We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies...

You're probably familiar, at least in passing, with the 'convexity' of long-term bonds - i.e. that yields dropping 1% produce a bigger price move than yields rising 1%. A significant amount of brainpower has gone into understanding all the ramifications of this convexity in the fixed income...

This paper uses accounting-based reverse engineering of market expectations to identify potentially mispriced stocks. Building upon the “errors-in-expectations” hypothesis, we develop a theoretically funded yet practical tool for stock screening in this paper. We use the Ohlson (1995) model...

The profitability of a trading system based on the momentum-like effects of price jumps was tested on the time series of 7 assets (EUR/USD, GBP/USD, USD/CHF and USD/JPY exchange rates and Light Crude Oil, E-Mini S&P 500 and VIX Futures), in each case for 7 different frequencies (ranging from...

The literature on stock return predictability has identified macroeconomic and technical predictors that when combined, leads to out-of-sample outperformance relative to the historical mean null. This paper investigates a new method for aggregating information beyond using forecast combination...

Average skewness, which is defined as the average of monthly skewness values across firms, performs well at predicting future market returns. This result still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. We also find that average...

The Baker and Wurgler (2006) sentiment index purports to measure irrational investor sentiment, while the University of Michigan Consumer Sentiment Index is designed to largely reflect fundamentals. Removing this fundamental component from the Baker and Wurgler index creates an index of investor...

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...

We examine the relationship between the tonality of news flow and the cross section of expected stock returns. We use a comprehensive definition of media coverage that includes both financial newspapers and mass media, represented by TV broadcasts. Using the total news flow with positive and...

This paper examines the cross-sectional properties of stock return forecasts based on Fama-MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999-December 2018 period. Our estimation approach is strictly out-of-sample, mimicking an investor who...