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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 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.

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.

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.

The failure of the efficient market hypothesis has a direct bearing on the Geometric Brownian Motion model of asset returns. The current paper aims to investigate the effect that the autocorrelation in the time‐series of returns has on the calculation of expected shortfall (ES) for an asset‐liability investor.

The regression model is selected according to the Akaike and the Schwarz information criterion. A series of tests are used to insure the stability of the autocorrelation parameters. Autocorrelation‐adjusted formulas for volatility and cross‐asset correlations are then employed for the computations.

The presence of autocorrelation changes the values of most of the correlation parameters used in the calculation of the ES of the risk bearing capital (RBC) – in some cases the cross‐asset correlation parameters double. Once the presence of smoothing is accounted for, the ES increases by 1 per cent in relative value.

Other asset classes may also feature smoothed time‐series requiring thus an account of their autocorrelation structure and their interaction with the property asset. An analysis of the time stability of the cross‐asset correlations may also improve the estimation of the optimal RBC.

The proposed method focuses on the proper calculation of the RBC through the judicious estimation of the relevant risk measure for an investor who, while not having access to the underlying data pool from which the property index is computed, cannot adjust the index for the potential presence of temporal aggregation and market illiquidity.

This paper seeks to investigate the relationship between volatility and autocorrelation in major European stock index futures markets.

The methodology is based on the exponential autoregressive model with conditionally heteroskedastic errors (EAR‐GARCH).

The evidence points to a negative relationship between volatility and autocorrelation. Specifically, autocorrelation is low during volatile periods and high during calm periods. This evidence is in agreement with LeBaron's findings for US stock market returns, suggesting that return dynamics are similar across asset categories.

An obvious limitation of this study is the lack of a theoretical justification for the observed relationships in futures markets, an area where future research should be directed.

The observed relationships suggest that futures prices are non‐linearly predictable so that short‐term trading could produce abnormal returns.

The paper documents a negative relationship between volatility and autocorrelation in major European futures markets. This finding should be of interest to researchers and market participants.