Integrating Grey Theory and Neural Networks in Investigating the Information Contents of Futures Prices in Non-Cash-Trading Period: Evidence From the SGX-DT Nikkei 225 and MSCI Taiwan Index Futures Contracts
碩士 === 輔仁大學 === 金融研究所 === 88 === This study investigates the information contents of Nikkei 225 and MSCI Taiwan index futures prices in non-cash-trading period by integrating grey theory and neural networks. Then we construct a model to predict the daily opening spot price. First, we use...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2000
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/93204581191684632100 |
| Summary: | 碩士 === 輔仁大學 === 金融研究所 === 88 === This study investigates the information contents of Nikkei 225 and MSCI Taiwan index futures prices in non-cash-trading period by integrating grey theory and neural networks. Then we construct a model to predict the daily opening spot price.
First, we use the index futures prices during non-cash-trading period in constructing a GM(1,1) model to predict the index futures price of 8:55am and 7:55am for MSCI Taiwan index futures and Nikkei 225 index futures, respectively. Then we use the futures price estimate to predict the daily opening spot price by employing neural networks method. The sample period is from 1998/10/1 to 1999/12/31. The data used are 5-minute intraday data of spot and futures index.
The empirical results show that the prediction result of our model is better than random walk model. This indicates that the futures price in non-cash-trading period contains valuable information. But, the conclusion above only works in MSCI Taiwan index not Nikkei 225 index.
|
|---|