• 아태비즈니스연구
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• 제14권1호
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• pp.265-279
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• 2023

Purpose - The purpose of this paper is to compare and review behavioral economics models that explain stock price changes after large-scale price shocks in the Korean stock market and to find a suitable model. In this paper, among the theories reviewed, it was confirmed that the anchoring heuristics theory has high explanatory power for stock prices after large-scale stock price fluctuations. Design/methodology/approach - This paper conducts an event study on stock price shocks in which the individual stocks that make up the KOSPI200 index show more than 10% fluctuation on a daily basis. In order to materialize the abstract predictions of heuristics theories in a varifiable form, this paper uses the daily stock price index change as a reference point for availability heuristics, and uses the 52-week highest and lowest price as reference point for anchoring heuristics. Research implications or Originality - As a result of the empirical analysis, the stock price reversals did not consistently appear for changes in the daily index. On the other hand, the stock price drifts consistently appeared around the 52-week highest and the 52-week lowest price. And in the multiple regression analysis that controlled for company-specific and event-specific variables, the results that supported the anchoring heuristics were more evident. These results suggest that it is possible to establish an investment strategy using large-scale price change in Korean stock market.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.125-134
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• 2020

This study examines the nominal price anomaly in the Vietnamese stock market, that is, whether stocks with low nominal price outperform stocks with high nominal price. Using a sample of all 351 companies listed on the Ho Chi Minh Stock Exchange (HOSE) from June 2009 to March 2018, we confirm our hypothesis and document that cheaper stocks yield higher subsequent abnormal returns. The results are robust after controlling for various stock characteristics that have been documented to be value-relevant in prior literature, including firm size, book-to-market ratio, intermediate-term momentum, short-term reversal, skewness, market risk, idiosyncratic risk, illiquidity and extreme daily returns, using both the portfolio analysis and the Fama-MacBeth cross-sectional regression. The negative effect persists in the long term (i.e., after up to 12 months), implying a slow adjustment of stock prices to their intrinsic value. Further analysis show that the observed nominal price anomaly is mainly driven by mispricing but not a latent risk factor proxied by stock price, thus the observed anomaly reflects a mispricing but not a fundamental risk. The study highlights the irrational behaviour of investors and market inefficiency in the Vietnamese stock market and provides important implication for investors in the market.

• The Journal of Asian Finance, Economics and Business
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• 제7권8호
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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 1^st 2000 to October 25^th 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.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• 한국컴퓨터정보학회논문지
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• 제22권11호
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

• Journal of East Asia Management
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• 제3권1호
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• pp.41-55
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• 2022

This study investigates whether foreign investors' behavior is involved in firm-specific investor sentiment. Because the mixed role of foreign investors on investor sentiment formation seems to exist in the Korean stock market, it needs to examine the moderate or incremental effect of foreign investors on the stock price crash risk which is due to investor sentiment. The analysis results using Korea Stock Exchanges - listed firms for the period of 2011-2019 show the increased future stock price crash risk which is attributable to high investor sentiment is mitigated for firms with the high foreign ownership, indicating the moderate effect. This study expands the literature on the foreign investors' behavior in the Korean stock market, by showing foreign investors are not involved in firm-specific investor sentiment, which improves market's efficiency in the Korean stock market. Also, the paper is valuable to the academic and practice field in that the findings shed light on the foreign investors' mitigating role in stock price crashes in the behavioral finance perspective.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.601-611
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• 2021

There are many asset prices which are interlinked and have a bearing on the stock market index. Studies have shown that the interrelationship among these asset prices vary and are inconsistent. The ultimate aim of this study is to examine the dynamic relationship between gold price, oil price, exchange rate and stock index. Monthly time series data has been utilized by the researcher to examine the interrelationship between four variables. The relationship among stock exchange rate index, oil price and gold price have been undertaken using regression and granger causality test. The results indicate that the exchange rate and oil price have an indirect influence on NIFTY; whereas gold price had a direct impact on NIFTY. It is evident from the results that volatility in the price of gold is mainly dependent on the exchange rate and vice versa. All the variables affect NIFTY in some way or the other. However, gold has a direct and vital relationship. From the study findings, it can be concluded that macroeconomic variables like commodity prices and foreign exchange rate, gold and oil, have a strong relationship on the return on securities at the national stock exchange of India.

• 산업경영시스템학회지
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• 제41권3호
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.157-165
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• 2022

As the stock market is affected by various circumstances including economic and political variables, predicting the stock market is considered a still open problem. When combined with corporate financial statement data analysis, which is used as fundamental analysis, and technical analysis with a short data generation cycle, there is a problem that the time domain does not match. Our proposed method, LSTE the operating profit and market outlook of a petrochemical company and estimates the sales and operating profit of the company, it was possible to solve the above-mentioned problems and improve the accuracy of stock price prediction. Extensive experiments on real-world stock data show that our method outperforms the 8.58% relative improvements on average w.r.t. accuracy.

• 유통과학연구
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• 제21권5호
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• pp.101-111
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• 2023

Purpose: The purpose of this paper is to examine the effect of the VKOSPI index on short-term stock returns after a large-scale stock price shock of individual stocks of firms in the distribution industry in Korea. Research design, data, and methodology: This study investigates the effect of the change of the VKOSPI index or investor mood on abnormal returns after the event date from January 2004 to July 2022. The significance of the abnormal return, which is obtained by subtracting the rate of return estimated by the market model from the rate of actual return on each trading day after the event date, is determined based on T-test and multifactor regression analysis. Results: In Korea's distribution industry, the simultaneous occurrence of a bad investor mood and a large stock price decline, leads to stock price reversals. Conversely, the simultaneous occurrence of a good investor mood and a large-scale stock price rise leads to stock price drifts. We found that the VKOSPI index has strong explanatory power for these reversals and drifts even after considering both company-specific and event-specific factors. Conclusions: In Korea's distribution industry-related stock market, investors show an asymmetrical behavioral characteristic of overreacting to negative moods and underreacting to positive moods.