We find that aggregate profitability and asset investment exhibit robust joint predictive power for aggregate excess stock returns, consistent with the investment model of Hou, Xue, and Zhang (henceforth HXZ, 2015). These results provide out-of-sample empirical support for HXZ, as the same...

The author analyzes the statistics of words and phrases related to financial market trading practices in millions of volumes from Google's book collection and available at Google Ngram Viewer. In recent almost 30 years, as the analyzed data shows, the scholars and practitioners' interest in the...

We start this paper by presenting compelling evidence of short-term momentum in the excess returns on the S&P Composite stock price index. For the first time ever, we assume that the excess returns follow an autoregressive process of order p, AR(p), and evaluate the parameters of this process....

Using the long-term wavelet component of monthly S&P 500 excess returns as supervision information, we employ a machine learning method to extract the common predictive information of 14 prevalent macroeconomic variables, and construct a new macroeconomic index aligned for predicting stock...

We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and...

This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time-Series Momentum (TSM). Relying on time-series models, empirical residual distributions and copulas we overcome two key drawbacks of conventional backtesting procedures. We create...