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Although momentum strategies result in abnormal profitability, thereby challenging the efficient market hypothesis (EMH), concerns persist regarding their reliability due to their significant volatility and susceptibility to substantial losses. In this study, we investigate the limitations of these strategies and propose a solution. Our literature review reveals that the volatile profits are due to statistical analyses that assume the persistence of past patterns, leading to unreliable results in out-of-sample scenarios when underlying mechanisms evolve. Statistical analysis, the predominant method in financial economics, often proves inadequate in explaining market fluctuations and predicting crashes. To overcome these limitations, a paradigm shift towards dynamic approaches is essential. Drawing inspiration from three groundbreaking economists, we introduce the extended Samuelson model (ESM), a dynamic model that connects price changes to market participant actions. This paradigm transition uncovers several significant findings. First, timely signals indicate momentum initiations, cessations, and reversals, validated using S&P 500 data from 1999 to 2023. Second, ESM predicts the 1987 Black Monday crash weeks in advance, offering a new perspective on its underlying cause. Third, we classify sequential stock price data into eight distinct market states, including their thresholds for transitions, laying the groundwork for market trend predictions and risk assessments. Fourth, the ESM is shown to be a compelling alternative to EMH, offering potent explanatory and predictive power based on a single, realistic assumption. Our findings suggest that ESM has the potential to provide policymakers with proactive tools, enabling financial institutions to enhance their risk assessment and management strategies. [ABSTRACT FROM AUTHOR]

This study examines the relationship between extreme weather conditions and the S&P500 return index, representing the U.S. stock market. The literature review and analysis show extreme weather events can impact the S&P500 return index. This effect is observed in two ways. First, extreme weather events create a market anomaly in the U.S. stock market, indicating that prices move in a way that cannot be explained by a rational model. Second, extreme weather events create financial uncertainty and have a negative impact on firms' future cash flows. These findings suggest that investors and financial markets should be more cautious about extreme weather events. In addition, the impact of extreme weather on the U.S. stock market is weak. This can be explained in two ways. First, extreme weather events are predictable and seasonally recurring. Second, the American stock market is close to the efficient market hypothesis. [ABSTRACT FROM AUTHOR]

Abstract Although momentum strategies result in abnormal profitability, thereby challenging the efficient market hypothesis (EMH), concerns persist regarding their reliability due to their significant volatility and susceptibility to substantial losses. In this study, we investigate the limitations of these strategies and propose a solution. Our literature review reveals that the volatile profits are due to statistical analyses that assume the persistence of past patterns, leading to unreliable results in out-of-sample scenarios when underlying mechanisms evolve. Statistical analysis, the predominant method in financial economics, often proves inadequate in explaining market fluctuations and predicting crashes. To overcome these limitations, a paradigm shift towards dynamic approaches is essential. Drawing inspiration from three groundbreaking economists, we introduce the extended Samuelson model (ESM), a dynamic model that connects price changes to market participant actions. This paradigm transition uncovers several significant findings. First, timely signals indicate momentum initiations, cessations, and reversals, validated using S&P 500 data from 1999 to 2023. Second, ESM predicts the 1987 Black Monday crash weeks in advance, offering a new perspective on its underlying cause. Third, we classify sequential stock price data into eight distinct market states, including their thresholds for transitions, laying the groundwork for market trend predictions and risk assessments. Fourth, the ESM is shown to be a compelling alternative to EMH, offering potent explanatory and predictive power based on a single, realistic assumption. Our findings suggest that ESM has the potential to provide policymakers with proactive tools, enabling financial institutions to enhance their risk assessment and management strategies.

This study examines the relationship between extreme weather conditions and the S&P500 return index, representing the U.S. stock market. The literature review and analysis show extreme weather events can impact the S&P500 return index. This effect is observed in two ways. First, extreme weather events create a market anomaly in the U.S. stock market, indicating that prices move in a way that cannot be explained by a rational model. Second, extreme weather events create financial uncertainty and have a negative impact on firms’ future cash flows. These findings suggest that investors and financial markets should be more cautious about extreme weather events. In addition, the impact of extreme weather on the U.S. stock market is weak. This can be explained in two ways. First, extreme weather events are predictable and seasonally recurring. Second, the American stock market is close to the efficient market hypothesis.

We test the efficient market hypothesis (EMH) in emerging markets by simultaneously considering gradual shifts and common factors. Findings indicate that (i) while the test with sharp breaks does not support EMH, the tests with gradual/smooth shifts and common factors support EMH in emerging markets; (ii) considering structural breaks as a gradual process within a common factor framework is important for emerging financial markets; and finally (iii) increasing time span sheds light more on EMH. [ABSTRACT FROM AUTHOR]

The efficient markets hypothesis argues that stock prices reflect all information in the market and that it is not possible for investors to obtain abnormal returns. However, in some of the academic studies, it was determined that this hypothesis was not valid and these situations were stated as anomalies. The aim of this study is to examine whether there is a Ramadan effect, which is among the periodic anomalies in Borsa Istanbul. The research covers a 166-month period between January 2011 and May 2024. In the study, the power ratio method was used to detect the Ramadan anomaly in the Participation Index. As a result of the study, it was determined that the highest monthly average return occurred in the month of Rabi al-Ahir and the lowest monthly average return occurred in the month of Cemaziyelahir. As a result of the study, it was concluded that there was a periodic anomaly in the months of Rajab and Ramadan, which are the three months considered sacred in Islamic belief, and that there was no effect of Shaban. [ABSTRACT FROM AUTHOR]

This study explores market efficiency and behavior by integrating key theories such as the Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational Efficiency and Random Walk theory. Using LSTM enhanced by optimizers like Stochastic Gradient Descent (SGD), Adam, AdaGrad, and RMSprop, we analyze market inefficiencies in the Standard and Poor's (SPX) index over a 22-year period. Our results reveal "pockets in time" that challenge EMH predictions, particularly with the AdaGrad optimizer at a size of the hidden layer (HS) of 64. Beyond forecasting, we apply the Dominguez–Lobato (DL) and General Spectral (GS) tests as part of the Martingale Difference Hypothesis to assess statistical inefficiencies and deviations from the Random Walk model. By emphasizing "informational efficiency", we examine how quickly new information is reflected in stock prices. We argue that market inefficiencies are transient phenomena influenced by structural shifts and information flow, challenging the notion that forecasting alone can refute EMH. Additionally, we compare LSTM with ARIMA with Exponential Smoothing, and LightGBM to highlight the strengths and limitations of these models in financial forecasting. The LSTM model excels at capturing temporal dependencies, while LightGBM demonstrates its effectiveness in detecting non-linear relationships. Our comprehensive approach offers a nuanced understanding of market dynamics and inefficiencies. [ABSTRACT FROM AUTHOR]

This study proposes new generalized spectral tests for multivariate martingale difference hypotheses, specifically geared toward high-dimensionality scenarios where the dimension of the time series is comparable or even larger than the sample size in practice. We develop an asymptotic theory and a valid wild bootstrapping procedure for the new test statistics, in which the dimension of the time series is fixed. We demonstrate that a bias-reduced version of the test statistics effectively addresses the high-dimensionality concerns. Comprehensive Monte Carlo simulations reveal that the bias-reduced statistic performs substantially better than its competitors. The application to testing the efficient market hypothesis on the U.S. stock market illustrates the usefulness of our proposal. [ABSTRACT FROM AUTHOR]

Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate. [ABSTRACT FROM AUTHOR]

Exchange rate fluctuations profoundly impact macroeconomic variables, making them a focal point for policymakers. This study is grounded in the efficient market hypothesis (EMH), asserting that such forecasts offer no utility due to all relevant information being embedded in asset prices. The research problem is rooted in the dynamics of currency exchange rate fluctuations and their impact on the Albanian economy. The research aims to analyse Albanian lek (ALL)/euro (EUR) exchange rate dynamics and their impact on Albania's economy, assessing market efficiency and providing insights for monetary and fiscal policy design. The method used is a detrended fluctuation analysis (DFA) which consists of a root mean square analysis of a random walking pattern (Peng et al., 1994). This method is used to assess adherence to the weak form of EMH. Findings indicate that the Albanian foreign exchange market closely resembles a random walk, suggesting efficiency during specific periods. The study underscores the exchange rate's dual impact: its influence on domestic price levels and its broader implications for the real economy and balance of payments. This paper is relevant as it offers crucial insights into currency exchange rate dynamics and their economic impacts, supporting policymakers in improving Albania's economic stability and market efficiency. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to identify and rank the financial, nonfinancial, and behavioral components affecting companies' ERC. Methodology: Using data from 153 companies listed on the Tehran Stock Exchange over the period 2013 to 2022, and employing data mining techniques and two methods: stepwise forward regression and regression decision tree, the influential components on ERC were identified and ranked. Findings: The results of the stepwise forward regression method indicated that the components of auditor's opinion type, earnings per share (EPS), stock liquidity, growth opportunities, earnings stability, inflation rate, sales growth, operating profit ratio, relative strength index per share, and market index return respectively influence ERC. The regression decision tree method results also showed that the components of earnings stability, stock liquidity, EPS, stock price synchronicity, stock trading imbalance, adjusted trading volume, psychological line index, relative strength index per share, earnings conservatism, and financial statement restatements respectively influence ERC. The Wilcoxon test results also showed that the ranking of components influencing ERC is not the same in the two methods. Additionally, comparing the mean absolute error of the two methods indicated that the regression decision tree method identifies and ranks the influential components on ERC more accurately. Conclusion: The results of both methods confirm the high impact of EPS, stock liquidity, earnings stability, and relative strength index per share on the ERC of companies. [ABSTRACT FROM AUTHOR]

Purpose: This study analyzes how political-economic events have affected the Brazilian capital market over 13 years by evaluating the abnormal returns of the five most liquid stocks (Top-5). Originality/value: This research proceeds from previous studies by analyzing various political-economic events that impacted, to some extent, the prices of companies listed on the Brazilian Stock Exchange over 13 years. Considering both favorable and opposing evidence to the efficient market hypothesis (EMH), this study provides an original and robust test to evaluate market efficiency, considering various events and companies. Design/methodology/approach: We used the event study methodology to measure the impact of each event on stock prices by using the day before and after the event analyzed. We performed capital asset pricing model (CAPM) estimations with 110 observations before the event to assess abnormal returns. To assess the research hypotheses, we used the average abnormal return test around the event (Window – 1.1). Findings: We found that the five major companies in the Brazilian stock market were efficient in the EMH semi-strong form test despite 13 years of extreme events. Their returns did not change significantly after the events, differentiating themselves from studies that question market efficiency. Therefore, market participants should not expect abnormal returns in similar events. [ABSTRACT FROM AUTHOR]

This work applies a bootstrap multivariate panel Granger causality test to examine the causal relationship between the coronavirus disease 2019, Delta and Omicron pandemic eras and the maritime shipping freight industry across 15 Chinese marine transportation freight stocks through the data from Jannuary 23, 2020 to December 6, 2022. After accounting for both dependency and heterogeneity across these industries, we find support for six of the 15 maritime shipping freight enterprises which display a unidirectional Granger relationship between stock returns and pandemic-related shocks and five of them which demonstrate a bidirectional association. On the other hand, the stock values of three firms in this cohort Granger "lead" the epidemic, suggesting that the adjustment of expectations to external shocks and the efficient market hypothesis could elucidate apparent causal connections. The empirical findings of this work provide important policy implications for the maritime shipping freight industry. [ABSTRACT FROM AUTHOR]

The issue of forecasting or predicting asset returns is fundamental to financial economics and most often the Capital Asset Pricing Model (CAPM) is adopted for estimating the expected rate of return based on systematic risk. This paper focuses on the empirical testing of the CAPM in the Indian market based on the Nifty 50 index from April 2008 to March 2023, which covers the period of the COVID-19 pandemic, the Goods and Services Tax (GST) reforms and demonetisation. In the context of this study, Lintner, Miller and Scholes, Black, Jensen and Scholes tests are used with a new dataset to examine the CAPM. This study examines the empirical validity of the CAPM in the context of the Indian market, more specifically the Nifty 50 index, during a period of significant economic change. Thus, the analysis is centred around the interaction of systematic risk, residual variance and average returns. The study identifies a positive but non-significant relationship between average returns with systematic risk and a slightly higher risk-free return compared to that of Lintner. The variance of residuals also helps to explain average returns, while at the same time, actual returns are higher than market returns. The findings of the current study partially support Lintner, Miller and Scholes' earlier work while not supporting Black, Jensen and Scholes'(BJS) conclusions. In the context of the BJS methodology, 38 per cent of the securities have significant non-zero alphas, which means CAPM does not operate perfectly in the Indian market. The findings that alphas are non-zero indicate the inefficiency of the National Stock Exchange (Nifty 50), therefore, the need for regulation by agencies such as the Securities and Exchange Board of India (SEBI) arises. Equity analysts and portfolio managers must not only use CAPM for asset valuation blindly without appreciating its empirical flaws. This paper revisits the CAPM with recent data thus enriching the limited literature on the CAPM in the Indian scenario where CAPM has however a very restricted application in explaining asset returns in the context of the Nifty 50. [ABSTRACT FROM AUTHOR]

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This study investigates how the Global Financial Crisis has affected the weak-form Efficient Market Hypothesis (EMH) on the stock prices of sixteen nations throughout the globe based on a suite of Fourier unit root tests. Considering the smooth structural breaks, we employed the Fourier-based unit root tests to assess the weak-form efficient market hypothesis. We used multiple frequency datasets of global financial stock market indexes that span over 20 years to have comprehensive analysis and robustness in the results. The study is performed from distinct sub-sample periods of the global financial crisis, including the pre-crisis period (2000–2007), the crisis and post-crisis period (2008–2020), and the overall sample period (2000–2020). We observed seven stock markets in the total sample period and twelve in the pre-crisis period, which were weak-form efficient across different frequency data sets. During the crisis and post-crisis period, just four out of sixteen stock market indexes were found to be weak in efficiency based on Fourier unit root tests. Given the superior properties of the Fourier unit root tests, this study reiterates that investors may receive a stream of arbitrage benefits in all markets due to the inefficiency of these countries. We offer investment implications that enable forecasting future stock price changes based on past performance and creating trading methods that produce anomalous profits.

This study aims to analyze the efficient market form of the Jakarta Islamic Index. The methodology used is an event study with a window period of 20 days, including ten days before and ten days after the announcement of composition changes held twice a year. The sample consists of 102, with 30 companies listed during the study period and 36 companies excluded and re-listed following the composition change announcements. The analysis technique used is the Paired Sample T-test. The findings reveal no differences in abnormal returns and trading volume activity before and after the announcement. This suggests that the market had already absorbed the announcement information before the event, resulting in a weak reaction and indicating that the announcement did not significantly impact the Jakarta Islamic Index. This implies that the index operates in a semi-strong form of market efficiency. Consequently, companies listed in the index may need additional strategies to improve their stock performance. To provide further insight, this study also examines the average stock trading activity of companies that enter and exit the composition of the Jakarta Islamic Index for the 2019-2023 periods.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps. The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers. The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events. There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias. Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives. This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This article examines the adaptive market hypothesis in the five most important Latin American stock indices. To that end, we apply three versions of the variance ratio test, as well as the Brock-Dechert-Scheinkman test for nonlinear predictability. Additionally, we perform the Dominguez-Lobato and generalized spectral tests to evaluate the Martingale difference hypothesis. Moreover, we consider salient news related to the plausible market inefficiencies detected by these four tests. Finally, we apply a GARCH-M model to assess the risk-return relationship through time. Our results suggest that the predictability of stock returns varies over time. Furthermore, the efficiency in each market behaves differently over time. All in all, the analyzed emerging market indices satisfy the adaptive market hypothesis, given the switching behavior between periods of efficiencies and inefficiencies, since the adaptive market hypothesis suggests that market efficiency and market anomalies might coexist in capital markets.

Purpose – This study’s goal was to identify how several markets have developed over time and what determinants have influenced this process, based on adaptive markets hypothesis (AMH). In this regard, the authors consider that agents are driven by the seeking for abnormal returns to stay “alive” and their environment could somehow modify their decision-making processes, as well as influence the degree of efficiency of the market. Design/methodology/approach – The authors collected the daily closing-of-the-market index from 50 countries, between 1990 and 2022. The sample includes emerging countries, developed countries and frontier markets. Then, the authors ran multilevel modeling using Hurst exponent as an informational efficiency metric estimated by two different moving windows: 500 and 1,250 observations (approximately 2 and 5 years). Findings – The results indicate that the efficiency of the markets is not constant over time. The authors also have identified that markets follow a cyclical pattern of efficiency/inefficiency, and they are currently in a period of convergence to efficiency, possibly explained by the increase in computational capacity and speed of the available information to agents. In addition, this study identified that country characteristics are associated with market efficiency, considering institutional factors. Originality/value – Studies of this nature contribute to the literature, considering the importance of better comprehension of market efficiency dynamics and their determinants, specially observing other theories on the relationship between information and markets (like AMH), which work with other investor assumptions than those used by efficient market hypothesis.

Purpose – This study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine. Design/methodology/approach – This study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making. Findings – On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process. Practical implications – Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy. Originality/value – This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.

Purpose – This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system. Findings – Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information. Practical implications – It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market. Social implications – There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions. Originality/value – This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.

AbstractHenri Poincaré is well known for his work in mathematics, physics, and the philosophy of science. From 1900 onwards, Poincaré had the opportunity to become a forerunner of the Efficient Market Hypothesis (EMH), a concept that was formally developed 65 years later, but he never seised it. We show that Poincaré’s view on randomness was incompatible with the EMH framework. Although he did not work directly on financial topics, his work has helped to understand the limits of random walks in asset pricing; a way to reconcile the mathematician with the field of finance. [ABSTRACT FROM AUTHOR]

Identifying anomalies, or empirical facts that contradict theoretical predictions, is a common form of proof by contradiction in economics and finance. We identify an apparent pricing anomaly in the market for trading cards: large and persistent death effects of about 20% are observed following the death of legendary baseball players. Rational investors should anticipate each player's eventual death, so these results suggest that profit opportunities are being left on the table. The arguably causal estimates come from interrupted time series models that adjust flexibly for time pre- and post-death and the time of sale using high-frequency sales data. The effect is largest in the days immediately following the death, but prices remain elevated for at least 60 days. Generally, our findings reaffirm the presence of a causal "nostalgia effect" of celebrity deaths on consumer demand for collectibles and products associated with the celebrity. [ABSTRACT FROM AUTHOR]

International portfolio management is influenced by the existence of "frictions", factors or events that interfere with trade, which are linked in financial literature to market-specific factors, such as available information, restrictions, investor protection, or market liquidity. Given the wide variety of factors that can be included in these categories, scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact. We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values. We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction. By employing grey clustering analysis, we cluster these capital markets into three categories—strongly recommended, recommended with some reserve, and not recommended—based on the importance given by the decision maker to these factors. The results show that some of the studied markets are in the same cluster, irrespective of the chosen scenario. The only market always included in the "strongly recommended" category is Hungary, indicating that it is a good investment option for international participants. Bulgaria and Slovakia are always regarded as "recommended with reserve" markets, whereas the Republic of Moldova is part of the "not recommended" category. The other markets show a degree of variability that can be explained by different investor perspectives. This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis. Investors can use this approach during the decision-making process related to their investments. [ABSTRACT FROM AUTHOR]

The efficient market hypothesis assumes that financial markets fully incorporate all available information, rendering past information irrelevant for predicting future prices. However, numerous studies challenge this notion and suggest the presence of long-term memory in market dynamics. Understanding long-term memory in financial markets has important implications for investors and policymakers. The aim of this study was to empirically investigate long term memory in financial markets. This study employed a Hurst model for a sample of 5 financial markets from June 1, 2018, to June 1, 2023. The findings revealed that four out of the five sampled financial market exhibits long term memory which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjust portfolio weights to take advantage of potential return predictability and manage risk. [ABSTRACT FROM AUTHOR]

The weak-form of market efficiency states that stock price reflects all the historical price data thus abnormal return cannot be earned based on historical price. Many studies observed a pattern in generating returns over the week, where Fridays have been documented as a day providing significantly higher returns while Mondays as a day providing the lowest or negative return. The presence of a such pattern in the return violates the efficient market hypothesis in the weak form which provide a golden opportunity for smart and knowledgeable investors to exploit such a pattern and generate an abnormal return. This paper tries to critically review and synthesize the relevant research in the field of the days of the week and weekend effect by using a systematic literature review approach, from 1973 to 2023. This study concluded that the direction and magnitude of the days-of-the-week and weekend effects, for different stock markets across different periods are not the same, it may be due to different institutional arrangements, and religious and cultural beliefs.

While nature loves symmetry inasmuch as many natural laws remain invariant under various transformations, description of the natural diversity warrants some kind of breakdown of symmetry. Such a breakdown of symmetry may be either explicit or spontaneous. Many significant developments have taken place in recent times in diverse fields of study. Algorithmic information theory has been formulated by Kolmogorov and others. Spontaneous breakdown of symmetry and its use in the standard model of particle physics, discovery of the Higgs Boson are all notable events. Work of Ilya Prigogine to accommodate biological self-organisation within the ambit of the second law of thermodynamics is also quite noteworthy. According to some, his principle of minimum entropy production has added a dynamic content to the second law of thermodynamics, which otherwise is viewed as a mere kinematic constraint. It appears that non-linear dynamics and the catastrophe theory of Rene Thom can play an important role to understand a sudden qualitative change of a dynamical system triggered by certain value of a parameter. Phase change of water subjected to external heating or change from a stable economy to one marked by trade cycles are examples of such sudden transitions. Advent of econophysics and behavioural finance has facilitated a new way of looking logically at the problems of finance and economics with the help of established physical laws as well as rigorous mathematical and statistical analyses. Importance of such an approach is evident from the sad plight of Louis Bachelier. In this article, it has been our effort to link up such apparently disjoint developments from diverse fields of study within the ambit of a single logically consistent framework. [ABSTRACT FROM AUTHOR]

This study examines the day-of-the-week effect on the returns of different classifications of South African REITs. Ordinary least squares regression (OLS), generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) (2,1), and Kruskal–Wallis (KW) tests were performed on data obtained from the IRESS Expert database from 2013 to 2021. We found statistical differences in the day-of-the-week effects for SAREITs; the best day to invest in office REITs is Friday, for diversified REITs Thursday, and for industrial REITs Friday. Generally, Wednesday was found to be the least profitable day to invest in all REIT classifications because it had the least average daily return. Tuesdays were the most profitable days for all REIT classifications, with the highest average daily return. REITs traded the most on Fridays, while REITs traded the least on Mondays. Returns were the most volatile on Monday, while volume was the least volatile on Thursday. The KW test revealed a statistically significant difference between the median returns across days of the week. Based on the above, profitability is expressed on Tuesdays in South African REITs. By recognizing the day-of-the-week effect, investors can buy and sell South African REITs more effectively. This study, apart from being the first in the context of South African REITs, provides updated evidence of the contested calendar anomaly issues. [ABSTRACT FROM AUTHOR]