應用深度學習於機器人理財之投資組合最佳化

Deep Learning for Robo-Advisors in Portfolio Optimization

李政剛(Jheng-Gang Li)

指導教授 ： 鄭東光

共同指導教授 ： 戴敏育(Min-Yuh Day)

淡江大學/商管學院/經濟學系碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00678

摘要

本研究旨在開發執行自動化投資管理、時間序列分析以及投資組合最佳化的理財機器人系統。本研究開發的核心演算法，利用人工智慧執行金融資產價格預測，並作為理財機器人的子系統。演算法交易以及投資組合最佳化的研究中已有許多實務上的應用，然而只有少部分文獻專注於理財機器人的應用開發。本研究利用深度學習中的長短期記憶 (Long Short-Term Memory) 方法，來解決時間序列分析中序列相依的複雜問題，並進一步開發自動化系統來執行時間序列預測於當沖交易策略以及投資組合最佳化的應用。本研究開發的可擴充系統可做為自動化投資管理中理財機器人系統的開發基礎。

關鍵字

人工智慧 ； 機器人理財 ； 深度學習 ； 投資組合最佳化 ； 時間序列分析 ； 金融市場預測

並列摘要

This research aims to develop the system for Robo-Advisor to perform time-series forecasting and portfolio optimization in automated investment management. We designed the core algorithm, artificial intelligence to perform time-series forecasting in the financial market, as part of the system for Robo-Advisor in automated investment. There are many studies in algorithmic trading and portfolio management in various forms of applications; however, there is a paucity of literature focusing on the applications which are designed for Robo-Advisors. In this research, we used a deep learning approach, Long Short-Term Memory (LSTM), to develop our algorithm to solve the complexity of sequence dependence in time-series forecasting. We developed an automated system to perform time-series forecasting and used the result to construct a day trading strategy and to perform portfolio optimization to show that LSTM based algorithm added value. The system we built is expandable and can be used as a framework when developing Robo-Advisors for automated investment management.

並列關鍵字

Artificial Intelligent (AI) ； Robo-Advisors ； Deep Learning ； Portfolio Optimization ； Black-Litterman ； Time-Series Forecasting ； Financial Market Prediction

參考文獻

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