• Smart Media Journal
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• v.11 no.1
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• pp.38-45
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• 2022

A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.127-132
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• 2015

This study attempts to empirically investigate if relative momentum strategy is effective in the Korean stock market. The sample of the study is comprised of companies which are traded in both Kospi and Kosdaq stock markets in Korea for the period between 2001~2014. The study observes that the momentum strategy buying past winner stocks and selling past loser stocks is negatively correlated with the value strategy buying value stocks with high book to market ratio and selling glamour stocks with low book to market ratio. And each strategy is alternatively effective from period to period. The study demonstrates that the momentum strategy is effective when both strategies which are negatively correlated are treated as one system by estimating Fama and French's[1] 3 factor regression model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.5
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• pp.384-390
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• 2005

The purpose of this study is to test empirically the effects of Entry Barriers for the stock prices of Venture Business using the Ohlson Model which is modifying and extending in terms of growth and the potential growth energy. Because the traditional Ohlson model(1995) on which the firm's value is determined only based on abnormal earnings and book value have numerous limitations when we evaluate the value of venture Businesses with high technology and new emerging market. In order to overcome these limitations, We can introduce items of net sales growth ratios and industrial property-to-net asset ratios into as proxy variables of the growth and potential growth energy. In the process of analyzing these research tests, we have set three kinds of hypotheses and tested then empirically compared with KOSDAQ ordinary listing business and KOSDAQ venture businesses between long-term analysis and short-term analysis. According to the degree of concentration reflecting HHI index, our empirical research were performed in depth. Therefore, the results of this study show us that all three kinds of Hypotheses are accepted.

• 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 Industrial Convergence
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• v.19 no.5
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• pp.21-34
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• 2021

The purpose of this study is to find out what the investment efficiency of BW is from an investor's point of view and to suggest an efficient investment plan to investors. The research method is to investigate the coupon interest rate, maturity interest rate, issuance date, right exercise start and end date, maturity date, exercise price, etc. for BW issued from 2014 to July 2021. By connecting them, it was attempted to quantitatively understand the efficiency of investment in BW and the effect of new stock acquisitions. As a result of the study, the ratio of the number of days in excess of the exercise price was 41.3% of the available days for new stocks, so it was analyzed that the investment efficiency of bonds with warrants was not high. The return on the exercise start date was 24.8% on average and the return on the end date was 52.6% on average, showing a positive return on average, so it was derived in line with investor expectations. The number of stocks with negative returns on the exercise start date was 1.47 times higher than the number of stocks with positive returns, and the number of stocks with negative returns on the end date was 1.16 times higher than the number of positive stocks.

• Journal of Industrial Convergence
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• v.18 no.5
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• pp.1-13
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• 2020

The purpose of this study is to find out how much the investment effect of convertible bond(CB) is from the perspective of investors and to present efficient investment plans to investors. The research method is to investigate the coupon interest rate, maturity interest rate, conversion price, etc. for CBs. As a result of the study, it was analyzed that CB's investment efficiency was low because the conversion price excess days ratio was only about 1/4 of the conversion date. The conversion day yield was -6.3% and the maturity day yield was -5.2% on average. It was analyzed that the number of stocks with negative conversion day yield was 2.4 times higher than the number of positive stocks and 3.7 times higher than the number of positive stocks with a maturity day yield, so the expected return on equity conversion of CB was low.

• Management & Information Systems Review
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• v.36 no.4
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• pp.117-127
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• 2017

In recent times the size of the world IPO in general has skyrocketed. Specifically, China's financial market development is becoming important as both the size of China's capital market and the number of companies going public are gradually increasing. This has led to a rapid development of venture vapital(VC) institutions in China for the past couple of decades. This study focuses on one of the three markets of China's Shenzhen Stock Exchange-the Growth Enterprise Board((GEB) hereafter, ChiNext). The ChiNext is established in October, 2009 to enable hi-tech or high growth potential technology companies that find it relatively difficult to fulfil the listing requirements of either the Shenzhen Main Board or Small and Medium Size Enterprise Board(SMEB) to go public. This study covers a three-year period(2012/01/-2015/01) and analyze first day initial return of 83 venture capital-backed companies and 53 non-venture capital-backed companies using T-test. Regression analysis is used as to examine the variables affecting IPO's first-day return. The empirical results are four-fold. First, the level of first day return of venture-backed is significantly lower than non venture capital backed support in the Chinese venture capital market. Second, the level of first-day return of listed companies supported by foreign venture capital is significantly higher than that of companies receiving domestic venture capital support. Third, the firms that have a large number of venture capital firms showed a low level of first-day return. Fourth, regression result for the IPO first-day return which is as dependent variable indicates that the venture capital support(VCAP), number of venture capital(VCNum), offering size(Lnsize) and PER all affect have negative effect on the first day initial return. Also, the venture capital type(VCType), turnover ratio and the the firm type(Tech-firms) statistically affect IPO first day return positively. Finally, by shedding more light on the IPO first-day return, this paper provides meaningful information to investors about the Chinese IPO market.

• Journal of Service Research and Studies
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• v.10 no.4
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• pp.71-88
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• 2020

CB(Convertible bond) is mezzanine security that have the characteristics of bonds and stocks. From the perspective of investors, the purpose of the research is to empirically investigate the degree of investment efficiency of CB and to suggest efficient investment plans. The research method investigated the maturity interest rate, conversion price, and conversion date for CB, and then linked it with daily stock price fluctuations after the conversion date to determine the degree of investment efficiency and stock conversion effect of CB. As a result of the study, it was analyzed that the ratio of the conversion price exceeded days was only about 1/4 of the conversion date, so the investment efficiency was low. The conversion day yield was -6.3% on average and the maturity day yield was -5.2% on average, showing a minus return on average, which was calculated differently from investor expectations. It was analyzed that the number of stocks with a minus conversion day is 2.4 times greater than the number of plus stocks and 3.7 times more than the number of plus stocks with a minus maturity return, so the expected return on stock conversion of CB is low. The research contribution was derived from the problem that the expected rate of return of CB is not high, and it is that the investor's point of view when purchasing CB was established.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1263-1274
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• 2013

VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.