Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan
碩士 === 國立成功大學 === 統計學系碩博士班 === 92 === In this low-interest era, interest incomes of deposit cannot catch up the inflation rate. Therefore, the diverse investment products start to be popular. Among all outlets for investment, mutual fund is one of investor’s favorites. Due to its characteristic o...
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ndltd-TW-092NCKU53370172016-06-17T04:16:56Z http://ndltd.ncl.edu.tw/handle/45009877707971780073 Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan 統計方法與類神經網路應用於國內開放式股票型基金投資績效分類及投資報酬率預測之研究 Chi-Nien Huang 黃綺年 碩士 國立成功大學 統計學系碩博士班 92 In this low-interest era, interest incomes of deposit cannot catch up the inflation rate. Therefore, the diverse investment products start to be popular. Among all outlets for investment, mutual fund is one of investor’s favorites. Due to its characteristic of accumulation and less risk, investors having less financial support also can get a chance to make the profit from investment portfolio. Moreover, authorizing a professional manager to handle their funds could save cost of time, so mutual fund gradually becomes a popular product in the commercial market. The purpose of this research focuses on Equity Mutual Funds and includes two main directions. First of all, different funds are classified based on their performance. Data is collected from Jan. 2001 to Dec. 2003, and the evaluation index of Mutual Funds includes net asset value, turnover rate, Sharpe Index, Beta coefficient, and Treynor Index; Secondly, based on historical data of rate of return from Jan. 1999 to Dec. 2003, this research explores the relationship between ROR and the macroeconomic indicators including the wholesale price index, M1b and M2 of money supply, Prosperity Score, refunding rate, interest rate, net value of foreign exchange, and import and export balance of trade. This research proceeds by using Statistical Methods and Artificial Neural Networks and compares to get the best result. For classification, judging model good or not by the rate of accurate classification, and matching up SOM and PNN as result get better effect. As for Forecasting, judging from Residual, the result of BPN is better than other models. In conclusion, we infer that Artificial Neural Networks could be more appropriate than statistical methods based on the data type of this research. Chung-Cheng Wu 吳宗正 2004 學位論文 ; thesis 96 zh-TW |
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碩士 === 國立成功大學 === 統計學系碩博士班 === 92 === In this low-interest era, interest incomes of deposit cannot catch up the inflation rate. Therefore, the diverse investment products start to be popular. Among all outlets for investment, mutual fund is one of investor’s favorites. Due to its characteristic of accumulation and less risk, investors having less financial support also can get a chance to make the profit from investment portfolio. Moreover, authorizing a professional manager to handle their funds could save cost of time, so mutual fund gradually becomes a popular product in the commercial market.
The purpose of this research focuses on Equity Mutual Funds and includes two main directions. First of all, different funds are classified based on their performance. Data is collected from Jan. 2001 to Dec. 2003, and the evaluation index of Mutual Funds includes net asset value, turnover rate, Sharpe Index, Beta coefficient, and Treynor Index; Secondly, based on historical data of rate of return from Jan. 1999 to Dec. 2003, this research explores the relationship between ROR and the macroeconomic indicators including the wholesale price index, M1b and M2 of money supply, Prosperity Score, refunding rate, interest rate, net value of foreign exchange, and import and export balance of trade.
This research proceeds by using Statistical Methods and Artificial Neural Networks and compares to get the best result. For classification, judging model good or not by the rate of accurate classification, and matching up SOM and PNN as result get better effect. As for Forecasting, judging from Residual, the result of BPN is better than other models. In conclusion, we infer that Artificial Neural Networks could be more appropriate than statistical methods based on the data type of this research.
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author2 |
Chung-Cheng Wu |
author_facet |
Chung-Cheng Wu Chi-Nien Huang 黃綺年 |
author |
Chi-Nien Huang 黃綺年 |
spellingShingle |
Chi-Nien Huang 黃綺年 Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan |
author_sort |
Chi-Nien Huang |
title |
Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan |
title_short |
Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan |
title_full |
Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan |
title_fullStr |
Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan |
title_full_unstemmed |
Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan |
title_sort |
using statistical methods and artificial neural networks to classify the investment performance and forecast the rate of return - a study of open-end equity mutual funds in taiwan |
publishDate |
2004 |
url |
http://ndltd.ncl.edu.tw/handle/45009877707971780073 |
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