英文摘要
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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.
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参考文献
|
-
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.
連結:
-
De Villiers, J., & Barnard, E. (1993). Backpropagation neural nets with one and two hidden layers. IEEE Transactions on Neural Networks, 4(1), 136–141.
連結:
-
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.
連結:
-
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.
連結:
-
Han, J., & Kamber, M. (2001). Data mining: concepts and techniques. Morgan Kaufmann San Francisco, Calif, USA.
連結:
-
Kumar, M., & Thenmozhi, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian Institute of Capital Markets 9th Capital Markets Conference Paper.
連結:
-
Liao, S.-H., Chu, P.-H., & Hsiao, P.-Y. (2012). Data mining techniques and applications – A decade review from 2000 to 2011. Expert Systems with Applications, 39(12), 11303–11311.
連結:
-
Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89–93.
連結:
-
Ou, P., & Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), p28.
連結:
-
Yufeng Han, Ke Yang, & Guofu Zhou. (2013). A new anomaly: The cross-sectional profitability of technical analysis. Journal of Financial and Quantitative Analysis, 48(5), 1433–1461.
連結:
-
Zhang, Y., & Wu, L. (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36(5), 8849–8854.
連結:
-
Zhu, X., Wang, H., Xu, L., & Li, H. (2008). Predicting stock index increments by neural networks: The role of trading volume under different horizons. Expert Systems with Applications, 34(4), 3043–3054.
連結:
-
李天行, & 邱志洲. (2000). 類神經網路於現貨開盤指數之預測-以新加坡交易所日經 225 指數期貨為例. Asia Pacific Management Review, 5(4), 557–570.
連結:
-
李惠妍, 吳宗正, & 溫敏杰. (2006). 迴歸模式與類神經網路在台股指數期貨預測之研究. 經營管理論叢.
連結:
-
余尚武, & 黃雅蘭. (2003). 台灣股價指數期貨套利之研究:類神經網路與灰色理論之應用. 電子商務學報, 5(2), 87–115.
連結:
-
洪才元. (2009). 結合基因演算類神經網路預測台股指數之模型. 中原大學資訊管理研究所學位論文, 1–95.
連結:
-
翁振益, & 張瑛琦. (2007). 決策分析: 方法與應用. 華泰文化事業股份有限公司.
連結:
-
陳昌捷. (2015). 以倒傳遞類神經網路預測股市指數. 宜蘭大學多媒體網路通訊數位學習碩士在職專班學位論文, 1–46.
連結:
-
陳淑玲, 吳安琪, & 費業勳. (2011). 臺灣股票市場技術指標之研究─不同頻率資料績效比較. 東海管理評論, 12(1S), 187–225.
連結:
-
陳秉洋. (2012). 結合技術指標、籌碼變動及期貨基差建構整合性投資策略─以台灣50指數型基金為例. 國立成功大學統計學系碩士學位論文.
連結:
-
張育維. (2013). 改良式類神經網路預測模式於股價預測之研究. 北商學報, (23), 1–18.
連結:
-
連立川, & 葉怡成. (2008). 以遺傳神經網路建構台灣股市買賣決策系統之研究. 資訊管理學報, 15(1), 29–51.
連結:
-
葉俞佛. (2014). 應用資料探勘技術結合股票分析方法建構投資策略. 中原大學資訊管理研究所學位論文, 1–71.
連結:
-
黃建勳. (2014). 台灣股票籌碼集中度指標與股價之間的動態關係研究-以週轉率和公司特徵觀點探討. 臺灣大學經濟學研究所學位論文.
連結:
-
黃鐘億. (2008). 以改良式倒傳遞類神經網路運用成分股與技術指標預測台灣 50 指數報酬率之研究. 臺北大學企業管理學系學位論文, 1–79.
連結:
-
楊勝凱. (2011). 運用類神經網路與時間序列分析建構台灣50股價預測模型. 臺北大學企業管理學系碩士學位論文.
連結:
-
蔡瑞昌, & 陳安斌. (2010). 應用多重類神經網路進行台灣指數期貨跨日走勢行為研究.
連結:
-
蘇哲宇. (2014). 台灣加權股價指數整合性預測之研究. 成功大學統計學系學位論文, 1–54.
連結:
-
譚克平. (2008). 極端值判斷方法簡介. 臺東大學教育學報, 19(1), 131–150.
連結:
-
林如茵, & 陳安斌. (2003). 基於籌碼面分析利用學習分類元系統於股票市場.
-
吳東曄, & Wu Tung-Yeh. (2009). 應用類神經網路於預測台股指數之研究, Application of Artificial Neural Network to Forecast the Weighted Price Index in Taiwan.
-
高慶恩. (2014). 結合基本面與技術面之複合選股模型, Stock selection model based on combination of fundamental analysis and technical analysis. 中華大學資訊管理學系碩士班學位論文.
-
張斐章, 張麗秋, & 黃浩倫. (2003). 類神經網路: 理論與實務. 臺北市: 臺灣東華書局.
-
許育彰. (2003). 模擬飽和砂土抗剪行為之倒傳遞網路特性及其於有限元素法之應用.
-
郭英哲. (2004). 應用倒傳遞類神經網路技術於臺灣指數期貨預測之研究.
-
黃俞翔. (2013). 結合交易點預測之動態投資組合管理系統; Dynamic Portfolio Management with Trading Signal Prediction. 國立中央大學資訊工程學系碩士學位論文.
-
黃華山, & 邱一勳. (2005). 類神經網路預測台灣 50 股價指數之研究. 資訊, 科技與社會學報, 5, 19–42.
-
蔡玉娟, 林家妃, & 張修明. (2012). 應用倒傳遞類神經網路及時間序列法建構股價報酬率預測模型-以台灣股市為例, A Study for Forecasting Taiwan Stock Return Model- Using Back Propagation Network and Time Series Model. 國立屏東科技大學.
-
鄭超文. (2008). 點線賺錢術: 技術分析詳解 20 年增訂版. 財信出版社.
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