Dynamic Quantile Panel Data Analysis of Stock Returns Predictability

Abstract

<p>This paper analyses the effect of financial ratios on stock returns using quantile regression for dynamic panel data with fixed effects. Eighty three firms of manufacturing industry, which were traded on the Borsa Istanbul for 2000-2014 period, are covered in the study. The most of financial variables have heterogeneous structure so they generally include extreme values. Thus, panel quantile regression technique, suggested by Koenker (2004), is used. Since the technique yields robust estimator in the case of extreme values the Gaussian estimators will be biased and not efficient. The sensitivity of relationship, on the other hand, can be studied for different parts of the stock returns’ conditional distribution by using quantile regression technique. However, because of that the lagged of dependent variable is used as an explanatory variable in dynamic panel models, fixed effect estimators will be biased. Thereby, in this study the instrumental variable approach suggested by Chernozhukov and Hansen (2006) is used to produce unbiased and consistent estimators.</p>The results show that the stock returns respond to the changes on the financial leverage ratio, the dividend yield, the market-to-book value ratio, financial beta and the total active profitability variables differently for the different parts of the stock returns’ conditional distribution. They also indicate that, at high quantiles, return fluctuations in the current period will be more effective for investors’ transaction attitudes on stocks for the next period.

Return predictability and valuation ratios: sector-level evidence on the Johannesburg stock exchange

“…Moreover, due to the influence of bubbles on asset pricing, this study could be extended by employing a present value model that is adjusted to the presence of bubbles which warrants the use of a regime-switching model that incorporates bubbles as previously done by McMillan (2010). Lastly, future studies could further disaggregate the data and assess the predictive ability of financial statement information using firmlevel panel data as done by Güloğlu et al (2016) and Alexakis et al (2010), among others. You are free to: Share -copy and redistribute the material in any medium or format.…”

“…Alexakis et al (2010) found that valuation ratios, asset utilisation, debt, investment and liquidity ratios could predict stock returns of 47 non-financial firms on the Athens stock market. Güloğlu et al (2016) showed that financial leverage, dividend yield and market-to-book ratios could predict stock returns of 83 Turkish firms. Kheradyar et al (2011) found that dividend yield, earnings yield and book-to-market ratios could predict returns of 960 companies on the Malaysian Stock Exchange.…”

The Relationship Between Energy Consumption and Growth in Emerging Markets by Panel Quantile Regression: Evidence from VISTA Countries

“…Quantile regression models allow the researcher to account for unobserved heterogeneity and heterogeneous covariates effects, while the availability of panel data potentially allows the researcher to include fixed effects to control for some unobserved covariates (Canay, 2011). Recently, some researchers associated these two methodologies and named it Panel Quantile Regression (Koenker, 2004;Geraci and Bottai, 2007;Abrevaya and Dahl, 2008;Galvao, 2008;Rosen, 2009;Lamarche, 2010;Guloglu, et al 2016). Koenker (2011) explains the panel quantile regression with fixed effect like these: suppose that the conditional quantile functions of the response of the j th observation on the i th individual y ij takes the form:…”