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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/76500

Title:	Predicting trends of stock prices with text classification techniques
Authors:	Chen, Jiun Da;Wang, T.-P.;Liu, Chao-Lin 陳俊達;劉昭麟
Contributors:	資科系
Keywords:	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
Date:	2007
Issue Date:	2015-07-13 15:35:48 (UTC+8)
Abstract:	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.
Relation:	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
Data Type:	conference
Appears in Collections:	[資訊科學系] 會議論文

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