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Though stock portfolio return autocorrelation is well documented in the literature, its cause is still not clearly understood. Presently, evidence of private information induced stock return autocorrelation is still very limited. The difficulty in obtaining foreign country information by small investors makes the private information of institutional investors in the ADR (American Depository Receipt) market more significant and influential. As such, the ADR market provides a favorable environment for testing the effect of private information on return autocorrelation. The purpose of this paper is to address this issue.

In this paper, ADRs are sorted annually into three groups based on market equity capitalization. Within each capitalization group, ADRs are further sorted into three groups based on the fraction of shares held by institutional investors. Each ADR is assigned to one of the nine groups and group membership is rebalanced each year. The return autocorrelation of individual ADR securities and ADR portfolios for each group are then calculated.

The results demonstrate that ADR individual stock and portfolio daily return autocorrelations are positively related to institutional ownership. It is also found that other explanations, such as non‐synchronous trading, bid‐ask spread and volatility of ADR, cannot explain the positive relation between daily return autocorrelations and institutional ownership of ADR.

Since ADR market is more suitable than other markets for testing the role of private information, stronger and clearer results are got accordingly. This paper suggests that trading strategy based on private information of institutional investors can lead to stock return autocorrelation in ADR daily returns.

This study examines the cross‐autocorrelation of size‐based portfolio returns in a sample of 15 major European markets using daily data from January 1990 through December 1999. Previous studies have primarily used U.S. data. This study extends previous research by considering results in multiple European exchanges. We examine whether a difference in size‐based portfolios exists by testing cross‐autocorrelation, granger‐causality, and asymmetric responses in the European markets. The results confirm that large stock portfolio returns lead small stock portfolio returns in most European countries, and that cross‐autocorrelation is present both within and between European financial markets.

The main purpose of this paper is to investigate autocorrelation structure of stock and portfolio returns in a unique market setting of Saudi Arabia, where nearly all active traders are the retail individuals and the market operates under severe limits to arbitrage. Specifically, the authors examine how return autocorrelation of Saudi Arabian stock market is related to factors such as the day of the week, stock trading, performance on the preceding day and volatility.

The sample consists of the daily stock price and index data of 159 firms listed in Tadawul (Saudi Arabian Stock Exchange) for the period from January 2004 through December 2015. The methodology of Safvenblad (2000) is primarily used to investigate the autocorrelation structure of individual stock and index returns. The authors also use the Sentana and Wadhwani (1992) methodology to test for the presence of feedback traders in the Saudi stock market.

Results show that there is significantly positive autocorrelation in individual stock, size portfolio and market returns and that the last two are almost always larger than the first. Return autocorrelation is negatively related to firm size. Interestingly, return autocorrelation is positively related to trading frequency. For portfolios, autocorrelation of returns following a high absolute return day is significantly higher than that following a low absolute return day. Similarly, return autocorrelation during volatile periods is generally larger than that during tranquil periods. Return correlation between weekdays is usually larger than that between the first and last days of the week. Overall, the results suggest that the possible reason for positive autocorrelation in stock returns could be the presence of negative feedback traders who are engaged in frequent profit-taking activities.

This is the first paper that thoroughly investigates the autocorrelation structure of the returns of the Saudi stock market using both index and individual stock returns. As this US$583bn (as of August 21, 2014) market opened to foreign institutional investors in June 2015, the results of this paper should be of significant value for the potential uninformed foreign investors in this relatively lesser known and previously closed yet highly prospective market.

The phenomenon of positive autocorrelation in daily stock index returns is often viewed as a consequence of stable behavioural patterns of certain investor groups (see, e.g., Sentana & Wadhwani, 1992; Koutmos, 1997). However, such patterns may change due to extreme events, that is, financial crises, and thus affect the autocorrelation in returns. Emerging markets and especially BRIC countries have experienced severe crises in the last 20 years and are therefore a suitable object for studying this effect.

The focus of this chapter is on identifying substantial changes in the autocorrelation of BRIC markets' index returns after experiencing upheavals of the financial system. For this purpose, we look for structural breaks in the parameters of an ARMA–GARCH model with the standard endogenous search procedure.

Our approach yields no statistically significant evidence of the autocorrelation changes due to the crises. Only in India the decline in autocorrelation in 1998 seems to be economically relevant, but is not significant statistically. Significant shifts that we could identify were rather related to microstructural changes, such as abolishment of price change limits by China and the removal of a leading player in India's market in 1992. All in all our results suggest that even though extreme negative events on financial markets may induce changes in feedback trading strategies, their influence on autocorrelation is not pronounced enough. The impact of other factors, in the first place of regulatory changes, seems to be of larger relevance.

This chapter considers the relationship between stock market autocorrelation and (i) the presence of international investors which is proxied by the level of capital market integration and (ii) stock market volatility. Drawing from a sample of nine Asia-Pacific stock indices, significant evidence of a relationship between the presence of international investors and the level of stock market autocorrelation is found. This evidence is consistent with the view that international investors are positive feedback traders. Robustness testing of this model suggests that the trading strategy of international investors changed as a result of the Asian currency crisis. The evidence for the role of volatility in explaining autocorrelation is, however, is generally weak and varies across the sample countries.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market.

It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market.

The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only.

This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

Motivated by recent evidence that securitized real estate returns exhibit higher levels of predictability than stock market returns and that feedback trading (FT) can induce returns autocorrelation and market volatility, the purpose of this study is to examine the impact of FT strategies on long-term market volatility of eight international real estate markets (UK, Germany, France, Italy, Sweden, Australia, Japan and Hong Kong).

Assuming that the return autocorrelation may vary over time and the impact of positive feedback trading (PFT) or negative feedback trading (NFT) could be a function of return volatility, the authors use a combination of a FT model and a fractionally integrated Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model.

The results are mixed, revealing that both PFT and NFT strategies persist. Specifically, the authors detect PFT in the real estate markets of France, Hong Kong and Italy as opposed to the real estate markets of Australia, Germany, Japan and Sweden where NFT was present. A noteworthy exception is the UK real estate market, with important and rational FT strategies to sustain. With respect to the long-term volatility persistence, this seems to capture the mean reversion of real estate returns in the UK and Hong Kong markets. In general, the results are not consistent with those reported in previous studies because NFT dominates PFT in the majority of real estate markets under consideration.

The main contribution of this study is the investigation of the link between short-term PFT or NFT and long-term volatility in eight international real estate markets, symmetrically. Particular attention has been given to the link between short-term FT and long-term volatility, by means of a fractionally integrated GARCH approach, a symmetric one. Moreover, investigating the relationship between returns’ volatility and investors’ strategies based on FT entails significant implications because real estate assets offer a good alternative investment for many investors and speculators.

The purpose of this paper is to examine cross-correlation in stock returns of SAARC countries, conditional and unconditional volatility of stock markets and to test efficient market hypothesis (EMH).

Stock indices of India, Bangladesh, Sri Lanka and Pakistan are considered to serve as proxy for stock markets in SAARC countries. Data consist of daily closing price of stock indices from 2000 to 2011. Since preliminary testing indicated presence of serial autocorrelation and volatility clustering, family of GARCH models is selected.

Results indicate presence of serial autocorrelation in stock market returns, implying dependence of current stock prices on stock prices of previous times and leads to rejection of EMH. Significant relationship between stock market returns and unconditional volatility indicates investors’ expectation of extra risk premium for exposing their portfolios to unexpected variations in stock markets. Cross-correlation revealed level of integration of South Asian economies with global market to be high.

Business cycles and other macroeconomic developments affect most companies and lead to unexplained relationships. The paper finds stock markets to exist at different levels of development as economic liberalization started at different points of time in SAARC countries.

Correlation between stock indices of SAARC economies are found to be low which is in line with intra-regional trade being one of lowest as compared to other regional groups. Results point towards greater need for economic cooperation and integration between SAARC countries. Greater financial integration leads to development of markets and institutions, effective price discovery, higher savings and greater economic progress.

The paper focuses on EMH and risk return relation for SAARC nations.

This paper empirically assesses the determinants of conditional stock index autocorrelation with particular emphasis on the impact of return volatility that are theoretically linked through the behaviour of feedback traders.

The S&P 100, 500 and the NASDAQ 100 index are considered and volatility in each series is captured using option‐implied estimates taken from the Chicago Board Options Exchange. A seemingly unrelated regression approach is used in which trading volume and volatility are simultaneously modelled.

The results of this study suggest that low or even negative return autocorrelations are more likely in situations where: return volatility is high; price falls by a large amount; traded stock volumes are high; and the economy is in a recessionary phase.

The results confirm that previous related work showing a link between autocorrelation and volatility is not induced by a mechanical relation.

Usage of endogenously determined volatility measures in this area of the literature is justified.

This study provides a robustness test of the autocorrelation/volatility relation, as well as a further exploration of the utility inherent in option‐implied volatility.