论文标题
情感和资产价格预测投资组合优化的融合
Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization
论文作者
论文摘要
以文本形式与股票价格预测形式的公共情感数据融合是金融界越来越兴趣的话题。但是，研究文献很少探讨投资者情绪在投资组合选择问题中的应用。本文旨在解开并增强对情感意识到投资组合选择问题的理解。为此，研究使用语义注意模型来预测对资产的情绪。我们通过情感感知的长期记忆（LSTM）复发性神经网络选择最佳投资组合，以进行价格预测和均值差异策略。我们的情感组合策略平均达到了超过非闻名模型的收入大幅增加。但是，结果表明，从稳定的角度来看，我们的策略并不能优于传统的投资组合分配策略。我们认为，与价格预测和投资组合优化的组合相结合的情感预测的融合融合得出了增强的投资组合选择策略。
The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem. To this end, the study uses a Semantic Attention Model to predict sentiment towards an asset. We select the optimal portfolio through a sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for price prediction and a mean-variance strategy. Our sentiment portfolio strategies achieved on average a significant increase in revenue above the non-sentiment aware models. However, the results show that our strategy does not outperform traditional portfolio allocation strategies from a stability perspective. We argue that an improved fusion of sentiment prediction with a combination of price prediction and portfolio optimization leads to an enhanced portfolio selection strategy.
