• Title/Summary/Keyword: Stock Log Return

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Structural Change in the Price-Dividend Ratio and Implications on Stock Return Prediction Regression

  • Lee, Ho-Jin
    • The Korean Journal of Financial Management
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    • v.24 no.2
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    • pp.183-206
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    • 2007
  • The price-dividend ratio is one of the most frequently used financial variables to predict long-horizon stock return. However, the persistency of the price-dividend ratio is found to cause the spuriousness of the stock return prediction regression. The stable relationship between the stock price and the dividend, however, seems to weaken after World War II and to experience structural break. In this paper, we identify a structural change in the cointegrating relationship between the log of the stock price and the log of the dividend. Confirming a structural break in 1962, we subdivide the sample and apply the fully modified estimator to correct for the nonstationarity of the regressor. With the subdivided sample, we exercise the nonparametric bootstrap procedure to derive the empirical distribution of the test statistics and fail to find return predictability in each subsample period.

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The Impact of Firms' Environmental, Social, and Governancial Factors for Sustainability on Their Stock Returns and Values (지속가능경영을 위한 기업의 환경적, 사회적, 지배구조적 요인이 주가수익률 및 기업 가치에 미치는 영향)

  • Min, Jae H.;Kim, Bumseok;Ha, Seungyin
    • Journal of the Korean Operations Research and Management Science Society
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    • v.39 no.4
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    • pp.33-49
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    • 2014
  • This study empirically examines the impact of firms' environmental (E), social (S), and governancial (G) factors on their short-term and long-term values. To measure firms' non-financial performance, we use ESG performance grades published by KCGS (Korea Corporate Governance Service). We employ stock log return as the proxy of each firm's short-term value, and Tobin's Q ratio as that of its long-term value. From a series of regression analyses, we find each of the ESG factors generally has a negative impact on stock return while it has a positive impact on the Tobin's Q ratio. These results imply that firms' effort for enhancing their non-financial performance may adversely affect their financial performance in a short term; but in the long-term point of view, firms' values increase through their good images engraved by their respective social, environmental and governancial efforts. In addition, we compare the relative strength of impact among E, S, G, the three non-financial factors on the firms' value measured in Tobin's Q ratio, and find that S (social factor) and G (governancial factor) give statistically significant impact on the firms' value respectively. This result tells us it would be advised to strategically embed CSV (creating shared value) pursuing both of profits and social responsibility in the firms' future agenda. While E (environmental factor) is shown to be an insignificant factor for the firms' value, it should be emphasized as a major concern by all the stakeholders in order to form a sound business ecosystem.

Level Shifts and Long-term Memory in Stock Distribution Markets (주식유통시장의 층위이동과 장기기억과정)

  • Chung, Jin-Taek
    • Journal of Distribution Science
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    • v.14 no.1
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    • pp.93-102
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    • 2016
  • Purpose - The purpose of paper is studying the static and dynamic side for long-term memory storage properties, and increase the explanatory power regarding the long-term memory process by looking at the long-term storage attributes, Korea Composite Stock Price Index. The reason for the use of GPH statistic is to derive the modified statistic Korea's stock market, and to research a process of long-term memory. Research design, data, and methodology - Level shifts were subjected to be an empirical analysis by applying the GPH method. It has been modified by taking into account the daily log return of the Korea Composite Stock Price Index a. The Data, used for the stock market to analyze whether deciding the action by the long-term memory process, yield daily stock price index of the Korea Composite Stock Price Index and the rate of return a log. The studies were proceeded with long-term memory and long-term semiparametric method in deriving the long-term memory estimators. Chapter 2 examines the leading research, and Chapter 3 describes the long-term memory processes and estimation methods. GPH statistics induced modifications of statistics and discussed Whittle statistic. Chapter 4 used Korea Composite Stock Price Index to estimate the long-term memory process parameters. Chapter 6 presents the conclusions and implications. Results - If the price of the time series is generated by the abnormal process, it may be located in long-term memory by a time series. However, test results by price fixed GPH method is not followed by long-term memory process or fractional differential process. In the case of the time-series level shift, the present test method for a long-term memory processes has a considerable amount of bias, and there exists a structural change in the stock distribution market. This structural change has implications in level shift. Stratum level shift assays are not considered as shifted strata. They exist distinctly in the stock secondary market as bias, and are presented in the test statistic of non-long-term memory process. It also generates an error as a long-term memory that could lead to false results. Conclusions - Changes in long-term memory characteristics associated with level shift present the following two suggestions. One, if any impact outside is flowed for a long period of time, we can know that the long-term memory processes have characteristic of the average return gradually. When the investor makes an investment, the same reasoning applies to him in the light of the characteristics of the long-term memory. It is suggested that when investors make decisions on investment, it is necessary to consider the characters of the long-term storage in reference with causing investors to increase the uncertainty and potential. The other one is the thing which must be considered variously according to time-series. The research for price-earnings ratio and investment risk should be composed of the long-term memory characters, and it would have more predictability.

The Analysis on the Relationship between Firms' Exposures to SNS and Stock Prices in Korea (기업의 SNS 노출과 주식 수익률간의 관계 분석)

  • Kim, Taehwan;Jung, Woo-Jin;Lee, Sang-Yong Tom
    • Asia pacific journal of information systems
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    • v.24 no.2
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    • pp.233-253
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    • 2014
  • Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

Comparison of realized volatilities reflecting overnight returns (장외시간 수익률을 반영한 실현변동성 추정치들의 비교)

  • Cho, Soojin;Kim, Doyeon;Shin, Dong Wan
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.85-98
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    • 2016
  • This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.

A Study on the Effect on Net Income of the Shipbuilding Industry through Exchange Hedge - Focused on the Global Top 5 Shipbuilders - (환헤지가 조선업체의 당기순이익에 미치는 영향에 관한 연구)

  • Cho, In karp;Kim, Jong keun
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.10 no.3
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    • pp.133-146
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    • 2015
  • This study is to investigate the causal relationship between exchange hedge and the net income of the shipbuilder through the unit root test and co-integration and vector autoregressive model(Vector Autoregressive Model: VAR). First, quarter net income of shipbuilders to order a unit root tests from 2000 to 2013 was used as a value after the Johnson transformation. In the same period, the return on bond futures(KTBF), three years bond yield(KTB3Y), America-Korea exchange differences are weekly data for each quarterly difference in value was converted by utilization, shipbuilding shares after log transformation which it was used. Also, structural change point investigation analysis to verify that looked to take advantage of the structural changes occur in the exchange hedge strategies affecting net income in the shipbuilding industry. Between the exchange hedge and net income of shipbuilders in structural change points detection and analysis showed that structural changes occur starting in 2004. In other words, strategy of shipbuilders about exchange hedge has occurred from "passive exchange hedge" to "active exchange hedge". The exchange hedge of the Korea shipbuilders through the estimation of the VAR was able to grasp that affect the profitability of mutual shipbuilders. Macroeconomic variables and stock prices could also check to see that affected the net income of the shipbuilding industry.

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A Study of Option Pricing Using Variance Gamma Process (Variance Gamma 과정을 이용한 옵션 가격의 결정 연구)

  • Lee, Hyun-Eui;Song, Seong-Joo
    • The Korean Journal of Applied Statistics
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    • v.25 no.1
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    • pp.55-66
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    • 2012
  • Option pricing models using L$\acute{e}$evy processes are suggested as an alternative to the Black-Scholes model since empirical studies showed that the Black-Sholes model could not reflect the movement of underlying assets. In this paper, we investigate whether the Variance Gamma model can reflect the movement of underlying assets in the Korean stock market better than the Black-Scholes model. For this purpose, we estimate parameters and perform likelihood ratio tests using KOSPI 200 data based on the density for the log return and the option pricing formula proposed in Madan et al. (1998). We also calculate some statistics to compare the models and examine if the volatility smile is corrected through regression analysis. The results show that the option price estimated under the Variance Gamma process is closer to the market price than the Black-Scholes price; however, the Variance Gamma model still cannot solve the volatility smile phenomenon.