Stock Return Autocorrelation Research Articles

Stock Return Autocorrelations and Expected Option Returns

We show that the return autocorrelation of underlying stock is an important determinant of expected equity option returns. Using an extended Black-Scholes model incorporating the presence of stock return autocorrelation, we demonstrate that expected returns of both call and put options are increasing in the return autocorrelation coefficient of the underlying stock. Consistent with this insight, we find strong empirical support in the cross-section of average returns of equity options. Our paper highlights the necessity to control for stock return autocorrelation when studying option return predictability. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03071 .

Stock Return Autocorrelations: Evidence from the Asia‐Pacific Stock Markets

AbstractThis study uses quantile regression to examine the stock autocorrelations of eight Asian markets for the period 1990–2014. First, we find that the impacts of their previous returns are basically positive under most of the quantiles. Second, if we distinguish previous returns as positive or negative, the basic positive autocorrelations are strengthened. Third, when the previous return soars or plummets, the basic positive autocorrelations are not obviously changed. Last, through the cross‐comparison of two different financial crises, geographical correlations are shown to be a potentially major factor in the spread of the impact of the financial crisis.

Beliefs About the Stock Market and Investment Choices: Evidence from a Field Experiment

We survey retail investors at an online bank to study beliefs about the autocorrelation of aggregate stock returns, and how these beliefs shape investment decisions measured in administrative account data. Individuals’ beliefs exhibit substantial heterogeneity and predict trading responses to market movements. We inform a random half of our respondents that historically the autocorrelation of aggregate returns was close to zero, which persistently changes their beliefs. Among those initially believing in mean reversion, treated respondents buy significantly less equity during the COVID-19 crash four months later. Our results highlight how heterogeneity in subjective models causally drives trade in asset markets.

Stock Return Autocorrelation and Individual Equity Option Prices

This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. The relation between stock return autocorrelation and option price structure is more pronounced when market is volatile, especially during financial crisis. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. Thus, stock return autocorrelation can help differentiate the price structure across individual equity options.

Stock returns, quantile autocorrelation, and volatility forecasting

We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast stock return volatility. Our empirical results show that the strength of the autoregression varies across the quantiles of the returns' distribution in terms of both magnitude and persistence. Specifically, the autoregression order and magnitude of the coefficients is lower in the left tail in comparison with the right tail. Additionally, we show that the quantile autoregressive (QAR) framework proposed in this study improves out-of-sample volatility forecasting performance compared to the generalised autoregressive conditional heteroscedasticity (GARCH)-type models and other quantile-based models. We also observe greater outperformance in QAR estimates during periods of financial turmoil. Moreover, the QAR method also explains the stylized ‘leverage effect’ associated with asset returns in the presence of volatility asymmetry.

Interactions Between Housing Market and Stock Market in the United States: A Markov Switching Approach

This study uses the Markov switching vector autoregressive model (MSVAR) model to examine dynamic relationships between stock and housing market returns in the United States covering the period from 1987 to 2017. The results show significant regime-dependent auto-correlations in stock and housing returns in both the high volatility and low volatility regimes studied. The feedback effects are stronger in the housing market than the stock market. We observe significant positive cross-market spillovers, consistent with the wealth story. Increases in stock returns in low volatility regimes create positive spillover effects into housing markets; likewise, positive spillovers in the reverse direction from housing market to stock market occur in high volatility regimes. We also find significant negative correlations between lagged stock returns and current housing returns in the high volatility regime, which implies that capital switching occurs as investors move their investments out of the stock market and into the housing market. In this manner, the housing market becomes a hedge against a volatile stock market.

Weather-Induced Moods and Stock-Return Autocorrelation

Abstract Moods affect investors’ attention, memory, and capacity to process information. Inattentive investors delay the price adjustment process, thus leading to a positive autocorrelation of asset returns. In this study, I investigate the relationship between weather-induced moods and stock-return autocorrelation in the Stock Exchange of Thailand from January 2, 1991, to December 29, 2017. Only good moods contribute significantly to return autocorrelation.

Underreaction, Overreaction, and Dynamic Autocorrelation of Stock Returns

I document that aggregate stock market returns correlate negatively with past returns in the first half of the earnings seasons, and positively in the second half. While these two arms of correlation are individually strong, they cancel with each other, averaging to a weak unconditional autocorrelation. A stylized model featuring both underreaction and overreaction explains this pattern. Similar return predictability pattern in the cross section of industry returns and the international stock markets provides out-of-sample evidence for the model. The paper shows that the traditional view on stock return autocorrelation misses important information, which is that it varies over time.

Stock Return Autocorrelations and the Cross Section of Option Returns

We present a new finding that the return autocorrelation of underlying stock is an important determinant of expected equity option returns. Using an extended Black-Scholes model incorporating the presence of stock return autocorrelation, we show that expected returns of both call and put options are increasing in return autocorrelation coefficient of the underlying stock. Consistent with this insight, we find strong empirical support in the cross-section of average returns of equity options. Average returns of calls and puts as well as average returns of straddles all show monotonically increasing relationship with the degree of underlying stock's return autocorrelation coefficient. Additional equity option portfolio analysis shows that the information on stock return autocorrelation helps investors to significantly improve the out-of-sample performance of their portfolios.

Do stock bulletin board systems (BBS) contain useful information? A viewpoint of interaction between BBS quality and predicting ability

AbstractThis study explores whether information on internet stock bulletin board systems (BBS) is valuable for stock return prediction, taking advantage of data derived from the biggest stock BBS in China. Using a text classification algorithm, we find the online messages significantly predict stock return with negligible R‐squared. However, we find that accuracy of individual BBS posts is below 50 percent and there is no distinction at prediction accuracy between high‐ and low‐quality stock BBS. Due to the autocorrelation of stock returns, we argue that BBS predicts stock returns because of its reflection on the simultaneous stock return rather than revelation on valuable information.

Risk, uncertainty and stock returns predictability – a case of emerging equity markets

PurposeThis paper aims to investigate the predictability of stock returns under risk and uncertainty of a set of 11 emerging equity markets (EEMs) during the pre- and post-crash periods.Design/methodology/approachListed indices are considered to serve the proxy of stock markets with a structural break in data for the period: 2000-2014. As preliminary results highlight the significant autocorrelations in stock returns, Threshold-GARCH (1,1) model is used to estimate the conditional volatility, which is further decomposed into expected and unexpected volatility.FindingsResults highlight that the volatility has symmetric impact on stock returns during the pre-crash period and asymmetric impact during the post-crash period. While testing the relationship of stock returns, a significant positive (negative) relationship is found with expected volatility during the pre-crash (post-crash) periods. The stock returns are found positively related to unexpected volatility.Research limitations/implicationsBusiness, political and other market conditions of sample stock markets are fundamentally different. These economies were liberalized in different years, which may affect the degree of integration with international equity markets.Practical implicationsThe findings highlight that investors consider the impact of expected volatility in forecasting of stock returns during the growth period. They realize returns in commensurate to risk of their portfolios. However, they significantly reduce their investments in response to expected volatility during the recession period. The positive relationship between stock returns and unexpected volatility highlights the fact that investors realize extra returns for exposing their portfolios to unexpected volatility.Originality/valuePioneer efforts are made by using T-GARCH (1,1) procedure to analyse the problem. Given the emergence of emerging equity markets, new insight in dynamics of stock returns provide interesting findings for portfolio diversification under risk and uncertainty.

A robust and powerful test of abnormal stock returns in long-horizon event studies

Abstract This paper proposes a novel standardized test for abnormal returns in long-horizon event studies that takes into account cross-sectional correlation, autocorrelation, and heteroskedasticity of stock returns. Extensive simulation analyses demonstrate improved size and power of testing relative to existing long-run test methodologies. Application to initial public offerings and seasoned equity offerings further demonstrates robustness to extreme return outliers inherent in these long-run studies.

Elements of decisional dynamics: An agent-based approach applied to artificial financial market

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Interaction of Housing Market and Stock Market in the U.S. - A Markov Switching Model Analysis

This paper uses the Markov Switching VAR model to examine the dynamics relationships between stock returns and housing returns in the US covering the periods from 1987 to 2017. The results show significant regime-dependent auto-correlations in stock and housing returns in both the high volatility and the low volatility regimes. The feedback effects are stronger in than housing markets than the stock markets. We observe significant positive cross-market spillovers, which are consistent with the wealth story. Increases in stock return in the low volatility regime create positive spillover effects into housing markets; and likewise, the positive spillovers in the reverse direction from housing market to the stock market occur in the high volatility regime. We also find significant negative correlations between the lagged stock returns and the current housing returns in the high volatility regime, which imply that capital switching occur where investors move their investments out of the housing market into the stock market.

Does the predictive power of variable moving average rules vanish over time and can we explain such tendencies?

Recent studies offer striking evidence that, in stock markets, the predictive power of fundamental variables and seasonal effects tends to diminish over time. In this article, we analyse whether this also holds for the popular variable moving average (VMA) rules of Brock et al. (1992). While previous research on this issue has strongly concentrated on US and emerging stock market indices, we fill a research gap by focussing on a wide range of developed market indices and individual stocks. Using a trend regression approach for a dataset covering 1972 to 2015, we find that most analysed trading rule specifications show negative trend coefficients. These results, robust in a variety of settings, indicate that VMA rule signals have steadily lost their ability to forecast future price movements accurately. Analysing several rationales for this outcome, we find that negative trends in the autocorrelation of stock returns are a highly promising explanation for the poorer performance of VMA rules because they are designed to capture autocorrelation.

Financial instability, integration and volatility of emerging South Asian stock markets

PurposeThe purpose of this paper is to examine the short- and long-run spillover effect of international financial instability on emerging South Asian stock markets. The paper also investigates the financial integration regionally.Design/methodology/approachGranger causality test is used for short-run causal relations. Since results of preliminary test highlight the significant autocorrelations in stock returns, GARCH class models with extreme shocks in international financial market are utilized to test the long-run spillover impact on stock returns.FindingsResults indicate significant short- and long-run spillover impacts of international financial instability on the stock returns. They highlight the significant co-integration of South Asian stock markets with the international market. Significant correlations in stock returns and volatility reveal the degree of regional integration to be high between India, Pakistan and Sri Lanka.Research limitations/implicationsBusiness, political and market conditions of South Asian stock markets are fundamentally different from each other. These economies were liberalized at different time, which in turn may affect the degree of integration with international equity markets and regionally alike.Practical implicationsFinancial liberalization has linked the South Asian stock markets to the rest of the world. Stock prices move in the same line with the emergence of global expected and unexpected economic shocks. The benefits that arise from the diversification of funds will be eradicated in the long run. Investors with long investment horizons will not actually benefit from portfolio diversification in South Asian equity markets. The Bangladesh stock market does not respond to volatility in international market in the short run and may be a good destination for short-term investment.Originality/valuePioneer efforts are made by utilizing a novel approach with the use of net volatility change in world financial instability for measuring the short- and long-run impacts. Given the emergence of South Asian stock markets, new insights into their vulnerability to world financial shocks provide interesting findings for portfolio diversification.

Stock return autocorrelation, day of the week and volatility

PurposeThe 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.Design/methodology/approachThe 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.FindingsResults 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.Originality/valueThis 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.

Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models

This paper applies the threshold quantile autoregressive model to study stock return autocorrelations and predictability in the Chinese stock market from 2005 to 2014. The results show that the Shanghai A-share stock index has significant negative autocorrelations in the lower regime and has significant positive autocorrelations in the higher regime. It attributes that Chinese investors overreact and underreact in two different states. These results are similar when we employ individual stocks. Besides, we investigate stock return autocorrelations by different stock characteristics, including liquidity, volatility, market to book ratio and investor sentiment. The results show autocorrelations are significantly large in the middle and higher regimes of market to book ratio and volatility. Psychological biases can result into return autocorrelations by using investor sentiment proxy since autocorrelations are significantly larger in the middle and higher regime of investor sentiment. The empirical results show that predictability exists in the Chinese stock market.

Dynamic autocorrelation of intraday stock returns

We discover three significant periodicities in the autocorrelation of intraday stock returns. We demonstrate that (i) the autocorrelation is 64% more negative during afternoons than during mornings, (ii) the autocorrelation is more negative Tuesdays through Fridays than on Mondays, (iii) overall serial correlation becomes less negative when salient information events arrive, i.e., earnings months, but measures less negative during mornings and on Mondays. Our results support the hypothesis that informational demand is more critical following daily and weekly market closures when information accumulated cannot easily be traded on, while liquidity demand intensifies closer to the no-trading periods.

Identifying Financial Bubbles Using Finite-time Singularity GARCH Model: A Case Study of Tehran Stock Exchange

The purpose of this study was to identify financial bubbles using finite-time singularity GARCH model in the case study of Tehran Stock Exchange for the period 2004 to 2013. Statistical sample of study included 102 manufacturing companies listed in Tehran Stock Exchange Tehran. To achieve to this goal, two hypotheses were proposed. To test the hypotheses, Dickey-Fuller test, GARCH model (1, 1) vector auto-regression, and BEKK method were used. The results of this study suggest that both hypotheses were confirmed, in other words, in the financial bubbles situation, autocorrelation of stock returns is significant. I addition, by adding a regression component to see the possible positive feedback effects of price on return or return on stock return, we can see the returns autocorrelation in GARCH (1, 1) model. DOI: 10.5901/mjss.2016.v7n4S2p109