• Title/Summary/Keyword: Stock Index

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Uncertainty and Manufacturing Stock Market in Korea

  • Jeon, Ji-Hong
    • The Journal of Industrial Distribution & Business
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    • v.10 no.1
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    • pp.29-37
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    • 2019
  • Purpose - We study the dynamic linkages of the economic policy uncertainty (EPU) in the US on the manufacturing stock market returns in Korea. In detail, we examine the casual link between EPU index in the US and the manufacturing stock indexes in Korea. Research design, data, and methodology - We measure mainly the distribution effect of the US EPU on the manufacturing stock market in Korea of 1990-2017 by the vector error correction model (VECM). Result - In result, we estimate the impact of the US EPU index has significantly a negative response to the manufacturing stock market in Korea such as non-metal stock index, chemical stock index, food stock index, textile·clothes stock index, automobile·shipbuilding stock index, machinery stock index, steel·metal stock index. Also the remaining variables such as electric·electronics stock index, S&P 500, and producer price index in Korea have a negative relationship with US EPU index. Conclusions - We find out that the relationship between EPU index of the US and the manufacturing stock market in Korea has the negative relationships. We determine the EPU of the US has the spillover effect on the industry stock markets in Korea.

Two-Stage Forecasting Using Change-Point Detection and Artificial Neural Networks for Stock Price Index (주가지수예측에서의 변환시점을 반영한 이단계 신경망 예측모형)

  • Oh, Kyong-Joo;Kim, Kyoung-Jae;Han, In-Goo
    • Asia pacific journal of information systems
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    • v.11 no.4
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    • pp.99-111
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    • 2001
  • The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

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Change of Stock Earning Rate on Korean Quality Award Recipients - The comparison between KQA Index and Baldrige Index-

  • Suh, Yung-Ho;Lee, Hyun-Soo
    • International Journal of Quality Innovation
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    • v.1 no.1
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    • pp.106-120
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    • 2000
  • The purpose of this research is to understand the effects of Quality Management Award on stock prices movement and to examine the comparative advantages of quality award system in Korea and the U.S. This study compares the performances of QM Award companies in the stock market with those of the market index in both countries. We develop Korean Quality Award Index(KQA Index) based on the Baldrige Index of NIST in the U.S. We inspect three studies. Study 1 tests if the performances of MB Award winners and S&P500 index have a difference in the stock market. Study 2 tests if the performances of KQA winners and KOSPI(Korean Composite Stock Price Index) have a difference in the stock market. Study 3 tests if the performances of KQA winners and MB Award winners have a difference in the stock market. From the empirical tests, the performances of KQA winners are superior to those of KOSPI and the performances of MB Award winners are superior to those of S&P500 and the performances of MB Award winners are superior to those of KQA winners.

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Two-Stage forecasting Using Change-Point Detection and Artificial Neural Networks for Stock Price Index

  • Oh, Kyong-Joo;Kim, Kyoung-Jae;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.11a
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    • pp.427-436
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    • 2000
  • The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

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Dynamic Relationship between Stock Prices and Exchange Rates: Evidence from Nepal

  • Kim, Do-Hyun;Subedi, Shyam;Chung, Sang-Kuck
    • International Area Studies Review
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    • v.20 no.3
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    • pp.123-144
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    • 2016
  • This paper investigates the linkages between returns both in foreign exchange and stock markets, and uncertainties in two markets using daily data for the period of 16 July 2004 to 30 June 2014 in Nepalese economy. Four hypotheses are tested about how uncertainty influences the stock index and exchange rates. From the empirical results, a bivariate EGARCH-M model is the best to explain the volatility in the two markets. There is a negative relationship from the exchange rates return to stock price return. Empirical results do provide strong empirical confirmation that negative effect of stock index uncertainty and positive effect of exchange rates uncertainty on average stock index. GARCH-in-mean variables in AR modeling are significant and shows that there is positive effect of exchange rates uncertainty and negative effect of stock index uncertainty on average exchange rates. Stock index shocks have longer lived effects on uncertainty in the stock market than exchange rates shock have on uncertainly in the foreign exchange market. The effect of the last period's shock, volatility is more sensitive to its own lagged values.

The Impact of Investor Sentiment on Energy and Stock Markets-Evidence : China and Hong Kong

  • Ho, Liang-Chun
    • Journal of Distribution Science
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    • v.12 no.3
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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.

Determinants and Prediction of the Stock Market during COVID-19: Evidence from Indonesia

  • GOH, Thomas Sumarsan;HENRY, Henry;ALBERT, Albert
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.1
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    • pp.1-6
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    • 2021
  • This research examines the stock market index determinants and the prediction using the FFT curve fitting of the Jakarta Stock Exchange (JKSE) Composite Index during the COVID-19 pandemic. This paper has used daily data of Jakarta Stock Exchange (JKSE) Composite Index, interest rate, and exchange rate from 15 October 2019 to 15 September 2020, and a total of 224 observations, retrieved from Indonesia Stock Exchange (IDX), Indonesia Statistics Central Bureau and Observation & Research of Taxation. The study covers descriptive statistics, multicollinearity test, hypothesis tests, determination test, and prediction using FFT curve fitting. The results unveil four fresh and robust evidence. Partially, the interest rate has affected positively and significantly the stock market index. Partially, the exchange rate has affected negatively and significantly the stock market index. The F-test result, interest rate, and exchange rate have significantly affected the stock market index (JKSE) simultaneously. Furthermore, the FFT curve fitting has predicted that the stock market fluctuates and increases over time. The results have shown a strong influence of the independent variables and the dependent variable. The value of Adjusted R-Square is 0.719, which means that the independent variables have simultaneously impacted the dependent variable for 71.9%; other factors have influenced the remaining 28.1%.

The Impacts of Oil Price and Exchange Rate on Vietnamese Stock Market

  • NGUYEN, Tra Ngoc;NGUYEN, Dat Thanh;NGUYEN, Vu Ngoc
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.8
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    • pp.143-150
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    • 2020
  • This study aims to investigate the effect of oil price and exchange rate on the two Vietnamese stock market indices: VN index and HXN index. This study uses the daily data from August 1st 2000 to October 25th 2019 of the two Vietnamese stock indices: VN index and HNX index, the two oil price indices: BRENT and WTI, and the two exchange rates: US dollar to Vietnamese dong and Euro to Vietnamese dong. Due to the presence of heteroskedasticity in our data, we use GARCH (1,1) regression model to perform our analysis. Our findings show that the oil price has a significant positive effect on the two Vietnamese stock market indices. In terms of the stock index volatility, both the VN index and HNX index volatilities are negatively impacted by the return of oil price. While the conclusion about the impact of oil price remained consistent through all three robustness tests, the effect of exchange rate on Vietnamese stock market indices is not consistent. We find thatchanges of the USD/VND exchange rate significantly impact the return and volatility of HNX index only in GARCH (1,1) setting. Our analysis also survives a number of robustness tests.

An Empirical Analysis on the Relationship between Stock Price, Interest Rate, Price Index and Housing Price using VAR Model (VAR 모형을 이용한 주가, 금리, 물가, 주택가격의 관계에 대한 실증연구)

  • Kim, Jae-Gyeong
    • Journal of Distribution Science
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    • v.11 no.10
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    • pp.63-72
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    • 2013
  • Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

Stock Market Sentiment and Stock Returns

  • Kim, Taehyuk;Ryu, Hoyoung
    • Journal of the Korean Data Analysis Society
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    • v.20 no.6
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    • pp.2759-2769
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    • 2018
  • The behavioral finance view on the existence of asset pricing anomalies is based on two factors: investors' sentiment and limits to arbitrage. This paper tries to examine the effect of investors' sentiment on the stock price in the Korean stock market. In order to measure investors' sentiment, we constructed the sentiment index using principal component of five sentiment variables. By using sentiment index as an additional independent variable to three risk factors, impacts of the sentiment index on individual stocks and 25 portfolios sorted by BM-size are examined. Main results found are as follows: 1) not only all three risk factors show positive impacts on the return of individual stock, but also the sentiment index has a positive impact. SI alone explains 15% of individual return variation. 2) among four independent variables, the most important factor turned out to be the market risk factor and investors' sentiment has better explanatory power on stock price than the size effect. 3) after controlling the market risk factor, the coefficient of the sentiment index for the smallest size and highest book/market value portfolios is significantly positive. 4) all the coefficients of the sentiment index for 25 portfolios sorted by BM-size have significant positive value after controlling size or (and) value.