The predictability of stock returns – a nonparametric approach
用非参数模型预测股票指数月度收益方向，准确率达74%，并发现基于预测的交易策略在考虑交易成本后仍优于买入持有策略。
This paper reexamines the predictability of stock returns with a nonparametric model. We first identify, through a set of diagnostic tests, five lagged predictive factors from a linear model. Using these factors, we predict one-month-ahead stock index returns with a nonparametric approach. We find that our nonparametricmodel. We first identify, through a set of diagnostic tests, five lagged predictive factors from a linear model. Using these factors, we predict on -month-ahead stock index returns with a nonparametric approach. We find that our nonparametric model can correctly predict about 74% of stock index return signs. With various ex ante trading rules based on nonparametric predictions and transaction cost schedules, we then compare the performance of "managed" portfolios with that of the buy and hold portfolios. We fmd that the managed portfolios are mean-variance dominant over the buy-and-hold strategies when no or low transaction costs are assumed. When high transaction costs are assumed instead, the mean-variance dominance diminishes However,the Sharpe index of risk-adjusted portfolio performanceindicates that the managed portfolios significantly outperform the buy-and-hold strategies even for the high-transaction cost scenario. We show that the difference in performance between the managed portfolios and the buy-and-hold strategies can be partially explained by the January effect or the small firm effect. In sum, this paper demonstrates the merits of using a nonparametric approach for predicting stock returns and testing market efficiency.