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題 名 | 現貨盤後期貨交易資訊內涵之研究--以新加坡交易所日經225指數期貨為例=The Information Content of Futures Prices in Non-Cash-Trading Periods: Evidence from the SGX-DT Nikkei 225 Futures Contracts |
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作 者 | 李天行; 陳能靜; 蔡榮裕; | 書刊名 | 管理學報 |
卷 期 | 18:4 2001.12[民90.12] |
頁 次 | 頁567-588 |
分類號 | 561.76 |
關鍵詞 | 類神經網路; 期貨預測; 隨機漫步; Neural networks; Futures; GARCH; Random walk; Nikkei 225; |
語 文 | 中文(Chinese) |
中文摘要 | 本研究探討新加坡交易所日經 225 指數現貨盤後期貨交易資訊之內涵,利用領先現貨開盤5分鐘之期貨指數及現貨前一日之收盤指數當作輸入層之輸入變數建構類神經網路模式,進行現貨開盤指數價格之預測。在類神經網路預測模式之建構方面,首先利用敏感度分析決定模式之參數,而有關建構模式之穩健性評估方面,則利用訓練樣本佔總樣本之不同資料比例加以分析。而為了評估建構模式之預測效果,以 1998年 10 月 1 日至 1999 年 3 月 31 日現貨與期貨指數5分鐘日內資料為實証之時間序列資料,實証結果顯示建構之類神經網路預測模式相較於隨機漫步模型、以現貨前一日之收盤指數為輸入變數之類神經網路模式及GARCH模型顯著有較好之預測能力,顯示非現貨交易時段之期貨交易價格內含豐富之資訊,可做為預測現貨開盤指數之參考,且類神經網路相較於GARCH模型而言,為一較優良之預測工具。 |
英文摘要 | This study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period. The futures price at 07:55 and previous day’s closing index at 14:00 in the cash market are used as the input nodes to predict the 08:00 opening cash market price index by the backpropagation neural network model. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate setup of the topology of the networks. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. To demonstrate the effectiveness of our proposed method, the five-minute intraday data of spot and futures index from October 1, 1998 to March 31, 1999 was evaluated using the designed neural network model. Analytic results demonstrate that the proposed neural network model outperforms the neural network model with previous day’s closing index as the input variable, random walk and the GARCH model forecasts. It therefore indicates that there is valuable information involved in the futures prices during the NCT period that can be used to forecast the opening cash price index.Besides, the neural network model provides better forecasting results than the GARCH model. |
本系統中英文摘要資訊取自各篇刊載內容。