• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1199-1215
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• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.97-125
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• 2014

This study investigates the relationship between stock index and its associated nearby futures markets based on the cost-of-carry model. The purpose of this study is to explore the existence of mispriced futures contracts, and to test whether traders can earn trading profits in real financial market using the information about the mispriced futures contracts. This study suggests the concordance correlation coefficient to investigate the existence of mispriced futures contracts. The concordance correlation coefficient gives a desirable result for trading profits that results from a comparative analysis among profits from trading at the time to indicate trading opportunities determined by the degree of the difference between the observed market price and the theoretical price of a futures contract. In addition, this study also explains that the concordance correlation coefficient developed from the mean square error (MSE) has a statistically theoretical meaning. In conclusion, this study shows that the concordance correlation coefficient is appropriate for analyzing the relationship between the observed stock index futures market price and the theoretical stock index futures price derived from the cost-of-carry model.

• Journal of Agricultural Extension & Community Development
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• v.24 no.1
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• pp.9-19
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• 2017

This paper analyses the interrelationship among wholesale price, trading volumes, import volumes and demand for three agricultural products (cabbage, onions, and garlic) by using the consumer panel and the data from the Korea Rural Economic Institute and the Korea Customs Service with a VAR model. The results are summarized as below. (1) The prices of three agricultural products decrease when trading volumes increase while the price of cabbage and onions decreases when import volumes increase. But the prices of three agricultural products have little effects on trading volumes. (2) The demand of three agricultural products increases when trading volumes increase while the demand of cabbage and onions increases when import volumes increase. (3) when demand of garlic and cabbage increases by 10%, their price increases by 2.5% and 1.3% respectively. And the demand of garlic has positive effects on import volumes of garlic.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.7-12
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• 2019

The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.

• Journal of Korea Trade
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• v.27 no.5
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• pp.153-166
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• 2023

Purpose - This research empirically analyzes the influence of economic policy uncertainty and free trade agreements (FTAs) on bilateral trade volumes between Korea and its trading partners. The study investigates whether fluctuations in the Economic Policy Uncertainty Index (EPUI) for both Korea and its trading partners significantly impact trade volumes and whether the implementation of FTAs mitigates these effects. Design/methodology - The study employs dynamic panel data analysis using the system generalized method of moments (system GMM) estimation method to achieve its research objectives. It utilizes country-month-level panel data, including the EPUI, trade volume between Korea and its trading partner countries, and other pertinent variables. The use of system GMM allows for the control of potential endogeneity issues and the incorporation of country-specific and time-specific effects. Findings - The analysis yields significant results regarding the impact of economic policy uncertainty on Korea's exports and imports, particularly before the implementation of FTAs. An increase in the EPUI of trading partners leads to a notable increase in Korea's exports to them. Conversely, an increase in Korea's EPUI negatively affects its imports from trading partners. However, post-FTA implementation, the influence of each country's EPUI on trade volume is neutralized, with no significant difference observed. Originality/value - This research contributes to the existing literature by providing empirical evidence on the interaction effects between economic policy uncertainty and FTAs on bilateral trade volumes. The study's uniqueness lies in its examination of how FTAs mitigate the impact of economic uncertainty on trade relations between countries. The findings underscore the importance of trade agreements as mechanisms to address economic risks and promote international trade relations. In a world where global market uncertainties persist, these insights can aid policymakers in Korea and other countries in enhancing their trade cooperation strategies and navigating challenges posed by evolving economic landscapes.

• Management & Information Systems Review
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• v.38 no.4
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• pp.79-93
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• 2019

This study analyzed the information effect of KOSPI200 market and KOSPI200 futures market and volume synchronized probability of informed trading (VPIN). The data period is 760 days from July 8, 2015 to August 9, 2018, and the intraday trading data is used based on the trading period of the KOSPI 200 Index. The findings of the empirical analysis are as follows. First, as a result of regression analysis of the same parallax, when the level of VPIN is high, the return and volatility of KOSPI200 are high. Second, the KOSPI200 returns before and after the VPIN measurement and the return of the KOSPI200 future had a positive relationship with the VPIN. The cumulative returns of KOSPI200 futures were positive for about 15 minutes.Finally, we find that portfolios with high levels of VPIN showed high KOSPI200 and KOSPI200 futures return. These results confirmed the applicability of VPIN as a trading strategy index. The above results suggest that KOSPI200 and KOSPI200 futures markets will be able to explore volatility and price changes, and also be useful indicators of financial market risk.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.359-364
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• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1471-1479
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• 2017

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.

• The Journal of Industrial Distribution & Business
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• v.11 no.11
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• pp.7-18
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• 2020

Purpose: The Emission Trading Scheme (ETS), which enables structuring emission credits as a financial product, is taking a crucial position of global collaboration against climate change. Previous studies that have covered ETS subjects from the macro perspective contribute to facilitating legal enactment of this scheme. However, they have rarely addressed challenges aligned with issues arising from labor burdens for ETS works from the business perspective. Research Design, data and methodology: This study presents conceptual models that are expected to help design an electronic system. The study model contains four modules: emission allocation, data interface, reduction technology sharing, and emission trading. Two validation approaches, the Analytic Hierarchy Process (AHP) and regression analysis, are applied in confirming the feasibility of the proposed model. Results: This study suggests an IT system methodology to help improvement of the current K-ETS mechanism. In particular, this study addresses effectiveness for real businesses and the adaptability of this mechanism to other nations. Conclusions: The proposed IT platform diagram can contribute to successful operation of ETS by providing multiple benefits to participating companies through in-house allocation mechanisms, the soft-landing of ETS adoption to participating companies through reduction of technology-sharing, group purchases, and transaction costs through the trading system.