This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...

Predictions of asset returns and volatilities are heavily discussed and analyzed in the finance research literature. In this paper, we compare linear and nonlinear predictions for stock- and bond index returns and their covariance matrix. We show in-sample and out-of-sample prediction accuracy...

Stock markets proved to be statistically predictable on an economically interesting scale over the past decade by fully data driven automatically constructed maps that associate to a set of new factor values a return prediction that is the average of historically observed returns for an area in...

This paper is an attempt to predict stock returns using classical (AR) and intelligent (ANN) techniques. AR and ANN techniques are also used to test the efficient market hypotheses using long time-series of daily data of BSE Sensex for the period of January 1997 to September 2005. An attempt has...

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty...

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal...

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure topological stock market changes as well as the...

Motivated by recurrent neural networks, this paper proposes a recurrent support vector regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR based ARMA model is compared with five...