What Do We Learn from Daily Leaders and Laggards in Stock Investment? Do They Help Outperform the Market Average?

Investors generally believe that rising stocks are more likely to maintain their trend and rise going forward, whereas the losing stocks look more price attractive. This belief can lead the investors to expect that they can outperform the average market by trading the stocks purely based on the price movements. However, this research finds that this simple trading strategy does not effectively outperform the market. Nonetheless, we find five sectors of rising stocks and three sectors of declining stocks that outperform the average market in this limited study.

The Initial Analysis of the Share Suitability for the Inclusion into an Active Trading Strategy

The article lists the problems inherent to the Russian stock market at the present stage, special attention is paid to the liquidity issues. The authors evaluate the shares of all issuers listed on the Moscow Stock Exchange for the possibility of their inclusion in an active strategy based on average trading volumes. The article calculates the effectiveness of using the methods of average values in assessing the compliance of the selected instruments with the minimum required liquidity values. In the course of the work, the industry features of the Russian market are taken into account. The classifier of the Moscow Exchange is used to distribute issuers by industry. In parallel, the liquidity imbalance between the branches of the Russian stock market is being investigated. The conclusion is given about the real number of stock market instruments suitable for use in active trading strategies. The result of the study is a formed set of shares distributed by industry.

The predictive ability of stock market factors

Purpose This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions. Design/methodology/approach Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns. Findings Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns. Research limitations/implications The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy. Practical implications The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement. Originality/value The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.