研究生: |
黃昱凱 Huang, Yu-Kai |
---|---|
論文名稱: |
利用 Radius Neighbors Regressor 模型預測台灣股市加權指數並賦予強弱指標 Enhancing Stock Market Predictions with Dynamic Radius Neighbors Regressor: A Feature Weighted Approach |
指導教授: |
蔡芸琤
Tsai, Yun-Cheng 林順喜 Lin, Shun-Shii |
口試委員: |
蔡芸琤
Tsai, Yun-Cheng 林順喜 Lin, Shun-Shii 許軒 Hsu, Hsuan 邱嘉豪 Chiu, Chia-Hao |
口試日期: | 2024/07/02 |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 64 |
中文關鍵詞: | 股票 、機器學習 、Radius Neighbor Regressor |
英文關鍵詞: | Stock Market, Radius Neighbor Regressor, Machine Learning |
研究方法: | 實驗設計法 、 主題分析 |
DOI URL: | http://doi.org/10.6345/NTNU202401777 |
論文種類: | 學術論文 |
相關次數: | 點閱:62 下載:0 |
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股票投資是現代人在累積資產上不可或缺的工具,雖然投資理財有賺有賠,但是若能夠找到一套良好的交易策略,以及善用各種分析工具,達到長期穩定獲利也是一件可以期盼的事情。本論文使用Radius Neighbor Regressor之機器學習方法並結合個人之股票交易經驗,在特定時間窗口之大盤強弱指標以及大盤中長期的多空頭判斷上取得了良好的結果。
在實作上,我們使用Radius Neighbor Regressor與個人交易經驗所挑選出的特徵值作為產生強弱指標的依據。資料收集樣本時間為2013/1/25~2023/12/21,總共2668個交易日。主要資料來源取自XQ全球贏家之資料庫,並且使用所經加權後的強弱指標分別在幾種預測時間的長短進行比較與分析。
從實驗結果驗證,我們發現使用Radius Neighbor Regressor搭配個人交易經驗所挑選出的特徵值,在以60個交易日預測後20個交易日的結果準確率高達73%,且在傳統多空頭的分析上也得到了良好的結果。另外,還證明了在特徵值選擇上以個人交易經驗做選擇的優勢,最後也彌補了單純使用Radius Neighbor Regressor機器學習方法的缺點,得出最佳的一種大盤強弱指標之模型。
Stock investment is an indispensable tool for modern people to accumulate wealth. Although investment and financial management come with risks, finding a good trading strategy and utilizing various analytical tools can lead to long-term stable profits. This thesis uses the Radius Neighbor Regressor machine learning method combined with personal stock trading experience to achieve good results in determining the strength and weakness indicators of the market and the long-term bullish or bearish trends within a specific time window.
In practice, we use the Radius Neighbor Regressor and the features selected based on personal trading experience to generate strength and weakness indicators. The data collection period spans from January 25, 2013 to December 21, 2023, covering a total of 2668 trading days, with the main data source being the XQ Global Winner database. We then use the weighted strength and weakness indicators to conduct comparisons and analyses over various prediction periods.
The experimental results confirm that using the Radius Neighbor Regressor combined with the features selected based on personal trading experience achieves an accuracy rate of up to 73% when predicting the results for the 20 trading days following a 60-day prediction period. This approach also yielded good results in traditional bullish and bearish analyses, demonstrating the advantage of selecting features based on personal trading experience. Ultimately, this method addresses the shortcomings of solely using the Radius Neighbor Regressor machine learning method, resulting in the optimal model for market strength and weakness indicators.
[1] 周俊志 (2008). 自動交易系統與策略評價之研究。
[2] 于慎為 (2011). 價值型投資考量法人籌碼動能下的選股策略中:以台灣股市為例。
[3] Breiman, L. (2001). Random Forests. Machine Learning.
[4] Ho, K. (1995). Random Decision Forest. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, August 14-18.
[5] Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. (1984). Classification and Regression Trees. Monterey, CA: Chapman & Hall/CRC.
[6] Chen, Y. & Hao, Y. (2017). A Feature Weighted Support Vector Machine and K-nearest Neighbor Algorithm for Stock Market Indices Prediction.
[7] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O. & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. The Journal of Machine Learning Research, 12, 2825-2830.
[8] Lo, A. W. & Wang, J. (2010). Stock Market Trading Volume. In Handbook of Financial Econometrics: Applications (pp. 241-342). Elsevier.
[9] 陳怡均 & 陳安斌 (2007). 應用類神經網路對台股籌碼面與技術面之領先-落後研究分析 (Doctoral dissertation).
[10] Huang, S. & Liu, S. (2019). Machine Learning on Stock Price Movement Forecast: the Sample of the Taiwan Stock Exchange. International Journal of Economics and Financial Issues, 9(2), 189.
[11] Su, I. F., Lin, P. L., Chung, Y. C. & Lee, C. (2023). Forecasting of Taiwan's Weighted Stock Price Index Based on Machine Learning. Expert Systems, 40(9), e13408.
[12] Kusuma, R. M. I., Ho, T. T., Kao, W. C., Ou, Y. Y. & Hua, K. L. (2019). Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market. arXiv preprint arXiv:1903.12258.
[13] Kumbure, M. M., Lohrmann, C., Luukka, P. & Porras, J. (2022). Machine Learning Techniques and Data for Stock Market Forecasting: A Literature Review. Expert Systems with Applications, 197, 116659.