• 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 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.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.327-335
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• 2022

The purpose of this study is to examine the Kuwaiti Stock Exchange's (KDE) response to the COVID-19 pandemic and the precautions taken by Kuwaiti authorities to protect their citizens and other residents. To achieve this objective, daily data from four different indexes published by the Kuwait Stock Exchange (KSE) for the period between 24 February and 30 June 2020, as well as daily data on the number of people infected with COVID-19, the daily number of recovered people, the daily number of deaths, lockdown days, and days the country was under curfew. The findings show a significant positive association between the daily recovery of persons infected by COVID-19 and all indexes published by the KSE except for the Boursa Kuwait Main Market 50, where the association was positive but insignificant. A negative and significant association was also found between the closure of the country and each of the four indexes. Although the curfew imposed by the Kuwaiti authorities at an early stage of the pandemic appeared to have a negative effect on the four indexes, the level of association was statistically significant only in the cases of the Main Market index and Boursa Kuwait Main Market 50 index.

• East Asian Economic Review
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• v.18 no.3
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• pp.253-276
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• 2014

We examine which traditional asset pricing variables together with bank-specific accounting variables explain the cross-sectional variation of future bank stock returns, using a firm-level data of eight Asian countries. Our empirical evidence shows that exchange rate risk, firm size, the book-to-market ratio, and the net income ratio are important in explaining future bank stock returns during normal times. However, during the Global Financial Crisis period, different variables such as local market beta, illiquidity risk, equity ratio, and off-balance sheets ratio were statistically significant. Thus, researchers and policy practitioners should monitor these variables during normal times as well as during times of crisis.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.79-86
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• 2021

In recent years, firm performance has been a topic that attracts many researchers. It is extremely important to identify the factors that change firm performance. In the current trend of competition and integration, foreign ownership, product market competition is found to reduce agency costs and impact firm performance. The purpose of this research is to investigate the relationship between foreign ownership, product market competition, and firm performance. Our research using a quantile regression model, through panel data of 290 companies listed on the Vietnam stock exchange (include Ho Chi Minh and Hanoi stock exchanges) from 2017 to 2019 that was collected by Thomson - Reuters DataStream has shown that foreign ownership and product market competition have a positive impact on Tobin's Q but are not statistically significant with ROA. Critically, our quantile regression results suppose foreign ownership, product market competition have a significantly larger positive impact in high-performing firms relative to low-performing firms. The results help propose solutions to planners and managers to change foreign ownership and product market competition to increase business performance. Besides, through quantile regression analysis, managers need to pay attention to the impact on foreign ownership, product market competition; there will be a difference between high-performing firms relative to low-performing firms.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.578-588
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• 2017

The purpose of this paper was to prove the January Effect and Efficiency of the KOSPI Market, and then suggest as a result. KOSPI data was divided into two of section, Before and After IMF Financial Crisis, and this paper utilized Market Capitalization of common stock to conduct a study. As the main findings of this result, in KOSPI 1st section(Before IMF Financial Crisis), this paper proved the January Effect and Size Effect for Small-capital stock. On the other hand, in KOSPI $2nd-{\alpha}$ & ${\beta}$ section(After IMF Financial Crisis), this paper couldn't prove the January Effect. And then, this paper couldn't prove the Efficient Market hypothesis in KOSPI 1st with January Effect, however, proved the weak efficient market in KOSPI 2nd(${\alpha}$ & ${\beta}$) without January Effect. Finally, this paper deducted implications and limitation as the results.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• Management & Information Systems Review
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• v.34 no.1
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• pp.65-80
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• 2015

In previous studies concerning turnover, they argue individual stock's turnover must be identical to market portfolio's turnover under one condition where 2 funds separation theorem holds. In this kind of world, all market participants hold and trade the same portfolio and this should be only market portfolio. If one's trading portfolio's shape is different from market portfolio's, this would mean he or she has an advantage over others in information and this kind of information would be private. In accordance with this theory, we develop a metric which measures how far one's trading portfolio from market's and name it as Stock Selection by Investor(SSI). We apply this measurement to the various types of investor groups classified as individual, institutional and foreign who participate in Korea stock market. To test the validity of measure, we regress price ratio on this measurement using SUR method. As a result, individual investor group shows large number in SSI, but the coefficient in regression is not significant and economically meaningless. In case of institutional investor group, the coefficient proves to be significantly negative. We can infer from this fact that their trading is somehow far from informed trading. Stock selection activity by foreign investor groups proves to be informed trading by showing significantly positive coefficient and the magnitude of coefficient is economically meaningful, especially in sell activity.