We present evidence on the asset pricing implications of salience theory. In our model, investors overweight salient past returns when forming expectations about future returns. Consequently, investors are attracted to stocks with salient upsides, which are overvalued and earn low subsequent returns. Conversely, stocks with salient downsides are undervalued and yield high future returns. We find empirical support for these predictions in the cross section of U.S. stocks. The salience effect is stronger among stocks with greater limits to arbitrage and during high-sentiment periods. Our results are not explained by common risk factors, return reversals, lottery demand, and attention-grabbing news events.

We propose a hybrid approach for estimating beta that shrinks rolling window estimates towards firm-specific priors motivated by economic theory. Our method yields superior forecasts of beta that have important practical implications. First, hybrid betas carry a significant price of risk in the cross-section even after controlling for characteristics, unlike standard rolling window betas. Second, the hybrid approach offers statistically and economically significant out-of-sample benefits for investors who use factor models to construct optimal portfolios. We show that the hybrid estimator outperforms existing estimators because shrinkage towards a fundamentals-based prior is effective in reducing measurement noise in extreme beta estimates.

This study provides European evidence on the ability of static and dynamic specifications of the Fama and French (1993) three-factor model to price 25 size-B/M portfolios. In contrast to US evidence, we detect a small-growth premium and find that the size effect is still present in Europe. Furthermore, we document strong time variation in factor risk loadings. Incorporating these risk fluctuations in conditional specifications of the three-factor model clearly improves its ability to explain time variation in expected returns. However, the model still fails to completely capture cross-sectional variation in returns as it is unable to explain the momentum effect.

This paper examines the impact of option trading on individual investor performance. The results show that most investors incur substantial losses on their option investments, which are much larger than the losses from equity trading. We attribute the detrimental impact of option trading on investor performance to poor market timing that results from overreaction to past stock market returns. High trading costs further contribute to the poor returns on option investments. Gambling and entertainment appear to be the most important motivations for trading options while hedging motives only play a minor role. We also provide strong evidence of performance persistence among option traders.

AFA 2022, EFA 2022, 2019 Federal Reserve Board Workshop on Monetary and Financial History, 2018 ESSFM Gerzensee

This paper provides evidence on the financial consequences of insider trading for outsiders. We collect a novel data set that contains all equity trades of all corporate insiders and outsiders in an era without restrictions on informed trading. These data features allow us to study the profitability of insider trades and the expected loss outsiders incur due to insider trading. We show that access to private information creates a performance gap of 7% per year between insiders and outsiders. Nonetheless, outsiders’ unconditional expected losses from insider trading are small because the probability of trading with an insider is low.

Best Paper Award, GRASFI Conference on Sustainable Investing, INQUIRE Europe Research Grant, and Netspar Topicality Grant

We propose a novel approach for measuring the impact of climate change on long-horizon equity risk and optimal portfolio choice. Our method combines historical data about the impact of climate change on return dynamics with prior beliefs elicited from the temperature long-run risk (LRR-T) model of Bansal, Kiku, and Ochoa (2019). Our Bayesian framework incorporates this prior information to obtain more precise estimates of long-term climate risks than existing methods that solely rely on historical data. Compared to the benchmark investor without climate change, we document that the LRR-T Bayesian investor predicts higher equity premia for all investment horizons, with per period variance increasing considerably over the horizon. This results in relatively (high) low allocations to equities in the (short) long run. Investors that optimize between portfolios that are vulnerable and non-vulnerable to climate change only diversify in the long run.

This paper presents evidence of a bias towards carbon-intensive companies in popular value-weighted stock market indices that are tracked by index funds and ETFs and serve as benchmark for active equity strategies. The average carbon bias in the U.S. Russell 1000 is close to 70% and the bias in the MSCI Europe index is about 90%. This means that the carbon intensity of the U.S. and European market indices is 70% and 90% higher than that of the U.S. and European economy, respectively. The carbon bias arises because firms operating in carbon-intensive sectors, such as mining, manufacturing, and electricity, tend to be more capital intensive and more likely to be publicly listed. These companies therefore issue more equity than firms in low-carbon sectors and receive a larger weight in the value-weighted stock market index than in the real economy. The carbon bias is problematic because it exposes institutional investors such as pension funds to carbon-transition risks and is at odds with their drive towards sustainability. We therefore explore several strategies for investors to mitigate the carbon bias in their equity allocation.

Best Paper Award, FMA Consortium on Hedge Fund Research

Presented at Annual Meetings of the Econometric Society (ES) and Society for Financial Econometrics (SoFiE)