| Summary: | 碩士 === 國立政治大學 === 國際經營與貿易研究所 === 100 === 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.
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