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Encyclopedia of Financial Models II by Frank J. Fabozzi

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Forecasting Stock Returns

FRANK J. FABOZZI, PhD, CFA, CPA

Professor of Finance, EDHEC Business School

PETTER N. KOLM, PhD

Director of the Mathematics in Finance Masters Program and Clinical Associate Professor, Courant Institute of Mathematical Sciences, New York University

SERGIO M. FOCARDI, PhD

Partner, The Intertek Group

Abstract: One of the key tasks in seeking to generate attractive returns is producing realistic and reasonable return expectations and forecasts. In the Markowitz mean-variance framework, an investor's objective is to choose a portfolio of securities that has the largest expected return for a given level of risk (as measured by the portfolio's variance). In the case of common stock, by return (or expected return) of a stock, we mean the change (or expected change) in the stock price over the period, plus any dividends paid, divided by the starting price. Of course, since we do not know the true values of the securities’ expected returns and covariances, these must be estimated or forecasted. Equity portfolio managers have used various statistical models for forecasting returns and risk. These models, referred to as predictive return models, make conditional forecasts of expected returns using the current information set. Predictive return models include regressive models, linear autoregressive models, dynamic factor models, and hidden-variable models.

In contrast to forecasting events such as the weather, forecasting stock prices and returns is difficult ...

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