臺灣博碩士論文加值系統

本研究以2008年至2019年中國市場之指數和股票為樣本，探討機器學習模型與量化投資的結合是否能有效提升投資績效，並進一步探討不同的交易期間、量化投資策略(包括：均線策略、布林通道策略)之選擇，針對投資人常應用的量化投資策略，以及機器學習模型(包括：支持向量機器、隨機森林、人工神經網路)建模並且進行交互分析，並且分別將交易期間分為2019年與2018年至2019年進行驗證，探討分析各策略之績效表現。在實證結果中發現，人工神經網路之投資報酬績效表現較穩健，而基於機器學習模型之量化投資策略則在均線策略與支持向量機器、隨機森林的搭配，績效表現較佳。

This study uses the index and stocks of the Chinese market from 2008 to 2019 as samples, explore the relationship of the combination of machine learning models and quantitative investment can effectively improve investment performance or not. This study further explore the different trading periods, the choice of quantitative investment strategies (including: moving average strategy, Bollinger Bands strategy) commonly used by investors, and machine learning models (including: support vector machines, random forests, artificial Neural network) modeling and interactive analysis, In addition, the trading period is divided into 2019 and 2018 to 2019 for verification, to discuss and analyze the performance of each strategy. In the empirical results, it is found that the performance of the return on investment of artificial neural networks is relatively stable, while the quantitative investment strategy based on the machine learning model performs better in the combination of the moving average strategy and the support vector machine and random forest.

目錄
摘 要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
壹、緒論 1
一、研究背景 1
二、研究目的 2
三、研究架構 2
貳、文獻回顧 4
一、機器學習概述與文獻 4
二、交易策略與相關文獻 5
參、研究方法 7
一、研究流程 7
二、資料前處理 7
(一)特徵製作 8
1.簡單移動平均線(SMA) 8
2.加權移動平均線(WMA) 8
3.動量指標(MOM) 9
4.隨機指標(K) 9
5.隨機指標(D) 9
6.相對強弱指標(RSI) 10
7.指數平滑異同移動平均線(MACD) 10
8.威廉指標R(Larry William’s R%) 11
9.累積分布指標(AD Accumulation/Distribution Oscillator) 11
10.順勢指標CCI 12
(二)特徵縮放 14
(三)資料標籤定義 14
(四)資料切片 15
三、建立模型 16
(一)支援向量機器(Support Vector Machine，SVM) 16
(二)隨機森林法(Random Forest) 17
(三)人工神經網路(Artificial Neural Network) 18
四、投資策略 19
(一)買進持有策略 19
(二)量化投資策略 19
1.均線策略(Moving Average,MA) 19
2.布林通道策略(Bollinger Bands,BBands) 21
(三)機器學習買進賣出策略 23
(四)結合策略 23
肆、實證結果與分析 26
一、研究資料與敘述統計 26
二、市場趨勢分析 29
三、分析模型準確度 33
四、基於機器學習之投資策略分析 34
(一)投資策略一年期回測結果 34
(二)投資策略兩年期回測結果 37
伍、結論 42
參考文獻 43

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中文部分：
1. 李厚德，107，機器學習應用於台指期貨報酬率之實證，國立高雄科技大學，碩士論文
2. 黃紹輔，102，應用三重濾網下之技術指標探討台灣股市交易策略，淡江大學，碩士論文
3. 周建維，100，買賣訊號操作與買進持有策略之投資績效比較‐以台灣市場為例，宜蘭大學