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績效與策略研究

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篇名 大數據之應用平行K-means演算法-建構股市決策分析
卷期 14:1
並列篇名 Big Data Application Using Parallel K-means Clustering to Construct Stock Decision Support Analysis
作者 李國誠李彥賢何宗燁
頁次 021-046
關鍵字 大數據帄行K-means 演算法決策支援系統技術指標HadoopBig DataParallel K-means clusterDecision Support SystemTechnical Analysis
出刊日期 201703

中文摘要

本研究以Hadoop 帄台為基礎讓股票先透過技術指標公式帄行運算 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。

英文摘要

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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