Firm Return Volatility Research Articles

The January and Turn-of-The-Month Effect on Firm Returns and Return Volatility

In this paper, we test whether January and turn-of-the-month (TOM) affect firm returns and firm return volatility differently depending on their sector and size. We use time series data for 560 firms listed on the NYSE and find evidence of both January and TOM affecting returns and return volatility of firms. The effects are, however, different for different firms and are dependent on the sectoral location of firms and on firm sizes. These findings imply that January and TOM have an heterogeneous effect on firm returns and firm return volatility.

Firm return volatility and economic gains: The role of oil prices

In this paper we investigate whether the oil price contributes to stock return volatility for 560 firms listed on the NYSE. Using daily data, we find that the oil price is a significant determinant and predictor of firm return variance. We devise trading strategies based on forecasts of firm return variance using the oil prices and historical averages. We find that investors can make substantial gains in returns by using the oil price in forecasting firm return variances.

New evidence on turn-of-the-month effects

In this paper, we test whether the turn-of-the-month (TOM) affects firm returns and firm return volatility differently depending on their sector and size. We use time series data for 560 firms listed on the NYSE and find evidence that the TOM affects returns and return volatility of firms. The effects are, however, different for different firms and are dependent on the sectoral location of firms and on firm sizes. These findings imply that the TOM has a heterogeneous effect on firm returns and firm return volatility.

Information Content of Public Firm Disclosures and the Sarbanes-Oxley Act

We find evidence that public firm disclosure, in the form of Management Discussion and Analysis (Sections 7 and 7a of annual reports), is more informative about the firm's future risk following the passage of the Sarbanes-Oxley Act of 2002. Employing a novel text regression, we are able to predict, out of sample, firm return volatility using the Management Discussion and Analysis section from annual 10-K reports (which contains forward-looking views of the management). Using the relative performance of the text model as a proxy for the informativeness of reports, we show that the MD&A sections are significantly more informative after the passage of SOX. We further show that this additional information is associated with a reduction in share illiquidity, suggesting that the information divulged was new to investors. Finally, we find that the increase in informativeness of MD&A reports is most pronounced for firms with higher costs of adverse selection.

Text Based Portfolio Choice

Portfolio optimization requires, as an input, a description of the assets' joint return distribution. In estimating forward-looking distributional parameters from historical data, such as means, variances, and correlations, investors face a conundrum. Estimates with small measurement error require a long time-series, but a longer time-series necessarily contains less forward-looking information unless the distribution of returns is known to be static over time. We propose to investigate the usefulness of textual company-level materials in producing better estimates for firms' return distributions. Our preliminary investigations have already yielded success. Employing a novel {\em text regression}, we have been able to predict, out of sample, firm return volatility using the Management Discussion and Analysis section from annual 10-K reports (which contains forward-looking views of the management). Our text-based predictor does as well or better than recent historical return volatility, suggesting that one can supplement contemporary textual data with historical returns to better deduce the contemporaneous properties of firms' return distribution. Our project's goal is to investigate how to extend the analysis into out-of-sample predictions of correlations. The end objective is to combine textual and quantitative data to best predict the covariance structure of firms' returns. An immediate and important application is to generate mean-variance efficient portfolios that suffer less from the measurement error and backward-looking bias documented in the literature.