论文标题
S＆P500上短跨跨的监督机学习分类
Supervised machine learning classification for short straddles on the S&P500
论文作者
论文摘要
在本工作论文中，我们介绍了我们目前在培训机器学习模型的过程中，以执行S＆P500上的简短选项策略。作为第一步，本文将这个问题分解为监督分类任务，以决定是否应每天执行S＆P500的短期跨。我们描述了我们的二手框架，并概述了有关不同分类模型的评估指标。在这项初步工作中，使用标准的机器学习技术并且没有超参数搜索，我们发现对简单的“贸易始终”策略没有统计学意义的超出效果，但是就我们如何在进一步的实验中进行的方式获得了更多的见解。
In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised classification task to decide if a short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview over our evaluation metrics on different classification models. In this preliminary work, using standard machine learning techniques and without hyperparameter search, we find no statistically significant outperformance to a simple "trade always" strategy, but gain additional insights on how we could proceed in further experiments.
