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題名 台灣股市的成交量預測_以主成分分析為例
Forecasting the Trading Volume in Taiwan Stock Market by Principle Components作者 陳鈺淳
Chen, Yu Chun貢獻者 郭維裕<br>鄭鴻章
Kuo, Wei Yu<br>Cheng, Hung Chang
陳鈺淳
Chen, Yu Chun關鍵詞 主成分分析
成交量預測
總體因子
principle components
forecast
macroeconomic data日期 2011 上傳時間 30-Oct-2012 10:55:23 (UTC+8) 摘要 本論文探討利用總體因子預測台灣股市的月成交量，並討論其預測準確度。總體因子主要利用主成分分析法從大量的總體資料中抽出，台灣股市月成交量資料主要來自TEJ資料庫，並將其分為九類：水泥窯業、食品業、塑膠化工業、紡織業、機電業、造紙業、營建業、金融業和加權指數。 結果發現三個月後的預測值比一個月後的預測值準確，而從RMSE跟MAE的結果，發現食品業、塑膠化工業、紡織業、機電業、造紙業預測的準確度較高。
This paper discusses forecasting monthly turnover by static principle components method, and testing accuracy of forecasting. The monthly turnover is from Taiwan stock market as nine turnover classification, Cement & Kiln industry, Food industry, Plastic & Chemical industry, Textile industry, Mechanical & Electrical industry, Paper-making industry, Construction industry, Financial industry and Value-Weighted Index. The principle components extracted from large macroeconomic datasets have the explanatory power to monthly turnover. In addition, for basic forecasting, the accuracy of three-month prediction is better than one-month prediction in both subsamples. To test accuracy, RMSE (PC) and MAE (PC) are outperformed the same in Food industry, Textile& Fibers industry. However, MAE (PC) in Plastic & Chemical industry, RMSE (PC) in Mechanical & Electrical industry and Paper-making industry still show the good prediction as well.參考文獻 References Bialkowski, J., Darolles, S., Le Fol, G. (2008). "Improving VWAP Strategies: A Dynamic Volume Approach," Journal of Banking & Finance 32: 1709-1722. Boivin, J., Ng, S. (2005). "Understanding and comparing factor-based forecasts," International Journal of Central Banking 01, December 2005: 117-151. Chang, E. C., Cheng, J. W., Pinegar J. M. (2008). "The factor struture of time-varying conditional volume," Journal of Empirical Finance 15: 251-264. Chen, G. M., Firth, M., Rui, O. M. (2001). " The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility," Financial Review 36(3): 153-174. Connolly, R. A., Strivers, C. (2005). "Macroeconomic News, Stock Turnover, and Volatility Clustering in Daily Stock Returns," Journal of Financial Research 28: 235-259. Easley D., O’hara, M. (1987). "Price, Trade Size, and information in securities markets," Journal of Financial Economics 19: 60-90. Groen, J. J. J., Kapetanios, G. (2009). "Revisiting useful approaches to data-rich macroeconomic forecasting," FRB of New York Staff Report 327. Heij, C., Dijk, v. D., Groenen, P. J. F. (2008). "Macroeconomic forecasting with matched principal components," International Journal of Forecasting 24(1): 87-100. Kosfeld, R., Lauridsen, J. (2008). "Factor Analysis Regression," Stat Papers 49: 653-667. Lo, A. W., Wang, J. (2000). "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," Review of Financial Studies 13(2, Summer): 257-300. Madhavan, A. N. (2002). "VWAP Strategies Transaction Performance," Institutional Investor Journals Spring 2002 (Transaction Performance): 32-39. Martell, F. T., Wolf, A. S. (1987). "Determinants of Trading Volume in Futures Markets," Journal of Futures Markets 7 (3): 233-244. Schumacher, C., Bundesbank, D. (2007). "Forecasting German GDP Using Alternative Factor Models Based on Large Datasets," Journal of Forecasting 26(4): 271-302. Stock, J. H., Watson, M. W. (2002). "Forecasting Using Principle Components from a Large Number of Predictors," Journal of the American Statistical Association 97: 1167-1179. 描述 碩士
國立政治大學
國際經營與貿易研究所
99351031
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099351031 資料類型 thesis dc.contributor.advisor 郭維裕<br>鄭鴻章 zh_TW dc.contributor.advisor Kuo, Wei Yu<br>Cheng, Hung Chang en_US dc.contributor.author (Authors) 陳鈺淳 zh_TW dc.contributor.author (Authors) Chen, Yu Chun en_US dc.creator (作者) 陳鈺淳 zh_TW dc.creator (作者) Chen, Yu Chun en_US dc.date (日期) 2011 en_US dc.date.accessioned 30-Oct-2012 10:55:23 (UTC+8) - dc.date.available 30-Oct-2012 10:55:23 (UTC+8) - dc.date.issued (上傳時間) 30-Oct-2012 10:55:23 (UTC+8) - dc.identifier (Other Identifiers) G0099351031 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54394 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營與貿易研究所 zh_TW dc.description (描述) 99351031 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 本論文探討利用總體因子預測台灣股市的月成交量，並討論其預測準確度。總體因子主要利用主成分分析法從大量的總體資料中抽出，台灣股市月成交量資料主要來自TEJ資料庫，並將其分為九類：水泥窯業、食品業、塑膠化工業、紡織業、機電業、造紙業、營建業、金融業和加權指數。 結果發現三個月後的預測值比一個月後的預測值準確，而從RMSE跟MAE的結果，發現食品業、塑膠化工業、紡織業、機電業、造紙業預測的準確度較高。 zh_TW dc.description.abstract (摘要) This paper discusses forecasting monthly turnover by static principle components method, and testing accuracy of forecasting. The monthly turnover is from Taiwan stock market as nine turnover classification, Cement & Kiln industry, Food industry, Plastic & Chemical industry, Textile industry, Mechanical & Electrical industry, Paper-making industry, Construction industry, Financial industry and Value-Weighted Index. The principle components extracted from large macroeconomic datasets have the explanatory power to monthly turnover. In addition, for basic forecasting, the accuracy of three-month prediction is better than one-month prediction in both subsamples. To test accuracy, RMSE (PC) and MAE (PC) are outperformed the same in Food industry, Textile& Fibers industry. However, MAE (PC) in Plastic & Chemical industry, RMSE (PC) in Mechanical & Electrical industry and Paper-making industry still show the good prediction as well. en_US dc.description.tableofcontents Content Chapter 1 Introduction 2 Chapter 2 Literature Review 4 Chapter 3 Model 6 3.1 Principle Component Method 6 3.2 Factor Forecast Regression 7 Chapter 4 Empirical Result 8 4.1 Data Description 8 4.2 In-Sample test 9 4.3 Basic forecast 13 4.4 Out-of-sample forecast 17 4.5 Test forecast accuracy 19 Chapter 5 Conclusion 22 References 23 Appendix 24 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099351031 en_US dc.subject (關鍵詞) 主成分分析 zh_TW dc.subject (關鍵詞) 成交量預測 zh_TW dc.subject (關鍵詞) 總體因子 zh_TW dc.subject (關鍵詞) principle components en_US dc.subject (關鍵詞) forecast en_US dc.subject (關鍵詞) macroeconomic data en_US dc.title (題名) 台灣股市的成交量預測_以主成分分析為例 zh_TW dc.title (題名) Forecasting the Trading Volume in Taiwan Stock Market by Principle Components en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) References Bialkowski, J., Darolles, S., Le Fol, G. (2008). "Improving VWAP Strategies: A Dynamic Volume Approach," Journal of Banking & Finance 32: 1709-1722. Boivin, J., Ng, S. (2005). "Understanding and comparing factor-based forecasts," International Journal of Central Banking 01, December 2005: 117-151. Chang, E. C., Cheng, J. W., Pinegar J. M. (2008). "The factor struture of time-varying conditional volume," Journal of Empirical Finance 15: 251-264. Chen, G. M., Firth, M., Rui, O. M. (2001). " The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility," Financial Review 36(3): 153-174. Connolly, R. A., Strivers, C. (2005). "Macroeconomic News, Stock Turnover, and Volatility Clustering in Daily Stock Returns," Journal of Financial Research 28: 235-259. Easley D., O’hara, M. (1987). "Price, Trade Size, and information in securities markets," Journal of Financial Economics 19: 60-90. Groen, J. J. J., Kapetanios, G. (2009). "Revisiting useful approaches to data-rich macroeconomic forecasting," FRB of New York Staff Report 327. Heij, C., Dijk, v. D., Groenen, P. J. F. (2008). "Macroeconomic forecasting with matched principal components," International Journal of Forecasting 24(1): 87-100. Kosfeld, R., Lauridsen, J. (2008). "Factor Analysis Regression," Stat Papers 49: 653-667. Lo, A. W., Wang, J. (2000). "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," Review of Financial Studies 13(2, Summer): 257-300. Madhavan, A. N. (2002). "VWAP Strategies Transaction Performance," Institutional Investor Journals Spring 2002 (Transaction Performance): 32-39. Martell, F. T., Wolf, A. S. (1987). "Determinants of Trading Volume in Futures Markets," Journal of Futures Markets 7 (3): 233-244. Schumacher, C., Bundesbank, D. (2007). "Forecasting German GDP Using Alternative Factor Models Based on Large Datasets," Journal of Forecasting 26(4): 271-302. Stock, J. H., Watson, M. W. (2002). "Forecasting Using Principle Components from a Large Number of Predictors," Journal of the American Statistical Association 97: 1167-1179. zh_TW
