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題名 卷積神經網路結合投資組合理論之交易策略實證研究： 以台灣股市為例
The Empirical Research of Trading Strategies for Convolutional Neural Network and Portfolio Theory on Taiwan Stock Market作者 莊承勳
Chuang, Cheng-Hsun貢獻者 廖四郎
Liao, Szu-Lang
莊承勳
Chuang, Cheng-Hsun關鍵詞 量化交易
卷積神經網路
投資組合
平均-變異數分析
動能交易日期 2019 上傳時間 7-Aug-2019 16:10:36 (UTC+8) 摘要 本研究從台灣前60大市值比上市公司中，挑出49家公司為樣本，蒐集2006-2018間的資料，採用技術指標作為變數，以卷積神經網路預測為選股策略，選取投資組合成分股， 再利用「平均－變異數」分析配置權重，並根據不同風險趨避程度，建構不同投組。結果卷積神經網路的投資策略，在訓練樣本期間(2010～2016年)內的績效表現相當好，但應用在樣本外期間(2008～2009年，2017～2018年)則表現不佳。若使用此種交易策略與簡單動能策略比較，則動能策略建構的投資組合能在訓練樣本外期間表現的較佳。
This Research selects 49 companies from the top 60 companies in Taiwan as a sample, collects stock data from 2006 to 2018. Choose technical indicators as variables, and use convolutional neural network prediction as a stock selection strategy to form a portfolio. In the selected stocks, the “Mean-Variance Analysis” is used to allocate the asset weights, and different investment groups are constructed according to different risk aversion levels. The result of this study shows that: the investment strategy of the convolutional neural network is quite good during the training period (2010~2016) of data. However, the strategy make negative return during the out-of-sample period (2008-2009, 2017~2018). With this performance, compare to a simple momentum strategy, the momentum portfolio can perform better during the out-of-sample period.參考文獻 [1] 王春峰、屠新曙、厉斌(2002)，效用函数意义下投资组合有效选择问题的研究，中国管理科学，第10卷第2期，4月，頁15-19。[2] 李顯儀、吳幸姬(2009)，技術分析資訊對共同基金從眾行為的影響，臺大管理論叢，第20卷第1期，12月，頁227-260。[3] 陳嘉惠、高郁惠、劉玉珍(2002)，投資人偏好與資產配置。臺灣管理學刊，第1卷第2期，2月，頁213-232。[4] 詹錦宏、吳莉禎(2011)，動能投資策略於台灣股票市場之應用—含金融海嘯之影響，會計學報，第3卷第2期，5月，頁1-22。[5] Allen, F. & R. Karjalainen (1999). ‘‘Using Genetic Algorithms to Find Technical Trading Rules,’’ Journal of Financial Economics, 51, 245-271.[6] Bai, S., J. Kolter & V. Koltun (2018). ‘‘An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,’’ Retrieved from https://arxiv.org/abs/1803.01271.[7] Bodie, Z., A. Kane & A. Marcus (1999). Investments, 4th ed. McGraw-Hill Companies, 178-193.[8] Cesarone, F., A. Scozzari & F. Tardella (2010). ‘‘Portfolio selection problems in practice: a comparison between linear and quadratic optimization models,’’ Retrieved from https://arxiv.org/abs/1105.3594[9] De Bondt, W. & R. Thaler (1985). ‘‘Does the Stock Market Overreact?,’’ Journal of Finance, 40, 793-805.[10] Jegadeesh, N. & S. Titman (1993). ‘‘Returns to Buying Winners and Selling Losers: Implications for Market Efficiency,’’ Journal of Finance, 48, 65-91.[11] LeCun, Y., L. Bottou, Y. Bengio & P. Haffner (1998). ‘‘Gradient-based learning applied to document recognition,’’ Proc. IEEE, 86, 2278-2324.[12] Lo, A. W. and A. C. MacKinlay (1990). ‘‘When Are Contrarian Profits Due to Stock Market-Overreaction,’’ Review of Financial Studies, 3, 175-208.[13] Markowitz, H. (1952). ‘‘Portfolio Selection,’’ Journal of Finance 7, 77-91.[14] Thawornwong, S., D.Enke & C. Dagli (2003). ‘‘Neural Networks as a Decision Maker for Stock Trading: A Technical Analysis Approach,’’ International Journal of Smart Engineering System Design, 5(4), 313-325.[15] Vejendla, A. & D. Enke (2013). ‘‘Evaluation of GARCH, RNN and FNN Models for Forecasting Volatility in the Financial Markets,’’ Journal of Financial Risk Management, 10(1), 41-49.[16] White, H. (1988). ‘‘Economic prediction using neural networks: the case of IBM daily stock returns,’’ Proc. IEEE int. conf. on neural networks, 2, 451-458.[17] Wood, D. & B. Dasgupta (1996). ‘‘Classifying trend movements in the MSCI U.S.A. capital market index－A comparison of regression, arima and neural network methods,’’ Computers & Operations Research, 23 , 611-622. 描述 碩士
國立政治大學
金融學系
106352017資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106352017 資料類型 thesis dc.contributor.advisor 廖四郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (Authors) 莊承勳 zh_TW dc.contributor.author (Authors) Chuang, Cheng-Hsun en_US dc.creator (作者) 莊承勳 zh_TW dc.creator (作者) Chuang, Cheng-Hsun en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 16:10:36 (UTC+8) - dc.date.available 7-Aug-2019 16:10:36 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 16:10:36 (UTC+8) - dc.identifier (Other Identifiers) G0106352017 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124729 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 106352017 zh_TW dc.description.abstract (摘要) 本研究從台灣前60大市值比上市公司中，挑出49家公司為樣本，蒐集2006-2018間的資料，採用技術指標作為變數，以卷積神經網路預測為選股策略，選取投資組合成分股， 再利用「平均－變異數」分析配置權重，並根據不同風險趨避程度，建構不同投組。結果卷積神經網路的投資策略，在訓練樣本期間(2010～2016年)內的績效表現相當好，但應用在樣本外期間(2008～2009年，2017～2018年)則表現不佳。若使用此種交易策略與簡單動能策略比較，則動能策略建構的投資組合能在訓練樣本外期間表現的較佳。 zh_TW dc.description.abstract (摘要) This Research selects 49 companies from the top 60 companies in Taiwan as a sample, collects stock data from 2006 to 2018. Choose technical indicators as variables, and use convolutional neural network prediction as a stock selection strategy to form a portfolio. In the selected stocks, the “Mean-Variance Analysis” is used to allocate the asset weights, and different investment groups are constructed according to different risk aversion levels. The result of this study shows that: the investment strategy of the convolutional neural network is quite good during the training period (2010~2016) of data. However, the strategy make negative return during the out-of-sample period (2008-2009, 2017~2018). With this performance, compare to a simple momentum strategy, the momentum portfolio can perform better during the out-of-sample period. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與背景 1第二節 研究目的 2第三節 研究架構 3第二章 文獻回顧 4第一節 神經網路應用於股市預測 4第二節 動能交易策略與投資組合理論 5第三節 總結 5第三章 研究方法 6第一節 研究對象 6第二節 股市交易技術指標 6第三節 卷積神經網路 9第四節 馬可維茲 平均－變異數分析 18第四章 實證研究 22第一節 實驗架構 22第二節 實驗結果 25第五章 結論與建議 37第一節 結論 37第二節 未來展望 37參考文獻 38 zh_TW dc.format.extent 3581306 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106352017 en_US dc.subject (關鍵詞) 量化交易 zh_TW dc.subject (關鍵詞) 卷積神經網路 zh_TW dc.subject (關鍵詞) 投資組合 zh_TW dc.subject (關鍵詞) 平均-變異數分析 zh_TW dc.subject (關鍵詞) 動能交易 zh_TW dc.title (題名) 卷積神經網路結合投資組合理論之交易策略實證研究： 以台灣股市為例 zh_TW dc.title (題名) The Empirical Research of Trading Strategies for Convolutional Neural Network and Portfolio Theory on Taiwan Stock Market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] 王春峰、屠新曙、厉斌(2002)，效用函数意义下投资组合有效选择问题的研究，中国管理科学，第10卷第2期，4月，頁15-19。[2] 李顯儀、吳幸姬(2009)，技術分析資訊對共同基金從眾行為的影響，臺大管理論叢，第20卷第1期，12月，頁227-260。[3] 陳嘉惠、高郁惠、劉玉珍(2002)，投資人偏好與資產配置。臺灣管理學刊，第1卷第2期，2月，頁213-232。[4] 詹錦宏、吳莉禎(2011)，動能投資策略於台灣股票市場之應用—含金融海嘯之影響，會計學報，第3卷第2期，5月，頁1-22。[5] Allen, F. & R. Karjalainen (1999). ‘‘Using Genetic Algorithms to Find Technical Trading Rules,’’ Journal of Financial Economics, 51, 245-271.[6] Bai, S., J. Kolter & V. Koltun (2018). ‘‘An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,’’ Retrieved from https://arxiv.org/abs/1803.01271.[7] Bodie, Z., A. Kane & A. Marcus (1999). Investments, 4th ed. McGraw-Hill Companies, 178-193.[8] Cesarone, F., A. Scozzari & F. Tardella (2010). ‘‘Portfolio selection problems in practice: a comparison between linear and quadratic optimization models,’’ Retrieved from https://arxiv.org/abs/1105.3594[9] De Bondt, W. & R. Thaler (1985). ‘‘Does the Stock Market Overreact?,’’ Journal of Finance, 40, 793-805.[10] Jegadeesh, N. & S. Titman (1993). ‘‘Returns to Buying Winners and Selling Losers: Implications for Market Efficiency,’’ Journal of Finance, 48, 65-91.[11] LeCun, Y., L. Bottou, Y. Bengio & P. Haffner (1998). ‘‘Gradient-based learning applied to document recognition,’’ Proc. IEEE, 86, 2278-2324.[12] Lo, A. W. and A. C. MacKinlay (1990). ‘‘When Are Contrarian Profits Due to Stock Market-Overreaction,’’ Review of Financial Studies, 3, 175-208.[13] Markowitz, H. (1952). ‘‘Portfolio Selection,’’ Journal of Finance 7, 77-91.[14] Thawornwong, S., D.Enke & C. Dagli (2003). ‘‘Neural Networks as a Decision Maker for Stock Trading: A Technical Analysis Approach,’’ International Journal of Smart Engineering System Design, 5(4), 313-325.[15] Vejendla, A. & D. Enke (2013). ‘‘Evaluation of GARCH, RNN and FNN Models for Forecasting Volatility in the Financial Markets,’’ Journal of Financial Risk Management, 10(1), 41-49.[16] White, H. (1988). ‘‘Economic prediction using neural networks: the case of IBM daily stock returns,’’ Proc. IEEE int. conf. on neural networks, 2, 451-458.[17] Wood, D. & B. Dasgupta (1996). ‘‘Classifying trend movements in the MSCI U.S.A. capital market index－A comparison of regression, arima and neural network methods,’’ Computers & Operations Research, 23 , 611-622. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900301 en_US
