• Korea Trade Review
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• v.45 no.2
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• pp.229-262
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• 2020

The price-to-earnings ratio (PER) is an important indicator to measure the stock price and profitability of a firm; it is also the most used valuation indicator among investors. When using the PER to compare the investment values of different stocks, these stocks must come from the same sector. This study mainly focuses on the China's listed manufacturing firms. By learning from previous research results and analyzing the current situation, we studied the correlation between the manufacturing sector's PER and its influencing factors from both macro and micro perspectives, the combination of which eventually sheds light on such correlation. Analyzing GDP growth rate data, Manufacturing Purchasing Managers' Index, and other macroeconomic variables from 2008 to 2018, we conclude that these variables jointly have a certain impact on the average PER of the manufacturing sector. We then form panel data based on relevant (2014-2018) data gathered from 317 of China's A-listed manufacturing firms to study the impact of micro-variables on PER. By using Stata and other software to analyze the panel data, we reach the conclusion that the Debt to Asset Ratio, Return on Equity, EPS growth rate, Operating Profit Ratio, Dividend Payout Ratio, and firm size have a significant impact on PER. The Current Ratio, Treasury Stock ratio and Ownership Concentration have no distinct effect on PER. Based on our empirical findings, we design a theoretical model that affects the PER.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.51-58
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• 2024

This paper examines the differences between the Korean and American stock markets using the Kospi and S&P 500 indices and discusses policy implications through them. To this end, in addition to the existing time series analysis method, a deep learning method was used to compare markets, and the comparison was made in terms of stock price forecasting ability and data generation ability. In monthly data, the difference between time series was not large, and in daily data, the difference in terms of stability was weak, and there was no significant difference in predictive power or simulation data generation. As shown in the results of this study, if there is not much difference in market price movement patterns between Korea and the United States, tax benefits for long-term stocks investment will be effective against the side effects of short selling.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Journal of Climate Change Research
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• v.4 no.2
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• pp.77-93
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• 2013

Global warming accelerates both carbon (C) input through increased forest productivity and heterotrophic C emission in forest soils, and a future trend in soil C dynamics is uncertain. In this study, the Korean forest soil carbon model (KFSC model) was applied to 1,467,458 ha of Pinus densiflora forests in Korea to predict future C dynamics under RCP 8.5 climate change scenario (RCP scenario). Korea was divided into 16 administrative regions, and P. densiflora forests in each region were classified into six classes by their stand ages : 1 to 10 (I), 11 to 20 (II), 21 to 30 (III), 31 to 40 (IV), 41 to 50 (V), and 51 to 80-year-old (VI+). The forest of each stand age class in a region was treated as a simulation unit, then future net primary production (NPP), soil respiration (SR) and forest soil C stock of each simulation unit were predicted from the 2012 to 2100 under RCP scenario and constant temperature scenario (CT scenario). As a result, NPP decreased in the initial stage of simulation then increased while SR increased in the initial stage of simulation then decreased in both scenarios. The mean NPP and SR under RCP scenario was 20.2% and 20.0% higher than that under CT scenario, respectively. When the initial age class was I, IV, V or VI+, predicted soil C stock under CT scenario was higher than that under RCP scenario, however, the countertrend was observed when the initial age class was II or III. Also, forests having a lower site index showed a lower soil C stock. It suggested that the impact of temperature on NPP was higher when the forests grow faster. Soil C stock under RCP scenario decreased at the end of simulation, and it might be derived from exponentially increased SR under the higher temperature condition. Thus, the difference in soil C stock under two scenarios will be much larger in the further future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.239-276
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• 2009

The paper investigates the possible relationship between earnings prediction by security analysts and special ownership ties that link security companies those analysts belong to and firms under analysis. "Security analysts" are known best for their role as information producers in stock markets where imperfect information is prevalent and transaction costs are high. In such a market, changes in the fundamental value of a company are not spontaneously reflected in the stock price, and the security analysts actively produce and distribute the relevant information crucial for the price mechanism to operate efficiently. Therefore, securing the fairness and accuracy of information they provide is very important for efficiencyof resource allocation as well as protection of investors who are excluded from the special relationship. Evidence of systematic distortion of information by the special tie naturally calls for regulatory intervention, if found. However, one cannot presuppose the existence of distorted information based on the common ownership between the appraiser and the appraisee. Reputation effect is especially cherished by security firms and among analysts as indispensable intangible asset in the industry, and the incentive to maintain good reputation by providing accurate earnings prediction may overweigh the incentive to offer favorable rating or stock recommendation for the firms that are affiliated by common ownership. This study shares the theme of existing literature concerning the effect of conflict of interests on the accuracy of analyst's predictions. This study, however, focuses on the potential conflict of interest situation that may originate from the Korea-specific ownership structure of large conglomerates. Utilizing an extensive database of analysts' reports provided by WiseFn(R) in Korea, we perform empirical analysis of potential relationship between earnings prediction and common ownership. We first analyzed the prediction bias index which tells how optimistic or friendly the analyst's prediction is compared to the realized earnings. It is shown that there exists no statistically significant relationship between the prediction bias and common ownership. This is a rather surprising result since it is observed that the frequency of positive prediction bias is higher with such ownership tie. Next, we analyzed the prediction accuracy index which shows how accurate the analyst's prediction is compared to the realized earnings regardless of its sign. It is also concluded that there is no significant association between the accuracy ofearnings prediction and special relationship. We interpret the results implying that market discipline based on reputation effect is working in Korean stock market in the sense that security companies do not seem to be influenced by an incentive to offer distorted information on affiliated firms. While many of the existing studies confirm the relationship between the ability of the analystand the accuracy of the analyst's prediction, these factors cannot be controlled in the above analysis due to the lack of relevant data. As an indirect way to examine the possibility that such relationship might have distorted the result, we perform an additional but identical analysis based on a sub-sample consisting only of reports by best analysts. The result also confirms the earlier conclusion that the common ownership structure does not affect the accuracy and bias of earnings prediction by the analyst.

• KDI Journal of Economic Policy
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• v.14 no.4
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• pp.3-26
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• 1992

The pupose of this paper is to construct a price determination model of the Korean economy and to find out the propogation mechanism of monetary and fiscal policies. The model is a small-size macroeconometric model consisted of ten core equations : consumption, investment, exports, imports, consumer price index, wage rate, corporate bond rate, potential GNP, capital stock, and GNP identity. The model is a Keynesian model : consumer price index is determined by markup over costs, and wage rate is expressed by Phillipse curve ralation. Two features of the model, however, distinguish this model from other macroeconometric models of the Korean economy. First of all, the estimation of potential GNP and the capital stock is endogenized as suggested by Haque, Lahiri, and Montiel (1990). This allows us to calculate the level of excess demand, which is defined as the difference between the actual GNP and the potential GNP. Second, interest rate, inflation and wages are all estimated as endogenous variables. Moreover, all quantity variables include price variables as important determinants. For instance, interest rate is an important determinant of consumption and investment. Exports and imports are determined by the real effective exchange rate. These two features make the interactions between excess demand and prices the driving forces of this model. In the model, any shock which affects quantity variable(s) affects excess demand, which in turn affects prices. This strong interaction between prices and quantities makes the model look like a classical model over the long run. That is, increases in money supply, government expenditures, and exchange rate (the price of the U.S. dollar in terms of Korean won) all have expansionery effects on the real GNP in the short run, but prices, wage, and interest rate all increase as a result. Over the long run, higher prices have dampenning effects on output. Therefore the level of real GNP turns out to be not much different from the baseline level ; on the other hand, the rates of inflation, wage and interest rate remain at higher levels.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.225-231
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• 2010

Transaction value of business-to-business(B2B) is composed of various factors for groups and time series. In this paper, we use the panel data for finding various variables and using this we analyse the factors that is major influence to transaction value of business-to-business. For analysis we looked at transaction value of business-to-business of 7 groups such as manufacturing industry, electric, gas and piped water industry, construction industry, retail & wholesale trade, traffic industry, publish, image; broad-casting & telecommunication and information service industry, etc. In our analysis we looked at the transaction value of business-to-business during the period from 2005.01 to 2009.12. We examined the data in relation to the transaction value of cyber shopping mall, company bond, composite stock price index, transaction value of credit card, loaned rate of interest in deposit bank, rate of exchange looking at the factors which determine the transaction value of business-to-business, evidence was produced supporting the hypothesis that there is a significant positive relationship between the transaction value of cyber shopping mall, composite stock price index and loaned rate of interest in deposit bank, rate of exchange. The company bond is negative relationship, transaction value of credit card is positive relationship and they are not significant variables in terms of the transaction value of business-to-business.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• Journal of Korean Society of Forest Science
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• v.90 no.3
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• pp.314-323
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• 2001

This study aimed to clarify the influencing factors of mesopore ratio on a pore geometry of surface soil in coniferous stands as an index of the water retention capacity. Twenty three factors including site conditions and soil properties were analyzed by spss/pc + for the data collected during March to October of 1993. The factors influencing the mesopore ratio(pF2.7) on the surface soil were as follows; macropore ratio(pF1.6), slope, crown-cover rates, thickness of F layer, organic matter contents, and the growing stock. And influencing factor on the ratio of mesopore in the soil surface was correlated with percentage of amount of clay, soil surface, A and B horizon soil hardness shows high negative significance. Also, multiple regression equations for mesopore ratios of surface soil and surface soil hardness, clear length, growing stock, B horizon of soil hardness, organic matter contents show high significance($R^2$; 0.80). In coniferous stands, it is effective in promoting development on the ratio of mesopore that forest practice for enhancing of the water resource retention capacity should be carried out when the crown-cover rates of stands are more than 80 percentages.