• Journal of Korean Philosophical Society
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• v.126
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• pp.213-233
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• 2013

The aim of this paper is to reveal the ethical problem of insider trading. 'Insider trading' refer to obtaining information from non-public sources such as private acquaintances about trade secret, using it purposes of enhancing insider's financial advantages. And sometimes such a practice can be conducted fraudulently. Therefore, the focus of this paper will be on fairness or justice arguments against insider trading. And all kinds of discussion this paper are to focus the underlying consideration behind these arguments, that is, the underlying consideration about violation of ethical standards of fairness. First, one of these arguments argues that insider trading does necessarily involve defrauding general investors such as general employees, general stockholders. And economic power and unjust advantage of insider can be exercised to the detriment of this non-insider's interests. Second, another argument argues that insider trading undermines competition which is the principle of any free market. And insider trading is not only a complication in the free market mechanism, but also thwarts free competition which free markets depend. Third, the final argument argues that insider trading will be made something unfair about the concept of equal access to information. This argument argues, therefore, that to permit insider trading would be to set up stock market trading rules that are unfair to non-insiders.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of Korea Trade
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• v.23 no.7
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• pp.147-155
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• 2019

Purpose - As the U.S.-China trade war has become considerably worse, the Chinese government is considering applying non-tariff barriers to trade, especially local contents rule. The main purpose of this research is to check whether it is suitable for Korean investors to invest in the current Chinese capital market. Design/methodology - In order to check the stability of the recent Chinese capital market, we investigated the behavior of foreign equity investment (including Korean equity investment) in the Chinese capital market after China announced the Shanghai-Hong Kong Stock Connect (SH-HK Connect). In this paper, we researched whether international portfolio investment would or would not contribute to an increase the volatility of an emerging market's stock market (Chinese capital market) when foreign investors make investment decisions based on the objective of short-term gains by rushing into countries whose markets are booming and fleeing from countries whose markets are falling. Findings - The empirical results indicate that foreign investors show strong, negative feedback trading behavior with regard to the stock index of the Shanghai Stock Exchange (SSE), and when the performance of foreign investors in the Chinese stock market was fairly good. Also, we found evidence that the behavior of foreign investors significantly decreased volatility in SSE stock returns. Consequently, the SH-HK Connect brought on a win-win effect for both the Chinese capital market and foreign investors. Originality/value - It appeared that the Chinese capital market was very suitable for Korean investors after the China's declaration of the SH-HK Connect. However, the win-win effect was brought on by the Chinese government's aggressive capital control but the capital controls could possibly cause financial turmoil in the Chinese capital market. Therefore, Chinese reform in industrial structure and the financial sector should keep pace with suitable capital control policies.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.87-93
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• 2023

Determining the timing of buying and selling in stock investment is one of the most important factors to increase the return on stock investment. Buying low and selling high makes a profit, but buying high and selling low makes a loss. The price is determined by the quantity of buying and selling, which determines the price of a stock, and buying and selling is also related to corporate performance and economic indicators. The fear and greed index provided by CNN uses seven factors, and by assigning weights to each element, the weighted average defined as greed and fear is calculated on a scale between 0 and 100 and published every day. When the index is close to 0, the stock market sentiment is fearful, and when the index is close to 100, it is greedy. Therefore, we analyze the trading criteria that generate the maximum return when buying and selling the US S&P 500 index according to CNN fear and greed index, suggesting the optimal buying and selling timing to suggest a way to increase the return on stock investment.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.15-25
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• 2020

Purpose - The purpose of this study is to examine whether the change of settlement procedures have an impact on the distribution of day of the week effect in the UK and French markets or not. U.K and France changed their systems from fixed settlement date systems to fixed settlement lag systems Design/methodology/approach - This study adopted the data of the specific stock market indices such as FTSE 100 in the U.K market and FRCAC 40 in the French market, This study constructs a test of the differences in mean returns across the days of the week by computing the regression equations for each country index. Findings - First, this study found that the evolving settlement procedures in stock exchanges have an effect on stock return of day of the week. Second, long-run improvements in market efficiency may have diminished the effects of certain anomalies in recent periods. Improvements in market efficiency and evolving settlement systems may cause the disappearance of the weekend effect. Research implications or Originality - The Implication of this study is that recent settlement systems contributed to the disappearance of the weekend effect and explains improvements in market efficiency and diminishments of market anomaly. This study may be the first study which examines whether evolving settlement systems have an effect on the disappearance of the weekend effect in the market or not.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.193-200
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• 2023

This paper selects 100 IT companies listed on the Shenzhen Stock Exchange from 2016 to 2020, and the public announcement in Hwajung collects ESG integrated ratings and grades for each sector and empirically verifies the relationship between ESG ratings and stock returns. Huazheng ESG level data and QIANZHAN database Using corporate financial data, a total of 500 samples were selected through correlation analysis and linear regression analysis with SPSS23 to analyze the effect of ESG on Return. As a result of the analysis, first, the impact on stock returns was found to be a significant positive (+) value for ESG integrated ratings and ratings by E (environment), S (social), and G (governance) sectors, confirming that ESG ratings have a positive mold of corporate stock returns. Currently, the world's major economies have proposed sustainable development strategies and "carbon neutral" goals. Development strategies are very consistent with ESG concepts, and companies that agree and execute ESG concepts may have higher ratings than other companies in the same industry, resulting in certain evaluation premiums. In addition, capital market performance in recent years shows that companies with ESG concepts or "carbon neutrality" concepts are generally considered to have higher growth potential and stronger anti-risk capabilities in the market. For listed companies, they should focus on ESG investment, improve ESG performance, and actively disclose related information to investors. Improving ESG performance should deliver positive information to society, enhance corporate image, increase market confidence in the future development of listed companies, and positively improve corporate value to actively increase financial, financial, trading, and other aspects of negotiation.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.389-396
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• 2023

This paper selects 100 IT companies listed on the Shenzhen Stock Exchange from 2016 to 2020, and the public announcement in Hwajung collects ESG integrated ratings and grades for each sector and empirically verifies the relationship between ESG ratings and stock returns. Huazheng ESG level data and QIANZHAN database Using corporate financial data, a total of 500 samples were selected through correlation analysis and linear regression analysis with SPSS23 to analyze the effect of ESG on Return. As a result of the analysis, first, the impact on stock returns was found to be a significant positive (+) value for ESG integrated ratings and ratings by E (environment), S (social), and G (governance) sectors, confirming that ESG ratings have a positive mold of corporate stock returns. Currently, the world's major economies have proposed sustainable development strategies and "carbon neutral" goals. Development strategies are very consistent with ESG concepts, and companies that agree and execute ESG concepts may have higher ratings than other companies in the same industry, resulting in certain evaluation premiums. In addition, capital market performance in recent years shows that companies with ESG concepts or "carbon neutrality" concepts are generally considered to have higher growth potential and stronger anti-risk capabilities in the market. For listed companies, they should focus on ESG investment, improve ESG performance, and actively disclose related information to investors. Improving ESG performance should deliver positive information to society, enhance corporate image, increase market confidence in the future development of listed companies, and positively improve corporate value to actively increase financial, financial, trading, and other aspects of negotiation.

• Knowledge Management Research
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• v.22 no.1
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• pp.21-37
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• 2021

The purpose of this study is to explore financial investment knowledge related to multi-strategy, which is not generally shared. Through case studies, we will share it with the domestic hedge fund market. Since the era of full-fledged private equity hedge funds in Korea opens, many funds are created; however, reality is that there is a lack of diversity in strategies. Initially, it started with a simple stock long/short strategy, and various strategies such as mezzanine and alternative investments are in use but funds using multi-strategy are limited. This study aims to present an empirical application plan for hedge fund management strategies using a case study. It will specifically focus on process of achieving Absolute Return using the Multi Strategy technique actively used in securities firms' Prop Trading. With the results of this study, we intend to contribute to those fund managers and desired researchers who are utilizing multiple strategies in the hedge fund management to pursue Absolute Return and to help them strengthening their financial knowledge and competitiveness.