This paper uses hand-collected historical data to provide empirical evidence on the strategic trading behavior of insiders and its consequences for outsiders. Specifically, we collect all equity trades of all insiders and outsiders in an era without legal restrictions on insider trading and a market where trading is non-anonymous. We find that access to private information creates a significant gap between the post-trade returns of insiders and outsiders. Consistent with theory, insiders capitalize on their information advantage by hiding their identity and timing their trades. Both experienced and inexperienced outsiders face expected losses due to this strategic insider trading.

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.

WFA 2023, INQUIRE Europe Fall Seminar 2023, SoFiE Annual Meeting 2022, INQUIRE UK Spring Seminar 2022, CREDIT Conference on Long Run Risks 2022, Frontiers of Factor Investing 2022, FMA Annual Meeting 2021
Best Paper Award, GRASFI Conference on Sustainable Investing, INQUIRE Europe Research Grant, and Netspar Topicality Grant
Covered by Institutional Investor magazine and by BNP Paribas Asset Management

We study how investor beliefs about the economic impact of climate change affect long-horizon portfolio choice. We show that buy-and-hold investors who elicit prior views from a temperature long-run risks (LRR-T) model allocate less capital to equities at longer horizons. These investors perceive stocks to be riskier over longer holding periods because climate change weakens their beliefs in mean reversion and increases estimation risk and uncertainty about future expected returns. Climate risk also reduces the optimal allocation to equity for long-term investors with LRR-T beliefs who dynamically rebalance their portfolio, because it generates a negative intertemporal hedging demand.

Netspar Industry Paper Series - Design Paper #218

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.