近日，44118太阳成城集团梁龙跃老师及其学生2018级金融专硕蔡铉烨的研究成果《Time-sequencing European options and pricing with deep learning–Analyzing based on interpretable ALE method》被国际期刊《Expert Systems With Applications》刊发。《Expert Systems With Applications》是中国人工智能学会认定的A级期刊，影响因子6.954，CiteScore 12.70，2021年JCR分区中属于Q1。梁龙跃老师及其研究团队长期致力于人工智能、金融科技领域的研究，这是距去年两位作者发文《Forecasting peer-to-peer platform default rate with LSTM neural network》后的又一相关作品。此次文章发表在国际高水平期刊上有助于提升我院的科学研究和学科建设水平。
Abstract
In this paper, we investigated the feasibility of pricing European options with time-sequencing data processing method and deep learning models, based on two European options, the ETF50 options of China and the S&P 500 options of America. Four competing models were built to verify the improvement of the 1D-CNN and LSTM models on the option pricing task. Methods like cross-validations and statistical tests were also used to make our experiments more robust. Besides, in order to increase the stability and the interpretability of our pricing models, we selected the ALE method to interpret and analyze the behavior of the deep learning models. The empirical results indicate that, in both ETF50 option and S&P500 option pricing tasks, the 1D-CNN and LSTM models had significant advantages in forecasting accuracy and robustness under moneyness, trading date or maturity dimension irrespectively. Especially for the LSTM model, which has robust performance using different kinds of cross-validation methods. With the help of ALE method, we proved that the improved performance brought by the 1D-CNN and LSTM models could be attributed to their capability of capturing time-series information and their different emphasis on input features and lags.
摘要
本文以中国的ETF50期权和美国的S&P500期权为例，研究了用时间序列数据处理方法和深度学习模型对欧洲期权定价的可行性。为了验证1D-CNN和LSTM模型对期权定价任务的改进，我们建立了四个竞争模型。还使用了交叉验证和统计测试等方法，使实证结果更加稳健。此外，为了提高我们定价模型的稳定性和可解释性，选择了ALE方法来解释和分析深度学习模型的行为。实证结果表明，在ETF50期权和S&P500期权的定价任务中，1D-CNN和LSTM模型在货币性、交易日或到期日维度下分别具有明显的预测准确性和稳健性优势。特别是LSTM模型，它在使用不同种类的交叉验证方法时具有稳健性。利用ALE方法，我们证明了1D-CNN和LSTM模型所带来的性能改善可以归因于它们捕捉时间序列信息的能力以及它们对不同输入特征和滞后期的重视。
论文的具体信息,详见：https://doi.org/10.1016/j.eswa.2021.115951
作者简介
梁龙跃（1986-），44118太阳成城集团讲师，硕士生导师，2014年毕业于中国科学院大学数学科学学院，获概率论与数理统计学博士学位。主要研究方向为数量经济学、金融数学、风险管理及人工智能算法在经济金融中的应用。
蔡铉烨（1994-），44118太阳成城集团2018级金融专硕研究生，目前就读于中央财经大学统计与数学学院，攻读数量经济学博士学位，主要研究方向为智能风控、资产定价、碳中和及深度学习。
