Estimating the Dynamics of Mutual Fund Alphas and Betas

This article develops a Kalman filter model to track dynamicmutual fund factor loadings. It then uses the estimates to analyzewhether managers with market-timing ability can be identifiedex ante. The primary findings are as follows: (i) Ordinary leastsquares (OLS) timing models produce false positives (nonzeroalphas) at too high a rate with either daily or monthly data.In contrast, the Kalman filter model produces them at approximatelythe correct rate with monthly data; (ii) In monthly data, thoughthe OLS models fail to detect any timing among fund managers,the Kalman filter does; (iii) The alpha and beta forecasts fromthe Kalman model are more accurate than those from the OLS timingmodels; (iv) The Kalman filter model tracks most fund alphasand betas better than OLS models that employ macroeconomic variablesin addition to fund returns.     

Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

Technical analysis, also known as 'charting,' has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis—the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head-and-shoulders or double bottoms—we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.     

Improved Forecasting of Mutual Fund Alphas and Betas

This paper proposes a simple back testing procedure that isshown to dramatically improve a panel data model's ability toproduce out of sample forecasts. Here the procedure is usedto forecast mutual fund alphas. Using monthly data with an OLSmodel it has been difficult to consistently predict which portfoliomanagers will produce above market returns for their investors.This paper provides empirical evidence that sorting on the estimatedalphas populates the top and bottom deciles not with the bestand worst funds, but with those having the greatest estimationerror. This problem can be attenuated by back testing the statisticalmodel fund by fund. The back test used here requires a statisticalmodel to exhibit some past predictive success for a particularfund before it is allowed to make predictions about that fundin the current period. Another estimation problem concerns theuse of a single statistical model for all available mutual funds.Since no one statistical model is likely to fit every fund,the result is a great deal of misspecification error. This papershows that the combined use of an OLS and Kalman filter modelincreases the number of funds with predictable out of samplealphas by about 60%. Overall, a strategy that uses very modestex-ante filters to eliminate funds whose parameters likely deriveprimarily from estimation error produces an out of sample risk-adjustedreturn of over 4% per annum.