A Profitable Portfolio Allocation Strategy Based on Money Net-Flow Adjusted Deep Reinforcement Learning
Publish place: Iranian Journal of Finance، Vol: 7، Issue: 4
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
View: 55
This Paper With 31 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJFIFSA-7-4_003
تاریخ نمایه سازی: 24 آذر 1402
Abstract:
Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.
Keywords:
Portfolio Optimization Strategy , Automate Trading , Deep reinforcement learning , Money Net Flow Indicator
Authors
Samira Khonsha
Ph.D. Candidate in Computer Engineering, Department of Computer Engineering, Yazd University, Yazd, Iran.
Mehdi Agha Sarram
Associate Prof., Department of Computer Engineering, Yazd University, Yazd, Iran.
Razieh Sheikhpour
Assistant Prof.,, Department of Computer Engineering, Faculty of Engineering, Ardakan University, P.O. Box ۱۸۴, Ardakan, Iran.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :