Forecasting Fundamental Asset Return Distributions and Tests for Excess Volatility and Bubbles
This paper develops an augmented Artificial Neural Network forecast-simulation procedure for estimating both the current fundamental price of a financial asset and the state-dependent distribution (including volatilities) from which future returns will be fundamentally drawn. The results provide an improved method for valuing assets, such as stocks and stock options,and suggest new applications of tests for excess volatility and bubbles in asset prices.
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