NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

NBER Working Papers by Jason Karceski

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Working Papers

March 2003Analysts' Conflict of Interest and Biases in Earnings Forecasts
with Louis K. C. Chan, Josef Lakonishok: w9544
Analysts' earnings forecasts are influenced by their desire to win investment banking clients. We hypothesize that the equity bull market of the 1990s, along with the boom in investment banking business, exacerbated analysts' conflict of interest and their incentives to adjust strategically forecasts to avoid earnings disappointments. We document shifts in the distribution of earnings surprises, the market's response to surprises and forecast revisions, and in the predictability of non-negative surprises. Further confirmation is based on subsamples where conflicts of interest are more pronounced, including growth stocks and stocks with consecutive non-negative surprises; however shifts are less notable in international markets.
May 2001The Level and Persistence of Growth Rates
with Louis K.C. Chan, Josef Lakonishok: w8282
Expected long-term earnings growth rates are crucial inputs to valuation models and for cost of capital estimates. We analyze historical long-term growth rates across a broad cross-section of stocks using several operating performance indicators. We test whether growth persists, and whether it is forecastable. Cases of very high growth have occurred, but are relatively rare. There is scant persistence in growth beyond chance, and limited ability to identify firms with high future long-term growth. IBES forecasts are too optimistic, and have low predictive power for long-term growth. Regressions using a variety of predictors confirm the low predictability in growth. Valuations that assume persistently high growth over prolonged periods rest on shaky foundations.
March 1999On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model
with Louis K.C. Chan, Josef Lakonishok: w7039
We evaluate the performance of different models for the covariance structure of stock returns, focusing on their use for optimal portfolio selection. Comparisons are based on forecasts of future covariances as well as the out-of-sample volatility of optimized portfolios from each model. A few factors capture the general covariance structure but adding more factors does not improve forecast power. Portfolio optimization helps for risk control, but the different covariance models yield similar results. Using a tracking error volatility criterion, larger differences appear, with particularly favorable results for a heuristic approach based on matching the benchmark's attributes.
July 1997The Risk and Return from Factors
with Louis K. C. Chan, Josef Lakonishok: w6098
The ability to identify which factors best capture systematic return covariation is central to applications of multifactor pricing models. This paper uses a common data set to evaluate the performance of various proposed factors in capturing return comovements. Factors associated with the market, size, past return, book-to-market and dividend yield help explain return comovement on an out-of-sample basis (although they are not necessarily associated with large premiums in average returns). Except for the default premium and the term premium, macroeconomic factors perform poorly. We document regularities in the behavior of the more important factors, and confirm their influence in the Japanese and U.K. markets as well.

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