论文标题
通过使用机器学习分类模型的黑色 - 链式方程的数值方法来解决股票期权预测问题
Solving the Stock Option Forecast problem by a numerical method for the Black-Scholes Equation with Machine Learning Classification Model
论文作者
论文摘要
我们提出了利用准可逆性方法的结果的分类模型，该模型求解了黑色 - choles方程，以预测期权价格提前一天。将QRM的最小化器与我们的机器学习分类相结合，我们可以将选项分类为增加或减少的价值。根据选项的不同分类，我们可以采用各种交易策略，我们旨在找出改善QRM推断结果的方法。为了进一步测试模型的生存能力，我们从现实世界市场收集了23548个选项数据，然后我们将进食数据，以及QRM的最小化器以形成决策树和随机森林，以后我们将测试准确性，精度和回忆。
We proposed classification models that utilize the result from the Quasi-Reversibility Method, which solves the Black-Scholes equation to forecast the option prices one day in advance. Combining the minimizer from QRM with our machine learning classifications, we can classify the option as an increase or decrease in value. Based on the different classifications of the options, we can apply various trading strategies which we aim to figure out ways to improve the results from QRM's extrapolations. To further test the viability of our model, we collected 23548 options data from the real-world market for our model, and we will then feed in the data along with the minimizer from QRM to form decision trees and random forests, which we will later test for accuracy, precision, and recall.
