臺灣博碩士論文加值系統

本文採用1998年九月2日到2003九月30日的台灣指數期貨每日收盤價，總共1304筆資料。我們希望能藉由濾嘴法則以收盤價及交易量和未平倉量來衡量台灣指數期貨的效率性。而實證結果也證實可以藉由濾嘴法則濾除掉市場上的小波動，並進而預測出主要的價格趨勢。

This thesis adopts futures data, which are the daily closing prices of the Taiwan Stock Exchange Capitalization Weighted Stock Index futures contracts. The sample period is from September 2, 1998 to September 30, 2003, a total of 1304 transaction days. The goal we want to achieve is to test and verify the momentum by filter rules based on price and volume in the futures market in Taiwan. In addition, the open interest is substituted for the trading volume to exam its effect on the futures price. The empirical results show that we can predict the price trend as long as we employ an appropriate range value to filter out “the noise”.

Content
Abstract
1. Introduction............................................................................................................1
2. Methodology and Data ………………………………………………………...5
2.1 Methodology………………………………………………………..….………….5
2.1.1 Filter Rules of Price Strategy………………………………………………...….5
2.1.2 Filter Rules of Price and Trading Volume Strategy…………………….….……8
2.2 Data Description…………………………………………………………...……..10
3. Empirical Results……………………………………………………………....12
3.1 Overview of Empirical Results…………………….…………………….............12
3.2 Returns to Price-only strategies…………………………………………………..13
3.3 Returns to the Price-volume Filter Strategies…………………………………….14
3.4 Returns to Price-Open-Interest Strategies………………………………………..17
4. Conclusions and Recommendations………………………………………..20
4.1 Conclusions……………………………………………………………………....20
4.2 Recommendations for Further Researches……………………………………….20
Reference……………………………………………………………………………..22
Figure and Table Content
Figure 1 Demonstration of the average fluctuation…………………………….......26
Table 1 Sample statistics for TAIEX futures for the period September 2, 1998 - September 30, 2003…………………………………………………………………..27
Table 2 Price-only filter results…………………………………………………….28
Table 3 Returns to Price-Volume filters with 1-day moving average and 4% price filter level…………………………………………………………………………….29
Table 4 Returns to Price-Volume filters with 1-day moving average and 5% price filter level…………………………………………………………………………….30
Table 5 Returns to Price-Volume filters with 1-day moving average and 6% price filter level…………………………………………………………………………….31
Table 6 Returns to Price-Volume filters with 3-day moving average and 4% price filter level…………………………………………………………………………….32
Table 7 Returns to Price-Volume filters with 3-day moving average and 5% price filter level…………………………………………………………………………….33
Table 8 Returns to Price-Volume filters with 3-day moving average and 6% price filter level…………………………………………………………………………….34
Table 9 Returns to Price-O.I filters with 1-day moving average and 4% price filter level…………………………………………………………………………………..35
Table 10 Returns to Price-O.I filters with 1-day moving average and 5% price filter level...………………………………………………………………………………...36
Table 11 Returns to Price-O.I filters with 1-day moving average and 6% price filter level…………………………………………………………………………………..37
Table 12 Returns to Price-O.I filters with 4-day moving average and 4% price filter level…………………………………………………………………………………..38
Table 13 Returns to Price-O.I filters with 4-day moving average and 5% price filter level…………………………………………………………………………………..39
Table 14 Returns to Price-O.I filters with 4-day moving average and 6% price filter level…………………………………………………………………………………..40
Table 15 Returns to Price-O.I filters with 10-day moving average and 4% price filter level…………………………………………………………………………………..41
Table 16 Returns to Price-O.I filters with 10-day moving average and 5% price filter level…………………………………………………………………………………..42
Table 17 Returns to Price-O.I filters with 10-day moving average and 6% price filter level…………………………………………………………………………………..43

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