傳統財經指標與深度學習模型於股價預測上之方法比較

股票市場在許多投資人眼中一直是望而卻步的領域，而股價波動也是各路投資人一直在探討的議題，過去在股票市場中絕大多數投資人會參考各類傳統財經指標並依據圖形走勢來判斷是否要進行股票交易，而近年來隨著科技的進步，分析方法也逐漸增加，如本文中提到的類神經網路模型RNN、LSTM等，其他還有處理PANEL DATA的資料探勘方法，諸如此類都是這幾年備受矚目的分析方法。本文以臺灣、日本與美國的股票作為研究對象進行綜合整理，比較ARIMA模型、資料探勘REEM tree演算法與深度學習模型三種不同領域方法對於股價預測的能力。

關鍵字

股價預測；技術指標； ARIMA ； REEMtree ； LSTM ；方法比較

並列摘要

From the view of investors, the stock market has always been an area where people always yearn for a gleam of hope but also fear of the wretchedness to see their investment experience a sudden slump. In addition, price fluctuation has also become a subject of discussion among investors. In this paper, four prediction techniques in different fields are selected to be analyzed and discuss their advantages and disadvantages. In the past, most investors will refer to various traditional financial indicators such as moving average, Stochastic Oscillator, RSI indicators and others to determine whether it is suitable to conduct their stock trading based on the graphical trends. However, with the advance of science and technology, the number of ways to carry out the analysis is gradually increasing. One example of the methods mentioned in this paper is the LSTM deep learning model. We also apply other techniques such as traditional ARIMA or REEM Tree in data mining to deal with panel data. The case analysis for the research target will be based on stock markets at Taiwan, Japan and the United States.

並列關鍵字

Stock price prediction ； Technical Analysis ； Autoregressive Integrated Moving Average model ； RE-EM tree ； Long Short Term Memory ； Methods Comparison