• Journal of Intelligence and Information Systems
• /
• v.3 no.1
• /
• pp.47-68
• /
• 1997

This paper discusses a method of modified Elman network(1990) for nonlinear predictions and its a, pp.ication to forecasting daily exchange rate returns. The method consists of two stages that take advantages of both time domain filter and modified feedback networks. The first stage straightforwardly employs the filtering technique to remove extreme noise. In the second stage neural networks are designed to take the feedback from both hidden-layer units and the deviation of outputs from target values during learning. This combined feedback can be exploited to transfer unconsidered information on errors into the network system and, consequently, would improve predictions. The method a, pp.ars to dominate linear ARMA models and standard dynamic neural networks in one-step-ahead forecasting exchange rate returns.

• KDI Journal of Economic Policy
• /
• v.40 no.1
• /
• pp.45-66
• /
• 2018

This paper measures the extent of comovements in stock returns between Korea and three major countries (China, Japan and the U.S.) using industry-level data for Korea from 2003 to 2016 in the spirit of the international capital asset pricing model. It also examines what drives the comovements between Korea and the three countries. We find that the comovements of Korean stock returns with those of the U.S. and Japan became smaller after the global financial crisis. In contrast, the comovement in stock returns between Korea and China became larger after the crisis. After an additional analysis, we conclude that trade linkage is the main driver of the comovements between Korea and the three countries.

• The Journal of Information Technology
• /
• v.9 no.3
• /
• pp.77-89
• /
• 2006

The event study analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Stock split announcements are generally associated with positive abnormal returns. It is important to investigate the responses of stocks to new information contained in the announcements of stock splits. So It is important to study the long term performance in the case of Stock Split. This Study forced to two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model.

• Asia pacific journal of information systems
• /
• v.9 no.3
• /
• pp.93-110
• /
• 1999

The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

• Journal of Distribution Science
• /
• v.10 no.7
• /
• pp.13-22
• /
• 2012

The effect of the September 2008 global financial crisis weighed heavily on stock markets around the world. The purpose of this study is to empirically investigate the impact of the crisis on Amman Stock Exchange. Event study methodology has been adopted on a period of 24 months, from January 2008 to December 2009. Monthly average abnormal returns across a sample of 52 industrial and services companies have been tested separately. The results reveal that Amman Stock Exchange experienced significant negative abnormal returns in the fourth quarter of the year 2008. However, there were no significant abnormal returns observed thereafter. This means that Amman Stock Exchange managed to overcome its adverse consequences. Since the event study tests for market efficiency, as well, the results show that Amman Stock Exchange reaction is consistent with the semi-strong form of the efficient market hypothesis.

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.141-154
• /
• 2002

Foreign investors'shares and their influences on the Korean stock market have never been larger and greater before since the market was completely open to foreign investors in 1992 Quantitatively and qualitatively as well, as a result, changes in the patterns of foreign investments have caused enormous effects on the interactions of major macroeconomic indices of the Korean economy. This paper is intended to investigate the causal relations of the four variables, foreigners'buy-sell ratios, stock returns, ￦/＄ exchange rates and $\yen$/＄ exchange rates, over the two time periods of the pre-IMF (1996.1.1-1997.8.15) and the post-IMF (1997.8.16-2000.6.15) based on the daily data of the variables. Granger Causality Test, Forecast Error Variance Decomposition(FEVD) using VAR model and Impulse Response Function were implemented for the empirical analysis.

• Communications for Statistical Applications and Methods
• /
• v.20 no.1
• /
• pp.41-52
• /
• 2013

The stationary bootstrap of Politis and Romano (1994) is adopted to develop prediction intervals of returns and volatilities in a generalized autoregressive heteroskedastic (GARCH)(p, q) model. The stationary bootstrap method is applied to generate bootstrap observations of squared returns and residuals, through an ARMA representation of the GARCH model. The stationary bootstrap estimators of unknown parameters are defined and used to calculate the stationary bootstrap samples of volatilities. Estimates of future values of returns and volatilities in the GARCH process and the bootstrap prediction intervals are constructed based on the stationary bootstrap; in addition, asymptotic validities are also shown.

• Asia-Pacific Journal of Business
• /
• v.5 no.1
• /
• pp.1-11
• /
• 2014

The study provides new empirical evidence on the level of profit efficiency and returns to scale of the Bangladesh banking sector. We employ the Slack-Based Data Envelopment Analysis (SBM-DEA) method to assess the level of profit efficiency of individual banks over the years 2004 to 2011. The empirical findings indicate that the Bangladesh banking sector has exhibited the highest and lowest level of profit efficiency during years 2004 and 2011 respectively. We find that only eight banks have been profit efficient throughout the period under study. The empirical findings seem to suggest that most of the Bangladesh banks have been experiencing economies of scale due to being at less than the optimum size, or diseconomies of scale due to being at more than the optimum size. Thus, decreasing or increasing the scale of production could result in cost savings or efficiencies.

• Korean Management Science Review
• /
• v.24 no.2
• /
• pp.57-72
• /
• 2007

Many Applications of scientific research have coupled with functional data signal clustering techniques to discover novel characteristics that can be used for the diagnoses of several issues. In this article we present an interpretable multi-scale cluster hierarchy framework for clustering functional data using its multi-aspect frequency information. The suggested method focuses on how to effectively select transformed features/variables in unsupervised manner so that finally reduce the data dimension and achieve the multi-purposed clustering. Specially, we apply our suggested method to mutual fund returns and make superior-performing funds group based on different aspects such as global patterns, seasonal variations, levels of noise, and their combinations. To promise our method producing a quality cluster hierarchy, we give some empirical results under the simulation study and a set of real life data. This research will contribute to financial market analysis and flexibly fit to other research fields with clustering purposes.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.5
• /
• pp.1083-1088
• /
• 2013

Detection and classification of undersea mines in shallow waters using active sonar returns is a difficult task due to complexity of underwater environment. Support vector machine(SVM) is a binary classifier that is well known to provide a global optimum solution. In this paper, classification experiments of sonar returns from mine-like objects and non-mine-like objects are carried out using the SVM, and classification performance is analyzed and presented with discussions depending on parameter values of SVM kernel functions.