Stock Time Series Forecasting Using Support Vector Machines Employing Analyst Recommendations

Abstract

This paper discusses the application of support vector machine (SVM) in stock price change trend forecasting. By reviewing prior research, thirteen technical indicators are defined as the input attributes of SVM. By training this model, we can forecast if the stock price would rise the next day. In order to make best use of market information, analyst recommendations about upgrading stocks are employed. So we put forward an improved method to evaluate if an upgrade classification of SVM is reliable. In our method, recommendation accuracy is first calculated according to historical advice. Then the more objective relative accuracy is deduced by considering the influence of total stock market index. Moreover, improved model is examined with the real data in Shanghai stock exchange market. Finally, we discuss some interesting hints to help readers understand this model more explicitly.

This work is supported by the National Science Foundation of China (60435010), National Great Basic Research Priorities Programme (973 Program: Grant No.2003CB317004) and the Nature Science Foundation of Beijing (4052025).