The Realized Information Ratio and the Cross-Section of Expected Stock Returns
Mehran Azimi (University of Massachusetts Boston)
This study investigates the predictability of asset returns with the information ratio and its specific variant, the Sharpe ratio. We find that the realized Sharpe ratio (rsr ) negatively predicts the cross-section of stock returns. The predictability is not due to the components of the rsr, that is, past returns and volatility, nor is it due to size, book-to-market, momentum, and several market microstructure and investor preference-related variables. A factor portfolio based on the short-term rsr generates alphas in the range of 0.67% to 0.89% per month against ten prominent factor models with t-stats well above 5. Our results suggest that a return-to-risk ratio metric is a strong predictor of cross-sectional stock returns, stronger than both risk and return. We provide evidence that the rsr is a proxy for stock-level sentiment. We find similar results using several information ratios and formation periods of up to one year when we control for momentum. The average monthly Carhart alpha of 60 factors constructed using five information ratios, each with formation periods ranging from one to 12 months, is 0.63%. The average of the corresponding t-stats is 4.5. The findings suggest that the predictive power of the information ratio, and the rsr in particular, differs fundamentally from its constituents.
Economic Fundamentals and Stock Market Valuation: A CAPE-based Approach
Maria Ludovica Drudi and Federico Nucera (Bank of Italy)
This paper estimates a fair-value model, based on macroeconomic fundamentals, of the Shiller Cyclically Adjusted Price-to-Earnings (CAPE) ratio. By performing a multi-country analysis, we find that CAPE – a widely used metric for stock market valuations – is, in general, positively related to economic growth and negatively related to the real long-term interest rate and to measures of economic volatility computed using industrial production and inflation data. Empirical evidence arising from predictive regressions of real stock market returns indicates that deviations of CAPE from its estimated fair value are negatively related to future stock returns. A prediction model based on these deviations outperforms, in many cases, a model based on the CAPE levels both in sample and out of sample.
War Discourse and the Cross-Section of Expected Stock Returns
David A. Hirshleifer (Marshall School of Business, USC), et al.
A war-related factor model derived from textual analysis of media news reports explains the cross section of expected asset returns. Using a semi-supervised topic model to extract discourse topics from 7,000,000 New York Times stories spanning 160 years, the war factor predicts the cross section of returns across test assets derived from both traditional and machine learning construction techniques, and spanning 138 anomalies. Our findings are consistent with assets that are good hedges for war risk receiving lower risk premia, or with assets that are more positively sensitive to war prospects being more overvalued. The return premium on the war factor is incremental to standard effects.
Do Emotions Influence Investor Behaviour?
Ron Bird (University of Waikato), et al.
Despite much discussion in the psychology and marketing literature as to how emotions influence decision-making, this area of analysis has been largely neglected in the financial economics literature. We redress this important gap by using proxies for emotions drawn from the news and social media to evaluate their influence on investment decisions and ultimately asset pricing. We find strong evidence to support that emotions do influence investor decision-making and provide important insights into the nature of this relationship. In general, we find those positive emotions such as trust, and optimism are more influential in shaping investors’ reactions than are negative emotions. Finally, the emotions based on the news media listings have a greater influence on stock valuations than those based on social media listings.
Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
Alejandro Lopez-Lira and Yuehua Tang (University of Florida)
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 document a positive correlation between these “ChatGPT scores” and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. ChatGPT-4’s implied Sharpe ratios are larger than ChatGPT-3’s; however, the latter model has larger total returns. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies. Predictability is concentrated on smaller stocks and more prominent on firms with bad news, consistent with limits-to-arbitrage arguments rather than market inefficiencies.
Factor Momentum Versus Stock Price Momentum: A Revisit
Nusret Cakici (Fordham university), et al.
Does factor momentum drive the stock price momentum? Inspired by the recent findings from the United States, we revisit this relationship across 51 markets. The factor momentum effect remains strong—both within and across countries—regardless of typical drivers of return predictability. However, its ability to capture the stock momentum profits depends fundamentally on methodological and dataset choices. Consequently, the factor momentum cannot robustly subsume the stock momentum in global markets. On the contrary, the latter explains the former better than vice versa. Our conclusions challenge the view that momentum only times other factors rather than constituting a distinct anomaly.
Risk Premia – The Analysts’ Perspective
Pascal Büsing and Hannes Mohrschladt (University of Muenster)
We examine the time-series and cross-section of stock market risk premia from the perspective of financial analysts. Our novel approach is based on the notion that analysts’ stock recommendations reflect both their subjective return expectations and their perceived stock risk. Thus, we can empirically infer presumed risk premia from recommendations and target price implied expected returns. We show that analysts’ presumed risk premia are strongly countercyclical such that their correlation with the VIX is 72%. Moreover, they predict future stock market returns and are closely related to the price-dividend ratio and other cyclical state variables. In the cross-section, the presumed risk premia are comparably large for high-beta, small, and value stocks lending support to a risk-based interpretation of these characteristics.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno