has roots in fundamentals. Higher market risk predicts greater idiosyncratic earnings volatility as well as dispersion and …From 1963 through 2015, idiosyncratic risk (IR) is high when market risk (MR) is high. We show that the positive …

demand arise as a risk factor. Motivated by theory, we use shocks to the ratio of residential-to-aggregate investment to … capture the housing demand risk. The single-factor model exhibits strong explanatory power for expected returns across various … equity characteristic-sorted portfolios and non-equity asset classes with positive risk price estimates that are similar in …

during times of crisis. In this study, we consider the potential for precious metals to mitigate downside risk when combined … with equities, and evaluate the impact on portfolio risk-adjusted returns. Each of gold, silver and platinum are found to … contribute to downside risk reduction at short horizons, but diversifi cation into silver and platinum may result in increased …

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows … that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this … puzzling result that the anomalous negative risk-return relation is only confined to those stocks held by rich households …

equally after taking risk into account, and examines the predictive power of reward-to-risk ratios for expected market returns …. We place special emphasis on downside risk by calculating both nonparametric and parametric value at risk. We find that … when all 52 markets are ranked based on their alternative reward-to-risk ratios, almost all of the countries in the top …

We establish that the risk-return tradeoff of cryptocurrencies (Bitcoin, Ripple, and Ethereum) is distinct from those …

specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can … findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than … standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors …

how these volatility measures can be used for risk management. We find that momentum risk management significantly …We introduce a new class of momentum strategies, the risk-adjusted time series momentum (RAMOM) strategies, which are … based on averages of past futures returns, normalized by their volatility. We test these strategies on a universe of 64 …

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based …