Weak-form Market Efficiency Research Articles

Advanced Classification Technique to Detect the Changes of Regimes in Financial Markets by Hybrid CNN-based Prediction

Traders' tactics shift in response to the shifting market circumstances. The statistical features of price fluctuations may be significantly altered by the collective conduct of traders. When some changes in the market eventuate, a "regime shift" takes place. According to the observed directional shifts, this proposed study attempts to define what constitutes between normal and abnormal market regimes in the financial markets. The study begins by using data from ten financial marketplaces. For each call, a time frame in which major events may have led to regime change is chosen. Using the previous returns of all the companies in the index, this study investigates the usage of a CNN with SVM deep learning hybrid to anticipate the index's movement. The experiment findings reveal that this CNN model can successfully extract more generic and useful features than conventional technical indicators and produce more resilient and lucrative financial performance than earlier machine learning techniques. Most of the inability to forecast is due to randomness, and a small amount is due to non-stationarity. There is also a statistical correlation between the legal regimes of various marketplaces. Using this data, it is conceivable to tell the difference between normal regimes and lawful regimes. The results show that the stock market efficiency has never been tested before with such a large data set, and this is a significant step forward for weak-form market efficiency testing.

Modelling Economic Time Series in the Presence of Variance Non-Stationarity: A Practical Approach

Although non-stationarity in the level of a time series is always tested (and there is a variety of tests for this purpose), non-stationarity in the variance is sometimes neglected in applied research. In this work, the consequences of neglecting variance non-stationarity in economic time series, and the conceptual difference between variance non-stationarity and conditional variance are discussed. An ad hoc method for testing and correcting for variance non-stationarity is suggested. It is shown that the presence of variance non-stationarity leads to misspecified univariate ARIMA models and correcting for it, the number of model parameters is vastly reduced. The implications of the tests for the hypothesis of weak form market efficiency (WFME) are discussed. More specifically it is argued that the usual autocorrelation tests are inappropriate when based on the differences of asset prices. Finally, it is shown how the analysis of outliers is affected by the presence of variance non-stationarity.

Market efficiency and volatility within and across cryptocurrency benchmark indexes

With initial coin offerings and token offerings remaining at the forefront of alternative investments, the study of peer groups can be important for comparing investors’ tastes and preferences for particular classes of cryptocurrency on a more equal footing. The aim of this paper is to identify violations of the weak-form market efficiency hypothesis for comparable cryptoassets that are conditional on market segmentation and those conditional on benchmarks. We use daily frequency data of 57 cryptocurrencies that account for more than 90% of the total market capitalization (market cap). We construct seven thematic market cap indexes that are able to represent the whole cryptocurrency universe. Against this background, we test for the presence of four empirical anomalies: risk premium, leverage, regime switch and calendar effects, both across and within these benchmark indexes. The main results support the existence of a switch between two states and positive excess returns toward the end of the week for both cases. Our methodology and findings contribute to the emerging literature on introducing active and passive portfolio management strategies that track benchmark crypto indexes.

Market Efficiency and Volatility Spillover in Bitcoin and Ethereum Prices: Comparisons during the Pre-COVID-19 Period and COVID-19 Pandemic

Purpose – This study attempts to establish if the markets for the two most popular cryptocurrencies in the world, Bitcoin and Ethereum, follow weak-form market efficiency across various landmarks in time. Design/Methodology/Approach – Traditional testing for establishing weak-form market efficiency rests on whether the price series exhibits a random walk process, which implies that future prices cannot be predicted. However, not all random walk series automatically imply weak-form market efficiency, since some asset price behaviors may exhibit non-constant variance. In such cases, the GARCH model can be used to test for the presence of market efficiency. Since structural breaks in the prices of both cryptocurrencies are common, tests for market efficiency were carried out using sub-temporal price windows. In both price series, the last time window coincided with the 2020 COVID-19 pandemic period. Findings – Results of the GARCH analyses showed that the volatility and persistence parameters (α and β, respectively) in the Bitcoin and Ethereum models were all statistically significant, implying that prices in their sub-temporal markets were generally weak-form inefficient. The observed market inefficiency in both cryptocurrencies can be attributed to various factors like the price manipulation of crypto whales, security issues, and increased media attention, which led to inflows of information that helped big investors beat and gain from the market by successfully predicting the trend in future prices. During the 2020 COVID-19 pandemic period, both cryptocurrencies’ prices were observed to rise significantly, similar to the case of the 2017 Bitcoin price bubble. A cointegrating regression between Bitcoin and Ethereum prices during this period, however, showed a spurious relationship. Despite the absence of a long run relationship between these two price series, the current price bubbles in the cryptocurrency markets are speculated to be tied together. Research Implications – Players in the cryptocurrency market must always be cautious in making investment decisions regarding this type of asset since the markets are generally price inefficient and risky; any idiosyncratic decision that may be triggered by a price bubble burst in one cryptocurrency market may or may not serve as a signal that the other market will do the same. Since the Bitcoin and Ethereum prices were shown to exhibit volatility spillover and persistence, investors can use this information to make informed decisions as to whether to invest in these cryptocurrencies despite the huge risks that are magnified during the COVID-19 pandemic.

Changing efficiency of BRICS currency markets during the COVID-19 pandemic

This paper studies the information efficiency of BRICS currency markets during the COVID-19 pandemic using daily data spanning 3rd February 2020–31st August 2021. In our preliminary analysis, which consists of tests for random walk and martingale processes, we provide evidence of time-varying weak-form market efficiency in the currency markets. In our main empirical analysis, we use wavelet coherence tools to examine the time–frequency relationship between COVID-19 death rates and BRICS currency returns, and we find higher frequency components dominate periods of panic and financial turmoil. However, subsequent to government intervention in financial markets and the more recent rollout of mass vaccination programmes, we find that higher frequency oscillations disappear, and only very low-frequency co-movements remain. Important academic, market and policy implications derived from our study are discussed.

A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes

The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period.

How does price (in)efficiency influence cryptocurrency portfolios performance? The role of multifractality

This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk–return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.

Modeling Asymmetric Effects and Long Memory in Conditional Volatility of Dhaka Stock Exchange: New Evidence from Family of FIGARCH Models

This paper investigates modeling the conditional volatility of the Bangladesh equity market, namely the Dhaka Stock Exchange benchmark index (DSEX) and the Shariah Index (DSES), to explore the presence of leverage effects and long memory behavior covering the period from July 01, 2004 to December 31, 2020. We employ a family of Fractionally Integrated GARCH models FIFARCH BBM, FIGARCH CHUNG, FIEGARCH, FIAPARCH BBM, FIAPARCH CHUNG, and HYGARCH to capture both asymmetric effects and long memory behavior in conditional variance, a unique study in volatility literature. We detect strong evidence in favor of asymmetric effects and long memory behavior in the conditional volatility of DSEX and DSES returns, which repudiates the weak-form efficient market hypothesis. The study reveals FIEGARCH and FIAPRACH CHUNG outperform the other fractionally integrated GARCH specifications in modeling conditional volatility of equity returns. The paper further examines the diagnostic test of misspecification of the conditional variance equation based on the news impact curve, and results ensure that all models are fairly specified. This study also looks into the risk-return tradeoff in time-varying volatility and finds no evidence of the positive relationship between equity returns and volatility dynamics. The findings have pragmatic implications for retail and wholesale investors and other market players to initiate the investments and hedging strategies before investing in an emerging equity market like Bangladesh.

Random Walk Hypothesis for Stock Prices in Fiji

The main goal of this paper is to investigate the random walk hypothesis in Fiji using monthly data from January 2000 to October 2017. Applying augmented Dickey Fuller (ADF 1979, 1981) and Phillips-Perron (1988), Zivot-Andrews (1992), and Narayan and Popp (2010) unit root tests, this study finds that stock prices is best characterized as non-stationary. The estimated multiple structural break dates in the stock prices corresponds with devaluation of Fijian dollar by 20 percent in 2009 and General Elections in September 2014, which Fiji First Party won by majority votes. The empirical results indicate that stock prices are best characterized as a unit root (random walk) process, indicating that the weak-form efficient market hypothesis holds in Fiji’s stock market. Hence, it will be difficult to predict future returns based on historical movement of stock prices in Fiji’s stock market.

Market Efficiency of US REITs: A Revisit

The findings on the efficient market hypothesis in the US real estate investment trust (REIT) sector are mixed, and applied methodologies may not be adequate. This paper investigates the weak-form efficient market hypothesis for US REIT stocks. The variance ratio test indicates that the REIT and general stock markets are not efficient in the weak-form: the log price series for REIT stocks violates the random walk theory as a model specification. As an alternative, we applied the quantum harmonic oscillator to provide robust evidence. The quantum harmonic oscillator, including the solution for a random walk as a ground state solution, proved to be a better method for testing the efficient market hypothesis. Contrary to variance ratio test, quantum harmonic oscillator provides results that REIT stocks are more efficient than general stocks, and their market efficiency is close to that of bonds. We argue that large market size and substantial institutional ownership of REIT shares have enhanced market efficiency. The findings suggest that arbitrage in the REIT market cannot be achieved simply by analyzing the historical price trend.

Does The Efficient Market Theory In The Weak Form Exist? Evidence From Indonesia

In the weak-form market efficiency theory, investors cannot predict the movement of all prices because of randomness. This circumstance happens because of a quick market reaction to new information. Conversely, suppose the market is not efficient in this shape; in that case, the investors can obtain an abnormal return. One of the reasons is the thin market, where many inactive stocks to be traded are available. Based on these issues, this research intends to examine this theory by employing runs testing on the daily returns of the Indonesia Composite Index (ICI) between January 2014 and December 2018 for each year and a whole. Once performing this test, this research demonstrates that the daily returns of the ICI are random for both situations. By denoting these facts, this research concludes that the capital market in Indonesia is efficient in a weak form and experiences a decrease in the thin level, reflected by the escalation in trading frequency, volume, and value, as well as the number of dynamic shares transacted. This research suggests that investors without sufficient information should utilize the service of the securities analysts to select the stocks they buy and sell to get the capital gain. 
 
 Keywords – an efficient market in the weak form; market index return; runs test; thin market

Deviations from fundamental value and future closed-end country fund returns

PurposeThis article examines whether deviations from fundamental value or closed-end country fund's discounts or premiums forecast future share price returns or net asset returns.Design/methodology/approachThe main empirical (econometric) tool is a vector autoregressive (VAR) model. The authors model share price returns and net asset returns as a function of their lagged values, the discounts or premiums, and a control variable for local market returns. The authors also conduct Dickey Fuller and Granger causality tests as well as impulse response functions.FindingsIt was found that deviations from fundamental value do predict share price returns. This predictability is contrary to weak-form market efficiency. Premiums or discounts predict net asset returns but weakly.Originality/valueThe findings point to the idea that the closed-end fund market is somewhat predictable and inefficient (in its weak form) since the market appears to be able to anticipate a fund's future returns using information contained in the premiums (or discounts). In particular, the market has the ability to anticipate future behaviour because growing premiums forecast declining share price returns for one or two periods ahead.

Testing the Efficiency of Globally Listed Private Equity Markets

This study is the first to investigate the efficient market hypothesis in its weak form and the random walk behaviour of globally listed private equity (LPE) markets represented by nine global, regional, and style indices based on weekly data covering the period from January 2004 to December 2020. Autocorrelation tests, variance ratio tests, and a non-parametric runs test are employed. The results of the autocorrelation tests and the variance ratio tests tend to correspond for all indices, and they reject the random walk hypothesis for the returns of all LPE indices under investigation. In contrast, the runs test for direct weak-form market efficiency cannot reject the null hypothesis of a random walk process for almost all LPE indices under investigation. Furthermore, there is no evidence that the market efficiency of globally listed private equity markets has improved after the global financial crisis. Due to the fact that the rapidly growing asset class of LPE as a form of private equity is still relatively unknown, the implications of the results of our paper are relevant for investors, policy makers, and academics alike. In addition, the results provide valuable insights to better understand the emerging asset class of LPE.

Higher moments and US industry returns: realized skewness and kurtosis

PurposeThe purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies.Design/methodology/approachDaily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships.FindingsPast skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns.Research limitations/implicationsThe calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor.Practical implicationsGiven the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking.Originality/valueWhile there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

Testing weak-form market efficiency in the Stock Exchange of Thailand

This research tests the weak-form market efficiency, one of the most important financial theories, on the stock indices of the Stock Exchange of Thailand (SET), namely, SET, SET50, SET100, SETHD, sSET, and MAI indices during the period from January 2002 to May 2019. This study employs three well-known weak-form market efficiency tests: autocorrelation tests, runs tests, and variance ratio tests (Lo and MacKinlay, 1988). The results show that SET, SET50, SET100, and SETHD indices become more weak-form efficient over time, suggesting that any strategy based on past returns to predict future returns is futile. However, sSET and MAI indices are not weak-form efficient, and show no sign of improved efficiency over time, thereby enabling the creation of trading strategies to earn abnormal returns, using past returns to predict future returns.

Can Machine Learning Unlock the Continuous Alpha? Empirical Study Based on China A-Share Market

With the development of fintech and artificial intelligence, machine learning algorithms are widely used in quantitative investment. Based on the listed companies in China A-share market from February 2005 to July 2020, quantitative stock selection models with machine learning algorithms are established to obtain continuous alpha returns. The results show that machine learning algorithms can effectively identify the relationship between factors and returns and then improve the performance of the quantitative stock selection model. China A-share market is a weak-form efficient market. By mining the factors that are not fully digested by the market, continuous alpha returns can be obtained. The ensemble algorithms represented by the extremely randomized tree (ET) and light gradient boosting machine (LGBM) perform best in stock market prediction.

Ho Chi Minh Stock Exchange Market: Operations and Efficiency

This paper presents an up-to-date account of market operations of the Ho Chi Minh Stock Exchange and examines its informational efficiency in recent years. The daily closing prices and rates of return of VN Index – the major market index of Ho Chi Minh Stock Exchange and 10 stocks chosen from different sectors are employed, from 2 January 2018 to 31 December 2019, to investigate the random walk hypothesis of market efficiency using the Lo-MacKinlay variance ratio test and Chow-Denning multiple variance ratio test. Our results show that the market index and individual sample stocks conform to the null hypothesis of a random walk type 3 model of a weak form market efficiency. The paper also presents the results of an Event Study to examine the semi-strong form market efficiency of the HOSE. The empirical results on this type indicate that there are significant abnormal returns and significant cumulative abnormal returns by trading the stocks around the events. These results are inconsistent with the requirements of a semi-strong form market efficiency, and it thus appears that further improvements in transmission of information and its speed within this market are needed to further improve the efficiency of this emerging market.

Testing weak-form market efficiency in the Stock Exchange of Thailand

Testing weak-form market efficiency in the Stock Exchange of Thailand

Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading.

Risk management and stock investment decision-making is an essential topic for investors and fund managers, especially in the context of the COVID-19 pandemic. The problem becomes easier if the market is efficient, where stock prices fully reflect potential risk. Nevertheless, if the market is not efficient, investors may have an opportunity to find an effective investment method. Vietnam is one of the emerging markets; the efficiency is still weak. Thus, there will be an opportunity for astute investors. This study aims to test the weak-form efficient market and provide a modern approach to investors' decision-making. To achieve that aim, this study uses historical data of stocks in the VN-Index and VN30 portfolio to buy and sell within a one-day period under the rolling window approach to test the Ho Chi Minh City Stock Exchange (HoSE) through a runs test and to perform stock trading using the support vector machine (SVM) and logistic regression. The buying/selling of stocks is guided by the forecasted outcomes (increase/decrease) of logistic regression and SVM. This study adjusted the return rate in proportion to the risks and compared it with index investments of VN-Index and VN30 to evaluate investment efficiency. The test results dismissed the weak-form efficient-market hypothesis, which opens up many opportunities for short-term traders. This study's primary contribution is to provide a stock trading strategy for short-term investors to maximize trading profits. Because logistic regression and SVM have proven effective trading methods, investors can use them to achieve abnormal returns.

Lack of Reform Effect on Exchange Efficiency- Empirical Evidence from Saudi Market Index –

As development of financial markets has been a growing issue after the financial crisis 2008, this paper has tested efficiency on one of the largest emerging securities markets in the world. It examines lack of reform impact on efficiency of Saudi stock exchange (TASI index) employing random walk hypothesis (RWH) through a battery of parametric and nonparametric tests including autocorrelations, unit root, variance ratio (VR) and Brock, Dechert, and Scheinkman (BDS) test.The study specifically aims to test weak form efficiency before and after massive efforts implemented to reform Saudi exchange. Findings have concluded in a consensus verdict that there is no change observed in TASI index behaviour, and hence it rejected to be a weak-form efficient market. Thus, policy makers and regulators should initiate further reforms; deep regulations and reorganization development to address inefficiency.