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題名:機器學習在股票價格變動預測:以台灣證券交易所股票為例
作者:劉譯聲
作者(外文):LIU, YI-SHENG
校院名稱:國立雲林科技大學
系所名稱:財務金融系
指導教授:黃金生
學位類別:博士
出版日期:2019
主題關鍵詞:類神經網路支持向量機隨機森林純粹貝氏預測Naive-Bayes classificationArtificial neural networksSupport vector machineRandom forestForecast
原始連結:連回原系統網址new window
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本文論述了預測台灣股票市場股票價格指數走勢的問題。該研究比較了四種預測模型,類神經網路(ANN)、支持向量機(SVM)、隨機森林(Random Forest)和純粹貝氏(Naive-Bayes)及兩種資料輸入方法,用於輸入這些模型。第一種數據預處理方法是以股票交易的資料來計算十個技術指標;第二種方法則是將這些技術指標表示為趨勢確定數據。評估兩種輸入方法對每個機器學習預測模型的準確度。資料來源是以台灣股票市場指數2000年至2018年的19年歷史資料為主要資料。實驗結果表示,對於輸入數據的第一種方法,其中十個技術參數表示為連續值,ANN在整體性能上優於其他三種預測模型。當這些技術參數轉換為+1、-1等二元趨勢確定資料時,所有預測模型的預測效能都得到改善。
This paper addresses the problem of predicting the movement direction of stock price index in the Taiwan stock market. The study compares four prediction models consisting of Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest and Naive-Bayes with two approaches applied to these models. The first data preprocess approach involves computation of ten technical parameters using stock trading data while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on 19 years of historical data from 2000 to 2018 of Taiwan Stock Market Index. The experimental results show that for the first approach of input data, ten technical parameters are expressed as continuous values, and the ANN is superior to the other three prediction models in overall performance. Experimental results also show that the performance of all the prediction models improves when these technical parameters are represented as binary trend deterministic data.
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