论文标题
通过机器学习模型组装和改进的交易策略来优化库存期权预测
Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies
论文作者
论文摘要
本文介绍了应用机器学习（ML）模型，改进的交易策略以及准可逆性方法（QRM）的关键方面，以优化股票期权预测和交易结果。它介绍了研究“使用准可逆性方法的卷积神经网络的应用，以预测的选择结果”。首先，该项目包括复发性神经网络（RNN）和长期记忆（LSTM）网络的应用，以提供一种新颖的预测股票期权趋势的方法。此外，它通过评估组合多个ML模型以改善预测结果和决策的实验方法来检查ML模型的依赖性。最后，提出了两种改进的交易策略和模拟投资结果。采用了具有离散时间随机过程分析和投资组合对冲的二项式资产定价模型，并提出了优化的投资期望。这些结果可以用于现实生活交易策略中，以根据历史数据优化股票期权投资结果。
This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.
