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Title:	應用深度強化學習將加密貨幣納入資產配置組合優化之探索 Applying Deep Reinforcement Learning to Explore Adding Cryptocurrencies in Asset Allocation Optimization
Authors:	李昂縣 Lee, Ang-Hsien
Contributors:	胡毓忠 Hu, Yuh-Jong 李昂縣 Lee, Ang-Hsien
Keywords:	深度強化學習 Soft Actor-Critic 加密貨幣資產配置現代投資組合理論均值-變異數最佳化 Deep reinforcement learning Soft Actor-Critic Cryptocurrency Asset portfolio Modern Portfolio Theory Mean-Variance Optimization
Date:	2023
Issue Date:	2023-12-01 14:09:06 (UTC+8)
Abstract:	隨著近年來加密貨幣市場的蓬勃發展，越來越多的投資者將加密貨幣納入其投資組合，以期望獲得更高的回報率。相較於傳統交易市場，加密貨幣市場提供更透明的交易資訊，更靈活地反應市場變化，且不受休市時間的限制。然而，加密貨幣市場的高波動及流動性不足等特性，使得投資者通常對納入其投資組合持保守態度，或者將其與其他傳統資產結合以對沖風險。深度強化學習在複雜環境中的自我學習和適應能力、對高維度數據的有效處理，以及良好的泛化性能，有望在加密貨幣投資決策中發揮顯著的優勢。因此本研究的目的在於應用Soft Actor Critic深度強化學習演算法，整合加密貨幣風險評估並探討加密貨幣納入資產配置組合的效益。透過實證分析，我們比較了加密貨幣資產配置組合與傳統金融資產配置組合的成效，以及DRL模型與現代投資組合理論的均值-變異數最佳化模型在加密貨幣資產配置優化的效益表現，為投資者提供具參考價值且系統性的投資建議。 With the rapid growth of the cryptocurrency market, an increasing number of investors are incorporating cryptocurrencies into their portfolios in pursuit of potential high returns. Compared to traditional markets, cryptocurrencies provide heightened transparency and flexibility, operating continuously around the clock. However, due to their unpredictable nature, some investors may approach them cautiously or combine them with traditional assets for risk management. Deep Reinforcement Learning (DRL) excels in navigating complex environments, handling high-dimensional data, and demonstrating robust adaptability. This study aims to integrate cryptocurrency risk assessment using the Soft Actor-Critic algorithm and explore the benefits of incorporating cryptocurrencies into asset allocation while determining suitable allocation ratios. By comparing the performance of cryptocurrency and traditional asset portfolios and evaluating the effectiveness of this research model, we aim to provide valuable and systematic investment guidance.
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Description:	碩士國立政治大學資訊科學系碩士在職專班 110971018
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110971018
Data Type:	thesis
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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