• Title/Summary/Keyword: In Stock Ratio

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An Empirical Study on the Determinants of Ownership Structure of Listed Companies in Korea : Evidence from Panel Data (우리나라 상장기업의 소유구조 결정요인에 관한 실증적 연구 : 패널자료로부터의 근거)

  • Lee, Hae-Young;Lee, Jae-Choon
    • The Korean Journal of Financial Management
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    • v.20 no.2
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    • pp.41-72
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    • 2003
  • The purposes of this paper are to build theoretical and empirically testable model to identify determining factors of ownership structure, and to analyze this model empirically using th Korea Stock Exchange panel data, and to test the impact of opening the stock market on the determinants of ownership structure. The determining factors of ownership structure identified in this paper include debt ratio, dividend, asset characteristics, profitability, growth business risk, size, institutional investors and chaebol-non chaebol dummy variable. Empirical panel estimation test reveals that this model can explain about $9\sim11%$ of the cross sectional variance in the equity ratio of large shareholders. The reasons that this model has too explanatory power are that some variables were measured with errors, and that there were some omitted variables in tested model. The regression results on the model variables ar generally in line with predictions. But the coefficient estimates on size is never significant. And it appears that the exogenous variable which explains opening the stock market has positive effect on the determinants of ownership structure.

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Influence factor analysis on the measurement of smoke density from floor materials in rolling stock (철도차량 바닥재 연기밀도 측정의 영향인자분석)

  • Kwon, Tae-Soon;Lee, Duck-Hee;Park, Won-Hee
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.11
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    • pp.629-634
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    • 2016
  • In this study, we investigated the effect of factors that influence the measurement of smoke density using synthetic rubber flooring. The characteristics of rolling stock in an enclosed environment can cause enormous loss of life by smoke inhalation during fires inside passenger cars. The amount of smoke generation from interior materials for rolling stock is strictly restricted domestically and in other countries. Precise measurement of smoke density is therefore required to assess the fire performance of interior materials. Major factors that influence the measurement of smoke density include the uniformity of the specimen, the variations in conditions and instruments, and the operational and maintenance environment of the instruments. The contribution of factors was analyzed by estimating the uncertainty to investigate the contribution ratios of the major factors. The results show a contribution ratio of about 86% for the variation from the measurement of light transmission using a photomultiplier tube. Thus, this factor was the most representative for the measurement of smoke density. The contribution ratio of the other factors was low at about 11%, including irradiant flux conditions (${\pm}0.5 kW/m^2$) and the influence of the operational and maintenance environment of the instrument. These results were obtained using specimens with high uniformity.

Predicting The Direction of The Daily KOSPI Movement Using Neural Networks For ETF Trades (신경회로망을 이용한 일별 KOSPI 이동 방향 예측에 의한 ETF 매매)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
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    • v.10 no.4
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    • pp.1-6
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    • 2019
  • Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

Determinants of Liquidity in Manufacturing Firms

  • VU, Thu Minh Thi;TRUONG, Tu Van;DINH, Dung Thuy
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.12
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    • pp.11-19
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    • 2020
  • This study examines the factors that affect firm's liquidity in manufacturing companies listed in Vietnam. Factors studied include the board size, the board independence, the firm size, the firm age, and its return. We use different metrics to measure firm's solvency status, including the cash ratio, the quick ratio, and the cash conversion cycle. Accordingly, three econometric models are built to test hypotheses proposed by researchers in order to explain the relationship between the five factors above and liquidity's measures. The study used the data set of manufacturing companies listed on the Ho Chi Minh City Stock Exchange in the period from 2015 to 2019. The final sample group comprises 139 firms with 633 observations. The results show that in manufacturing firms, while the cash ratio and the quick ratio are positively associated to the board size, the board independence, and the firm's profitability, the net operating cycle is negatively correlated to the board size, the firm size, the board independence, and the profitability. Therefore, larger firms with larger board size and more independent members can help to improve capital management efficiency.There is no evidence for the relationship between the firm age and solvency measurements, between cash conversion cycle and firm's profitability.

Association of Mutual Fund Risk Measures and Return Parameters: A Juxtapose of Ranking for Performance in Pakistan

  • KHURRAM, Muhammad Usman;HAMID, Kashif;JAVEED, Sohail Ahmad
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.2
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    • pp.25-39
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    • 2021
  • This purpose of this study is to investigate the association among mutual funds (MFs) risk measures and return parameters, evaluate mutual fund performance and also explore the best appropriate mutual fund performance measure for investment in Pakistan. Therefore, thirty-five mutual funds have been selected for the period 2007-2015. The Sharpe, Treynor, Jensen Alpha, Information ratio and Fama's Net Selectivity measures has been used to analyze MF performance. Our study findings show significant positive relation exist between Sharpe and Jenson alpha & information ratio (IR); Treynor ratio is negatively correlated to Jenson alpha and Jenson alpha is positively allied with IR. Moreover, association among performance measures, Fama's net selectivity is a major driver in leading to other measures but Sharpe and IR lead to Treynor ratio as well. Furthermore, performance measures are ranked in accordance standard deviation with the arrangement of Fama's net selectivity at top, Jenson Alpha at second, Sharpe ratio at third, IR at fourth and Treynor ratio at fifth position according to risk parameters in Pakistan. Overall, Jensen Alpha measure appears to be the best suitable mutual fund performance measure in Pakistan due to its practical nature. Finally, the Pakistani stock market index KSE100 (as benchmark) performs better than MF industry of Pakistan.

A Dynamic Asset Allocation Method based on Reinforcement learning Exploiting Local Traders (지역 투자 정책을 이용한 강화학습 기반 동적 자산 할당 기법)

  • O Jangmin;Lee Jongwoo;Zhang Byoung-Tak
    • Journal of KIISE:Software and Applications
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    • v.32 no.8
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    • pp.693-703
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    • 2005
  • Given the local traders with pattern-based multi-predictors of stock prices, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset allocated to each recommendation of the predictors, we design an asset allocation strategy called meta policy in the reinforcement teaming framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the total asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.

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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Study on Predicting the Designation of Administrative Issue in the KOSDAQ Market Based on Machine Learning Based on Financial Data (머신러닝 기반 KOSDAQ 시장의 관리종목 지정 예측 연구: 재무적 데이터를 중심으로)

  • Yoon, Yanghyun;Kim, Taekyung;Kim, Suyeong
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.17 no.1
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    • pp.229-249
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    • 2022
  • This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

Financial Security of Vietnamese Businesses and Its Influencing Factors

  • NGUYEN, Van Cong;NGUYEN, Thi Ngoc Lan
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.2
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    • pp.75-87
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    • 2020
  • This paper aims to not only investigate the nature of financial security and its measurement, but also to compare financial security level in 629 listed companies divided into four different industries (materials, industrials, health care, and consumer goods) before building a theoretical framework and regression models to examine the determinants of financial security. By gathering 2,167 financial statements published in Vietnamese Stock Exchange during eight years from 2012 to 2019, with the support of STATA, the research results indicate that six different internal factors, which are liquidity, profitability, firm size, debt management ratios, asset management ratios, and cash flows, explain 77.7% the change of financial security ratio and 3.4% the change in sustainable growth ratio. Specifically, while firm size has a positive impact on sustainable growth ratio but a negative impact on financial security ratio, deb management and profitability have an insignificant influence on the financial security level. Furthermore, an increase in asset management ratios would result positively in both two dependent variables whereas a rise in sustainable growth and a decline in financial security ratio are expected to witness if there is an increase in cash flows.

Dividend Policy and Companies' Financial Performance

  • KANAKRIYAH, Raed
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.10
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    • pp.531-541
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    • 2020
  • This study aims to determine the nature of the association between dividend policy and a corporation's financial performance in emerging countries, as well as the main variables that may have an effect on financial performance. The study included 92 industrial and service sector companies listed on the Amman Stock Exchange (ASE) during the period from 2015 to 2019. The study used Panel Data Analysis and cross-sectional time-series data and simple and multiple linear regression models. A multiple regression model was also developed in order to test whether guess factors may have a possible impact on financial performance (such as Dividend Yield, Dividend Pay-out Ratio, Firm Size, Leverage Ratio, Current Ratio). The data was collected from the annual reports and information that was available on the ASE website covering the period from 2015 to 2019. The results detect a strong relation between DY, DPR, and FSIZE variables that explain firm performance. Also leverage ratio is negatively and significantly associated with ROA and AOE. Moreover, no relations were detected between current ratio and financial performance. The study's conclusion is that dividend policy explains a lot of a company's financial performance, meaning that the dividend policy has a statistically significant impact on company financial performance.