Charles C. Y. Wang

4 Results


The Search for Benchmarks: When Do Crowds Provide Wisdom?

Finding appropriate economic benchmarks for individual firms is a fundamental issue. Firms, managers, investors, and researchers all need to identify fundamentally similar benchmarks for such tasks as performance evaluation, executive compensation, equity valuation, statistical arbitrage, and portfolio construction. While traditional benchmarking methods rely primarily on industry classification schemes, more recent approaches introduce new dimensions by utilizing novel data sources or fresh data analytic techniques. Some of these approaches suggest we may need to rethink the reliance on traditional industry classification for benchmarking purposes. In this paper, the authors conduct a comprehensive analysis of the state-of-the-art representatives of four broad categories of peer identification schemes nominated by either financial practitioners or recent academic studies as potential solutions to economic benchmarking. The study's results suggest that the class of bench-marking solutions that harnesses the collective wisdom of investors is a promising path for the future. This approach's effectiveness, however, depends on the sophistication of the individuals in the population (the inherent level of collective wisdom attainable through sampling) and the quality of the information environment surrounding the firm, as well as the size of the sample itself. Read More

Measurement Errors of Expected Returns Proxies and the Implied Cost of Capital

In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. Though ICCs are intuitively appealing and have the potential to help researchers better understand the cross-sectional variation in expected returns, much remain unknown about the sources of their measurement errors and how to correct for them; thus their use in regression settings should be interpreted with caution. This paper studies the measurement errors properties of GLS, a popular implementation of ICCs developed by Gebhardt, Lee, and Swaminathan (2001). The paper finds that ICCs can have persistent measurement errors that are associated with firms' risk or growth characteristics, and thus produce spurious results in regression settings. It also finds that ICC measurement errors are driven by not only analyst forecast biases but also functional form assumptions, suggesting that correcting for the former alone is unlikely to fully resolve these measurement-error issues. Together, these findings emphasize the importance of complementing ICC regressions with realized returns to establish robust inferences on expected returns. Read More

Boardroom Centrality and Firm Performance

Economists and sociologists have long studied the influence of social networks on labor markets, political outcomes, and information diffusion. These networks serve as a conduit for interpersonal and inter-organizational support, influence, and information flow. This paper studies the boardroom network formed by shared directorates and examines the implications of having well-connected boards, finding that firms with the best-connected boards on average earn substantially higher future excess returns and other advantages. Read More

Cost of Capital Dynamics Implied by Firm Fundamentals

Despite ample evidence that expected returns are time varying, there has been relatively little empirical research on estimating the dynamics of firm-level expected returns. Capturing the dynamics of firm-level expected returns is important, because it allows for a better understanding of firm risk over time and can inform investors in tailoring their portfolios to match their desired investment horizons. Findings show that cost of capital is time varying and highly persistent. The authors also demonstrate that the model produces empirical proxies of expected returns that can predict future stock returns up to three years into the future and sorts portfolio returns with near monotonicity. Aside from its practical contributions, this paper adds to a budding finance and accounting literature that studies the properties of expected return dynamics. Read More