Excess Volatility and Predictability of Stock Prices in Autoregressive Dividend Models with Learning
分析两种递归学习模型，发现现值学习规则即使在很大样本下也会产生显著的股票价格过度波动，并能解释股票收益与滞后股息收益率之间的正相关关系。
To what extent can agents' learning and incomplete information about the "true" underlying model generating stock returns explain findings of excess volatility and predictability of returns in the stock market? In this paper we analyse two models of recursive learning in the stock market when dividends follow a (trend-)stationary autoregressive process. The asymptotic convergence properties of the models are characterized and we decompose the variation in stock prices into rational expectations and recursive learning components with different rates of convergence. A present-value learning rule is found to generate substantial excess volatility in stock prices even in very large samples, and also seems capable of explaining the positive correlation between stock returns and the lagged dividend yield. Self-referential learning, where agents' learning affect the law of motion of the process they are estimating, is shown to generate some additional volatility in stock prices, though of a magnitude much smaller than present value learning