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Politically-themed stocks mainly refer to stocks that benefit from the policies of politicians. This study gave the empirical analysis of the politically-themed stocks in the Republic of Korea and constructed politically-themed stock networks based on the Republic of Korea’s politically-themed stocks, derived mainly from politicians. To select politically-themed stocks, we calculated the daily politician sentiment index (PSI), which means politicians’ daily reputation using politicians’ search volume data and sentiment analysis results from politician-related text data. Additionally, we selected politically-themed stock candidates from politician-related search volume data. To measure causal relationships, we adopted entropy-based measures. We determined politically-themed stocks based on causal relationships from the rates of change of the PSI to their abnormal returns. To illustrate causal relationships between politically-themed stocks, we constructed politically-themed stock networks based on causal relationships using entropy-based approaches. Moreover, we experimented using politically-themed stocks in real-world situations from the schematized networks, focusing on politically-themed stock networks’ dynamic changes. We verified that the investment strategy using the PSI and politically-themed stocks that we selected could benchmark the main stock market indices such as the KOSPI and KOSDAQ around political events. 相似文献
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The aim of this paper is to expand the methodological spectrum of socially responsible investing by introducing stochastic sustainability returns into safety first models for portfolio choice. We provide a foundation of the notion of sustainability in portfolio theory and establish a general model for generalized safety first portfolio management with probabilistic constraints and three specifications of it. Moreover, we prove theorems about conditions for unique optimal solutions and for the constraints of one model being more restrictive than those of another. In an empirical part, we calculate the costs of investing according to our approach in terms of less financial return. 相似文献
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基于主题模型的半监督网络文本情感分类研究 总被引:1,自引:0,他引:1
针对网络评论文本的情感分类问题中存在的数据的不平衡性、无标记性和不规范性问题,提出一种基于主题的闽值调整的半监督学习模型,通过从非结构化文本中提取主题特征,对少量标注情感的文本训练分类器并优化指标调整闽值,达到识别用户评论的情感倾向的目的。仿真研究证明阈值调整的半监督模型对数据非平衡性和无标记性具有较强的适应能力。在实证研究中,对酒店评论文本数据构建的文本情感分类器显示该模型可以有效预测少数类评论样本的情感极性,证实了基于主题模型的闽值调整半监督网络评论文本情感分类模型在实际问题中的适用性与可行性。 相似文献
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考虑到传染病的传播与股民数量的增长之间的类似性,建立了刻画新增股民数量变化规律的数学模型,并进行了相应的数学实验.建立的模型较好地反映了短期内新增股民数量的变化规律. 相似文献
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《Operations Research Letters》2023,51(4):408-413
We develop deep learning models to learn the hedge ratio for S&P500 index options from options data. We compare different combinations of features and show that with sufficient training data, a feedforward neural network model with time to maturity, the Black-Scholes delta and market sentiment as inputs performs the best in the out-of-sample test under daily hedging. This model significantly outperforms delta hedging and a data-driven hedging model. Our results also demonstrate the importance of market sentiment for hedging. 相似文献
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利用历史累积交易金额数据,本文构造了中国股票市场增量注意风险补偿和存量注意风险补偿,并检验其对中国股票市场收益率的预测能力。样本外检验结果显示,以上两种注意风险补偿均能显著预测下个月中国股市的超额收益率,其R2分别达到了2.68%和2.50%;与中国股票市场中其他预测变量相对比,增量注意和存量注意风险补偿表现出更强的预测能力。此外,基于不同的样本外检验期、不同的风险厌恶参数以及五种不同的变量构造方式,投资者注意风险补偿均产生显著的预测能力。围绕着经济周期波动,本文对注意风险补偿的预测能力进行了解释,同时还发现,相较于经济衰退期间,经济繁荣期间的投资者注意风险补偿样本外预测能力更强。 相似文献
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