This paper documents a negative relationship between options trading volume and stock returns. The relationship is remarkably robust and cannot be explained by existing asset-pricing theorems. We find that strategies that require buying stocks with low options trading volume in the past and...

Four new prominent asset pricing factors have recently been proposed. We test whether these factors fulfill necessary conditions for qualifying those as risk factors. We show that the investment and betting-against-beta factors fulfill these conditions. However, the profitability and quality...

We study firm-level characteristics that a manager would employ as signalling tools in order to time the market (i.e. repurchases and issues). Following the market timing framework, we develop a two-factor asset pricing model comprising a “market” and a “mispricing” factor, which is able...

This is the first study to investigate the profitability of Barroso and Santa-Clara's (2015) risk-managing approach for George and Hwang's (2004) 52-week high momentum strategy in an industrial portfolio setting. The findings indicate that risk-managing adds value as the Sharpe ratio increases,...

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the...

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods...

Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and...