大數據之應用平行K-means演算法－建構股市決策分析

Big Data Application Using Parallel K-means Clustering to Construct Stock Decision Support Analysis

10.6736/JPSR.201703_14(1).0002

李國誠(Kuo-Chen Li)；李彥賢(Yen-Hsein Lee)；何宗燁(Tsung-Yeh Ho)

大數據 ； 平行K-means演算法 ； 決策支援系統 ； 技術指標 ； Hadoop ； Big Data ； Parallel K-means cluster ； Decision Support System ； Technical Analysis ； Hadoop

績效與策略研究

14卷1期（2017 / 03 / 01）

21 - 45

繁體中文

本研究以Hadoop平台為基礎讓股票先透過技術指標公式平行運算後，接著將K-means演算法套用於MapReduce框架上，藉此將股票作分群並同時提高運算效率，最後將分群結果定義決策後，再推薦投資者作買進或賣出之決策。本研究實證結果分群後的群集與大盤經過檢定後，大部分的檢定結果皆優於大盤，且能夠獲得更高之獲利，其分析出的結果呈現出來供投資者作為決策參考。

This research applies map-reduce parallel computing technologies to analyze the stock technical indicators on Hadoop platform. The computation efficiency is improved significantly; in the meantime, the target indicators are clustered by parallel K-means clustering algorithm and patterns are defined. Based on the found patterns, the most profitable buy-sell decisions will be recommended. The experiments were carried out to validate the proposed framework. Results show that most suggested buy-sell strategies beat the market and gain higher profit. In addition, the analyzed results could be used as decision support for stock investors.

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