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    題名: Predicting trends of stock prices with text classification techniques
    作者: Chen, Jiun Da;Wang, T.-P.;Liu, Chao-Lin
    陳俊達;劉昭麟
    貢獻者: 資科系
    關鍵詞: Aggregate demands;Bayesian model;Hybrid model;K-nearest neighbors;Stock price prediction;Stock trading;Taiwan stock markets;Text classification;Bayesian networks;Classification (of information);Commerce;Computational linguistics;Costs;Forecasting;Investments;Profitability;Speech processing;Text processing;Aggregates
    日期: 2007
    上傳時間: 2015-07-13 15:35:48 (UTC+8)
    摘要: Stocks` closing price levels can provide hints about investors` aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock`s closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock`s closing price level. For example, in case that one stock`s current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock`s closing price level correctly in advance. In this paper, we propose and evaluate three models for predicting individual stock`s closing price in the Taiwan stock market. These models include a naïve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the "UP" and "DOWN" categories.
    關聯: Proceedings of the 19th Conference on Computational Linguistics and Speech Processing, ROCLING 2007
    19th Conference on Computational Linguistics and Speech Processing, ROCLING 2007,6 September 2007 through 7 September 2007,Taipei
    資料類型: conference
    顯示於類別:[資訊科學系] 會議論文

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