• 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.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• 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.

• 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.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.18-30
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• 2023

We compared empirically the forecast accuracies of the LSTM model, and the ARIMA model. ARIMA model used auto.arima function. Data used in the model is 100 days. We compared with the forecast results for 50 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as "Samsung Electronics", and "LG Energy", "SK Hynix", "Samsung Bio". The collection period is from June 17, 2022, to January 20, 2023. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were rejected at the significance level of 5%. Graphs and boxplots confirmed the results of the hypothesis tests. The accuracies of ARIMA are higher than those of LSTM for four cases. For closing stock price of Samsung Electronics, the mean difference of error between ARIMA and LSTM is -370.11, which is 0.618% of the average of the closing stock price. For closing stock price of LG Energy, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. For closing stock price of SK Hynix, the mean difference is -830.7269 which is 1.00% of the average of the closing stock price. For closing stock price of Samsung Bio, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. The auto.arima function was used to find the ARIMA model, but other methods are worth considering in future studies. And more efforts are needed to find parameters that provide an optimal model in LSTM.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.99-110
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• 2021

This study aims to examine empirically the industry and economic fundamental factors that affect the stock prices of the leading palm oil company in Indonesia. The dynamics of stock price are analyzed using the autoregressive distribution lag (ARDL) model both for symmetric and asymmetric effects. The data used in this study are monthly data for the period from 2008:01 to 2020:03. In the long run, the company stock price moves in line with the competitor company stock price at the current time. The palm oil price has a positive effect on the stock price. Meanwhile, inflation negatively affects the stock price in the short run. The estimated equilibrium correction coefficient indicates a reasonably quick correction of the distortion of the stock price equilibrium in monthly dynamics. However, fundamental factors have asymmetric effects, especially the response of stock price when these factors decrease rather than increase in the short run. Stock prices that are responsive to declines in fundamental performance should be of particular concern to both investors and management in their strategic decision making. The results of this study will contribute to the enrichment of literature related to stock prices from the viewpoint of economic analysis on firm-level data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.833-843
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• 2021

This research examines the impact of audit quality on the extent to which firm-specific information is integrated with a firm's share price - which is determined inversely using stock price synchronicity. The study sample consists of non-financial companies listed on the Amman Stock Exchange i.e., the Jordanian Stock Market, from 2014-2018. After examining 810 firm-year observations from Jordanian industrial companies listed on the ASE, during the study period, we find that the companies using one of the BIG4 audit firms for auditing have less synchronous and more informative stock prices, suggesting high-quality audit improved governance and reduce information asymmetry between firms' insiders and investors which enhances the capitalization of firm's specific information into the stock price, thus less synchronous and more informative stock return. The findings remain consistent over 2 separate measurements of stock price synchronicity (Market and Industry model and Market Model) and show robustness for fixed effect tests. Our multivariate regression results are also robust after controlling for a number of features at the firm level with potential associations with stock price synchronicity. These include the firm size, leverage, return on assets (ROA), and market to book value (MBV).

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.535-545
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• 2013

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.107-115
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• 2020

This study is concerned with the relationship between firm's ownership structure and the co-movement of the stock return with the market return. Four different types of firm ownership, including managerial ownership, state ownership, foreign ownership, and concentrated ownership, are among the main features of the company's governance mechanism and have been separately documemented in the previous research to understand their impact on stock price synchronicity. We constructed the regression model, using stock price synchronicity as the dependent variable and the above four components of ownership structure as explanantory variables. The pooled OLS, the fixed effects model, and the random effects are employed to investigate the outcome of the study. Data used in the reserch are of public firms listed on the Ho Chi Minh City Stock Exchange (HOSE) during the five-year period term from 2015 to 2019. The data sample contains 235 companies from 10 industries with 1135 observations. The results revealed by the fixed effects model, the large ownership and the managerial ownership are found to have adverse effect on the stock price synchronicity, whereas the foreign ownership model is revealed to have positive influence on the stock return co-movement. The effect of the state ownership on the stock price synchronicity is not confirmed.

• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.26-32
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• 2006

Predictions on stock prices have been a hot issue in stock market as people get more interested in stock investments. Assuming that the stock price is moving by a trend in a specific pattern, we believe that a model can be derived from past data to describe the change of the price. The best model can help predict the future stock price. In this paper, our model derivation is based on automata over temporal data to which the model is explicable. We use Bayesian Information Criterion(BIC) to determine the best number of states of the model. We confirm the validity of Bayesian Information Criterion and apply it to building models over stock price indices. The model derived for predicting daily stock price are compared with real price. The comparisons show the predictions have been found to be successful over the data sets we chose.