In this paper, we propose a new way to predict market returns for multi-assets (equity, fixed-income and commodity) by extracting features from macroeconomic data and performing machine learning algorithms for both regression and classification. Our approach aims to select robust models to build...

Long-term country equity premium forecasts based on a cross-sectional global factor model (CS-GFM), where factors represent compensation for risks proxied by valuation and financial variables, are superior, statistically and economically, from forecasts based on time-series prediction models...

We study whether prices of traded options contain information about future extreme market events. Our option-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and probability of the market crashes, beyond and above...

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 liquid futures contracts and show that RAMOM strategies...

We comprehensively analyze the predictive power of several option implied variables for monthly S & P 500 excess returns and realized variance. The correlation risk premium (CRP) emerges as a strong predictor of both excess returns and realized variance. This is true both in- and out-of-sample....

We find out-of-sample predictability of commodity futures excess returns using forecast combinations of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean-variance investor....

We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as...

The risk premium of stocks due to priced variance risk is summarized to two variables -- the stock-specific price of variance risk (the difference between realized and option-implied variance) and the quantity (i.e., how stock prices respond to their variance shocks) of variance risk....

We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more...

We combine self-collected historical data from 1867 to 1907 with CRSP data from 1926 to 2012, to examine the risk and return over the past 140 years of one of the most popular mechanical trading strategies — momentum. We find that momentum has earned abnormally high risk-adjusted returns — a...