Suk Jun Lee

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.

An early warning system for financial crisis using a stock market instability index

: This paper proposes to utilize a stock market instability index (SMII) to develop an early warning system for financial crisis. The system focuses on measuring the differences between the current market conditions and the conditions of the past when the market was stable. Technically the system evaluates the current time series against the past stable time series modelled by an asymptotic stationary autoregressive model via artificial neural networks. Advantageously accessible to extensive resources, the system turns out better results than the conventional system which detects similarities between the conditions of the current market and the conditions of previous markets that were in crisis. Therefore, it should be considered as a more advanced tool to prevent financial crises than the conventional one. As an empirical example, an SMII for the Korean stock market is developed in order to demonstrate its potential usefulness as an early warning system.

Using decision tree to develop a soil ecological quality assessment system for planning sustainable construction

Research highlights? This study proposes a soil ecological quality assessment system. ? The system uses forward and backward DT models under GIS-based spatial analysis. ? The system may examine conservation and development areas strictly. ? The prediction results can be used for planning mega-construction projects. Soil ecology is the foundation of the entire biosphere and plays a significant role in global ecosystems. Soil ecology is important in the decision-making aspects of mega-construction projects. Despite its significance, soil ecological quality is not normally included in environmental impact assessments for sustainable development. This study develops and presents a new expert system to assess soil microbial diversity as an indicator of soil ecology quality using decision tree (DT) algorithms and GIS (geographic information system)-based spatial analysis. Our modeling results show that forward and backward DT models provide development-oriented and conservation-oriented information maps. To resolve potential conflicts by the different model predictions, a new mapping approach was developed for identifying strict conservation and potential development areas. These results suggest that the newly developed soil ecological quality assessment system can be used for planning mega-construction projects.

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.

How many reference patterns can improve profitability for real-time trading in futures market?

Investors in futures market used to employ trading system which depends on reference pattern (template) to detect real-time buy or sell signal from the market. Indeed they prepare in advance a number of reference patterns that market movement might follow, and then match the current market with one of reference patterns. One popular way to prepare templates is to fix a relatively small number of them which represent possible market movements efficiently. The underlying assumption of this approach is of course that the current market movement is close enough to one of the templates. However, there is always a calculated risk that the current market is close to none of them sufficiently. In this article we investigate the issue of appropriate number of templates (or template cardinality I) in terms of profitability. We will show that one may improve profitability by increasing I and that random pattern sampling plays a key role in such case. An empirical study is done on the Korean futures market.

Using neural networks to tune the fluctuation of daily financial condition indicator for financial crisis forecasting

Recently, Oh et al. [11, 12] developed a daily financial condition indicator (DFCI) which issues an early warning signal based on the daily monitoring of financial market volatility. The major strength of DFCI is that it is expected to serve as a quite useful early warning system (EWS) for the new type of crisis which starts as an instability of the financial markets and then develops into a major crisis (e.g., 1997 Asian crises). One of the problems with DFCI is that it may show a high degree of fluctuation because it handles daily variable, and this may harm its reliability as an EWS. The main purpose of this article is to propose and discuss a way of smoothing DFCI, i.e., it will be tuned using long-term (monthly or quarterly) fundamental economic variables. It turns out that such a tuning procedure could reveal influential macroeconomic variables on financial markets. Since tuning DFCI is done by the method of fitting various types of data simultaneously, neural networks are employed. Tuning the DFCI for the Korean financial market is given as an empirical example.

Using Rough Set to Support Investment Strategies of Rule-Based Trading with Real-Time Data in Futures Market

Investment strategies in stock market have gained unprecedented popularity in major financial markets around the world. However, it is a very difficult problem because of the fluctuation of the stock market. This study presents usefulness of rough set on the rule base to develop real-time investment strategies using technical analysis in futures market. This study consists of four phases. In the first phase, meaningful technical indicators are selected to reflect market movements. In the second phase, rough set is used to extract trading rules for identification of buy and sell patterns in the stock market. In the third phase, the investment strategies are developed in order to apply selected trading rules using rule-based reasoning to unpredictable stock market. Finally, investment strategies on the basis of rule base are evaluated by real-time trading. This study then examines the profitability of the proposed model.

Machine Learning Algorithm Selection for Forecasting Behavior of Global Institutional Investors

Recently Son et al. [32] proposed early warning system (EWS) monitoring the behaviors of global institutional investors (GII) against their possible massive pullout from the local emerging stock market. They used machine learning algorithm for lag l classifier to forecast the behavior of GII. The main aim of this article is to implement various machine learning algorithms in constructing the EWS and to compare their performances to select the proper one. Our results address various important issues for machine learning forecasting problem. In particular, a proper machine learning algorithm will be recommended for both long term and short term forecasting. This is empirically studied for the Korean stock market.