I use machine learning methods to identify stock characteristics important for predicting both stock returns and mutual fund performance. My customized machine learning models can successfully predict both stock returns and fund performance, and a nonlinear model delivers better performance....

We explain stock mispricing linked to long-term expectations of earnings growth in terms of managerial manipulation in high-growth conglomerates. Manipulation does not affect analysts’ forecasts of conglomerate earnings, which are more accurate relative to pseudo-conglomerates. The combined...

We examine the predictability of stock returns using implied volatility spreads (VS) from individual (non-index) options. Volatility spreads can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

Here, we introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability...

In this analysis we are concerned with the issue of whether market forecasts of volatility, as expressed in the Black-Scholes implied volatilities of at-the-money European options on the S&P500 Index, are superior to those produced by a new forecasting model in the GARCH framework which...

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However,...

Estimates of future quarterly earnings are of prime importance to capital market participants for formulating their investment decisions. Superior ability to forecast future earnings may enable investors to reap extraordinary returns by trading in the affected securities. The extant forecast...

This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Terasvirta (1998). Because it uses a forward-looking approach to weight the regimes, in contrast to the typical...