论文标题
使用准可逆性方法的卷积神经网络的应用结果预测结果
Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting
论文作者
论文摘要
本文提出了一种应用数学金融和机器学习（ML）以预测股票期权价格的新方法。根据纸质可逆性方法和神经网络机器学习的结果来解决黑色 - choles方程（出现在AMS当代数学杂志上），我们创建并评估了黑色choles方程的新的经验数学模型，以分析92,846家公司的数据。我们使用准可逆性方法（QRM）来及时解决黑色 - 甲梁方程（BS）方程，以预测未来一天的期权价格。对于每家公司，我们有13个要素，包括股票和期权每日价格，波动性，最小化器等。由于市场是如此复杂，没有完美的模型，因此我们将ML应用ML来培训算法以进行最佳预测。研究的当前阶段将QRM与卷积神经网络（CNN）结合在一起，该杂志同时学习了大量数据点的信息。我们通过验证和测试样本市场数据来实施CNN来生成新的结果。我们测试了应用CNN的不同方法，并将CNN模型与以前的模型进行比较，以查看是否可以实现更高的利润率。
This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible.
