• 경영과학
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• 제32권3호
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• pp.91-103
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• 2015

This study recognizes that there is a correlation between the movement of the financial market and the sentimental changes of the public participating directly or indirectly in the market, and applies the relationship to investment strategies for stock market. The concerns that market participants have about the economy can be transformed to the search terms that internet users query on search engines, and search volume of a specific term over time can be understood as the economic trend of big data. Under the hypothesis that the time when the economic concerns start increasing precedes the decline in the stock market price and vice versa, this study proposes three investment strategies using casuality between price of domestic stock market and search volume from Naver trends, and verifies the hypothesis. The computational results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior in domestic stock market.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

• The Journal of Asian Finance, Economics and Business
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• 제8권11호
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• pp.11-19
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• 2021

The aim of this paper is to evaluate the impact of the COVID-19 pandemic on the stock prices of the companies traded on the UAE financial markets (Abu Dhabi Securities Exchange and Dubai Financial Market). The time series regressions have been applied to estimate the impact of COVID-19 data on the companies' stock prices movements. The data cover the period between January 29^th, 2020, and January 5^th, 2021. The data was collected from the website of the Federal Competitiveness and Statistics Centre of the UAE. The empirical results of this study show that the stock prices are negatively and significantly affected by the number of COVID-19 positive cases and the number of death while they are positively and significantly affected by the number of recoveries. The results vary from one industry to another. These results would be important to the policymakers and financial regulators in developing the needed policies to improve the stock markets' resilience and maintain financial and economic stability. In addition, the findings would be useful to the investors and portfolio managers in taking the most appropriate investment decisions and managing more efficiently their portfolios. This paper will shed light on the responsiveness of the UAE financial market to the COVID-19 pandemic.

• 산업경영시스템학회지
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• 제41권4호
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• pp.81-90
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• 2018

Modern investment theory has empirically proved that stock returns can be explained by several factors such as market risk, firm size, and book-to-market ratio. Other unknown factors affecting stock returns are also believed to still exist yet to be found. We believe that one of such factors is the operational efficiency of firms in transforming inputs to outputs, considering the fact that operations is a fundamental and primary function of any type of businesses. To support this belief, this study intends to empirically study the relationship between firm efficiency and stock price performance. Firm efficiency is measured using data envelopment analysis (DEA) with inputs and outputs obtained from financial statements. We employ cross-efficiency evaluation to enhance the discrimination power of DEA with a secondary objective function of aggressive formulation. Using the CAPM-based performance regression model, we test the performance of equally weighted portfolios of different sizes selected based upon DEA cross-efficiency scores along with a buy & hold trading strategy. For the empirical test, we collect financial data of domestic firms listed in KOSPI over the period of 2000~2016 from well-known financial databases. As a result, we find that the porfolios with highly efficient firms included outperform the benchmark market portfolio after controlling for the market risk, which indicates that firm efficiency plays a important role in explaining stock returns.

• The Journal of Asian Finance, Economics and Business
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• 제9권7호
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• pp.53-62
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• 2022

Commercial banks have a significant impact on the economy of Vietnam because they provide the majority of transactional capital. Therefore, the operational efficiency of commercial banks is a viral topic for the study of the Vietnamese banking system. The research aims to examine the efficiency and inefficiency of joint-stock commercial banks in Vietnam from 2016 to 2020 and then classify them into the efficient group and inefficient group. The study employs the time series data of 29 joint-stock commercial banks during the period 2016-2020. Based on the data collected from the annual audited financial statements of 29 Vietnamese joint-stock commercial banks, the authors select input and output variables for the standard DEA models and anti-efficient DEA models. This research uses two stages, first, by applying the standard DEA model, we investigate the efficient banks; second, by employing the anti-efficient DEA model, we find out the inefficient banks. The results reveal that the average efficiency score of 29 joint-stock commercial banks tends to increase in the period 2016-2018 and decrease gradually in the period 2019-2020. The findings of this study suggest that several small and medium-sized banks in the Vietnamese banking sector have both promising and risky performances and the efficiency of state-owned commercial banks has also improved significantly during the study period.

• 국제지역연구
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• 제20권3호
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제27권3호
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• 한국기후변화학회지
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• 제7권4호
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• pp.485-492
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• 2016

For reporting national greenhouse gas inventory in forest sector, the forest growing stock from the National Forest Inventory (NFI) system has used as activity data sources. The National Forest Inventory system was changed from rotation system by province to annual system by 5 years across the country. The forest growing stocks based on the new inventory system produced a different trend compared to the previous estimations. This study was implemented to recalculate previous forest growing stocks for time series consistency at a national level. The recalculation of forest growing stock was conducted in an overlap approach by the IPCC guideline. In order to support the more consistency data, we used calibration factors between applied stand volumes in 1985 and 2012, respectively. As a result, the time series of recalculated forest growing stock was to be consistency using the overlap approach and the calibration factor with the lower middle/middle site index. According to the applied overlap period, however, we will recalculate activity data using more complete data from national forest inventory system.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.363-369
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• 2024

In this paper, we develope a LOB(Limit Order Book) analyzing tool for an automated trading system, which features real-time and offline analysis of LOB data in conjunction with execution data. The 10-tier LOB data analyzer developed in this paper, which contains ask/bid prices and the execution data, receivs transaction requests in real-time from the Kiwoom Open API+ server. In the OnReceiveTrData event, the transaction data from the server is received and processed. The real-time data, triggered by the transaction, is received and processed in the OnReceiveRealData event. These two types of data are stored in a database and replayed in the same way as if it were a real-time situation in simulation mode. The LOB data are selectively read and analyzed in a necessary time points. The tool provides various features such as bar chart analysis and pattern analysis of the total shares on the bid side and ask side, which are used to develop a tool to accurately determine the timing of stock trading.

• 아태비즈니스연구
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• 제14권1호
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• pp.73-91
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• 2023

Purpose - The purpose of this study is to re-examine the disclosure effect of stock splits and long-term performance after stock splits using stock split data over the past 10 years, and infer the motivation (signal or opportunism) of stock splits. In addition, we focus on exploring the determinants of the short- and long-term market response to stock splits. Design/methodology/approach - We measure the short-term market response to a stock split and the long-term stock performance after the stock split announcement using the event study method. We analyze whether there is a difference in the long-term and short-term market response to a stock split according to various company characteristics through univariate analysis and regression analysis. Findings - In the case of the entire sample, a statistically significant positive excess return is observed on the stock split announcement date, and the excess return during the 24-month holding period after the stock split do not show a difference from zero. In particular, the difference between short-term and long-term returns on stock splits is larger in companies with a large stock split ratio, small companies, large growth potential, and companies with a combination of financial events after a stock split. Research implications or Originality - The results of this study suggest that at least the signal hypothesis for a stock split does not hold in the Korean stock market. On the other hand, it suggests that there is a possibility that a stock split can be abused by the manager's opportunistic motive, and that this opportunism can be discriminated depending on the size of the stock split, corporate characteristics, and financing plan.