運用類神經網路建構台股指數期貨預測模型

廖玟柔

doi:10.6840/cycu201700811

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运用类神经网路建构台股指数期货预测模型

Constructing Forecast Model of Taiwan Stock Market Index Futures Based on Neural Network

廖玟柔 , 硕士　　导师：李维平　　

繁体中文 DOI： 10.6840/cycu201700811

类神经网路；股票股市；技术面指标；筹码面指标；国际股市指标；国际汇率指标； Neural Network ； Stock Market ； Technical Indicators ； Chip Indicators ； International Stock Market Indicators ； International Exchange Rate Indicators

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摘要 │ 参考文献 (39) │ 国际计量

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The developement of the financial industry is driven by globalization and the Internet's rise in popularity. Now, investors can trade stocks wherever that are. In this globalized market, investors can trade with more confidence and profit more than usual in return with the help of an accurate tool that predicts the market.
　　In recent years, the bumpy stock index has been valued by investors. We can observe that the variation of stock index is depending on the country economic status. It means that we can tell the economic development status from a bumpy stock index, and the investors can understand when is the best time to invest in the stock market. It is normal to forecast the variation of stock index by technical indicators and chip indicators. In addition, the international financial information communication is more convenient, we can tell that the financial transaction between different country can influence the variation of stock index from the international stock market transaction information. To predict the variation of the stock index for the next day more accurately , the research sums up the international stock market indicators and international exchange rate indicators to predict the variation.  
　　Therefore, a model based on the neural network and multi-oriented indicators was created from the research. By beginning with combining the technical indicators, chip indicators, international stock market indicators and international exchange rate indicators. Then conduct experiments to find out the best parameters on different training set length of time, and observed which multi-oriented indicators combination that can result in the optimal neural network model. The point is to find out the accurate neural network model to make the investors predict the stock market accurately every day, and provide the researchers and investors data for reference.

参考文献 ( 39 ) 〈TOP〉

Al-Hmouz, R., Pedrycz, W., & Balamash, A. (2015). Description and prediction of time series: A general framework of Granular Computing. Expert Systems with Applications, 42(10), 4830–4839.
连结：
De Villiers, J., & Barnard, E. (1993). Backpropagation neural nets with one and two hidden layers. IEEE Transactions on Neural Networks, 4(1), 136–141.
连结：
Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927–940.
连结：
Gutierrez-Villalobos, J. M., Rodriguez-Resendiz, J., Rivas-Araiza, E. A., & Mucino, V. H. (2013). A review of parameter estimators and controllers for induction motors based on artificial neural networks. Neurocomputing, 118, 87–100.
连结：
Han, J., & Kamber, M. (2001). Data mining: concepts and techniques. Morgan Kaufmann San Francisco, Calif, USA.
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使用DOI作为永久连结

引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。