| DC 欄位 |
值 |
語言 |
| DC.contributor | 統計研究所 | zh_TW |
| DC.creator | 李濬紘 | zh_TW |
| DC.creator | Chun-Hung Lee | en_US |
| dc.date.accessioned | 2023-7-26T07:39:07Z | |
| dc.date.available | 2023-7-26T07:39:07Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=110225024 | |
| dc.contributor.department | 統計研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在股票交易中，根據不同情況設計一個盈利的交易策略是一個重大挑戰。近年來，人工智能的發展為股票市場帶來了新的投資方法。Q-learning，一種強化學習演算法，可以幫助投資者學習市場趨勢並提供更合理的投資決策。在Q-learning中，狀態的制定尤其重要，因為不同的制定方法會影響其表現。本文提出了一種基於非監督式學習的數據驅動方法來設置Q-learning所需的狀態，將多維度的股票市場資料作為特徵，並藉由動態時間校正(DTW) 與 t-SNE 來找尋所需狀態。本文以台灣股市為例，建構單一資產的Q-learning投資決策，並相應地提出了一個由多個資產組成的適當投資組合。實證結果顯示，所提出的方法提供了不錯的投資表現。 | zh_TW |
| dc.description.abstract | Designing a profitable trading strategy based on different situations is a major challenge in
stock trading. In recent years, the development of artificial intelligence has brought new investment methods to the stock market. Q-learning, a reinforcement learning algorithm, can
help investors to learn market trends and recommend more reasonable investment decisions.
In Q-learning, the formulation of states is particularly important since different formulation methods can affect its performance. We propose a data-driven approach based on a
non-supervised learning method to set the states required in Q-learning. By utilizing multidimensional stock market data as features and leveraging Dynamic Time Warping (DTW) and t-SNE, the proposed approach efficiently identifies the desired states for Q-learning. In this work, using the Taiwan stock market as an example, we obtain the Q-learning investment decision of a single asset and propose an appropriate investment portfolio consisting of multiple assets accordingly. The empirical results reveal that the proposed method provides a satisfactory investment performance. | en_US |
| DC.subject | 動態時間校正 | zh_TW |
| DC.subject | 非監督式學習 | zh_TW |
| DC.subject | 投資組合選擇 | zh_TW |
| DC.subject | Q-learning | zh_TW |
| DC.subject | t-SNE | zh_TW |
| DC.title | 基於Q-learning與非監督式學習之交易策略 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | A Trading Strategy Based on Q-learning and Unsupervised Learning | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |