Jae Joon Ahn

Using rough set to support investment strategies of real-time trading in futures market

Finding proper investment strategies in futures market has been a hot issue to everyone involved in major financial markets around the world. However, it is a very difficult problem because of intrinsic unpredictability of the market. What makes things more complicated is the advent of real-time trading due to recent striking advancement of electronic communication technology. The real-time data imposes many difficult tasks to futures market analyst since it provides too much information to be analyzed for an instant. Thus it is inevitable for an analyst to resort to a rule-based trading system for making profits, which is usually done by the help of diverse technical indicators. In this study, we propose using rough set to develop an efficient real-time rule-based trading system (RRTS). In fact, we propose a procedure for building RRTS which is based on rough set analysis of technical indicators. We examine its profitability through an empirical study.

Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

Graphical abstractDisplay Omitted Research highlights? In this study, we propose a customer classification model. ? The model may be used for facilitating cross-selling in a mobile telecom market. ? The various data mining techniques are applied to our proposed model in two steps. ? In the first step, our model predicts the purchase of new products. ? In the second step, our model compromises prediction probabilities by using GA. As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom companys case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.

Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting

This study considers real estate appraisal forecasting problem. While there is a great deal of literature about use of artificial intelligence and multiple linear regression for the problem, there has been always controversy about which one performs better. Noting that this controversy is due to difficulty finding proper predictor variables in real estate appraisal, we propose a modified version of ridge regression, i.e., ridge regression coupled with genetic algorithm (GA-Ridge). In order to examine the performance of the proposed method, experimental study is done for Korean real estate market, which verifies that GA-Ridge is effective in forecasting real estate appraisal. This study addresses two critical issues regarding the use of ridge regression, i.e., when to use it and how to improve it.

Usefulness of support vector machine to develop an early warning system for financial crisis

Oh, Kim, and Kim (2006a), Oh, Kim, Kim, and Lee (2006b) proposed a classification approach for building an early warning system (EWS) against potential financial crises. This EWS classification approach has been developed mainly for monitoring daily financial market against its abnormal movement and is based on the newly-developed crisis hypothesis that financial crisis is often self-fulfilling because of herding behavior of the investors. This article extends the EWS classification approach to the traditional-type crisis, i.e., the financial crisis is an outcome of the long-term deterioration of the economic fundamentals. It is shown that support vector machine (SVM) is an efficient classifier in such case.

Intelligent forecasting for financial time series subject to structural changes

This paper is mainly concerned about intelligent forecasting for financial time series subject to structural changes. For example, it is well known that interest rates are subject to structural changes due to external shocks such as government monetary policy change. Such structural changes usually make prediction harder if they are not properly taken care of. Recently, Oh and Kim (2002a, 2002b) suggested a method that could handle such difficulties efficiently. Their basic idea is to assume that different probabilistic law (and hence different predictor) works for different situations. Their method is termed as two-stage piecewise nonlinear prediction since it is comprised of establishing various situations empirically and then installing a different probabilistic nonlinear law as predictor on each of them. Thus, for its proper prediction functioning, it is essential to identify the law dictating the financial time series presently. In this article we propose and study a mixing approach for better identification of the presently working probabilistic law.

Using Hybrid Data Mining Techniques for Facilitating Cross-Selling of a Mobile Telecom Market to Develop Customer Classification Model

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more ARPU (average revenue per user). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model. Our model uses the cumulated data on the existing customers including the patterns for using old products or services to find prospects for purchasing. The data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques are applied independently. In the second step, our model compromises all these probabilities by using genetic algorithm. To validate the usefulness of our model, we applied it to a real-world mobile telecom companys case in Korea.

Applying option Greeks to directional forecasting of implied volatility in the options market

Highlights? Examining movement in implied volatility to enhance options investment profits. ? ANN is employed for implementing and specifying our model. ? Empirical study shows the model could yield a reasonably strong performance. This paper examines movement in implied volatility with the goal of enhancing the methods of options investment in the derivatives market. Indeed, directional movement of implied volatility is forecasted by being modeled into a function of the option Greeks. The function is structured as a locally stationary model that employs a sliding window, which requires proper selection of window width and sliding width. An artificial neural network is employed for implementing and specifying our methodology. Empirical study in the Korean options market not only illustrates how our directional forecasting methodology is constructed but also shows that the methodology could yield a reasonably strong performance. Several interesting technical notes are discussed for directional forecasting.

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability.

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.

Bayesian forecaster using class-based optimization

Suppose that several forecasters exist for the problem in which class-wise accuracies of forecasting classifiers are important. For such a case, we propose to use a new Bayesian approach for deriving one unique forecaster out of the existing forecasters. Our Bayesian approach links the existing forecasting classifiers via class-based optimization by the aid of an evolutionary algorithm (EA). To show the usefulness of our Bayesian approach in practical situations, we have considered the case of the Korean stock market, where numerous lag-l forecasting classifiers exist for monitoring its status.

Lag-ℓ forecasting and machine-learning algorithms

: In this study, we discuss the problem of lag-l forecasting, which has been solved using a machine-learning algorithm. The main aim of this study is to define the lag-l forecaster based on a precise classification approach and to discuss the technical issues involved in the lag-l forecasting problem, including a comparison of various machine-learning algorithms for proper implementation of the technique. This study focuses on an application that uses the lag-l forecaster in an early-warning system.