An extensive, in-depth study of risk factors seems to be of crucial importance in the research of the financial market in order to prevent (or reduce) the chance of developing this return. It represents market anomalies. This study confirms that the [Formula: see text]-factors model is better than the other traditional asset pricing models in explaining individual stock return in the US over the 2000–2017 period. The main focus of data analysis is, on the use of models, to discover and understand the relationships between different factors of risk market anomaly. Recently, Fama and French presented a five-factor model that captures the size, value, profitability, and investment patterns in average stock market returns better than their three-factor model presented previously in 1993. This paper explores a shred of new empirical evidence to assess the asset pricing model through an extension of Fama and French model and a report on applying Bayesian Network (BN) modeling to discover the relationships across different risk factor. Furthermore, the induced BN was used to make inference taking into account sensibility and the application of BN tools has led to the discovery of several direct and indirect relationships between different parameters. For this reason, we introduce additional factors that are related to behavioral finance such as investor’s sentiment to describe a behavior return, confidence index, and herding. It is worth noting that there is an interaction between these various factors, which implies that it is interesting to incorporate them into the model to give more effectiveness to the performance of the stock market return. Moreover, the implemented BN was used to make inferences, i.e., to predict new scenarios when different information was introduced.

中文翻译：

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评估资产定价模型的新证据：基于贝叶斯网络的实证研究

对风险因素进行广泛、深入的研究似乎对金融市场研究至关重要，以防止（或减少）产生这种回报的机会。它代表市场异常。本研究证实 [公式：见正文]-因子模型在解释 2000-2017 年期间美国个股回报方面优于其他传统资产定价模型。数据分析的主要重点是，在模型的使用上，发现和理解风险市场异常的不同因素之间的关系。最近，Fama 和 French 提出了一个五因素模型，该模型比 1993 年之前提出的三因素模型更好地捕捉了股票市场平均回报的规模、价值、盈利能力和投资模式。本文通过对 Fama 和 French 模型的扩展以及关于应用贝叶斯网络 (BN) 模型发现不同风险因素之间关系的报告，探索了一些新的经验证据来评估资产定价模型。此外，引入的 BN 用于在考虑敏感性的情况下进行推理，并且 BN 工具的应用导致发现了不同参数之间的几种直接和间接关系。出于这个原因，我们引入了与行为金融相关的其他因素，例如投资者的情绪来描述行为回报、信心指数和从众。值得注意的是，这些不同因素之间存在相互作用，这意味着将它们纳入模型以提高股市回报表现的有效性是很有趣的。此外，实现的 BN 用于进行推理，即在引入不同信息时预测新场景。