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One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading. At the same time, significant structural changes in the industry have occurred, with passive investing gaining momentum. The intersection of these two major trends poses special challenges during market downturns, magnifying portfolio losses and leading to significant outflows. Emerging market (EM) investors have seen two major downturn events in the 2020s, namely the COVID-19 pandemic and the Russia-Ukraine conflict, both of which have strongly affected EM portfolios' risk-return profiles and increased their correlations with their developed market counterparts, eliminating much or all of EMs' diversification benefits. This has led to major capital outflows from EM countries, further destabilizing these fragile economies. Against this backdrop, we argue that capital need not exit these riskier markets during periods of turmoil and support this by developing a second-generation Automated Adaptive Trading System (AATS) back-tested on a relevant, diversified EM portfolio that tracks the Morgan Stanley Capital International (MSCI) Emerging Markets Index during a volatile period characterized by negative returns, high risk, and a high correlation with global markets for the buy-and-hold EM portfolio. The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods. The main strength of the AATS is its ability to allow the embedded hybrid forecasting model to adapt to the changing environments that characterize EMs. This is done by implementing a recursive window technique and running a user-specified fitness function to dynamically optimize the mean equation parameters throughout the lead time. Back-testing several configurations of the flexible AATS consistently reveals its superiority while assuring the robustness of the results. We conclude that with the right investment tools, EMs continue to offer compelling opportunities that should not be overlooked. The novel AATS proposed in this study is such a tool, providing active EM investors with substantial value-added through its ability to generate abnormal returns, and can help to enhance the resilience of EMs by mitigating the cost of crises for those countries. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field. [ABSTRACT FROM AUTHOR]

Following the implementation of capital market reforms, Saudi Arabia has opened up its stock market to international investors. This liberalisation has led to the inclusion of the stock exchange into leading global indices, which is expected to lead to an influx of foreign investment. Thus, it is important and timely to examine the efficiency of the Saudi stock market. To that end, this paper conducts a comprehensive investigation of the predictive ability and profitability of a popular trading rule, the moving average rule, using firm-level data for the Saudi stock market over the period 1st January 2008 to 31st December 2017. The results show that the moving average rule has predictive ability; the active rules for a majority of the sample companies outperformed the corresponding passive strategy. Furthermore, the results were economically significant as the rules were profitable even after the consideration of transaction costs. The results also showed that the most profitable moving average rules were those with short-moving average lengths, and that the introduction of a bandwidth, which serves to eliminate weak trading signals, had a positive impact on rule profitability. Importantly, the analysis showed that the most profitable component of the rule related to short selling as these trades resulted in higher profits than the long positions. A disaggregated analysis of sectors showed that the trading rules outperformed the buy-and-hold strategy in all seven industries considered, while the analysis of covariance revealed the importance of careful selection of filter size. Overall, the analysis documents significant inefficiencies in the Saudi stock market and suggests that the employment of a simple trading rule, based on past price data, can yield substantial profits across companies and sectors even in a costly trading environment. These findings suggest that the recent reforms that were implemented to improve the efficiency of the Saudi stock market have been suboptimal, and that further regulatory reform is required. [ABSTRACT FROM AUTHOR]

We document the influence of stock volatility on trend-following profits for a global sample of 1618 blue-chip stocks, across 43911 evaluation subperiods (2004–2018). We use the price sensitivity of trend signals (i.e. signal volatility) to isolate the detrimental impact of high stock volatility manifested through excessive/inefficient trading. Despite its almost null correlation with the stocks' mean-variance characteristics, the signal volatility greatly complements them in explaining the time series variation in trend-following excess returns. The results hold for both the buy and sell excess returns, are robust across stock markets, and conserve after considering explicit and implicit trading costs. Investors can use ex postsignal volatility estimates as a valid criterion to choose across potential trading rules, according to their specific levels of risk aversion and transaction costs. [ABSTRACT FROM AUTHOR]

The effect of each trading rule is not completely independent. From the perspective of market differentiation, we establish the linear simultaneous equation model to study the comprehensive influence of the price limit rule and the day trading rule on the quality of China's stock market and investigate whether it is a synergistic or an anti-synergistic effect. To eliminate the influence of other factors before or after the reform of the trading rules, we establish the difference-in-difference simultaneous equation model for further testing. Results show that when the stock market is divided into the Shanghai A-share market, the Shanghai B-share market, the Shenzhen A-share market, and the Shenzhen B-share market, the simultaneous implementation of the price limit rule and T + 1 trading rule significantly reduces the liquidity and market efficiency of the stock market, significantly increases the volatility of markets, and does harm to the market quality. The interaction effect further aggravates the adverse impact of a single trading rule on the liquidity and market efficiency of the stock market, and weakens the beneficial impact of implementing a single trading rule to stabilize the market. The interaction effect has an anti-synergistic effect, and the results are robust. [ABSTRACT FROM AUTHOR]

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This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation. First published online 29 November 2021

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation. [ABSTRACT FROM AUTHOR]

This paper tests for the ability of a variety of technical indicators to generate excess returns at the individual stock level in the seven emerging and frontier markets of the Gulf region. While technical indicators show some early profitability promise, after controlling for the data snooping bias using the False Discovery Rate (FDR) methodology and non-synchronous trading, we fail to find any predictive ability or profitability for technical analysis. We arrive at a similar finding when assessing the risk-adjusted performance of a portfolio composed of stocks chosen based on technical indicators. The findings go to show the failure of technical analysis on the stock level. The findings are also evidence of the Gulf region markets being at least weak-form efficient and carry implications for investors choice of investment tools. [ABSTRACT FROM AUTHOR]

This research uses Simple Moving Averages and four other well-known indicators to investigate the usefulness of the Technical Analysis approach to the Johannesburg stock exchange (JSE) in South Africa. Technical indicators are applied to two JSE indexes representing large cap and small cap companies over the period of 1/2/2002 to 12/31/18. The best trading rules for both the Small Cap and All Share indexes involve the use of two simple moving averages. For the Small Cap Index, our best two trading rules and a low-risk strategy have positive annual net excess return over the Buy and Hold (B&H) strategy for the entire period and each sub-period, thus trading rules can beat the B&H strategy. However, we cannot say the same for the All Share Index (representing large cap companies).

This research uses Simple Moving Averages and four other well-known indicators to investigate the usefulness of the Technical Analysis approach to the Johannesburg stock exchange (JSE) in South Africa. Technical indicators are applied to two JSE indexes representing large cap and small cap companies over the period of 1/2/2002 to 12/31/18. The best trading rules for both the Small Cap and All Share indexes involve the use of two simple moving averages. For the Small Cap Index, our best two trading rules and a low-risk strategy have positive annual net excess return over the Buy and Hold (B&H) strategy for the entire period and each sub-period, thus trading rules can beat the B&H strategy. However, we cannot say the same for the All Share Index (representing large cap companies). [ABSTRACT FROM AUTHOR]

The advocates of the efficient market hypothesis recommend buying the market index for the long run, the implication for the Portuguese investors are to buy the PSI-20 index and hold it for at least 15 years. In this paper, we compare two other strategies for PSI-20 over the period 1999 to 2020. The first strategy is based on moving average trading rules and the second strategy, Gold Momentum Strategy (GMS), is based on switching between gold and PSI-20 based on semi-annual performance. Our findings suggest that the moving average trading rules beat the buy and hold strategy by more than 10% per year over the entire period and each sub-period considering both risk and transaction costs. For the second strategy, GMS which is based on comparing the performance of the PSI-20 and the gold index on semi-annual basis and go with the best of two for the next 6 months, we find similar results as the moving average trading rules. [ABSTRACT FROM AUTHOR]

In this article, the results of an extensive study of the weak form efficiency of the Iceland stock market are presented. This study almost covers the market’s entire history, with the research starting at the beginning of 1993 and ending in July 2017. Four trading rules based on 70-day moving averages were constructed and compared with the passive investment strategy of buying the market index. All of these trading rules provided significantly better returns than the passive strategy, even when considering trading costs. This result indicates that the Icelandic stock market did not show weak form efficiency, and past returns predicted future returns during the period examined.

Historical financial data are frequently used in technical analysis to identify patterns that can be exploited to achieve trading profits. Although technical analysis using a variety of technical indicators has proven to be useful for the prediction of price trends, it is difficult to use them to formulate trading rules that could be used in an automatic trading system due to the vague nature of the rules. Moreover, it is challenging to determine a specified combination of technical indicators that can be used to detect good trading points and trading rules since different stock may be affected by different set of factors. In this paper, we propose a novel trading point forecasting framework that incorporates a bicluster mining technique to discover significant trading patterns, a method to establish the fuzzy rule base, and a fuzzy inference system optimized for trading point prediction. The proposed method (called BM-FM) was tested on several historical stock datasets and the average performance was compared with the conventional buy-and-hold strategy and five previously reported intelligent trading systems. Experimental results demonstrated the superior performance of the proposed trading system. [ABSTRACT FROM AUTHOR]

Due to the effects of many deterministic and stochastic factors, it has always been a challenging goal to gain good profits from the stock market. Many methods based on different theories have been proposed in the past decades. However, there has been little research about determining the temporal investment style (i.e., short term, middle term, or long term) for the stock. In this paper, we propose a method to find suitable stock investment styles in terms of investment time. Firstly, biclustering is applied to a matrix that is composed of technical indicators of each trading day to discover trading patterns (regarded as trading rules). Subsequently a k-nearest neighbor (KNN) algorithm is employed to transform the trading rules to the trading actions (i.e., the buy, sell, or no-action signals). Finally, a min-max and quantization strategy is designed for determination of the temporal investment style of the stock. The proposed method was tested on 30 stocks from US bear, bull, and flat markets. The experimental results validate its usefulness. [ABSTRACT FROM AUTHOR]

Technical analysis (TA) is used in evaluating its predictive power for the Morgan Stanley Capital International (MSCI) Emerging Market Index (EMI) that reflects 23 emerging market economies' equity indices. We conclude strong predictive power of technical analysis for the EMI. Given this predictive power of TA, we then investigate whether investors can exploit this predictive power to beat the profitability of the Buy‐and‐Hold strategy considering both risk and transaction costs. Applying Moving Average, Relative Strength Index, Moving Average Convergence Divergence, and Rate of Change trading rules to the MSCI Emerging Market Index over the period of 11/1/1988 to 5/1/2017 reveals strong empirical evidence that investors could use TA to out‐perform the Buy‐and‐Hold strategy even when considering risk and transaction costs. This research provides evidence against the Efficient Market Hypothesis for EMI. [ABSTRACT FROM AUTHOR]

In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.

This article investigates the performance of moving-average strategies and tests the validity of the weak form of the Efficient Market Hypothesis (EMH) for the Qatari Stock Exchange (QSE). This study uses statistical analyses and adopts the version of the variable moving-average rule where buy and sell signals are generated by comparing a share price’s short- and long-term moving averages. The data include the daily closing share prices of 44 Qatari-listed companies for the period 2004–2017. The analysis shows that the QSE is not weak form efficient because patterns and trends are present in share prices. Sectoral analyses suggest that securities in consumer goods and services, industrials and insurance are the most efficiently priced on the QSE. The evidence suggests that profitability depends on the moving-average strategy selected. The findings may thus benefit technical analysts, fund managers, accountants and academics. This study is one of the first to examine the market efficiency of the QSE using trading rules. This research also suggests the possibility of limited transparency and accounting disclosure in the QSE, which may help policy-makers devise regulations that could improve the QSE’s efficiency.

The efficient-market hypothesis (EMH) is one of the most important economic and financial hypotheses that have been tested over the past century. Due to many abnormal phenomena and conflicting evidence, otherwise known as anomalies against EMH, some academics have questioned whether EMH is valid, and pointed out that the financial literature has substantial evidence of anomalies, so that many theories have been developed to explain some anomalies. To address the issue, this paper reviews the theory and literature on market efficiency and market anomalies. We give a brief review on market efficiency and clearly define the concept of market efficiency and the EMH. We discuss some efforts that challenge the EMH. We review different market anomalies and different theories of Behavioral Finance that could be used to explain such market anomalies. This review is useful to academics for developing cutting-edge treatments of financial theory that EMH, anomalies, and Behavioral Finance underlie. The review is also beneficial to investors for making choices of investment products and strategies that suit their risk preferences and behavioral traits predicted from behavioral models. Finally, when EMH, anomalies and Behavioral Finance are used to explain the impacts of investor behavior on stock price movements, it is invaluable to policy makers, when reviewing their policies, to avoid excessive fluctuations in stock markets.

Abstract: Stock market in China is subject to the T + 1 rule, which requires investors to hold the asset for at least 1 day before selling. This rule was initially imposed in the mid‐1990s, replacing the previous T + 0 rule, to prevent excessive speculative trading. Given the considerable changes in China's financial market over the past 20 years, it is controversial whether the T + 1 rule should be replaced by the T + 0 rule in today's market. In this paper, we empirically test the effect of the T + 1 rule on market speculation. To identify potentially different impacts of the T + 1 and T + 0 rule, we choose a unique pair of CSI 300 ETFs, one subject to the T + 1 rule while the other to the T + 0 rule. Based on an error correction model, we develop an empirical methodology to test intraday speculation in the ETF price. Our empirical results show that, at least under current market condition, the T + 1 rule reduces the price efficiency and spurs more speculation when the market liquidity is not in a shortage. [ABSTRACT FROM AUTHOR]

This paper tests a few moving average technical trading rules for the NASDAQ Composite and Goldman Sack commodity indexes from 1972 to 2015. Our results indicate that moving average rules do exhibit strong predictive power for NADSAQ composite index but much weaker predictive power for GSCI. Can a trader use this predictive to beat the B&H strategy? We show that MA-100 days could most of the time make an abnormal profit in the case of NASDAQ composite index by considering both transaction costs and risk.