Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX

碩士 === 國立臺北大學 === 企業管理學系 === 96 === Taiwan’s security market has been established more than 40 years. In this period have been many times bullish market and many times bear market. Everyone who buys stock knows the only way to make money in the stock market is buys stock at low price and sells stock...

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Main Authors: Tsai, Chang-He, 蔡長河
Other Authors: Goo, Yeong-Jia
Format: Others
Language:zh-TW
Published: 2008
Online Access:http://ndltd.ncl.edu.tw/handle/45682909821296210029
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spelling ndltd-TW-096NTPU01210522016-05-16T04:09:53Z http://ndltd.ncl.edu.tw/handle/45682909821296210029 Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX 以總體經濟變數、技術指數變數、國際股市變數預測台股月報酬 Tsai, Chang-He 蔡長河 碩士 國立臺北大學 企業管理學系 96 Taiwan’s security market has been established more than 40 years. In this period have been many times bullish market and many times bear market. Everyone who buys stock knows the only way to make money in the stock market is buys stock at low price and sells stock at high. But investors don’t know when to buy stock and when to sell. This research used Back Propagation Neural Network to forecast the return of TAIEX, and used monthly data from August 1988 to June 2006 of the TAIEX, macroeconomic variables, Technical Indexes variables, Foreign Stock Indexes variables, all variables. From the result of the research, we have several findings. First, using the all variables to forecast has the best directional and explainable effect in training period and best directional effect in testing period. Second, the result of the Mean Absolute Percentage Error (MAPE) and root mean squared error (RMSE) are different in training period. Goo, Yeong-Jia Chen, Dar-Hsin 古永嘉 陳達新 2008 學位論文 ; thesis 52 zh-TW
collection NDLTD
language zh-TW
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description 碩士 === 國立臺北大學 === 企業管理學系 === 96 === Taiwan’s security market has been established more than 40 years. In this period have been many times bullish market and many times bear market. Everyone who buys stock knows the only way to make money in the stock market is buys stock at low price and sells stock at high. But investors don’t know when to buy stock and when to sell. This research used Back Propagation Neural Network to forecast the return of TAIEX, and used monthly data from August 1988 to June 2006 of the TAIEX, macroeconomic variables, Technical Indexes variables, Foreign Stock Indexes variables, all variables. From the result of the research, we have several findings. First, using the all variables to forecast has the best directional and explainable effect in training period and best directional effect in testing period. Second, the result of the Mean Absolute Percentage Error (MAPE) and root mean squared error (RMSE) are different in training period.
author2 Goo, Yeong-Jia
author_facet Goo, Yeong-Jia
Tsai, Chang-He
蔡長河
author Tsai, Chang-He
蔡長河
spellingShingle Tsai, Chang-He
蔡長河
Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX
author_sort Tsai, Chang-He
title Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX
title_short Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX
title_full Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX
title_fullStr Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX
title_full_unstemmed Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX
title_sort using macroeconomic variables, technical indexes variables, foreign stock indexes variables to forecast the monthly return of taiex
publishDate 2008
url http://ndltd.ncl.edu.tw/handle/45682909821296210029
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