Building a Type-2 Fuzzy Random Support Vector Regression Scheme in Quantitative Investment

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  • Wei Yicheng
    Graduate School of Information, Production and Systems, Waseda University
  • Watada Junzo
    Graduate School of Information, Production and Systems, Waseda University

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Financial markets are connected well these days. One class assets' price performance is usually affected by movements of other classes of assets. However, the relationship between them is hard to trace and predict along with increase in complexity of markets' behaviors these days. Nothing like stock market, money or bond market is an over-the-counter market, where assets' prices are often presented in the form of classes of discrete quotations by trader's subjective judgments, thus are hard to model and analyze. Given concern to this, we define the Type 2 fuzzy random variable (T2 fuzzy random variable) to quantify those bid/offer behaviors in this paper. Moreover, we build a T2 fuzzy random support vector regression (T2-FSVR)scheme to study relationships between these markets, thus form an effective trading strategy to predict the trend of market prices. We use matlab platform to implement and test the effectiveness of the new model, then train and test it with 2014 whole years price data of bond and money markets. We also compare T2-FSVRs prediction accuracy with type-2 fuzzy expected regression(T2-FER) and confidence-interval-based fuzzy random regression model(CI-FRRM). The result shows that T2-FSVR outperforms and has 98% accuracy while CI-FRRM has 81% accuracy and T2-FER has only 70% accuracy. Moreover,T2-FSVR can be developed into a automated trading strategy for practical business use, which is able to learn behaviors of different markets based on mass of available historical and real time data and earn profit automatically.

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