Are stock returns still mean-reverting?

Mean Reversion Expectations and the 1987 Stock Market Crash: An Empirical Investigation

The equity premium - the difference between the return achievable from investment in the equity market (RM ) and the risk-free rate of return (RF )- plays an important part in corporate finance. The expression equity premium (sometimes referred to as the equity risk premium) is used to denote the ex ante expectation of investors. The term excess return refers to the ex post achievement of stock returns over and above the risk-free return. If we compare US and UK returns, we find that total returns, real returns and the value of (RM - RF ) are all marginally higher for the UK. Summarized evidence appears in Table 1 and Table 6. Such greater returns may be due to an increased risk premium related to increasing unexpected inflation. Particularly important in estimating the equity risk premium is whether excess returns are measured using a geometric or an arithmetic mean return. To a significant extent, this question revolves around mean reversion in stock returns. Evidence of mean reversion is substantial, although it cannot be proved unequivocally. Given the weight of evidence of mean reversion, there may be a strong case for the use of a geometric mean with an equity premium of between 3% and 5% - or even less.

This study determines optimal portfolios of five major U.S. financial assets based on four performance measures consistent with modern portfolio theory and the Capital Asset Pricing Model, and analyzes their risk-return profiles, over different investment horizons. Intermediate-term government bonds offer the best tradeoff between the risk premium and systematic risk, while small company stocks provide the highest risk premium adjusted for systematic risk, for all investment periods. Diversified portfolios containing mainly intermediate-term government bonds, with smaller allocations to small and large company stocks, generally deliver the best tradeoff between the risk premium and total risk as well as the highest risk premium adjusted for total risk. These diversified optimal portfolios hold declining proportions of intermediate-term government bonds and rising allocations of small and large company stocks, for longer investment horizons. All the optimal portfolios identified by the different performance measures offer substantially better risk-adjusted performance over longer periods. Keywords Performance Measures, Optimal Portfolios, Risk-Return Tradeoff, Investment Horizon

This thesis examines predictability and seasonality in the cross-section of stock returns. The first chapter, titled ``Infrequent Rebalancing, Return Autocorrelation, and Seasonality,'' shows that a model of infrequent rebalancing can explain specific predictability patterns in the time series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross-sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross-section of stock returns, which can help explain available empirical evidence. The second chapter, titled ``Seasonalities in Anomalies,'' investigates return seasonalities in a set of well-known anomalies in the cross-section of U.S. stocks returns. A January seasonality goes beyond a size effect and strongly affects most anomalies, which can even switch sign in January. Both tax-loss selling and firm size are important in explaining the turn-of-the-year pattern. Return seasonality exists outside of January, with respect to the month of the quarter. Small stocks earn abnormally high average returns on the last day of each quarter, which significantly affects size, idiosyncratic volatility, and illiquidity portfolios. The results have implications for the interpretation and analysis of many anomalies, such as asset growth and momentum. The third chapter, titled ``The Cross-Section of Intraday and Overnight Returns,'' uses a thirty-year sample of U.S. stock returns to document substantial cross-sectional variation in returns over the trading day and overnight. Market closures have a large impact on returns. Small and illiquid stocks earn high average returns in the last thirty minutes of trading. In contrast, large and liquid stocks perform poorly at this time. I find support for institutional and information asymmetry theories. But these theories do not fully explain the cross-sectional evidence. Portfolios based on other characteristics, such as beta and idiosyncratic volatility, earn their return gradually throughout the trading day–contrary to the market and a benchmark based on random portfolios. These portfolios also tend to incur large negative returns overnight, consistent with mispricing at the open.

Liquidity, Price Behavior and Market-Related Events

Utilizing the standard linearity test of Luukkonen et al. (1988 Luukkonen, R, Saikkonen, P and Teräsvirta, T. 1988. Testing linearity against Smooth Transition Autoregressive Models. Biometrika, 75: 491–9. [Crossref], [Web of Science ®] , [Google Scholar]), the linear nature of all the Asian stock indices has been formally rejected. This finding warrants use of the nonlinear stationary test of Kapetanois et al . (2003), which is also constructed in the STAR framework, to investigate the mean reverting property of the stock prices series. As a whole, this study not only found convincing evidence of a nonlinear mean reverting pattern in all the Asian stock indices, but also demonstrates the risk of drawing the wrong inferences on mean reversion when the ADF test is applied to data governed by nonlinearity.

Dependence and mean reversion in stock prices: The case of the MENA region

Cross-Autocorrelation in Asian Stock Markets

This article documents the long-horizon mean reverting character of annual earnings and tests the implications of such mean reversion for security valuation. First, both theory-based and nonparametric measures of earnings persistence decrease as the estimation order increases, revealing 40 percent less long-horizon persistence than expected under the commonly used random walk model. Second, the return responses to the earnings shocks are more closely related across firms to the higher-order measures of persistence that reflect significant long-horizon mean reversion. Third, the persistence measure derived from classical valuation theory outperforms the generic measure in explaining the return responses. Taken as a whole, these results provide evidence for significant mean reversion in the higher-order properties of earnings and for the stock market incorporating these properties in a manner consistent with classical valuation theory.

The Sources of Portfolio Returns: Underlying Stock Returns and the Excess Growth Rate

The explosive growth in REITs over the past decade suggests that they provide an important alternative investment vehicle. Since REITs are a representation of the real estate market, they should theoretically provide similar diversification benefits of holding direct real estate in a portfolio. Even though in the long run REIT performance may be governed by real estate fundamentals, investigations in the return dynamics of REITs show that they do not necessarily move in tandem with the private real estate market, but rather exhibit properties similar to common stocks. This study examines the return dynamics of REITs using different models. While we find that REIT returns have a significant real estate factor, we show that the most significant determinant of REIT return performance is the returns of small cap value stocks. We also find that REITs behave more like small cap value stocks than small cap growth stocks or large stocks. In addition, we find a decline in the explanatory power of large stocks.

The purpose of this study was to determine the influence of the operating cash flow, the financing cash flow, and the influence of the investing cash flow to the stock returns, as well as to determine the influence of the operating cash flow, the financing cash flow, and the investing cash flow together equally against the stock returns. Number of sample that used in this research is 30 samples with the method purposive sampling. Based on the results of simultaneous research indicate that the operating cash flows, the financing cash flows and the investing cash flows did not have any influence on the stock returns. The result of research partially known that the operating cash flow has not an effect on stock returns, the financing cash flow has not an effect on stock returns, and the investment cash flow has not an effect on stock returns. The research concluded that the operating cash flow, the financing cash flow, and the investing cash flow did not have any influence on the stock returns, either simultaneously or partially. The suggestions of the research is for the investors and the potential investors or other stockholders taking into account the information of a company’s cash flow from the operating cash flows, the financing cash flows and the investing cash flows to predicting the stock returns.

This research uses the results of eleven surveys of Taiwan’s most admired firms conducted annually by 天下 magazine during the time period 1994-2005. By analyzing both the short-term and long-term performances of these companies and how they are related with the firms’ stock returns and trading volumes, I hope to answer the controversial question that has left investors wondering, “ Whether a good company’s stock is really worth investing ? ”. By examining the stock returns and trading volumes ten days before and after the survey is released, this paper finds that, in the short-run, abnormal trading volume can be observed one to three days before the survey is released and that the survey does have an announcement effect on investors. However, the stock prices of these benchmark companies seldom go up before an event, therefore the potential profit from trading using the survey results is limited. Using different regression designs, this paper finds that, in the long-run, the stock returns and trading volumes of companies nominated for benchmark companies are both lower than those of comparable companies. In addition, by examining the stock returns and trading volumes twenty weeks before and after the release of the survey, this survey finds that the stock return of a company worsens after being nominated. Company performance does not have any significant effects on trading volume. Lastly, this paper looks at different investment portfolios consisting of these benchmark companies and their performances in a 47-week time span and finds that investors can profit from buying stocks of companies that are not performing very well. Based on the above-mentioned analysis, this paper concludes that a good company’s stock may not be worth investing, which supports the views of behavioral finance scholars’ and researchers’ that investors often overestimate the stock price of a company because they are overly optimistic. Due to mean reversion, the stock returns of these companies are often not what the investors were expecting. A possible explanation of this finding is that investors adopt representative bias and regret aversion when making their investment decisions. This paper proves that a good company’s stock is not worth investing by comparing the trading volumes of these companies with other companies in the same sectors. Other parts of this research may need further research due to the fact that the results are not statistically significant.

ABSTRACTThis study explores the size effect in financial markets, focusing on how mergers, acquisitions, and other corporate transactions influence the returns of small versus large stocks. Employing a comprehensive data set of US‐listed companies from 1992 to 2021, which includes 51,780 events, this research improves upon previous methodologies by integrating detailed timing information on deal announcements and completions with stock size and return data. Our analysis shows that small stocks are often the targets of transactions that significantly enhance their returns, not limited to takeovers. We find that pre‐announcement returns are consistently higher for small stocks, likely due to less analyst coverage, resulting in largely unanticipated deal news. The study deepens our understanding of the size effect, suggesting that deal‐related dynamics are essential for analyzing performance variations across different stock sizes and contributing to discussions on market efficiency and the valuation effects of corporate actions.

The objective of this study is to examine the stock returns variation to specific economic variables by applying a multi-factor model. The firms relating to banking and textile sectors were selected for this study on the basis of data availability, profitability and performance on the Karachi Stock Exchange. The data for the selected firms and economic variables obtained for the period of 10 years. GARCH model used to analyze risk and returns relationship. The tests applied on the stock returns of each firm and on the data set of the entire industry to generalize the results. The results disclose that market return is mainly accounts variation in stock returns, however the inclusion of other macroeconomic and industry related variables has added additional explanatory power in describing the stock returns variation. It is found that economic exposure is higher at industry level than firm level stock returns. Results also indicate that stock returns of different firms behave differently in similar economic conditions that acquaint investors about the risk diversification opportunity in the stock market. Key words: Stock returns, multifactor model, macroeconomic variables.