Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/136570


Title: 應用強化學習於股票的投資選擇-以台灣股市為例
Applying Reinforcement Learning to Stock Investment–Taiwan Stock Market as an Example
Authors: 彭志偉
Phang, Chee-Wai
Contributors: 蕭明福
蔡瑞煌

彭志偉
Phang, Chee-Wai
Keywords: 金融股票市場
機器學習
強化學習
神經網路
股票選擇
Stock Market
Machine Learning
Reinforcement Learning
Neural Networks
Stock Selection
Date: 2021
Issue Date: 2021-08-04 16:01:10 (UTC+8)
Abstract: 強化學習在各領域都是一門不可或缺的學科,而在金融界的實際應用已有信用借貸/違約評估、風險控管、人工智慧客服及股市預測等等,金融科技則是運用數學模型來解決金融環境中的問題,本研究將應用強化學習演算法的學習框架套用於臺灣股票金融市場環境當中,設計一個股票投資的學習環境並模擬投資人在該環境中進行演算法超參數調整的實驗,代理人的最終目的在於控制投資風險的情況下將投資報酬最大化,本研究採用已上市達21年,且為臺灣股市總市值前15大之股票作為強化學習之環境模擬的訓練對象,使用2000年至2016年的股票歷史資料作為訓練數據資料集來進行訓練,2017年至2021年作為測試資料集,最後本研究將評估其實驗結果及跟其他的投資績效策略進行投資報酬績效的比較。
本研究在強化學習框架中所訓練之智慧代理人在環境模擬訓練的過程中,智慧代理人透過模擬學習在一定程度上捕捉到股票市場上股票價格的變動,並且藉由訓練達到有效的自我提升,在其後介紹的實驗測試結果中將會詳細介紹。而研究結果顯示,部分實驗測試的成果比加權股票指數及隨機分配投資策略的績效要好,在經過超參數調參後,仍以本研究之實驗二的成果為最佳選擇,並在測試結果中發現代理人在訓練的過程中有效的學習到了在控制投資風險的情況下進行投資獲利。
Reinforcement learning is an indispensable subject in various fields, and the practical applications in the financial sector include credit lending, default assessment, risk control, artificial intelligence customer service, stock market forecasting, etc., and financial technology uses mathematical tools to explain the problems of the financial environment, this research will apply the learning framework of reinforcement learning algorithm to the Taiwan stock financial market environment, design a stock investment learning environment and simulate the experiment of investors in the environment to adjust the hyper parameters of the algorithm, and the ultimate purpose of the reinforcement learning’s agent is putting effort on learning to minimize investment risks and maximize investment returns. The total time data set in this study is 21 years long, and the stock history data from year 2000 to 2016 is used as the training data set for training, from year 2017 to 2021 will be treated as a test data set. Finally, this research will evaluate its experimental results and compare its return on investment performance with other investment performance strategies.
In the process of environmental simulation training, the intelligent agent trained in this research in the framework of reinforcement learning is able to acquire the stock’s price movement that changes in the stock market in a certain extent and can achieve effective self-improvement. In experiments two, five and ten The results of the test are better than the weighted stock price index and random allocation of investment strategies. In the test results of the experiments, that is found the agent is able to learn to make investment profits while controlling investment risks during the training process.
Reference: 中文部分
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英文部分
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Description: 碩士
國立政治大學
經濟學系
108258044
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108258044
Data Type: thesis
Appears in Collections:[經濟學系] 學位論文

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