Date of Award

Spring 2016

Document Type

Thesis

Degree Name

Master of Science in Bioinformatics - (M.S.)

Department

Computer Science

First Advisor

Zhi Wei

Second Advisor

Usman W. Roshan

Third Advisor

Jason T. L. Wang

Abstract

Mean Reversion is the most commonly used model in quantitative trading. This model is associated with several factors, like ma5 and ma10 line. These factors are the most significant in stock markets. However, the disadvantages of this model are lag and inaccuracy.

In this research, we get the historical and current stock data by web crawler, analyze the quantitative data and build a new model involved with the KDJ. Taking biotech companies marketed in the United States and B-share marketed in China as the research subjects, the result shows increased profits compared with the Mean Reversion model. It also shows that as long as we clearly understand the relationship between the turnover and fluctuation of share price, we can find the trading signals more accurately and generate more profit.

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