• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.1113-1119
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• 2021

This study aims to determine the impact of the news coverage of the COVID-19 pandemic on the composite stocks' movement (IHSG) in Indonesia. This study used secondary data of daily time series with an observation range of March 2020-June 2020. This study used three main variables, namely, COVID-19 news, the daily price of a composite stock market index (IHSG), and interest rate. This study clarifies pandemic news into two forms to facilitate quantitative analysis, namely, good news and bad news. Both pandemic news conditions, which have been clarified, are then processed into the index and reprocessed along with two other variables using vector autoregressive (VAR). The results showed that the good news have a dominant effect on developing the composite stock price index (IHSG) in Indonesia during the COVID-19 pandemic. Although the good news dominates the composite stock price index (IHSG) movement in Indonesia, the bad news must also be anticipated. By implementing a series of macroeconomic policies that follow the conditions of the composite stock price index (IHSG) movements on the stock exchange floor, the bad news response can decrease the potential for a decline in investor confidence, so that the financial system's macroeconomic stability is maintained.

• 유통과학연구
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• 제12권3호
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• pp.75-83
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• 2014

Purpose - The oil price affects company value, which is the present value of the expected cash flow, by affecting the discount rate and cash flow. This study examines the nonlinear relationships between oil price and stock price using the AlphaShares Chinese Volatility Index as the threshold. Research design, data, and methodology - Data comprise daily closing values of the Shanghai Stock Exchange Composite Index, Shenzhen Stock Exchange Composite Index, and Hang Seng Index of ChinaWest Texas Intermediate crude oil spot price and AlphaShares Chinese Volatility Index from May 25, 2007 to May 24, 2012. The Threshold Error Correction Model is used. Results - The results demonstrate different relationships between the stock price index and oil price under different investor sentiments; however, the stock price index and oil price could adjust to a long-term equilibrium the long-term causality tests between them were all significant. Conclusions - The relationship between the WTI and HANG SENG Index is more significant than the Shanghai Composites Index and Shenzhen Composite Index, when using the AlphaShares Chinese Volatility Index (ASC-VIX) as the investor sentiment variable and threshold.

• 유통과학연구
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• 제17권1호
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• pp.11-19
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• 2019

Purpose - We document the impact of economic policy uncertainty (EPU) in the US and China on the dynamic spillover effect of macroeconomics such as stock price, housing price in Korea. Research design, data, and methodology - We use the nine variables to analyze the effect which produces a result among the EPU indexes of the US and China on economic variables which is the consumer price index (CPI), housing purchase price composite index, housing lease price, the stock price index in banking industry, construction industry and distribution industry, and composite leading indicator from January 1995 to December 2016 with the Vector Error Correction Model. Result - The US EPU index has significantly a negative relation on the CPI, housing purchase price index, housing lease price index, the stock price index in banking industry, construction industry, and distribution industry in Korea. Conclusions - We find the dynamic effect of the EPU indexes in the US and China on the macroeconomics returns in Korea. This study has an empirical evidence that the economy market in Korea is influenced by the EPU index of the US rather than it of China. The higher EPU, the more risky the economy of in Korea.

• The Journal of Asian Finance, Economics and Business
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• 제7권1호
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• pp.29-36
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• 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.

• 유통과학연구
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• 제14권1호
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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.

• 지능정보연구
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• 제1권2호
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• pp.103-121
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• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• 디지털산업정보학회논문지
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• 제6권4호
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• pp.297-305
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• 2010

Employment number by areas 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 analyzed the factors that is major influence to employment number by areas. For analysis we looked at employment number by areas, the region for analysis consist of seven groups, that is, the metropolitan city(such as Busan, Daegu, Incheon, Gwangiu, Daejeon, Ulsan.) and Seoul. Analyzing period be formed over a 63 time points(2005.01.- 2010.03). We examined the data in relation to the employment number by occupational job, unemployment rate, monthly household income, preceding business composite index, consumer price index, composite stock price index. In looking at the factors which determine employment number by areas job, evidence was produced supporting the hypothesis that there is a significant negative relationship between unemployment rate and monthly household income the consumer price index. The consumer price index and composite stock price index are significant positive relationship, preceding business composite index is positive relationship, it are not significant variables in terms of employment number by areas job.

• 한국건설관리학회논문집
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• 제11권5호
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• pp.41-52
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• 2010

리츠는 주식시장에 상장되어 있으면서 부동산 개발을 위한 자금조달의 성격과 부동산에 투자하는 특징도 있으므로, 주식 시장과 건설 및 부동산시장과 관계가 있을 것으로 예상할 수 있다. 본 연구에서는 리츠와 주식시장, 건설 및 부동산 경기와 관계된 지표들을 시계열 분석하여, 리츠와의 영향관계를 분석하였다. 시계열 분석은 백터자기회귀모형과 백터오차수정모형을 사용하였으며, 다음의 세 부분으로 분류하여 분석하였다. 첫째, 리츠와 건설 코스피 지수와의 관계를 분석한 결과, 건설 코스피 지수가 리츠에 영향을 주는 것으로 분석되었다. 둘째, 리츠와 건설경기 동행지수인 건축착공면적, 부동산 경기 지수인 오피스 임대지수와 주택매매가격지수와의 관계를 분석하였다. 각 지표들은 서로 인과관계는 없는 것으로 분석되었지만, 리츠와 주택매매가격지수는 서로에게 영향을 주는 것으로 분석되었다. 셋째, 리츠와 건설경기 선행지수인 건축허가면적의 관계를 분석하였다. 두 지표는 서로 인과관계가 없는 것으로 분석되었지만, 건축허가면적이 리츠에 영향을 미치는 것으로 분석되었다. 본 연구를 통해 리츠는 주식시장과 주택경기, 건설경기 선행지표인 건축허가 면적에 영향을 받지만, 건설경기 동행지표인 건축착공면적과 오피스 임대지수에는 상대적으로 영향을 작게 받는 것으로 분석되었다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.29-32
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• 2000

We proposed a neural network based “left shoulder”detector. The auto-associative neural network was trained with the “left shoulder”patterns obtained from the Korea Composite Stock Price Index, and then tested out-of-sample with a reasonably good result. A hypothetical investment strategy based on the detector achieved a return of 132% in comparison with 39% return from a buy and hold strategy

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.