• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.105-117
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• 2022

This study analyzes the effects between stock returns and interest rate spread, difference between long-term and short-term interest rate through the polynomial linear regression analysis. The existing research concentrated on the business forecast through the interest rate spread focusing on the US market. The previous studies verified the interest rate spread based on the leading indicators of business forecast by moderating the period of long-term/short-term interest rates and analyzing the degree of leading. After the 7th reform of composite indices of business indicators in Korea of 2006, the interest rate spread was included in the items of composing the business leading indicators, which is utilized till today. Nevertheless, there are a few research on stock returns of each industry and interest rate spread in domestic stock market. Therefore, this study analyzed the stock returns of each industry and interest rate spread targeting Korean stock market. This study selected the long-term/short-term interest rates with high causality through the regression analysis, and then understood the correlations with each leading period and industry. To overcome the limitation of the simple linear regression analysis, polynomial linear regression analysis is used, which raised explanatory power. As a result, the high causality was verified when using differences between returns of corporate bond(AA-) without guarantee for three years by leading six months and call rate returns as interest rate spread. In addition, analyzing the stock returns of each industry, the relation between the relevant interest rate spread and returns of the automobile industry was the closest. This study is significant in the aspect of verifying the causality of interest rate spread, business forecast, and stock returns in Korea. Even though it could be limited to forecast the stock price by using only the interest rate spread, it would be working as a strong factor when it is properly utilized with other various factors.

• Journal of Agricultural Extension & Community Development
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• v.2 no.1
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• pp.1-21
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• 1995

This study examines the socioeconomic returns to agricultural research and extension services in Korea, using multivariate time series technique and Akino-Hyami formula. Results find that the socioeconomic returns are quite competitive in case of agricultural research with 44.82% internal rate of return and very high in case of extension services with 207.82% internal rate of return. Agricultural production responds to the agricultural research shock about four years after the shock. The magnitudes of the impacts increase until a peak is reached nine years after the initial expenditures and the impacts declines to a zero level after about twenty years. This lag lengths are consistent to the usual literature on research, which finds lags of seven to thirty years. Agricultural production responds to an agricultural extension shock immediately and declines to a zero level after about four years. Thus, the lag lengths are much shorter than those by research shock.

• Journal of Distribution Science
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• v.13 no.9
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• pp.5-11
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• 2015

Purpose - The purpose of this research is to provide insights that can be used for deliberate decision making around challenging big data investments by measuring the economic value of such big data implementations. Research design, data, and methodology - We perform empirical research through an event study. To this end, we measure actual abnormal returns of companies that are triggered by their investment announcements in big data, or firm size information, during the three-year research period. The research period targets a timeframe after the introduction of big data at Korean firms listed on the Korea stock markets. Results - Our empirical findings discover that on the event day and the day after, the abnormal returns are significantly positive. In addition, our further examination of firm size impacts on the abnormal returns does not show any evidence of an effect. Conclusions - Our research suggests that an event study can be useful as an alternative means to measure the return on investment (ROI) for big data in order to lessen the difficulties or decision making around big data investments.

• 한국디지털정책학회:학술대회논문집
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• 2004.11a
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• pp.25-37
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• 2004

This paper compares long-run buy-and-hold returns of privatization initial public offerings to those of domestic stock markets of respective countries using a sample of 29 privatized telecom initial public offerings from 27 countries. The evidence indicates that the privatization IPOs significantly outperform their domestic stock markets if the returns are equally-weighted while they do not outperform the markets if value-weighted. In addition, this paper analyzes the cross-sectional determinants of long-run buy-and- hold returns of privatized telecom shares. The results indicate that the long-run performance of privatized telecom IPOs is moderately related to the proxies of policy uncertainty or systematic risk while the size of the firm and some market wide variables such as the accounting standard, origin of commercial law, and the corporate governance scheme significantly affect the stock performance of privatized telecom shares.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.79-86
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• 1997

This study is tested the information effects of convertible bond(CB). In orter to examine the abnormal stock returns of convertable day of CB, this study were selected 134 samples for the period from Jan.1988 to Dec.1994. There are some empirical studies which pesent evidents that CB are converted day of CB. The results of empirical study are summarized as follows. As in korea stock market, abnormal stock returns of CB have influenced on convertable day of CB. The day has some affirmative influences but it takes away stock price pressures, the amount of stock and dilution effects. As the results, related corporate stock price falled in preference to market abnormal returns.

• The Korean Journal of Financial Studies
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• v.2 no.1
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• pp.127-144
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• 1995

Previous literature in financial economics documents the existence of a liquidity premium in expected returns, measured by the bid-ask spread. This study provides a more comprehensive test of the egect of liquidity on common stock returns by including trading volume as an additional liquidity measure. we find that trading volume is a relevant measure of liquidity, and affects expected returns even aher controlling for the effects of systematic risk, firm size, and the relative bid-ask spread. We also find that trading volume complements the bid-ask spread as a liquidity measure, and provides additional information about the liquidity premium. The liquidity effect emerges in non-January months as a volume effect, in addition to the spread effect in January documented by Eleswarapu and Reinganum(1993).

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.512-514
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• 2005

Accurate DEM surface of forest floor is very important to extract any meaningful information regarding forest stand structure, such as tree heights, stand density, crown morphology, and biomass. In airborne lidar data processing, DEM data of forest floor is mostly generated by interpolating those elevation points obtained from last laser returns. In this study, we try to analyze the property of the last laser return under relatively dense forest canopy. Airborne laser data were obtained over the study area in relatively dense pine plantation forest. Two DEM data were generated by using all the points in the last laser returns and using only those points after removing non-ground points. From the preliminary analysis on these DEM data, we found that more than half of points among the last laser returns are actually hit from canopy, branches, and understory vegetation that should be removed before generating the surface DEM data.

• East Asian Economic Review
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• v.23 no.4
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• pp.333-351
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• 2019

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by Inoue and Kilian and applied to the US stock market data by Rapach and Wohar. The empirical findings show that stock returns are predictable not only in-sample but out-of-sample in Hong Kong, Malaysia, Singapore, and Korea with a few exceptions for some forecasting horizons. However, we find some significant disparity between in-sample and out-of-sample predictability in the Korean stock market. For Hong Kong, Malaysia, and Singapore, stock returns have predictable components both in-sample and out-of-sample. For the US, Australia, and Canada, we do not find any evidence of return predictability in-sample and out-of-sample with a few exceptions. For Japan, stock returns have a predictable component with price-earnings ratio as a forecasting variable for some out-of-sample forecasting horizons.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.410-418
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• 2007

This article suggests the methods to investigate adverse movement across global stock markets arising from insolvency of subprime mortgage in U.S. Our application deals with asymptotic tail dependence of daily stock index returns (KOSPI, DJIA, Shanghai Composite) of three countries; Korea, U.S., and China, over specific period via extreme value theory and copula functions. Daily stock index returns among three countries show higher extremal dependence during the period exposed to systematic shock. We confirm that extreme value theory and copula functions have potential to well describe the extreme dependence between three countries' daily stock index returns.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.47-68
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• 1997

This paper discusses a method of modified Elman network(1990) for nonlinear predictions and its a, pp.ication to forecasting daily exchange rate returns. The method consists of two stages that take advantages of both time domain filter and modified feedback networks. The first stage straightforwardly employs the filtering technique to remove extreme noise. In the second stage neural networks are designed to take the feedback from both hidden-layer units and the deviation of outputs from target values during learning. This combined feedback can be exploited to transfer unconsidered information on errors into the network system and, consequently, would improve predictions. The method a, pp.ars to dominate linear ARMA models and standard dynamic neural networks in one-step-ahead forecasting exchange rate returns.