Our Research

Implementing EBO/EVA® Analysis in Stock Selection

A model developed by Edwards, Bell, and Ohlson and the residual income valuation model (RIVM) introduced in this study are versions of "economic value-added" models in that they measure value from the point of view of a firm's capacity for ongoing wealth creation rather than simply wealth distribution. The RIVM is distinctive in its use of balance sheet and other financial data to reflect economic reality more accurately and in its refinement of the traditional CAPM approach to computing a firm's cost of capital. The author modifies three inputs of residual income models: the measurement of book value of assets, the estimation of the discount rate, and the projection of the time period of abnormal earnings. One of the inputs to the model is the beginning book value of assets; the author separates the book value into perpetual value assets (e.g. land) and operating capital. Because of the fundamental differences in these assets, the author makes separate adjustments to each category. For a twelve-year backtest period, the model appears to have been very effective in uncovering firms whose stock is underpriced considering expectations for strong earnings and growth.

Enhancing Earnings Predictability Using Individual Analyst Forecasts

There is considerable evidence suggesting that stock selection based on firms' anticipated earnings can generate excess returns. The earnings predictor model (EPM) introduced in this article uses individual analyst forecasts to generate an earnings forecast that is more accurate than the consensus in over 1,200 (non-independent) back tests using three alternative metrics. The model determines those firm-specific components that can best generate superior earnings forecasts for each company at each point in time. The EPM is shown to have been very effective for stock selection purposes, generating a total annualized Q1 minus annualized Q5 return differential of 15.57% over the period of the study.

Identifying Lead Analysts for Stock Selection

Analyst forecast revisions are the basis for many quantitative investment strategies. The authors' lead analyst model identifies superior analysts on an individual stock basis. It selects analysts who tend to make revisions away from rather than toward the consensus, and measures their skill in terms of attributes such as courage, forecast accuracy, and influence on other analysts, as well as stock price reaction to their forecasts. For the period 1994–2000 stock selection based on all lead analyst revisions is shown to generate average excess monthly returns of 1.51% over the S&P 500, better than results achieved by using all analyst revisions from any brokerage firm. The model is profitable in 74 of 84 months; excess returns increase to 1.8% when acting only on revisions away from the consensus.

The Persistence of Hedge Fund Risk: Evidence and Implications for Investors

In this article the authors examine persistence of hedge fund performance and find that returns are not persistent while risk (volatility in returns) and correlations to underlying markets are highly persistent. Less risky funds tend to be less correlated with underlying markets and more efficient at bearing risk than their more risky counterparts. They analyze various determinants of hedge fund performance and develop a multifactor model to identify funds whose superior performance is based on underlying investment skill, rather than on risk-taking or undue exposure to markets. For the period 1996 to 2001, portfolios of such funds generate significantly higher risk-adjusted returns (Sharpe ratio of 4.14) than portfolios containing all funds (1.38), portfolios constructed solely on the basis of past returns (0.75), and portfolios constructed based on past Sharpe ratios (2.42).