• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.233-265
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• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.173-179
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• 2006

In order to explain a U-shape pattern of stock returns, Fama and French(1988) suggested the state-space model consisting of I(1) permanent component and AR(1) stationary component. They concluded the autoregression coefficient induced from the state-space model follow the U-shape pattern and the U-shape pattern of stock returns was due to both negative autocorrelation in returns beyond a year and substantial mean-reversion in stock market prices. However, we found negative autocorrelation is induced under the assumption that permanent and stationary noise component are independent in the state-space model. In this paper, we derive the autoregression coefficient based on ARIMA process equivalent to the state-space model without the assumption of independency. Based on the estimated parameters, we investigate the pattern of the time-varying autoregression coefficient and conclude the autoregression coefficient from the state-space model of ARIMA(1,1,1) process does not follow a U-shape pattern, but has always positive sign. We applied this result on the data of 1 month retums for all NYSE stocks for the 1926-85 period from the Center for Research in Security Prices.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.4
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• pp.63-79
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• 2011

In this research, we have investigated the impact of servitization on firm value focused on Fortune 500 company's alliance announcement. The Firms are categorized to understand the impact of servitization and productization. The data includes Fortune 500 companies (2009, USA). Samples used for the hypothesis tests consist of 1,057 American companies that are opened on NYSE and NASDAQ, for the years 1990 through 2010. We test four research hypotheses based upon the various theoretical perspectives in servitization. The market is selective in reacting to alliance category; service company ${\rightarrow}$ manufacturing company, service company ${\rightarrow}$ service company. The findings show that alliance is significant between service company and manufacturing company. Also, the result shows significant relationship inter-service company's alliance. We have a significant relationship between company's alliance announcement and the market value of firms and shows significant AR in financial performance. Finally, this study presents implications for manufacturing companies that pursue service-led expansion as a strategic approach and are seeking to improve market value through alliance, partnership and cooperation. Continual effort must be placed to sustain market value of the firms.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.13-26
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• 2011

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.59-70
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• 2020

The purpose of this study is to construct the ACD model for the block trading volume duration. The ACD model based on the block trading volume duration is referred to as Volume ACD (VACD) in this study. By integrating with GARCH-type models, the VACD based GARCH type models, which include VACD-GARCH, VACD-IGARCH and VACD-FIGARCH models, are set up. This study selects Chunghwa Telecom (CHT) Inc., offering the America Depository Receipt (ADR) in NYSE, to investigate the block trading volume duration in Taiwanese equity market. The empirical results indicate that the long memory in volume duration series increases dependence at level of volatility clustering by VACD (2,1)-FIGARCH (3,d,1) model. Moreover, the VACD (2,1)-IGARCH (1,1) exhibits relatively better performance of prediction on capturing block trading volume duration. This volatility model is more appropriate in this study to portray the change of the CHT Inc. prices and provides more information about the volatility process for investment strategy, which can be a reference indicator of financial asset pricing, hedging strategy and risk management.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.415-438
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• 2004

This dissertation is assumed to continuously occur adjustment cost on present investment. So, I derived from time-nonseparable production-based CAPM and tested the performance of model through data. I also compared time-nonseparable production-based CAPM with time-separable production-based CAPM and CCAPM, CAPM through testifying the performance of model. At the part of applied application, I estimated time-nonseparable PCAPM-betas. The data of Korea consists of 320 listed companies on Korea Stock Exchange (KOSPI) from first quarter 1987 to first quarter 2002. This data also is categorized by scale and industries. Additionally, I estimated time-nonseparable PCAPM-betas through 500 listed companies of New York Stock Exchange (NYSE) from first quarter 1973 to first quarter 2002. I observed the statistical significance of 230 firms by 320 companies in Korea. After that, I compared time-nonseparable PCAPM-betas by firms with time-separable production-based CAPM-betas and CCAPM-betas, CAPM-betas through individual firms. At empirical test, I found that estimated parameter of adjustment cost on time-nonseparable production-based CAPM by scale and industries in Korea had positive value and statistical significance, Moreover, this approach proved to resolve the underestimation of adjustment cost on time-separable production-based CAPM by scale and industries. I also found that the time-nonseparable PCAPM performed better than time-separable production-based CAPM and CCAPM, CAPM. The result from U.S data proved to have similarity to that of Korea. Specifically, I found that time-nonseparable PCAPM-betas by firms performed better than CAPM-betas on individual firms in Korea.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.347-356
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• 2022

This article is concerned with asymmetric volatility models for financial time series. A generalization of standard single-threshold volatility model is discussed via multiple-threshold in which we specialize to twothreshold case for ease of presentation. An empirical illustration is made by analyzing S&P500 data from NYSE (New York Stock Exchange). For comparison measures between competing models, parametric bootstrap method is used to generate forecast distributions from which summary statistics of CP (Coverage Probability) and PE (Prediction Error) are obtained. It is demonstrated that our suggestion is useful in the field of asymmetric volatility analysis.

• The Journal of Small Business Innovation
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• v.19 no.3
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• pp.17-42
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• 2016

Financial Accounting Standards Board (FASB) Interpretation No. 48 (FIN 48), Accounting for Uncertainty in Income Taxes: An interpretation of FASB Statement No. 109, requires firms to evaluate uncertain tax positions and disclose information on their liabilities for these positions, unrecognized tax benefits (UTBs). We analyze the FIN 48 disclosures for calendar-year-end Korean companies listed on NYSE and NASDAQ and examine the Korean firms' tax aggressiveness utilizing the UTBs. The results suggest that stock exchange and firm size do not play a role in the Korean firms' tax aggressiveness, contrary to the matched U.S. firms and that the Korean firm in the miscellaneous retail industry is more tax aggressive than the firms in the communications, depository institutions and business services. In addition, we find evidence that the Korean firms are less tax aggressive than the matched U.S. firms. We also examine the Korean firms' tax avoidance tendencies using other measures of avoidance, leading to mixed results. Finally, we examine the association between the UTBs and other measures of tax avoidance and find a significant and negative association between the UTBs and the long-run cash effective tax rate.