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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/69680


    Title: 應用類神經網路預測國外股價指數期約
    Other Titles: Forecasting Foreign Stock Index Futures: An Application of Neural Networks.
    Authors: 蔡瑞煌;徐燕山
    Contributors: 資訊管理學系
    Keywords: 指數期貨;理解神經網路;投資決策
    Index futures;Reasoning neural network;Investment decision
    Date: 1996
    Issue Date: 2014-09-09 17:44:48 (UTC+8)
    Abstract: 本研究嘗試整合類神經網路與法則基礎(rule-based)系統技術,以建立S&P 500指數期貨的交易策略。本研究不同於先前研究之處有下列二方面:一、本研究採用法則基礎系統的方式提供神經網路的訓練範例;二、本研究以理解神經網路(Reasoning Neural Networks)取代後向傳導網路(Back propagation networks)以解決局部最小值與隱藏結點數未知的困境,而實證結果也顯示理解神經網路之表現優於後向傳導網路。 首先,由期貨的日價格資料計算出十種技術分析指標值,用這些指標值來表示期貨市場內的各種可能狀況(case)。接著,我們提出FFM(Futures Forecast Model)與EFFM (Extended Futures Forecast Model)來處理市場的各種狀況,預測出隔日的期貨價格改變方向。以法則基礎方法所建立的FFM是用來處理明顯的狀況(obvious cases),並且提供類神網路好的訓練範例。而EFFM包括四個理解神經網路系統與一個決策機置(voting mechanism),它被用來處理那些不明顯的狀況(non-obvious cases)。 從實證模擬的結果顯示,在預測市場時FFM與EFFM有良好的合作關係。因此,我們以FFM與EFFM為基礎建立一個整合的期貨交易系統(Integrated Futures Trading System, IFTS),並將它用於S&P 500指數期貨市場作模擬交易,結果我們發現在1988到1993年的測試期間,IFTS的投資報酬率高於買入持有投資策略。
    This research adopts a hybrid approach to implementing the trading strategies in the S&P 500 index futures market. The hybrid approach integrates both the rule-based systems technique and the neural networks technique. Our methodology is different from previous studies in two aspects. First, we employ Reasoning Neural Networks (RN) instead of back propagation networks to resolve the undesired predicaments of local minimum and the unknown of the number of hidden nodes. Second, the rule-based systems approach is applied to provide neural networks with good training examples. We, first, categorize the daily conditions of the futures market into a variety of cases through processing futures historical data. Then, the dual-forecast models, FFM (futures forecast model) and EFFM (extended futures forecast model), are proposed to predict the direction of daily price changes. The rule-based model, FFM, is designed to deal with the obvious cases and to provide the neural network-based model, EFFM, with good training examples. Meanwhile, EFFM, which consists of four RNs and a voting mechanism, is designed to handle the non-obvious cases. The simulation results show that the cooperation of FFM and EFFM does a good job in predicting the direction of daily price change of S&P 500 index futures. Based on FFM and EFFM, the integrated futures trading system (IFTS) is developed and employed to trade the S&P 500 index futures contracts. The results show that IFTS outperforms the passive buy-and-hold investment strategy over the six-year testing period from 1988 to 1993.
    Relation: 行政院國家科學委員會
    計畫編號NSC85-2418-H004-008
    Data Type: report
    Appears in Collections:[資訊管理學系] 國科會研究計畫

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