A Bayesian Panel Data Approach to Explaining Market Beta Dynamics

We characterize the process that drives the market betas of individual stocks by setting up a hierarchical Bayesian panel data model that allows a flexible specification for beta. We show that combining the parametric relationship between betas and conditioning variables specified by economic theory with the robustness of an autoregressive specification delivers superior estimates of firm-specific betas. Our model also improves the accuracy of beta forecasts, which we use to construct optimal portfolios subject to target beta constraints. We further provide empirical support for the prediction of conditional asset pricing theory that individual stocks exhibit different risk dynamics. Finally, we document strong cross-sectional heterogeneity in firm-specific betas within the 25 size-B/M portfolios that are commonly used to test asset pricing models

Pension fund performance and costs: small is beautiful.

Abstract Using the CEM pension fund data set, we document the cost structure and performance of a large sample of US pension funds. To date, self-reporting biases and a deficiency of comprehensive return and cost data have severely hindered pension fund performance studies. The bias-free CEM dataset resolves these issues and provides detailed information on fund-specific returns, benchmarks and costs for all types of pension plans and equity mandates. We find that pension fund cost levels are substantially lower than mutual fund fees. The domestic equity investments of US pension funds tend to generate abnormal returns (after expenses and trading costs) close to zero or slightly positive, contrasting the average underperformance of mutual funds. However, small cap mandates of defined benefit funds have outperformed their benchmarks by about 3% a year. While larger scale brings costs advantages, liquidity limitations seem to allow only smaller funds, and especially small cap mandates, to outperform their benchmarks. JEL Classifications : G23, G11, G14 Acknowledgements Our thanks to Keith Ambachtsheer, CEM Benchmarking Inc. for providing the pension fun

Salience theory and stock prices:Empirical evidence

We present empirical 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 strong 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 and not explained by common risk factors and proxies for lottery demand and investor attention

Carbon Bias in Index Investing

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