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博碩士論文 etd-0528123-180654 詳細資訊
Title page for etd-0528123-180654
論文名稱
Title
機器學習在台灣股票投資策略建構之應用
Application of Machine Learning in Constructing Investment Strategies for the Taiwan Stock Market
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
86
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2023-06-21
繳交日期
Date of Submission
2023-06-28
關鍵字
Keywords
股票分析、機器學習、深度學習、CatBoost、Gated Recurrent Unit
Stock Analysis, Machine Learning, Deep Learning, CatBoost, Gated Recurrent Unit
統計
Statistics
本論文已被瀏覽 85 次,被下載 6
The thesis/dissertation has been browsed 85 times, has been downloaded 6 times.
中文摘要
這篇研究主要探討了機器學習在股票分析中的應用。本研究將目標放在理解如何利用機器學習找出最佳的股票分析方法組合,並評估這種方法是否能帶來優異的投資績效。研究首先進行了廣泛的文獻回顧,瞭解了各種與股票分析相關的研究,同時深入探討了將機器學習融入投資領域的各種設計與應用。緊接著,本研究收集和整理了台灣上市股的資料來作為實驗的樣本,並設計了一套雙階段的機器學習流程,也就是先篩選出每一季表現較好的個股作為當季的股票池,然後再基於它們的資料去預測每日的進出場點位。最終搭配普通買賣規則,即可進行完整的回測。研究結果顯示,本篇論文的研究方法無論是在篩選股票或是在預測進出場訊號上都能取得不錯的預測效果,同時也發現隨著訓練集資料的增加,預測準確度也能逐步得到提升。接著,本研究也針對進出場訊號和回測設定進行多種調整與檢驗,以了解不同情況下本策略能夠取得的投資績效。回測的結果顯示本策略可以根據投資者的風險偏好去進行不同的設定來放寬或限縮對於交易的限制,並以此來反映對於報酬和風險之間的抉擇。最後本研究也以表現較好的設定展示其回測績效,並得到了高於同時期的加權報酬指數將近一倍的夏普比率。
Abstract
This study aims to understand how machine learning can be utilized to identify optimal stock analysis approaches and evaluate whether such methods can lead to superior investment performance. The study begins with examining various research related to stock analysis and exploring the applications of integrating machine learning into investment. Subsequently, this study collects and organizes data from stocks listed on the Taiwan Stock Exchange as samples. A two-stage machine learning process is designed, involving the selection of outperforming stocks as the quarterly stock pool, and predicting daily entry and exit points based on their data. The complete backtesting is conducted in conjunction with regular buy/sell rules. The research results demonstrate that the proposed methodology achieves favorable predictive accuracy in both stock selection and entry/exit signal forecasting. Furthermore, the study performs multiple adjustments on the entry/exit signals and backtesting settings to evaluate the investment performance under different scenarios. The backtesting results reveal that the strategy can be customized by adjusting trading restrictions based on investors' risk preferences, reflecting the trade-off between returns and risks. Finally, the study presents the backtesting performance using the well-performing settings and achieves a Sharpe ratio nearly twice as high as Taiwan Weighted Index during the same period.
目次 Table of Contents
論 文 審 定 書 ................................................................................................................... i 誌 謝 .................................................................................................................. ii 摘 要 ................................................................................................................. iii A b s t r a c t .................................................................................................................. iv 第一章 緒論 .................................................................................................................... 1
第一節 研究背景與動機............................................................................................. 1 第二節 研究目的......................................................................................................... 2 第三節 研究流程......................................................................................................... 2
第二章 文獻探討 ............................................................................................................ 4 第一節 股票分析方法................................................................................................. 4 第二節 機器學習與投資............................................................................................. 9
第三章 研究方法 .......................................................................................................... 11 第一節 樣本搜集與整理........................................................................................... 13 第二節 篩選股票....................................................................................................... 17 第三節 預測進出場訊號........................................................................................... 26 第四節 回測設定....................................................................................................... 33
第四章 實證結果 .......................................................................................................... 37 第一節 預測準確度................................................................................................... 37 第二節 回測結果....................................................................................................... 45 第三節 最佳結果....................................................................................................... 54
第五章 結論與建議 ...................................................................................................... 57 第一節 研究結論與貢獻........................................................................................... 57 第二節 研究建議與展望........................................................................................... 59
第六章 參考文獻 .......................................................................................................... 60 第一節 外文文獻....................................................................................................... 60 第二節 中文文獻....................................................................................................... 61
附 錄................................................................................................................. 62
參考文獻 References
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