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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 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.

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 study aims to explore the impact of key parameters of brake pads on the dynamic characteristics of the braking system.

This study conducted experimental research based on a friction testing machine with a slider-disc structure. The experiment studied the impact of key parameters of brake pads (rotation speed, pressure, mass, braking radius, etc.) and the braking environment (dry friction, wetness, sand, etc.) on the stability of the braking system. At the same time, a dynamic model of the brake pad braking system was established and compared with experimental results using the mathematical tool of autocorrelation coefficient.

The key parameters of brake pads have a significant impact on the dynamic characteristics of the braking system; under different conditions of brake pad mass, tribological parameters, brake pad radius and braking environment, the chaotic characteristics of the braking friction force signal show a trend of expansion or contraction, which can be suppressed by adjusting the key parameters of brake pads.

This study can provide a reference for optimizing the braking strategy and reducing noise and vibration in brake pad systems.

This study aims to examine whether earnings autocorrelation affects the risk of an accounting restatement.

This paper uses logistic regression and identifies restatements between 2004 and 2016. Following prior research, (Dao et al., 2012; Francis and Michas, 2013; Francis et al., 2013; Lennox and Li, 2014; Lobo and Zhao, 2013; Paterson and Valencia, 2011), this study allows time between the end of our sample period and the date that this study obtained the restatement data because it takes time for material misstatements to be identified.

Bryan et al. (2018) report a negative association between autocorrelation and audit fees, suggesting that auditors view lower autocorrelation as increasing inherent risk. This study finds that autocorrelation is negatively related to accounting restatements, implying that although auditors react to lower autocorrelation by increasing their risk assessments (Bryan et al., 2018), their risk response is not sufficient. This study finds that autocorrelation has a fairly large effect: a shift from the 75th to the 25th percentile of autocorrelation is associated with a 9.38% increase in the likelihood of a restatement.

This study contributes to the stream of research that investigates the determinants of restatements. Not only do this study identifies autocorrelation as a factor that contributes to restatements, but importantly, this study’s results reveal a fairly substantial effect size: a shift from the 75th to the 25th percentile of autocorrelation is associated with a 9.38% increase in the likelihood of a restatement. While Bryan et al. (2018) find that autocorrelation affects audit fees, this study links autocorrelation to a more drastic consequence: accounting restatements.

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.

This study aims to investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.

The paper aims to identify the effect of ignorance of correlatedness among process observations and to implement new sampling schemes; skip and mixed sampling, in order to reduce the effect of autocorrelation on process capability index (PCI) Cpm.

Autocorrelated observations are generated using autoregressive process of order two (AR (2)) using Monte Carlo simulations. The PCI is computed based on these observations assuming the independence. The skip and mixed sampling schemes are then used to form sub-groups among correlated observations. The PCI obtained using sub-groups from skip and mixed sampling schemes are assessed using sample mean and sample standard deviation.

The paper provides empirical insights into how the effect of autocorrelation decreases in the estimated value of PCI Cpm. The use of new sampling schemes, skip and mixed sampling, reduces the effect of autocorrelation on estimates of PCI Cpm.

This paper fulfills an identified need to study how to reduce the effect of autocorrelation on PCI Cpm.

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.