Search results

This research paper aims to analyze the stock exchanges of developed, emerging and developing countries to investigate the volatility in stock markets and to evaluate the rate of mean reversion.

The stock exchanges included in the research are NASDAQ, Tokyo stock exchange, Shanghai stock exchange, Bombay stock exchange, Karachi stock exchange and Jakarta stock exchange. Secondary daily data from Bloomberg are used to conduct the research for the period from January 2011 to December 2018. Generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model was applied to examine volatility and the half-life formula was used to calculate mean reversion in days.

The research concluded that all the stock exchanges included in the research satisfy the assumptions of mean reversion. Developing countries have the lowest volatility while emerging countries have the highest volatility which means that the rate of mean reversion is fastest in developing countries and slowest in emerging countries.

Future studies can determine the reasons for fastest rate of mean reversion in developing countries and slowest rate of mean reversion in emerging countries.

Developing countries show the lowest mean reversion in days while the emerging countries show the highest mean reversion in days indicating that developing countries take less time to revert to their mean position.

The majority of previous studies on univariate volatility models are mostly on applications of the models. Only a few researchers have taken the robustness of the models into account when applying them in emerging countries and not in developed, developing and emerging countries in one place. This makes the current study unique and more rigorous.

The purpose of this article is to revisit the mean reversion in profitability and earnings among Indian-listed firms, based on the idea that changes in profitability and earnings are somewhat predictable.

The study used a sample of 445 Bombay Stock Exchange (BSE)-listed companies and 309 companies from the manufacturing sector in India for the period from 2007 to 2020. The study employed cross-sectional regressions. Both linear and non-linear Partial Adjustment Models (PAM) were used to forecast profitability and earnings.

The study revealed that profitability and earnings mean revert for both the BSE-listed companies and the manufacturing sector companies from 2007 to 2012. However, for the years from 2013 to 2020, it was found that there is no significant evidence of mean reversion in both the BSE-listed companies or the manufacturing sector companies.

The findings have larger implications for security analysts who forecast future stabilisation or recovery of historically high or low growth rates. Investors and analysts would benefit from having a better understanding of how competitive attacks affect profitability as well as how the overall economic growth of a country affects earnings and valuations.

Most of the empirical research in India has focused on mean reversion in stock prices or stock returns. The present study looked at the mean reversion of profitability and earnings in Indian firms.

This study aims to analyze the effect of change in trading volume on the short-term mean reversion of the stock price in the Korean stock market. Through the variance ratio test, this paper finds that the market shows the mean reversion pattern after 2000, but not before. This study also confirms that the mean reversion property is significantly reduced if the effect of change in trading volume is excluded from the return of a stock with a significant contemporaneous correlation between return and change in trading volume in the post-2000 market. The results appear in both the Korea Composite Stock Price Index and Korea Securities Dealers Automated Quotation. This phenomenon stems from the significance of the return response to change in trading volume per se and not the sign of the response. Additionally, the findings imply that the trading volume has a term structure because of the mean reversion of the trading volume and the return also has a partial term structure because of the contemporaneous correlation between return and change in trading volume. This conclusion suggests that considering the short-term impact of change in trading volume enables a more efficient observation of the market and avoidance of asset misallocation.

This paper investigates whether stock‐price indexes of emerging markets can be characterized as random walk (unit root) or mean reversion processes. We implement a panelbased test that exploits cross‐sectional information from seventeen emerging equity markets during the period January 1985 to April 2002. The gain in power allows us to reject the null hypothesis of random walk in favor of mean reversion at the 5 percent significance level. We find a positive speed of reversion with a half‐life of about 30 months. These results are similar to those documented for developed markets. Our findings provide an interesting comparison to existing studies on more matured markets and reduce the likelihood of earlier mean reversion findings as attributable to data mining.

Reconsiders the double sinking fund problem by looking at each of the common methods used. Investigates the underlying assumptions and the residual errors or inconsistencies. Notes that the use of traditional dual rate valuations results in a mathematical error within the valuation and an under‐valuation of the interest. Concludes that the Double Sinking Fund Method must be recommended in preference to Pannell′s Method.

In this paper we empirically investigate the tendency for beta risk to mean‐revert across industries. Using a sample of Australian stocks over the ten‐year period 1989 to 1998, our key results are as follows. We generally observe evidence of a mean reversion tendency — in particular, this seems most appropriate for the Gold, Energy, Finance and Consumer industry groupings. Moreover, there is some evidence that the mean reversion of beta is different across industries. Furthermore, we see that the maximum mean reversion beta occurs for the Gold industry — a value of approximately 1.4 (1.6) for the OLS (Scholes‐Williams) beta analysis. On the other hand, the minimum mean reversion beta based on the ‘All Stocks’ OLS analysis occurs for Miscellaneous Industries with a value of 0.4, while a similar minimum mean reversion beta based on the Scholes‐Williams analysis occurs for the Consumer industry grouping.

Evidence of mean reversion in U.S. stock prices during the post‐World War II era is mixed. I find that using the standard portfolio formation method to construct size‐sorted portfolios is inadequate for detecting mean reversion. Using alternative portfolio formation methods and additional cross‐sectional power gained from size‐sorted portfolios during the period 1963 to 1998, I find strong evidence of mean reversion in portfolio prices. My findings imply a significantly positive speed of reversion with a half‐life of approximately three and a half years. Parametric contrarian investment strategies that exploit mean reversion outperform buy‐and‐hold and standard contrarian strategies.

Recent papers on target capital structure show that debt ratio seems to vary widely in space and time, implying that the functional specifications of target debt ratios are of little empirical use. Further, target behavior cannot be adjudged correctly using debt ratios, as they could revert due to mechanical reasons. The purpose of this paper is to develop an alternative testing strategy to test the target capital structure.

The authors make use of a major “shock” to the debt ratios as an event and think of a subsequent reversion as a movement toward a mean or target debt ratio. By doing this, the authors no longer need to identify target debt ratios as a function of firm-specific variables or any other rigid functional form.

Similar to the broad empirical evidence in developed economies, there is no perceptible and systematic mean reversion by Indian firms. However, unlike developed countries, proportionate usage of debt to finance firms’ marginal financing deficits is extensive; equity is used rather sparingly.

The trade-off theory could be convincingly refuted at least for the emerging market of India. The paper here stimulated further research on finding reasons for specific financing behavior of emerging market firms.

The results show that the firms’ financing choices are not only depending on their own firm’s specific variables but also on the financial markets in which they operate.

This study attempts to assess mean reversion in debt ratios in a unique but reassuring manner. The results are confirmed by extensive calibration of the testing strategy using simulated data sets.

There are several studies that investigate evidence for mean reversion in stock prices. However, there is no consensus as to whether stock prices are mean reverting or random walk (unit root) processes. The goal of this paper is to re‐examine mean reversion in stock prices.

The authors use five different panel unit root tests, namely the Im, Pesaran and Shin t‐bar test statistic, the Levin and Lin test, the Im, Lee, and Tieslau Lagrangian multiplier test statistic, the seemingly unrelated regression test, and the multivariate augmented Dickey Fuller test advocated by Taylor and Sarno.

The main finding is that there is no mean reversion of stock prices, consistent with the efficient market hypothesis.

One issue not considered by this study is the role of structural breaks. It may be the case that the efficient market hypothesis is contingent on structural breaks in stock prices. Future studies should model structural breaks.

The findings have implications for econometric modelling, in particular forecasting.

This paper adds to the scarce literature on the mean reverting property of stock prices based on panel data; thus, it should be useful for researchers.

The purpose of this paper is to investigate the profitability of European Union (EU) firms with the aim of confirming the mean‐reverting pattern documented by earlier research in the USA. In addition, the paper classifies firms by industry sectors across countries to investigate potential differences.

The paper follows closely a model where the forecasting of profitability is done through year‐by‐year regressions. This approach allows the use of large samples and the year‐by‐year variation in the slopes. Both a linear and a nonlinear partial adjustment models are used for forecasting profitability.

Findings show that the profitability does follow a mean‐reverting process and that profitability forecasting can be improved substantially by exploiting the mean‐reverting feature. Further analysis shows that mean reversion does not play an important role in EU countries as in the USA and there is no evidence of nonlinearity in mean reversion. It was also found that mean‐reverting speed differ across industries, with utilities, financial and manufacturing among the lowest.

The sample companies are not originated from a single economy, but from 15 different countries with different macro‐economic conditions that might influence their profitability.

Studying the European market, where the institutional and financial structure of firms are different from the USA allows us to observe whether the US results are sample specific or can be generalized and applied elsewhere. The difference observed in these sample results is probably due to the fact that the US economy is more competitive than that of EU.