• Title/Summary/Keyword: stock price data

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Management Performance and Announcement Effect of Seasoned Equity Offering (기업의 경영성과가 유상증자 공시효과에 미치는 영향)

  • Yoon, Hong-Geun;Lee, Young-Hwan;Park, Kwang-Suck
    • Journal of Digital Convergence
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    • v.11 no.2
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    • pp.101-114
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    • 2013
  • This paper investigates whether the announcement effect of seasoned equity offering is affected by management performance. We used Korean stock market data from 2000 to 2007 to analyze the possible relation between net income and seasoned equity offerings announcement effect. The sample of 308 firms are selected for the study from the original population of 750 seasoned equity offering announcements. and We analysis this article through event studty of Brown and J.Warner. We divide the data into two groups. - the previous offerings year's positive net income group and negative income group. Both positive and negative net income samples affect stock price positively. However, the CAR for the negative net income offerings becomes zero around 25days after the announcement date. To analyze the impact of accounting income on the seasoned equity offering announcement effects fully, we introduce a cross-sectional regression analysis by setting the cumulative abnormal returns as a dependant variable and net income as an explanatory variable. The beta coefficient of the net income shows a statistical significance. These results can be considered as an evidence to support our hypothesis.

Finding optimal portfolio based on genetic algorithm with generalized Pareto distribution (GPD 기반의 유전자 알고리즘을 이용한 포트폴리오 최적화)

  • Kim, Hyundon;Kim, Hyun Tae
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1479-1494
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    • 2015
  • Since the Markowitz's mean-variance framework for portfolio analysis, the topic of portfolio optimization has been an important topic in finance. Traditional approaches focus on maximizing the expected return of the portfolio while minimizing its variance, assuming that risky asset returns are normally distributed. The normality assumption however has widely been criticized as actual stock price distributions exhibit much heavier tails as well as asymmetry. To this extent, in this paper we employ the genetic algorithm to find the optimal portfolio under the Value-at-Risk (VaR) constraint, where the tail of risky assets are modeled with the generalized Pareto distribution (GPD), the standard distribution for exceedances in extreme value theory. An empirical study using Korean stock prices shows that the performance of the proposed method is efficient and better than alternative methods.

The Simplification of information visualization using metaphor (메타포를 적용한 정보시각화의 단순화)

  • Kim, Sungkon
    • The Journal of the Convergence on Culture Technology
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    • v.7 no.3
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    • pp.303-310
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    • 2021
  • A method for developing a visual information concept that analogously compares and analyzes macroscopic data changes in a simple form is needed. The development of the visual information concept requires the selection of visualization form, selection of rhetorical effects, and selection of digital expression elements. Among them, an example of a rhetorical effect selection method for effectively delivering visual information to a user is presented. In this study, metaphorical rhetoric, which allows data comparison and analysis from a macroscopic point of view, was selected for stock price analysis by period and industry. We present a two-dimensional three-stage shape change using a dandelion with spreading cockle hair as a metaphor and a three-dimensional three-stage shape change information expression method using a coral peony flower that changes shape and color according to time as a metaphor. Using this rhetorical metaphor, it is possible to compare macroscopic trading changes and stock prices by industry.

A Study on the Investment Portfolios of Stocks using DEA (DEA를 활용한 주식 포트폴리오 구성에 관한 연구)

  • Gu, Seung Hwan;Jang, Seong Yong
    • Korean Management Science Review
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    • v.31 no.3
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    • pp.1-12
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    • 2014
  • This study suggests the two types DEA models such as DEA CCR model and Super Efficiency model to evaluate the value of a company and to apply them for the investments. 14 kinds of real data of companies such as EV/EBITDA, EPS growth rate, PCR, PER, dividend yield, PBR, stock price/net current asset, debt ratio, current ratio, ROE, operating margin, inventory turnover, accounts receivable turnover, and sales growth ratio were used as input variables of DEA models. 12 year data from December 30, 2000 up to December 30, 2012 were collected, and the data with negative, missing and 0 values were removed reflecting the characteristics of the DEA. In order to verify the effectiveness of the models, we compared the historical variability and rate of return of both models those of the market. Study results are as follows. First, two DEA models are more stable than market in terms of rate of return because the historical variability of both models are less than that of market. Second, Super Efficiency model is more stable than CCR model. Lastly, the cumulative rate of return of Super Efficiency model (434%) is greater than that of the CCR model (420%) and that of the market (269%).

Determinants of Firm Value and Profitability: Evidence from Indonesia

  • SUDIYATNO, Bambang;PUSPITASARI, Elen;SUWARTI, Titiek;ASYIF, Maulana Muhammad
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.11
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    • pp.769-778
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    • 2020
  • The purpose of this study was to examine the role of profitability as a mediating variable in influencing firm value. This study uses a sample of manufacturing companies listed on the Indonesia Stock Exchange from 2016 to 2018. The data used is panel data, with data analysis using multiple regression. Based on the Sobel test, profitability plays a role in mediating the effect of firm size on firm value. The effect of firm size on firm value is indirect, however, through profitability. Therefore, the market price of the shares of large-scale companies will increase if the resulting profitability is high. The capital structure and managerial ownership directly influence firm value. The results showed that managerial ownership and firm size had a positive effect on profitability, while capital structure had no effect on profitability. Capital structure and managerial ownership have a negative effect on firm value, while firm size and profitability have a positive effect on firm value. The main finding of this study is that profitability acts as an intervening variable in mediating the relationship between firm size and firm value.

Robo-Advisor Algorithm with Intelligent View Model (지능형 전망모형을 결합한 로보어드바이저 알고리즘)

  • Kim, Sunwoong
    • 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.

Mutual Funds Trading and its Impact on Stock Prices (뮤추얼펀드의 자금흐름과 주식거래가 주가에 미치는 효과)

  • Kho, Bong-Chan;Kim, Jin-Woo
    • The Korean Journal of Financial Management
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    • v.27 no.2
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    • pp.35-62
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    • 2010
  • This paper examines the existence of the fund performance persistence and the smart money effect in Korean stock market and tests the flow-induced price pressure (FIPP) hypothesis, that is, fund flows affect individual stock returns and mutual fund performance. This paper also tests whether the FIPP effect can cause the performance persistence using the monthly returns and stock holdings data of 2,702 Korean mutual funds from January 2002 to June 2008. The empirical results indicate that the performance persistence exists significantly for a long time but the smart money effect does not. The hedge portfolio constructed by buying funds with the highest past 12 months performance and selling funds with the lowest past 12 months performance earns 0.11%~1.05% monthly abnormal returns, on average, in 3 years from portfolio formation month, but the hedge portfolio constructed by buying funds with the highest past net fund inflows and selling funds with the lowest past net fund inflows cannot earn positive monthly abnormal returns and the size of negative abnormal returns of the portfolio increase as time goes on. We find the evidence that the FIPP hypothesis is significantly supported. We first estimate the FIPP measure for each individual stock using the trading volume resulting from past fund flows and then construct the hedge portfolio by buying stocks with the highest FIPP measure and selling stocks with the lowest FIPP measure. That portfolio earns significantly positive abnormal return, 1.01% at only portfolio formation month and cannot earn significant abnormal returns after formation month. But, the FIPP effect cannot cause the performance persistence because, within the same FIPP measure group, funds with higher past performance still earn higher monthly abnormal returns than those with lower past performance by 0.08%~0.77%, on average, in 2 years. These results imply that the main cause of the performance persistence in Korean stock market is the difference of fund managers' ability rather than the FIPP effect.

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VKOSPI Forecasting and Option Trading Application Using SVM (SVM을 이용한 VKOSPI 일 중 변화 예측과 실제 옵션 매매에의 적용)

  • Ra, Yun Seon;Choi, Heung Sik;Kim, Sun Woong
    • 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.

The Gains To Bidding Firms' Stock Returns From Merger (기업합병의 성과에 영향을 주는 요인에 대한 실증적 연구)

  • Kim, Yong-Kap
    • Management & Information Systems Review
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    • v.23
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    • pp.41-74
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    • 2007
  • In Korea, corporate merger activities were activated since 1980, and nowadays(particuarly since 1986) the changes in domestic and international economic circumstances have made corporate managers have strong interests in merger. Korea and America have different business environments and it is easily conceivable that there exists many differences in motives, methods, and effects of mergers between the two countries. According to recent studies on takeover bids in America, takeover bids have information effects, tax implications, and co-insurance effects, and the form of payment(cash versus securities), the relative size of target and bidder, the leverage effect, Tobin's q, number of bidders(single versus multiple bidder), the time period (before 1968, 1968-1980, 1981 and later), and the target firm reaction (hostile versus friendly) are important determinants of the magnitude of takeover gains and their distribution between targets and bidders at the announcement of takeover bids. This study examines the theory of takeover bids, the status quo and problems of merger in Korea, and then investigates how the announcement of merger are reflected in common stock returns of bidding firms, finally explores empirically the factors influencing abnormal returns of bidding firms' stock price. The hypotheses of this study are as follows ; Shareholders of bidding firms benefit from mergers. And common stock returns of bidding firms at the announcement of takeover bids, shows significant differences according to the condition of the ratio of target size relative to bidding firm, whether the target being a member of the conglomerate to which bidding firm belongs, whether the target being a listed company, the time period(before 1986, 1986, and later), the number of bidding firm's stock in exchange for a stock of the target, whether the merger being a horizontal and vertical merger or a conglomerate merger, and the ratios of debt to equity capital of target and bidding firm. The data analyzed in this study were drawn from public announcements of proposals to acquire a target firm by means of merger. The sample contains all bidding firms which were listed in the stock market and also engaged in successful mergers in the period 1980 through 1992 for which there are daily stock returns. A merger bid was considered successful if it resulted in a completed merger and the target firm disappeared as a separate entity. The final sample contains 113 acquiring firms. The research hypotheses examined in this study are tested by applying an event-type methodology similar to that described in Dodd and Warner. The ordinary-least-squares coefficients of the market-model regression were estimated over the period t=-135 to t=-16 relative to the date of the proposal's initial announcement, t=0. Daily abnormal common stock returns were calculated for each firm i over the interval t=-15 to t=+15. A daily average abnormal return(AR) for each day t was computed. Average cumulative abnormal returns($CART_{T_1,T_2}$) were also derived by summing the $AR_t's$ over various intervals. The expected values of $AR_t$ and $CART_{T_1,T_2}$ are zero in the absence of abnormal performance. The test statistics of $AR_t$ and $CAR_{T_1,T_2}$ are based on the average standardized abnormal return($ASAR_t$) and the average standardized cumulative abnormal return ($ASCAR_{T_1,T_2}$), respectively. Assuming that the individual abnormal returns are normal and independent across t and across securities, the statistics $Z_t$ and $Z_{T_1,T_2}$ which follow a unit-normal distribution(Dodd and Warner), are used to test the hypotheses that the average standardized abnormal returns and the average cumulative standardized abnormal returns equal zero.

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A Study on Training Ensembles of Neural Networks - A Case of Stock Price Prediction (신경망 학습앙상블에 관한 연구 - 주가예측을 중심으로 -)

  • 이영찬;곽수환
    • Journal of Intelligence and Information Systems
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    • v.5 no.1
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    • pp.95-101
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    • 1999
  • In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.

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