以演化式RBF模型建構樣式辨認之投資決策模式

Applying an adaptive RBF neural network on the construction of investment decision model in candlestick chart

蘇品丞(Pin-Cheng Su)

指導教授 ： 邱志洲

國立臺北科技大學/管理學院/商業自動化與管理研究所/碩士(2010年)

https://doi.org/10.6841/NTUT.2010.00618

摘要

台灣股票市場成立迄今已四十六年，股票投資已成為一般民眾主要的理財工具，然而影響股價波動的因素眾多，包含總體經濟、國際股匯市、政治面、公司基本面、技術面、籌碼面、消息面等，市場上的投資大眾為了有效預測股價的走勢，遂有許多分析方法乃因應而生。基本上，這些方法主要可區分成「基本分析」或「技術方析」兩大類。而所謂的基本分析主要是以經濟、產業及公司營運等面向的資料為基礎進行股票價格分析；技術分析則是透過整體市場及個別股票資料的價格及成交量為基礎建構不同技術指標進行分析。在技術分析的眾多技術指標中，由於K線圖 (K-chart) 可以簡單的圖形化方式反應市場的趨勢且投資人可以藉由K線型態的成立與否來判斷市場未來走向，所以常被投資大眾所採用。
事實上，在學術上已有許多的研究是根據K線型態使用數值運算判斷型態轉折進行分析的。此外，有許多學者也嘗試應用資料分群(clustering)的概念，以自組織映射圖網路 (Self-organizing map, SOM) 建立K線型態分群模式，試圖找出指數資料中所有可能出現的K線型態，進行走勢的判斷。但是由於傳統的SOM模式在模式訓練前必須先給定拓撲網路 (Topology map) 的大小，所以在訓練過程中並無法根據資料的特性來增加單元 (Unit) 數。再者，SOM模式由於受限於二度空間拓撲座標之假設，亦無法適當呈現具有層級性的資料型態。換言之，自組織映射圖網路並無法有效地處理具有階層特性的指數資料。
在本研究中，我們針對股價加權指數及個股股價資訊，採用Dittenbach et al. (2000) 所提出之成長型階層式自組織映射圖網路 (Growing hierarchical self-organizing map, GHSOM) 技術進行K線型態分群模式的建構。藉由所建構之分群模式，成功的尋找出隱藏在累積資料中的K線型態及正確的資料層級特性。此外，我們也根據分群模式的分析結果採用分類迴歸樹(Classification and Regression tree, CART)的方法，進行即時指數資料的監控，以預測市場未來趨勢。

關鍵字

股價預測 ； 輻射基底函數類神經網路 ； 成長型階層式自組織網路映射圖 ； 分類迴歸樹

並列摘要

Because of low interest rates in recent years, the stock market has been a very popular financial investment channel. However, financial investment is a knowledge-intensive industry. Without sufficient understanding, it is hard for any investor to get profit from it. In the past decade, with the advances in electronic transactions, vast amounts of data have been collected. Thus, the emergence of knowledge discovery technology enables the building up a financial investment decision support system.
There are various techniques of knowledge discovery have been employed in the stock market. Basically, they can be divided into two parts, technical analysis and forecasting of stock time series data. The former applies the charting heuristics of technical analysis to identify the bull flag by template match and to establish trading rules. The latter focuses on the forecast of stock prices, which often employs a statistical or artificial intelligence (AI) approach to facilitate the trading strategy-making.
In this thesis, we propose a hybrid approach on the basis of the knowledge discovery methodology by using Growing hierarchical self-organizing map (GHSOM) model, integrating K-chart technical analysis for feature representation of stock price movements. Moreover, the possibility of integrated GHSOM and Classification and regression tree (CART) methodology is examed. The investigated results showthat the application of GHSOM and CART in data mining provides the efficient identification and classification methods for financial investment data. And in terms of the successful identification of the relationship within data, the better prediction modeling can be found.

並列關鍵字

Predict of Stock Index ； Radial Basis Function Neural Networks ； Growing hierarchical Self-Organizing Map ； Classification and Regression Tree

參考文獻

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