مدل احتمالی خلفی برای پیش بینی بازده سهام بر اساس رفتار توصیه شده تحلیلگر

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.

Forecasting stock price and return have attracted researchers’ attention for several decades [1], [2], [3], [4] and [5]. Accurate prediction might provide a sound foundation for government administrators to maintain the order of stock markets. On the other hand, it also helps investors to obtain more profits, and to avoid investment risk. By considering the returns as a time series, most of the forecasting methods utilize statistical models to reveal the hidden properties in the sequence, such as dependency and data distribution. Several kinds of statistical models, such as AR process [6], auto-regression moving average (ARMA), Markov process and the artificial neural network model [7], generalized auto-regression conditional heteroscedasticity (GARCH) model [8] and [9], hidden Markov model (HMM) [2], hierarchical hidden Markov chain model [10], and so on, have been employed to describe the statistical characteristics. The predictability of intraday stock market returns has been studied by using both linear and nonlinear time series models, including simple autoregressive, Markov switching and support vector machine models. The empirical results indicated that nonlinear models outperformed linear models [11]. The co-integration concept and the related vector error correction model (VECM) were employed to model daily high and daily low of stock price [13]. A fusion model combining the HMM, artificial neural network (ANN) and a genetic algorithm (GA) was proposed to achieve better forecast performance [12]. Model-based prediction methods are helpful in obtaining desired accurate results when the market trading is stable. However, parameters optimization is a critical problem in utilizing the time series based prediction models. Several methods have been devised for this problem. Artificial fish swarm algorithm (AFSA) and a K-means clustering algorithm were proposed to optimize basis function neural network (RBF) which was employed to forecast the stock indices of the Shanghai Stock Exchange [14]. A GA-based algorithm was used to build an associative classifier that can discover trading rules from the numerical indicators [15]. An integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system was proposed to achieve best results using minimum required input data [3]. In order to provide flexible methods to adjust the model parameters for further enhancement on the prediction models, a new architecture entitled as the Multi-level and Interactive Stock Market Investment System (MISMIS) which combined different areas – financial economics, prediction techniques, and dynamical systems theory to handle financial data was developed for investors to build their financial models to forecast stock price [16]. In addition, over fitting is one of the main problems in model optimization methods since they try to maximize objective function with limited number of return time series. In this paper, we apply behavioral finance theory to establish a new prediction approach. As we know, the stock market is a complex system that is characterized by dynamics, uncertainty, and chaos. The rate of price fluctuations depends on many factors, such as interest rate, temporary disturbance and merger of large financial corporations [17]. Behavioral finance theory emphasizes that the uncertainty and chaos in stock markets are largely due to information asymmetric. Financial analysts who can effectively process all kinds of information related to stock market are generally regarded as an important information source for stock investors [18]. They play an important role in promoting market efficiency by reducing the information asymmetry between company management and external market participants. Hence, researches on the behavior of security analysts’ forecasting and recommendation have attracted a lot of attention recently. Ref. [19] proposed a rational learning-based explanation for the predictability in financial analysts’ earnings forecasting errors. Using simulations and real data, they showed that the predictability evidence was more consistent with rational learning than with irrationality. Some literatures provided evidences to clarify analyst’s ability in predicting stock price or influencing price [20], [21] and [22]. A portfolio of the stocks with the most (least) favorable consensus analyst recommendations was found to provide an average annual abnormal gross return of 4.13 (−4.91) percent, after controlling for market risk, size, book-to-market, and price momentum effects [20]. The persistence in stock picking ability of security analysts was verified [23]. It was indicated that the performance of recommendations-based investment strategies could be significantly enhanced by transacting quickly following recommendation changes [21]. It was also demonstrated the usefulness of forecast-derived information in characterizing analysts’ recommending behavior, and their incremental informativeness even in the presence of information obtained from past recommendations [22]. Barniv et al. used large international samples and found that the relation between stock recommendations and future returns was negative in high investor participation countries but positive in low investor participation countries [24]. Bayesian rule was employed to model analyst’s predictive ability recently since it can provide a reasonable approach to update investor’s brief. Chen et al. developed a predictive model of investor learning in Bayesian manner about analysts’ ability to forecast quarter profit [25]. The model showed that when investors rely on an analyst’s record of history forecasting performance to update their beliefs about the analyst’s true predictive ability, they tend to increase weights on the analyst’s past performance and decrease weights on prior perceived ability. We propose to model stock return based on the behavior of analysts’ recommendations and Bayesian rule. Different from the approach in [25], we do not require a common prior which is an unconditionally normally distribution about the analyst’s true predictive ability. Hence, the model can be more appropriate in actual applications. The performance of the model is evaluated in terms of prediction accuracy which is commonly used in other similar researches. In addition, stability and persistence are also proposed to evaluate the model so that we can gain more intuition about the new model. This study is different from prior work in respect to the prediction method and the evaluation measurements. Firstly, we address the problem of stock return prediction by utilizing analysts’ recommendations issued at arbitrary date interval. The new method is based on the theory of behavioral finance which emphasizes on the relation between participant’s activities and the movement of stock price. Secondly, we establish the posterior return distribution based on Bayesian inference. It provides an estimation of return distribution for the next few days after recommendation according to Bayesian rule. Thus it can serve as a novel predictor from point of view of behavioral finance. Hence, the prediction method is basically different from time series analysis methods, such as ARMA, Markov process, and so on. Thirdly, the performance of the predictive model is extensively studied. The accuracy of model prediction and its variance are investigated by varying TL (Training Length) and DRR (Date Range of Recommendation). The ability in persistence of prediction is also investigated. The result is in accordance with early conclusion which indicates that stock analysts try to maintain their reputation when they publish recommendations. This finding also confirms the effectiveness of the proposed method. The remainder of this article is organized as follows. Data preparation for prediction modeling is described in the next section. The model structure and model training process are described in Section 3. Then, the evaluation and results are provided in Section 4, and the final section reports conclusion and future work.

Forecasting stock return has attracted much attention of researchers from different backgrounds, such as academic researchers, policy makers and practitioners. A new model that incorporates Bayesian theory and analyst’s behavior is proposed. By combining the history data of recommendations and stock return, posterior return distribution is established and utilized as the estimation of return distribution for next few days after recommendation. Based on the empirical studies on China stock analysts whose recommendation number is above 300 within two years, the maximum accuracy between 84.3% and 94.2% can be achieved, and the average accuracy falls between 58.6% and 60.3%, while it is just from 43.5% to 56.2% for hidden Markov-based method, and even lower for ANN-based method. The comparison results demonstrate the superior forecasting performance over state of the art models based on numeric analysis framework. The ability in persistence of prediction is in accordance with early research on stock analysts in maintaining their reputation. The result also confirms the effectiveness of the proposed method. Furthermore, stability and persistence of the predictor are also investigated with two important factors (TL and DRR) that have effect on the model. It is shown that the two factors should be taken into consideration simultaneously in order to achieve the best fitness and avoid uncertainty of recommendation behavior. On the other hand, being lack of sufficient recommendation data, the prediction model tend to become unstable when the model is trained using the analyst’s behavior whose recommendation number is below 300. Future work can be concentrated on improvement of the predictor which can incorporate several analysts’ recommendations, and distribute weights to the analysts. On the other hand, new prediction method that integrates the proposed model with current methods, such as time series analysis techniques and fuzzy AI techniques [31], can be developed to enhance the prediction performance.