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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
    鄭鴻章

    Kuo, Wei Yu
    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: 2012-10-30 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
    顯示於類別:[國際經營與貿易學系 ] 學位論文

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