• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.1-18
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• 2015

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.

• The Korean Journal of Financial Studies
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• v.2 no.1
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• pp.127-144
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• 1995

Previous literature in financial economics documents the existence of a liquidity premium in expected returns, measured by the bid-ask spread. This study provides a more comprehensive test of the egect of liquidity on common stock returns by including trading volume as an additional liquidity measure. we find that trading volume is a relevant measure of liquidity, and affects expected returns even aher controlling for the effects of systematic risk, firm size, and the relative bid-ask spread. We also find that trading volume complements the bid-ask spread as a liquidity measure, and provides additional information about the liquidity premium. The liquidity effect emerges in non-January months as a volume effect, in addition to the spread effect in January documented by Eleswarapu and Reinganum(1993).

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.129-134
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• 2008

Dividend is one of essential factors determining the value of a firm. According to the valuation theory in finance, discounted cash flow (DCF) is the most popular and widely used method for the valuation of any asset. Since dividends play a key role in the pricing of a firm value by DCF, it is natural that the accurate prediction of future dividends should be most important work in the valuation. Although the dividend forecasting is of importance in the real world for the purpose of investment and financing decision, it is not easy for us to find good theoretical models which can predict future dividends accurately except Marsh and Merton (1987) model. Thus, if we can develop a better method than Marsh and Merton in the prediction of future dividends, it can contribute significantly to the enhancement of a firm value. Therefore, the most important goal of this study is to develop a better method than Marsh and Merton model by applying artificial intelligence techniques.

• Journal of Korean Society for Quality Management
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• v.28 no.1
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• pp.1-12
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• 2000

Since Black Monday there has been a rash of systems developments which aimed at automating and upgrading the surveillance mechanism of monitoring the many facets of security trading. A more sophisticated mathematical model for detecting abnormal trading activities was created by Davis and Ord of Penn State along with Nobel prize laureates Solow and Modigliani of MIT. They used CAPM(Capital Asset Pricing Model) to explain the movements of stock price and applied an idea of residuals to detect unusual movements. In this paper, their idea is discussed and a new method is proposed, which involves a confidence interval of future observation in linear regression. One of the examples of the stock watch system adopting this statistical method is also presented.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.117-124
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• 2008

The liquidity risk is defined as an additional risk in the market due to the timing and size of a trade. A recent work by Cetin et ai. (2003) proposes a rigorous mathematical model incorporating this liquidity risk into the arbitrage pricing theory. A practical problem arising in a real market application is an estimation problem of a liquidity cost. In this paper, we propose to estimate the liquidity cost function in the context of Cetin et al. (2003) using the constrained least square (LS) method, and illustrate it by analyzing the Kellogg company data.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• Journal of Navigation and Port Research
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• v.41 no.2
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• pp.71-78
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• 2017

This paper aims to investigate the time-varying systematic risk of the stocks of Korean logistics firms. For this purpose, the period from January 1991 to October 2016 was examined with respect to 21 logistics companies that are listed on the Korea Exchange. The systematic risk of the logistics stocks is measured in terms of the Capital Asset Pricing Model (CAPM) beta for which the sensitivity of a stock is compared to the return changes of the whole market. Overall, the betas of the stocks of the Korean logistics companies are significantly lower than those of the market unity; however, it was revealed that the logistics betas are not constant, but are actually time-varying according to different economic regimes, which is consistent with the previous empirical findings. This finding is robust across different measurements of the logistics betas. In addition, the impact of macroeconomic factors on the logistics betas was examined. The present study shows that the logistics betas are positively associated with foreign exchange-rate changes.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.2
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• pp.101-107
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• 2010

Among a variety of asset dynamics models in order to explain the common properties of financial underlying assets, parametric models are meaningful when their parameters are set reliably. There are two main methods from which we can obtain them. They are to use time-series data of an underlying price or the market option prices of the underlying at one time. Based on the Girsanov theorem, in the pure diffusion models, the parameters calibrated from the option prices should be partially equivalent to those from time-series underling prices. We call this phenomenon model consistency. In this paper, we verify that the two-state regime switching Black-Scholes model is superior in the sense of model consistency, comparing with two popular conventional models, the Black-Scholes model and Heston model.

• Asian Journal of Innovation and Policy
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• v.6 no.2
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• pp.225-244
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• 2017

When evaluating the economic value of technology or business project, we need to consider the period and cost for commercialization. Since the discounted cash flow (DCF) method has limitations in that it can not consider consecutive investment or does not reflect the probabilistic property of commercialization cost, we often take it desirable to apply the concept of real options with key metrics of underlying asset value, commercialization cost, and volatility, while regarding the value of technology and investment as the opportunity value. We at this moment provide more elaborated real options model with the effective region of volatility, which reflects the uncertainty in the option pricing model (OPM).

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