We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...

This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won-U.S. dollar (KRW/USD) exchange rate and the U.S. and Korean stock market returns. We construct international asset allocation...

Previous research finds that machine learning methods predict short-term return variation in the cross-section of stocks, even when these methods do not impose strict economic restrictions. However, without such restrictions, the models' predictions fail to generalize in a number of important...

The loss function in supervised deep learning is a key element for training AI algorithms. For models aiming at predicting asset returns, not all prediction errors are equal in terms of impact on the efficiency of the algorithm. Indeed, some errors result in poor investment decisions while other...

We design a novel empirical framework to examine market efficiency through out-of-sample(OOS) predictability. We frame the classic empirical asset pricing problem as a machine learningclassification problem. We construct classification models to predict return states. The prediction- based...

Modelling and forecasting of asset volatility and covariance is of prime importance in the construction of portfolios. In this paper, we present a generalised multi-factor model that incorporates heteroskedasticity and dependence in the idiosyncratic error terms. We apply this model to...

I compute economic gains for a power utility investor from taking business cycle dependent return predictability into account. Recent studies show that stock returns are only predictable in recessions, and bond returns are only predictable in expansions. I examine whether this finding can be...

We investigate the question whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP and industrial production growth from the Federal Reserve's Survey of Professional Forecasters predict volatility...

We create a market-wide measure of dispersion in options investors' expectations by aggregating across all stocks the dispersion in trading volume across moneynesses (DISP). DISP exhibits strong negative predictive power for future market returns and its information content is not subsumed by...

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...