset. The RSI provides a relative evaluation of the strength of a security’s recent price performance, thus making it a momentum indicator. RSI values range from 0 to 100. The default time frame for comparing up periods to down periods is 14, as in 14 trading days.

Traditional interpretation and usage of the RSI is that RSI values of 70 or above indicate that a security is becoming overbought or overvalued, and therefore may be primed for a trend reversal or corrective pullback in price. On the other side of RSI values, an RSI reading of 30 or below is commonly interpreted as indicating an oversold of undervalued condition that may signal a trend change or corrective price reversal to the upside.

In a paper by Chong & Ng (2008), the authors performed a technical analysis on the London stock exchange, using the RSI rules and the FT30 (similar to the Dow Jones Industrial Average). This is the longest UK index and has a sample period from July 1935 to January 1994. They divided the sample in three sub-periods. They found that in the third sub-period, the RSI rule generates the highest number of significant returns. In the first sub-period it was less significant and in the second sub-period it was not significant at all. Overall, they concluded that the RSI rule outperformed the buy-and-hold strategy.

2.3.2. Trading ranges

A trading range occurs when a stock or average moves up and down between a consistent high and low for an extended period of time (days, to weeks, to months). The bottom of the range becomes fairly solid support as the top becomes fairly solid resistance the more times either holds. We play stocks within the trading ranges if they are loose enough to give us some room to manoeuvre, e.g., a 5-point range or more. A tight trading range is one that is significantly narrower than a particular stock’s usual trading fluctuations. A tight trading range on low volume is usually a very good indicator that a move up is coming.

A series of high, low and closing prices are plotted on a graph for a certain period of time, and support and resistance lines are drawn across the bottom and top of the range. A breakout occurs when the price sustains a movement, even for a period or two, above or below the range.

A buy signal is generated when the price penetrates the resistance level. The resistance level is defined as the local maximum. Technical analysts believe that many investors are willing to sell at the peak. This selling pressure will cause resistance to a price rise above the previous peak. However, if the price rises above the previous peak, it has broken through the resistance area. Such a breakout is considered to be a buy signal. Under this rule, a sell signal is generated when the price penetrates the support level which is the local minimum price. The underlying rationale is that the price has difficulties penetrating the support level because many investors are willing to buy at the minimum price. However, if the price goes below the support level, the price is expected to drift downward. In essence, technical analysts recommend buying when the price rises above its last peak and selling when the price sinks below its last trough (Brock, Lakonishok, & LeBaron, 1992).

2.3.3. Pattern analysis

This may be the form of technical analysis that is easiest to understand. The same price charts discussed above are analysed for specific patterns that have historically appeared in the same stock or for common patterns that have been seen in many stocks over time. The most commonly observed patterns are head-and-shoulders patterns, triangle-up or triangle-down patterns, rounded tops or rounded bottoms, cup-and-handle formation, and so on.

A chart pattern is a distinct formation on a stock chart that creates a trading signal, or a sign of future price movements. Chartists use these patterns to identify current trends and trend reversals and to trigger buy and sell signals.

Head and shoulder pattern – The formalization of the geometry of a head-and-shoulders pattern is as follows: three peaks, with the middle peak higher than the other two (Lo, Mamaysky, & Wang, 2000). The head-and-shoulders pattern is considered by practitioners to be one of the most, if not the most, reliable of all chart pattern. If trading based on this pattern generates excess profits, investigating other patterns may prove interesting. Conversely, if profits are insignificant, then this entire branch of visually based technical analysis may be called into question (Osler & Chang, 1995). These authors found that in order to summarize the predictive power or their trading strategy, they investigate profits from speculating in all six currencies simultaneously over the same time horizon. Their findings show that these aggregate profits would have been both statistically and economically meaningful regardless of transaction costs, interest differentials or risk. They conclude that head-and-shoulders signals have some predictive power for the mark and yen during the twenty years since the advent of floating exchange rates. Basically, this would mean that as an investor, you would have to locate the neckline, wait till the pattern is complete and when the neckline brakes, you would invest.

In the paper of Lo, Mamaysky & Wang (2000), the authors use a different type of technical analysis. The authors propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression. They find that several technical indicators indeed provide incremental information and may have some practical value. They mainly focus on three different technical indicators: head-and-shoulders (and inverted), rectangle tops (and bottoms) and double tops (and bottoms). They chose these indicators specifically, to illustrate the power of smoothing techniques in automating technical analysis. They claim that these patterns are most difficult to quantify analytically. The added value of their paper to this research, would definitely be the methodology part, where they describe how to quantify these techniques and so how to apply these rather difficult techniques to the dataset.

2.3.4. Trend analysis

Highly complex and mathematical, trend analysis looks at short and long-term trends and tries to identify crossovers, where prices cross over their long-term averages. The long-term averages are referred to as moving averages, where a price range is smoothed for a period of time by averaging a series of data points and plotting the smoothed line against the actual price line of the stock. The moving average convergence divergence (MACD) is used to identify crossovers, divergence and convergence, and overbought and oversold conditions.

Moving Average (crossover, MACD, ribbon) – Yule (1909), Wold (1938), Zoicas-Lenciu (2016) = Buy/sell signal when short-term crosses long-term (double or triple), Many moving averages are placed onto the same chart and are used to judge the strength of the current trend.

According to the moving average rule, buy and sell signals are generated by two moving averages of the level of the index – a long-period average and a short-period average. In its simplest form this strategy is expressed as buying (or selling) when the short-period moving average rises above (or falls below) the long-period moving average. The idea behind computing moving averages is to smooth out an otherwise volatile series. When the short-period moving average penetrates the long-period moving average, a trend is considered to be initiated (Brock, Lakonishok, & LeBaron, 1992).

Unlike existing studies that apply technical analysis to either market indices or individual stocks, Han, Yang & Zhou (2013) apply it to portfolios sorted by volatility or other characteristics of the stocks that reflect information uncertainty. Their use of technical analysis focuses on applying the popular technical tool, moving averages, to time investments. This is a trend-following strategy (TFS), and hence the profitability of the strategy relies on whether there are detectable trends in the cross section of the stock market. They find that the application of a moving average timing strategy generates investment timing portfolios, that substantially outperform the buy-and-hold strategy. They find that especially the combination with the volatility anomaly is of great economic significance. Their paper shows that the moving average technique could definitely enhance an investment strategy.

Han, Huang & Zhou (2015) argue that most anomalies are based on low frequency attributes and therefore ignore higher frequency information. In their research, they implement higher frequency data in order to test the consistency of the anomalies. They find that by doing so, significant economic value will be added. They find that for the eight major anomalies used in their research, the enhanced anomalies can double the average returns while having similar or lower risks.

The main part of their paper that is of interest to this research, is that they use both technical and traditional analysis. They use the traditional anomalies, but they use the moving average technique to enhance these strategies. The moving average is used to rebalance their portfolios.

In the paper of Brock, Lakonishok & LeBaron (1992), the authors test two of the most popular trading strategies: the moving average and the trading range break. Their results provide strong support for the technical strategies. The returns obtained from these strategies are inconsistent with the four popular null models: the random walk, the AR(1), the GARCH-M and the exponential GARCH. Next to that, they find that buy signals consistently generate higher returns than sell signals and these signals are also less volatile. Their paper shows again the importance of the moving average method, but in this case also the trading range break strategy, so in overall, a support for the use of technical analysis.

2.3.5. Gap analysis

A gap occurs when the opening price of a stock is significantly higher or lower than its closing price the previous day, possibly because of company news released overnight or some other factor. The gap trader is concerned with the performance of the stock above or below its open, which may indicate further movement in either direction. In this sense, the trader’s decisions may be closer in style to that of the momentum trader than the technical analyst.

Gap analysis refers to the process through which a company compares its actual performance to its expected performance to determine whether it is meeting expectations and using its resources effectively. Gap analysis seeks to define the current state of a company or organization and the target state of the same company or organization. By defining and analysing these gaps, a business management team can create an action plan to move the organization forward and will the gaps in performance.

Conducting a gap analysis can help a company re-examine its goals to determine whether it is on the right path for accomplishing them. Gap analysis consists of four steps, ending in a compilation report that identifies areas of improvement and outlines an action plan to achieve increased company performance. The steps are: construct organizational goals, benchmark the current state, analyse the gap data and compile a gap report.

2.4 Pricing models

When comparing the different strategies, it is usually tested whether a certain investment strategy generates a significant high risk-adjusted return. The returns of the strategy are compared to different version of a pricing model. Up until several decades ago, the CAPM functioned as a benchmark. This model was introduced by Treynor (1961), Sharpe (1964), Lintner (1965) and Mossin (1966) independently, building on earlier work of Markowitz on diversification and modern portfolio theory. CAPM is defined as:

E(R_i )=R_f+β_i (E(R_m )-R_f)

Where E(R_i)is the expected return on the capital asset, R_fis the risk-free rate of interest, such as interest arising from government bonds, β_i is the sensitivity of the expected excess asset returns to the expected excess market returns, E(R_m) is the expected market return and E(R_m )-R_f is known as the market premium. Nowadays, three different models are often used, which are all adding-based on the CAPM: the three, four- and five factor models.

2.4.1. Three factor model

The three factor model is introduced by Fama and French in 1993. It is an addition to the CAPM, which contains the risk premium, but also a size factor and a value factor. As was mentioned earlier, it is assumed that smaller firms generate higher excess returns than bigger firms (small minus big, SMB) and companies with high book-to-market ratios generate higher excess returns than low ratios (high minus low, HML). These two factors will now shortly be discussed.

E(R_i )=R_f+β_1i (E(R_m )-R_f )+β_2i SMB+β_3i HML

The magnitude of the size anomaly is around 2-4% per annum, mostly in the 1960s and 1970s. it is quite possible that after the discovery of the anomaly, investors traded on it and hence the magnitude decreased to insignificant levels. There are two economic motivations begin the size anomaly. First, small firms receive less analyst attention. This means that their prices are updated less often, meaning that this would carry a risk for which compensation would be required. Second, small firms are not traded much. This means that their prices update less often and that they are less liquid (and that they carry higher transaction costs). Again, this carries a risk for which compensation would be required.

According to the value anomaly, value stocks (stocks with a high B/M-ratio) earn higher returns than growth stocks (stocks with a low B/M-ratio), even after corrections have been made for their market risk. The magnitude of this anomaly is around 4%-6% per annum. The economic motivation behind the value anomaly starts from the fact that ultimately asset prices are determined by (expected) pay-outs. If market value is close to book value (high B/M), the firm appears to be in dire shape (no growth opportunities that have any value), and is therefore riskier. This risk raises the required return. (However, it must be said that this explanation is just one of the possible explanations behind the value anomaly.) The problem with the financial distress hypothesis is that the factor associated with the value anomaly has low correlations with measures of distress. However, (Lettau & Ludvigson, 2001) suggest the value factor may work primarily in times that are already bad.

2.4.2. Four factor model

Carhart build further on the three factor model, by adding a fourth factor in 1997. He found a momentum factor in which companies that could be considered as “winners”, would outperform the “losers” (winners minus losers, WML).

E(R_i )=R_f+β_1i (E(R_m )-R_f )+β_2i SMB+β_3i HML+β_4i WML

The momentum anomaly states that, based on middle-long term autocorrelation, assets (stocks) that have performed well in the recent past (say 3-12 months) will outperform ‘losers’ for another year. The magnitude of this anomaly is mostly 4%-6% per annum but can be as high as 2% per month. This outperformance can be obtained by sorting past ‘winners’ and ‘losers’ and then (for example) buy the 20% best performing stocks and finance this by short selling the 20% worst performing stocks. Obviously this requires careful selection and rebalancing because at some time winners stop winning and losers stop losing.
The momentum anomaly is not constructed into the SML and SDF because from an economical point of view it has nothing to do with risk (especially the when do we get returns part, because it is based on the past which is irrelevant). There is no economic argument to support the idea that returns are less desirable just because the stock value has increased the past period.

Trading on the momentum anomaly seems easy, why doesn’t it disappear rapidly? 2 possible explanations:

• Trading costs. The short positions are costly to obtain and maintain. There are also transaction costs that can make it expensive the replicate the strategy. Taking this into account there might not be an anomaly after all.
• Illiquidity effects. Especially in smaller stocks, shortselling might be encountered by illiquidity. Illiquid stocks may fall a lot further if you try to sell them in a decreasing market.
Both explanations are based on market imperfections (violation of our assumptions). The momentum anomaly is rather robust (if you correct of risk with the SDF for the other anomalies or macroeconomic factors it is still there). Furthermore, illiquidity effects are hard to measure so testing on this is also hard to do.

2.4.3. Five factor model

The five-factor model does not include the WML factor from Carhart. This model builds on the three factor model by adding two extra factors, the profitability factor (robust minus weak, RMW) and the investment factor (conservative minus aggressive, CMA).

The profitability factor states that companies with higher future earnings will have higher stock market returns. RMW is the return spread of the most profitable firms minus the least profitable ones. The problem here has always been finding a proxy today that predicts earnings tomorrow.

The investment factor states that companies with a conservative investment strategy will generate higher future returns than companies with an aggressive strategy. CMA is the return spread of firms that invest conservatively minus aggressively.

E(R_i )=R_f+β_1i (E(R_m )-R_f )+β_2i SMB+β_3i HML+β_4i RMW+β_5i CMA

The results also show that the Fama-French five factor model explains between 71% and 94% of the cross-section variance of expected returns for the size, value, profitability and investment portfolios. It has been proven that a five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns performs better than the three-factor model in that it lessens the anomaly average returns left unexplained (ValueWalk, 2015).

The new model shows that the highest expected returns are attained by companies that are small, profitable and value companies with no major growth prospects. The five-factor model’s main setback however is its failure to capture the low average returns on small stocks whose returns perform like those of firms that invest a lot in spite of low profitability as well as the model’s performance being indifferent to the way its factors are defined (Fama & French, 2016).

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News & Insights

How Money Management Principles Apply to Options Trading

March 14, 2024 — 12:50 pm EDT

Written by [email protected] for Schaeffer  ->

As new traders flood the market, a return to the basics may help novices understand the fundamentals of options trading. To better assist them, we will be running posts diving into the finer details of options education. This week, we are looking at how money management can be applied to options trading. 

It's no secret that options trading involves a fair amount of risk, but in recognizing it, it becomes easier to manage. One way traders limit risk is by diversifying their trades. In other words, it's not a good idea to put all of your available capital into a single trade, or "all your eggs into one basket," so to speak. In general, avoiding vulnerability to a downturn in one area by combining a mix of stocks, bullish and bearish options, bonds, and cash will help form that protection.

With that comes a certain amount of discipline and patience, as it can be easy to want to follow the herd, or enter and exit positions using rash decision-making. However, it is better to stick to your own carefully considered and well-researched plan, which should have an exit strategy at the start. Overall, maintaining composure in regard to each trade is an important aspect of risk management. 

When creating your strategy, the size of the trade plays a crucial part, as it is the money-making aspect after all. After deciding how much you're willing to pay for the option, use limit orders instead of market orders to control your entry price. Overall, one strategy is to see how many trades you can have at the same time, give yourself a hefty cash cushion, and spread your remaining capital evenly throughout the positions. 

Lastly -- and one of the more advantageous uses of options trading -- use partial closeouts to lock in profits or manage losses, particularly after a sharp move happens early on. Throughout it all, there will likely be losses, but they can be learning experiences that help you hone in on what approach works best for you. In an ever-evolving market, it's important to stay an active learner. 

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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Peer-to-peer trading price and strategy optimization considering different electricity market types, tariff systems, and pricing models

• Zhang, Ruixiaoxiao
• Lee, Minhyun
• Zhao, Dongqi
• Kang, Hyuna
• Hong, Taehoon

This study investigated the peer-to-peer (P2P) trading price and strategy based on market-driven methods within a residential microgrid consists of two prosumers (i.e., P1 and P2) and one consumer (i.e., C1), which establishes a diverse microgrid trading market in terms of peer characteristics (e.g., system configurations and electrical load profile). Five cases are proposed in this study considering different electricity market types (i.e., peer-to-grid and P2P markets), retail tariff systems (i.e., progressive and time-of-use (ToU) tariff systems) as well as market-driven P2P trading pricing models (i.e., uniform and individual pricing models). Peer electrical load data are collected from three typical four-person households in Hong Kong along with their domestic electrical appliance utilization pattern. The results indicate that the individual pricing model has led to the dynamics of P2P electricity trading price than the uniform pricing model, and P2P trading is more economically efficient under the ToU tariff system. The maximum electricity bill saving of the entire microgrid can be achieved by 31.33% in summer and by 43.02% in winter. Besides, it is observed that the installation of battery energy storage system (BESS) has facilitated the self-consumption ratio of the renewable energy system to 91.98% in summer and 100% in winter. This implies that the BESS plays a pivotal role in improving the flexibility in managing the P2P trading strategy, and enhancing the efficiency in electricity dispatching within the microgrid. This study contributes to the novelty in science since it provides a comprehensive framework that can adapt to changing market conditions.

• Peer-to-Peer energy trading;
• Time-of-Use;
• Energy storage system;
• Photovoltaic system;
• Price optimization

Coin Strategy Redefines Cryptocurrency Trading with AI-Powered Algorithms

Coin Strategy, a leading player in the cryptocurrency investment landscape, is making waves with its groundbreaking approach to trading.

Zurich, Switzerland - March 12, 2024 —

Coin Strategy, a leading player in the cryptocurrency investment landscape, is making waves with its groundbreaking approach to trading. Leveraging the power of artificial intelligence (AI) algorithms, Coin Strategy is not only outpacing the market but redefining the rules of engagement in the dynamic world of cryptocurrency.

In an industry marked by volatility and uncertainty, Coin Strategy's innovative use of AI technology has set a new standard for success. By harnessing advanced algorithms that analyze market data with unparalleled precision, Coin Strategy is consistently delivering superior results for its clients.

" Our AI-powered trading algorithms have completely transformed the way we approach cryptocurrency trading," says Kelvin Blake, CEO of Coin Strategy. "We've cracked the code to unlocking profitable opportunities in the market, enabling us to stay ahead of the curve and deliver exceptional returns for our investors."

Coin Strategy's AI algorithms are designed to adapt to changing market conditions in real time, identifying lucrative trading opportunities and executing trades with precision timing. This dynamic approach has enabled Coin Strategy to consistently outperform traditional market benchmarks and achieve remarkable growth for its clients.

"We're not just beating the market; we're rewriting the rules of engagement," adds Blake. "Our AI algorithms give us a competitive edge, allowing us to navigate market fluctuations with confidence and capitalize on emerging trends before others even see them coming."

With an amazing track record of success and a commitment to innovation, Coin Strategy is poised to continue leading the way in the cryptocurrency investment space. As the market evolves and new challenges emerge, Coin Strategy remains dedicated to leveraging the power of AI to deliver unrivaled results for its clients.

For more information about Coin Strategy and its AI-powered trading algorithms, please contact:

· Company Name: Coin Strategy

· Website: https://coinstrategy.io

· Contact Person: Kelvin Blake

· Email Address: [email protected]

· Address: Hardstrasse 221 Floor 6, 8005 Zurich, Switzerland

Contact Info: Name: Mustansar Ali Email: Send Email Organization: Digitalsyrup Address: 91 Grantham Road Website: https://digitalsyrup.co/

Disclaimer: This press release is for informational purposes only. Information verification has been done to the best of our ability. Still, due to the speculative nature of the blockchain (cryptocurrency, NFT, mining, etc.) sector as a whole, complete accuracy cannot always be guaranteed. You are advised to conduct your own research and exercise caution. Investments in these fields are inherently risky and should be approached with due diligence.

Release ID: 89123895

In the event of any inaccuracies, problems, or queries arising from the content shared in this press release, we encourage you to notify us immediately at [email protected]. Our diligent team will be readily available to respond and take swift action within 8 hours to rectify any identified issues or assist with removal requests. Ensuring the provision of high-quality and precise information is paramount to us.

The Philippines economy in 2024: Stronger for longer?

The Philippines ended 2023 on a high note, being the fastest growing economy across Southeast Asia with a growth rate of 5.6 percent—just shy of the government's target of 6.0 to 7.0 percent. 1 “National accounts,” Philippine Statistics Authority, January 31, 2024; "Philippine economic updates,” Bangko Sentral ng Pilipinas, November 16, 2023. Should projections hold, the Philippines is expected to, once again, show significant growth in 2024, demonstrating its resilience despite various global economic pressures (Exhibit 1). 2 “Economic forecast 2024,” International Monetary Fund, November 1, 2023; McKinsey analysis.

The growth in the Philippine economy in 2023 was driven by a resumption in commercial activities, public infrastructure spending, and growth in digital financial services. Most sectors grew, with transportation and storage (13 percent), construction (9 percent), and financial services (9 percent), performing the best (Exhibit 2). 3 “National accounts,” Philippine Statistics Authority, January 31, 2024. While the country's trade deficit narrowed in 2023, it remains elevated at $52 billion due to slowing global demand and geopolitical uncertainties. 4 “Highlights of the Philippine export and import statistics,” Philippine Statistics Authority, January 28, 2024. Looking ahead to 2024, the current economic forecast for the Philippines projects a GDP growth of between 5 and 6 percent.

Inflation rates are expected to temper between 3.2 and 3.6 percent in 2024 after ending 2023 at 6.0 percent, above the 2.0 to 4.0 percent target range set by the government. 5 “Nomura downgrades Philippine 2024 growth forecast,” Nomura, September 11, 2023; “IMF raises Philippine growth rate forecast,” International Monetary Fund, July 16, 2023.

For the purposes of this article, most of the statistics used for our analysis have come from a common thread of sources. These include the Central Bank of the Philippines (Bangko Sentral ng Pilipinas); the Department of Energy Philippines; the IT and Business Process Association of the Philippines (IBPAP); and the Philippines Statistics Authority.

The state of the Philippine economy across seven major sectors and themes

In the article, we explore the 2024 outlook for seven key sectors and themes, what may affect each of them in the coming year, and what could potentially unlock continued growth.

Financial services

The recovery of the financial services sector appears on track as year-on-year growth rates stabilize. 6 Philippines Statistics Authority, November 2023; McKinsey in partnership with Oxford Economics, November 2023. In 2024, this sector will likely continue to grow, though at a slower pace of about 5 percent.

Financial inclusion and digitalization are contributing to growth in this sector in 2024, even if new challenges emerge. Various factors are expected to impact this sector:

• Inclusive finance: Bangko Sentral ng Pilipinas continues to invest in financial inclusion initiatives. For example, basic deposit accounts (BDAs) reached $22 million in 2023 and banking penetration improved, with the proportion of adults with formal bank accounts increasing from 29 percent in 2019 to 56 percent in 2021. 7 “Financial inclusion dashboard: First quarter 2023,” Bangko Sentral ng Pilipinas, February 6, 2024.
• Digital adoption: Digital channels are expected to continue to grow, with data showing that 60 percent of adults who have a mobile phone and internet access have done a digital financial transaction. 8 “Financial inclusion dashboard: First quarter 2023,” Bangko Sentral ng Pilipinas, February 6, 2024. Businesses in this sector, however, will need to remain vigilant in navigating cybersecurity and fraud risks.
• Unsecured lending growth: Growth in unsecured lending is expected to continue, but at a slower pace than the past two to three years. For example, unsecured retail lending for the banking system alone grew by 27 percent annually from 2020 to 2022. 9 “Loan accounts: As of first quarter 2023,” Bangko Sentral ng Pilipinas, February 6, 2024; "Global banking pools,” McKinsey, November 2023. Businesses in this field are, however, expected to recalibrate their risk profiling models as segments with high nonperforming loans emerge.
• High interest rates: Key interest rates are expected to decline in the second half of 2024, creating more accommodating borrowing conditions that could boost wholesale and corporate loans.

Supportive frameworks have a pivotal role to play in unlocking growth in this sector to meet the ever-increasing demand from the financially underserved. For example, financial literacy programs and easier-to-access accounts—such as BDAs—are some measures that can help widen market access to financial services. Continued efforts are being made to build an open finance framework that could serve the needs of the unbanked population, as well as a unified credit scoring mechanism to increase the ability of historically under-financed segments, such as small and medium-sized enterprises (SMEs), to access formal credit. 10 “BSP launches credit scoring model,” Bangko Sentral ng Pilipinas, April 26, 2023.

Energy and Power

The outlook for the energy sector seems positive, with the potential to grow by 7 percent in 2024 as the country focuses on renewable energy generation. 11 McKinsey analysis based on input from industry experts. Currently, stakeholders are focused on increasing energy security, particularly on importing liquefied natural gas (LNG) to meet power plants’ requirements as production in one of the country’s main sources of natural gas, the Malampaya gas field, declines. 12 Myrna M. Velasco, “Malampaya gas field prod’n declines steeply in 2021,” Manila Bulletin , July 9, 2022. High global inflation and the fact that the Philippines is a net fuel importer are impacting electricity prices and the build-out of planned renewable energy projects. Recent regulatory moves to remove foreign ownership limits on exploration, development, and utilization of renewable energy resources could possibly accelerate growth in the country’s energy and power sector. 13 “RA 11659,” Department of Energy Philippines, June 8, 2023.

Gas, renewables, and transmission are potential growth drivers for the sector. Upgrading power grids so that they become more flexible and better able to cope with the intermittent electricity supply that comes with renewables will be critical as the sector pivots toward renewable energy. A recent coal moratorium may position natural gas as a transition fuel—this could stimulate exploration and production investments for new, indigenous natural gas fields, gas pipeline infrastructure, and LNG import terminal projects. 14 Philippine energy plan 2020–2040, Department of Energy Philippines, June 10, 2022; Power development plan 2020–2040 , Department of Energy Philippines, 2021. The increasing momentum of green energy auctions could facilitate the development of renewables at scale, as the country targets 35 percent share of renewables by 2030. 15 Power development plan 2020–2040 , 2022.

Growth in the healthcare industry may slow to 2.8 percent in 2024, while pharmaceuticals manufacturing is expected to rebound with 5.2 percent growth in 2024. 16 McKinsey analysis in partnership with Oxford Economics.

Healthcare demand could grow, although the quality of care may be strained as the health worker shortage is projected to increase over the next five years. 17 McKinsey analysis. The supply-and-demand gap in nursing alone is forecast to reach a shortage of approximately 90,000 nurses by 2028. 18 McKinsey analysis. Another compounding factor straining healthcare is the higher than anticipated benefit utilization and rising healthcare costs, which, while helping to meet people's healthcare budgets, may continue to drive down profitability for health insurers.

Meanwhile, pharmaceutical companies are feeling varying effects of people becoming increasingly health conscious. Consumers are using more over the counter (OTC) medication and placing more beneficial value on organic health products, such as vitamins and supplements made from natural ingredients, which could impact demand for prescription drugs. 19 “Consumer health in the Philippines 2023,” Euromonitor, October 2023.

Businesses operating in this field may end up benefiting from universal healthcare policies. If initiatives are implemented that integrate healthcare systems, rationalize copayments, attract and retain talent, and incentivize investments, they could potentially help to strengthen healthcare provision and quality.

Businesses may also need to navigate an increasingly complex landscape of diverse health needs, digitization, and price controls. Digital and data transformations are being seen to facilitate improvements in healthcare delivery and access, with leading digital health apps getting more than one million downloads. 20 Google Play Store, September 27, 2023. Digitization may create an opportunity to develop healthcare ecosystems that unify touchpoints along the patient journey and provide offline-to-online care, as well as potentially realizing cost efficiencies.

Consumer and retail

Growth in the retail and wholesale trade and consumer goods sectors is projected to remain stable in 2024, at 4 percent and 5 percent, respectively.

Inflation, however, continues to put consumers under pressure. While inflation rates may fall—predicted to reach 4 percent in 2024—commodity prices may still remain elevated in the near term, a top concern for Filipinos. 21 “IMF raises Philippine growth forecast,” July 26, 2023; “Nomura downgrades Philippines 2024 growth forecast,” September 11, 2023. In response to challenging economic conditions, 92 percent of consumers have changed their shopping behaviors, and approximately 50 percent indicate that they are switching brands or retail providers in seek of promotions and better prices. 22 “Philippines consumer pulse survey, 2023,” McKinsey, November 2023.

Online shopping has become entrenched in Filipino consumers, as they find that they get access to a wider range of products, can compare prices more easily, and can shop with more convenience. For example, a McKinsey Philippines consumer sentiment survey in 2023 found that 80 percent of respondents, on average, use online and omnichannel to purchase footwear, toys, baby supplies, apparel, and accessories. To capture the opportunity that this shift in Filipino consumer preferences brings and to unlock growth in this sector, retail organizations could turn to omnichannel strategies to seamlessly integrate online and offline channels. Businesses may need to explore investments that increase resilience across the supply chain, alongside researching and developing new products that serve emerging consumer preferences, such as that for natural ingredients and sustainable sources.

Manufacturing

Manufacturing is a key contributor to the Philippine economy, contributing approximately 19 percent of GDP in 2022, employing about 7 percent of the country’s labor force, and growing in line with GDP at approximately 6 percent between 2023 and 2024. 23 McKinsey analysis based on input from industry experts.

Some changes could be seen in 2024 that might affect the sector moving forward. The focus toward building resilient supply chains and increasing self-sufficiency is growing. The Philippines also is likely to benefit from increasing regional trade, as well as the emerging trend of nearshoring or onshoring as countries seek to make their supply chains more resilient. With semiconductors driving approximately 45 percent of Philippine exports, the transfer of knowledge and technology, as well as the development of STEM capabilities, could help attract investments into the sector and increase the relevance of the country as a manufacturing hub. 24 McKinsey analysis based on input from industry experts.

To secure growth, public and private sector support could bolster investments in R&D and upskill the labor force. In addition, strategies to attract investment may be integral to the further development of supply chain infrastructure and manufacturing bases. Government programs to enable digital transformation and R&D, along with a strategic approach to upskilling the labor force, could help boost industry innovation in line with Industry 4.0 demand. 25 Industry 4.0 is also referred to as the Fourth Industrial Revolution. Priority products to which manufacturing industries could pivot include more complex, higher value chain electronic components in the semiconductor segment; generic OTC drugs and nature-based pharmaceuticals in the pharmaceutical sector; and, for green industries, products such as EVs, batteries, solar panels, and biomass production.

Information technology business process outsourcing

The information technology business process outsourcing (IT-BPO) sector is on track to reach its long-term targets, with $38 billion in forecast revenues in 2024. 26 Khriscielle Yalao, “WHF flexibility key to achieving growth targets—IBPAP,” Manila Bulletin , January 23, 2024. Emerging innovations in service delivery and work models are being observed, which could drive further growth in the sector.

The industry continues to outperform headcount and revenue targets, shaping its position as a country leader for employment and services. 27 McKinsey analysis based in input from industry experts. Demand from global companies for offshoring is expected to increase, due to cost containment strategies and preference for Philippine IT-BPO providers. New work setups continue to emerge, ranging from remote-first to office-first, which could translate to potential net benefits. These include a 10 to 30 percent increase in employee retention; a three- to four-hour reduction in commute times; an increase in enabled talent of 350,000; and a potential reduction in greenhouse gas emissions of 1.4 to 1.5 million tons of CO 2 per year. 28 McKinsey analysis based in input from industry experts. It is becoming increasingly more important that the IT-BPO sector adapts to new technologies as businesses begin to harness automation and generative AI (gen AI) to unlock productivity.

Talent and technology are clear areas where growth in this sector can be unlocked. The growing complexity of offshoring requirements necessitates building a proper talent hub to help bridge employee gaps and better match local talent to employers’ needs. Businesses in the industry could explore developing facilities and digital infrastructure to enable industry expansion outside the metros, especially in future “digital cities” nationwide. Introducing new service areas could capture latent demand from existing clients with evolving needs as well as unserved clients. BPO centers could explore the potential of offering higher-value services by cultivating technology-focused capabilities, such as using gen AI to unlock revenue, deliver sales excellence, and reduce general administrative costs.

Sustainability

The Philippines is considered to be the fourth most vulnerable country to climate change in the world as, due to its geographic location, the country has a higher risk of exposure to natural disasters, such as rising sea levels. 29 “The Philippines has been ranked the fourth most vulnerable country to climate change,” Global Climate Risk Index, January 2021. Approximately $3.2 billion, on average, in economic loss could occur annually because of natural disasters over the next five decades, translating to up to 7 to 8 percent of the country’s nominal GDP. 30 “The Philippines has been ranked the fourth most vulnerable country to climate change,” Global Climate Risk Index, January 2021.

The Philippines could capitalize on five green growth opportunities to operate in global value chains and catalyze growth for the nation:

• Renewable energy: The country could aim to generate 50 percent of its energy from renewables by 2040, building on its high renewable energy potential and the declining cost of producing renewable energy.
• Solar photovoltaic (PV) manufacturing: More than a twofold increase in annual output from 2023 to 2030 could be achieved, enabled by lower production costs.
• Battery production: The Philippines could aim for a $1.5 billion domestic market by 2030, capitalizing on its vast nickel reserves (the second largest globally). 31 “MineSpans,” McKinsey, November 2023.
• Electric mobility: Electric vehicles could account for 15 percent of the country’s vehicle sales by 2030 (from less than 1 percent currently), driven by incentives, local distribution, and charging infrastructure. 32 McKinsey analysis based on input from industry experts.
• Nature-based solutions: The country’s largely untapped total abatement potential could reach up to 200 to 300 metric tons of CO 2 , enabled by its biodiversity and strong demand.

The Philippine economy: Three scenarios for growth

Having grown faster than other economies in Southeast Asia in 2023 to end the year with 5.6 percent growth, the Philippines can expect a similarly healthy growth outlook for 2024. Based on our analysis, there are three potential scenarios for the country’s growth. 33 McKinsey analysis in partnership with Oxford Economics.

Slower growth: The first scenario projects GDP growth of 4.8 percent if there are challenging conditions—such as declining trade and accelerated inflation—which could keep key policy rates high at about 6.5 percent and dampen private consumption, leading to slower long-term growth.

Soft landing: The second scenario projects GDP growth of 5.2 percent if inflation moderates and global conditions turn out to be largely favorable due to a stable investment environment and regional trade demand.

Accelerated growth: In the third scenario, GDP growth is projected to reach 6.1 percent if inflation slows and public policies accommodate aspects such as loosening key policy rates and offering incentive programs to boost productivity.

Focusing on factors that could unlock growth in its seven critical sectors and themes, while adapting to the macro-economic scenario that plays out, would allow the Philippines to materialize its growth potential in 2024 and take steps towards achieving longer-term, sustainable economic growth.

Jon Canto is a partner in McKinsey’s Manila office, where Frauke Renz is an associate partner, and Vicah Villanueva is a consultant.

The authors wish to thank Charlene Chua, Charlie del Rosario, Ryan delos Reyes, Debadrita Dhara, Evelyn C. Fong, Krzysztof Kwiatkowski, Frances Lee, Aaron Ong, and Liane Tan for their contributions to this article.

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Journal of Materials Chemistry A

A “two-birds-one-stone” strategy to enhance capacitive deionization performance of flexible ti 3 c 2 t x mxene film electrodes by surface modification †.

* Corresponding authors

a Department of Chemistry, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou 215123, P. R. China E-mail: [email protected]

b Department of Chemistry, University of Liverpool, Liverpool L69 7ZD, UK

c Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore E-mail: [email protected]

Two-dimensional transition metal carbides/nitrides (MXenes) have gained considerable prominence in capacitive deionization (CDI) due to their exceptional electrochemical activity and outstanding electronic conductivity. However, the further development of MXenes in practical applications in CDI is hampered by their limited narrow interlayer spacing and oxidation proneness. Herein, a dual-functional surface modification of Ti 3 C 2 T x MXene by sodium ascorbate (SA) is proposed to concurrently enhance the salt adsorption capacity and long-term stability. The modification by SA induces synergistic functions, including the enlargement of interlayer spacing, effective protection of Ti from oxidation, and exceptional electrical conductivity. Simultaneously, this film electrode is designed to be flexible and free-standing, devoid of binders or adhesives, showing promise for the large-scale production of CDI electrodes. Benefiting from these advantages, the SA-modified MXene exhibit excellent desalination performance, including high salt adsorption capacity (109.6 mg g −1 ), high salt adsorption rates (17.5 mg g −1 min −1 ), and impressive cycling stability (100% retention after 80 cycles). And the adsorption behavior of SA-modified MXenes is further investigated by in situ X-ray diffraction and density functional theory calculations. This work proposes an effective modification and explores theoretical aspects for fabricating MXene-based electrodes suitable for CDI and other electrochemical applications in moist or aqueous environments.

• This article is part of the themed collection: Journal of Materials Chemistry A HOT Papers

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A “two-birds-one-stone” strategy to enhance capacitive deionization performance of flexible Ti 3 C 2 T x MXene film electrodes by surface modification

C. Huang, T. Huang, X. L. Li, W. Zhou and M. Ding, J. Mater. Chem. A , 2024, Advance Article , DOI: 10.1039/D4TA00236A

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