基于神经网络LAMSTAR的短线股票预测研究  

Short-Term Stock Prediction:A LAMSTAR Approach

作　　者：谢秀珍[1] 李建洋[2] 

机构地区：[1]龙岩学院数学与计算机科学学院,福建龙岩364000 [2]合肥工业大学计算机网络所,安徽合肥230009

出　　处：《计算机工程与科学》2008年第5期150-153,共4页Computer Engineering & Science

基　　金：龙岩学院自然科学研究项目(Z0408);福建省教育厅科研基金资助项目(JA07175)

摘　　要：股票预测在金融领域是一个重要的课题。LAMSTAR是一个用于存储、识别、比较和决策的网络系统。本文尝试开发一个关于短期股票预测的LAMSTAR网络应用程序,每一次预测都会从历史数据里获取股票特征,然后输入LAMSTAR网络。网络会自动检测各特征之间的多维非线性关系并编码,然后根据预测的趋势进行交易。本文提供了三个公司的预测结果,该预测结果非常有效。Stock prediction is an important issue in finance. LAMSTAR is a system of networks for storage, recognition, comparison and decision. This paper attempts to explore the LAMb＇TAR network application in short-term stock market prediction. For each prediction, the stock features extracted from the historical data are fed to the LAMSTAR network, in which the multi-dimensional non-linear connections between the features are detected and encoded in link weights. If the stock price is predicted to go up in the following trading day, LAMSTAR will send out a buy signal to initiate a transaction. Three experimental results with exciting returns of different companies are presented to validate the efficiency of this approach.

关 键 词：神经网络 LAMSTAR 股票预测 短期预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]