• IE interfaces
• /
• v.13 no.3
• /
• pp.444-454
• /
• 2000

Due to the improvement of modern information technologies, sharing stock information among the supply chain members is a common practice nowadays. Many companies are planning to adopt the information systems to possess the real-time shared stock information. Thus, it is needed to quantify the value of shared stock information. The purpose of this paper is to evaluate the value of the shared stock information for two-echelon distribution systems. Existing reorder policies can be classified into installation stock policies and echelon stock policies. Since installation stock policies do not utilize the shared stock information, and both classes of policies may show poor performances for distribution systems, we cannot evaluate the value of the shared stock information with the existing policies. Thus, we provide a new type of reorder policy, named order risk policy. We define the order risk using marginal analysis, and prove the optimality. Through computational experiment that compares the order risk policy with the existing policies, it is shown that a significant cost reduction is achieved with the effective utilization of the shared stock information. We also show the effect of the system characteristics on the value of the shared stock information.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.4
• /
• pp.1099-1106
• /
• 2006

This study is an analysis of the stock trading value in terms of investor types in the Korean stock market for recent 12 years. We examined the characteristics in stock trading value variation according to each investor type and the interactive relationship in the trading value between types of investors. The results show that the trading value scale of every investor type increases overall while the proportion of the trading value by each investor type in the market exhibits variation. In addition, a statistically significant interactive relationship in the trading value between types of investors exists: the correlations are formed differently before and after events which largely influence the stock market.

• Journal of Distribution Science
• /
• v.21 no.12
• /
• pp.83-89
• /
• 2023

Purpose: Research on spin-off and treasury stock is necessary because the market has realized that this can be utilized for major shareholder private interest. Considering the unique characteristic of a spin-off and treasury stock in the Korean stock market, this study contributes to the literature by examining the effects on shareholder value in the Korean distribution industry. Research design, data, and methodology: The present study investigates literature, analyst reports, and news articles to examine the spin-off process and analyze how treasury stock magic happens. Results: Setting the exchange ratio favoring Spin-Co in the spin-off is the leading cause for reducing the minor shareholders' value. Moreover, treating treasury stock as an asset is also problematic, allowing the allocation of Spin-Co shares. This leads to an increase in the major shareholder controls of Spin-Co without any contribution from the major shareholders. Therefore, the exchange ratio should be calculated reasonably, and treasury stock from the stock repurchase should be treated as stock retirement. Conclusion: By analyzing the spin-off and how treasury stock magic occurs, this study provides recommendations to improve shareholder value. Moreover, it contributes to the maturation of the Korean capital market by promoting a discussion on the revision of spin-off and treasury stock.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.7
• /
• pp.423-431
• /
• 2021

This research (1) examines the effect of stock ownership structure on capital structure; (2) explains the effect of stock ownership structure on corporate value; and (3) investigates the influence of capital structure on corporate value. This research is categorized as a quantitative research, which is directed to test various theories. In this study, the population of all consumption companies listed on the Indonesia Stock Exchange (IDX) consist of 38 companies. Population data in this study are all consumption companies, which have gone public in the period from 2010 to 2015. In this study, given the objectives and problem formulation and hypothesis, the analysis method used is Generalized Structural Component Analysis (GSCA). Ownership structure has a significant effect on capital structure; ownership structure has no significant effect on corporate value; capital structure has a significant effect on corporate value; corporate value has a significant effect on capital structure. Previous research found different results. Some researchers found a positive relationship and other researchers found a negative relationship, and there are studies that found both significant and non-significant effects. The inconsistency of previous research results prompted the researchers to examine the effect of ownership structure on capital structure and corporate value.

• Management & Information Systems Review
• /
• v.27
• /
• pp.31-52
• /
• 2008

Intangible Assets are more important determinants of firm value than others in a digital information-based economy(Lev, 2001). Prior research reveals that investments in intangible assets such as R&D and advertising expenditures are associated with firm value. This paper examines the effects of the corporate investments in the information technologies(IT) on stock return and economic goodwill. The sample consists of 152 firms listed on the Korean stock market in 2002. To test hypothesis We employed multiple regression models. Results are as follows; First, IT environment, IT process, and IT human resource are positively associated with firm's IT value. Second, firm's IT value is positively correlated with firm's economic goodwill. Third, firm's IT value is positively correlated with firm's stock return. These results suggest that the investments related with IT are effective in cultivating firm's value and Stock investors can make the best use of firm's announcements related with IT value. Thus the authorities concerned need to expand the public announcements related IT value.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.8
• /
• pp.69-76
• /
• 2020

The study examines lagged economic effects of research and development (R&D) investment on the market value of manufacturing firms listed on the Shanghai Stock Exchange or the Shenzhen Stock Exchange in China. This study applies panel data analysis methods to address the following issues: 1) There might be an adjustment lag in the impact of R&D investment on corporate market value, and 2) Unobserved firm effects must be taken into account. The balanced panel data includes a total of 1,462 observations with 34 cross-sections of manufacturing firms listed on Chinese stock markets and with 27 time-specific quarterly periods from 2007 to 2017. The results indicate that the R&D investment of Chinese manufacturing firms tends to yield favorable market value of the firm with some adjustments to time. The results show that R&D investment exhibits a strong positive impact on their market value of manufacturing firms in Chinese stock markets. Moreover, R&D investment has a positive time-lag effect on the market value of the firm. Interestingly, the R&D investment of Chinese manufacturing firms generate a relatively constant positive effect on their market value, supporting the notion that the corresponding returns of R&D investment for such firms yield lagged but added market values.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.1
• /
• pp.29-36
• /
• 2020

Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying "the ratio of stock price to a value driver" by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

• The Journal of Information Systems
• /
• v.32 no.1
• /
• pp.179-199
• /
• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.

• Asia pacific journal of information systems
• /
• v.30 no.1
• /
• pp.31-52
• /
• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Journal of Distribution Science
• /
• v.13 no.9
• /
• pp.37-45
• /
• 2015

Purpose - This paper's aim is to investigate whether or not gross profitability explains the cross-sectional variation of the stock returns in the Korean stock market. Gross profitability is an alternative profitability measure proposed by Novy-Marx in 2013 to predict cross-sectional variation of stock returns in the US. He shows that the gross profitability adds explanatory power to the Fama-French 3 factor model. Interestingly, gross profitability is negatively correlated with the book-to-market ratio. By confirming the gross profitability premium in the Korean stock market, we may provide some implications regarding the well-known value premium. In addition, our empirical results may provide opportunities for the fund distribution industry to promote brand new styles of funds. Research design, data, and methodology - For our empirical analysis, we collect monthly market prices of all the companies listed on the Korea Composite Stock Price Index (KOSPI) of the Korea Exchanges (KRX). Our sample period covers July1994 to December2014. The data from the company financial statementsare provided by the financial information company WISEfn. First, using Fama-Macbeth cross-sectional regression, we investigate the relation between gross profitability and stock return performance. For robustness in analyzing the performance of the gross profitability strategy, we consider value weighted portfolio returns as well as equally weighted portfolio returns. Next, using Fama-French 3 factor models, we examine whether or not the gross profitability strategy generates excess returns when firmsize and the book-to-market ratio are controlled. Finally, we analyze the effect of firm size and the book-to-market ratio on the gross profitability strategy. Results - First, through the Fama-MacBeth cross-sectional regression, we show that gross profitability has almost the same explanatory power as the book-to-market ratio in explaining the cross-sectional variation of the Korean stock market. Second, we find evidence that gross profitability is a statistically significant variable for explaining cross-sectional stock returns when the size and the value effect are controlled. Third, we show that gross profitability, which is positively correlated with stock returns and firm size, is negatively correlated with the book-to-market ratio. From the perspective of portfolio management, our results imply that since the gross profitability strategy is a distinctive growth strategy, value strategies can be improved by hedging with the gross profitability strategy. Conclusions - Our empirical results confirm the existence of a gross profitability premium in the Korean stock market. From the perspective of the fund distribution industry, the gross profitability portfolio is worthy of attention. Since the value strategy portfolio returns are negatively correlated with the gross profitability strategy portfolio returns, by mixing both portfolios, investors could be better off without additional risk. However, the profitable firms are dissimilar from the value firms (high book-to-market ratio firms); therefore, an alternative factor model including gross profitability may help us understand the economic implications of the well-known anomalies such as value premium, momentum, and low volatility. We reserve these topics for future research.