Using the Bootstrap to test the performance of the Technical Analysis
| Autor: | Yu-hua Hsieh, 謝玉華 |
|---|---|
| Rok vydání: | 1999 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 87 In this paper, we use computer simulation to analyze some common quantitative technical analysis index - Relative Strength Index (RSI), Bias, Stochastic KD Line (KD), Williams Overbought/Oversold (%R), Moving Average (MA), Moving Average Convergence & Divergence (MACD) and Directional Movement Index (DMI) in the investing performance. And we also use an Empirical distribution extracted from Bootstrap to test the investment performance respectively. The investment objects in our paper includes five stock index of four countries, which are Stand & Poor 500 Index, Dow Jones Industrial Average Index, Ft-Se 100 Index, TOPIX and Taiwan Weighted Average Stock Index. The research period is from 1993 to 1998 and we use market index daily and weekly data as buying and selling point to run the simulation analysis and our conclusion is as follows: 1. To the investors whose buying and selling decision is based on the technical index, transaction cost is very important. Without considering the transaction cost, operation by technical analysis may display an excess return, however, considering the transaction cost, the return is subject to be negative. 2. MA, MACD and DMI are not effective. In order to avoid the fake-rise-and-fall situation, we have considered the fluctuating locus but the excess return is still negative. Therefore, these three technical analysis indices are not effective. 3. BIAS, RSI, %R and KD are effective and %R is the best index. Using this index to do the short sun investment can gain excess return in each country. 4. Stand & Poor 500 Index, Dow Jones Industrial Average Index, Ft-Se 100 Index, TOPIX and Taiwan Weighted Average Stock Index, each of these five countries is not a weak-form efficient market because investors all can use technical analysis index to gain excess return. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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