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Posterior probability model for stock return prediction based on analyst's recommendation behavior

机译：基于分析师推荐行为的股票收益预测的后验概率模型

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摘要

Existing studies on stock return forecasting mainly formulate the issue in a numeric analysis framework. Various kinds of time series models and optimization methods are applied. In this paper, we explore a new prediction approach based on the behavior of analysts' recommendations. By combining each recommendation and stock return, a posterior probability model associated with an analyst's recommendation is built based on Bayesian inference. It provides an estimation of stock return distribution for next several days after recommendation, and thus serves as a novel predictor from point of view of behavioral finance. Based on the empirical studies on China stock market, we demonstrate the superior forecasting performance over traditional methods. The model's maximum accuracy can be reached between 84.3% and 94.2%. The average accuracy falls between 58.6% and 60.3%, while it is just from 43.5% to 56.2% or lower by traditional prediction methods. We also find that most of the analysts can produce recommendations which fitness lies between 0.5 and 0.6 at the successive recommendation time. The finding is in accordance with early conclusion which indicates that stock analysts tend to maintain their reputation when they issue recommendations. The consistency also confirms the effectiveness of the proposed method.
机译：现有的关于股票收益预测的研究主要是在数值分析框架中提出问题。应用了各种时间序列模型和优化方法。在本文中，我们基于分析师建议的行为探索了一种新的预测方法。通过结合每个建议和股票收益，基于贝叶斯推断建立了与分析师建议相关的后验概率模型。它提供了推荐后接下来几天的股票收益分布的估计，因此从行为财务的角度来看，它可以作为一种新颖的预测指标。基于对中国股票市场的实证研究，我们证明了优于传统方法的预测性能。该模型的最大精度可以达到84.3％至94.2％。平均准确度介于58.6％至60.3％之间，而传统预测方法仅为43.5％至56.2％或更低。我们还发现，大多数分析人员可以在连续的推荐时间产生适合度在0.5到0.6之间的推荐。该发现符合早期结论，该结论表明股票分析师在发布建议时倾向于保持声誉。一致性也证实了所提出方法的有效性。

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