标题： 基于分数阶熵的风险决策模型 显示英文标题

标题： Fractional order entropy-based decision-making models under risk

摘要： 构建一个具有良好回报水平和最小风险的有效投资组合取决于选择股票的最佳组合。 本文介绍了一种基于Ubriaco的分数阶熵的股票选择新决策框架。 通过调整分数参数，该模型捕捉了个体对风险的不同态度。 接近1的分数参数值表示高风险容忍度（冒险态度），而接近0的值则反映了风险规避（保守态度）。 通过四个现实世界的投资组合模型，即大型股、中型股、多样化和假设模型，展示了分数阶熵对决策者风险偏好的变化敏感性。 此外，引入了两种新的风险度量方法，称为期望效用分数熵（EU FE）和期望效用分数熵与方差（EU FEV），以开发与投资者风险偏好一致的决策模型。 该决策模型的有效性进一步通过PSI指数的金融股市数据进行测试，借助人工神经网络找到投资组合的有效前沿。 显示英文摘要

摘要： The construction of an efficient portfolio with a good level of return and minimal risk depends on selecting the optimal combination of stocks. This paper introduces a novel decision-making framework for stock selection based on fractional order entropy due to Ubriaco. By tuning the fractional parameter, the model captures varying attitudes of individuals toward risk. Values of fractional parameter near one indicate high risk tolerance (adventurous attitude), while those near zero reflect risk aversion (conservative attitude). The sensitivity of the fractional order entropy to changing risk preferences of decision makers is demonstrated through four real world portfolio models, namely, large cap, mid cap, diversified, and hypothetical. Furthermore, two new risk measures, termed as expected utility fractional entropy (EU FE) and expected utility fractional entropy and variance (EU FEV), are introduced to develop decision models aligned with investors risk preferences. The effectiveness of the decision model is further tested with financial stock market data of PSI index by finding efficient frontiers of portfolio with the aid of artificial neural network.