• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.393-394
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• 2022

본 논문에서는 과학기술정보통신부와 통계청에서 주관하고 한국산업인력공단에서 시행(한국데이터산업진흥원 위탁)하는 「빅데이터분석기사」에 대한 필기 및 실기 시험의 내용을 설명하고 지금까지 2회에 걸쳐 시행된 시험에 대한 문제점과 이에 대한 해결방안을 제시하였다. 2021년 처음 시행된 국가기술자격으로써 기존 자격증과의 차별성, 난이도 조정, 수험생들의 각종 민원 발생 등의 문제를 해결하기 위한 체계적인 시스템 마련이 요구되며, 향후 데이터 과학자들에 대한 수요 급증에 대비하기 위해 빅데이터분석 실무 능력을 평가하기 위한 바람직한 제도와 정책이 병행되어야 한다.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.41-48
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• 2019

The Management Discussion and Analysis(MD&A) provides investors with an opportunity to gain insight into the company from a manager's perspective and enables short-term and long-term analysis of the business. And MD&A is an important channel through which companies and investors can communicate, providing a useful source of information for analyzing financialstatements. MD&A is measured by the quality of disclosure and there are many previous studies on the usefulness of disclosure information. Therefore, it is very important for the financial analyst who is the representative information user group in the capital market that MD&A Disclosure Quality is measured in real-time in combination with IT information technology and provided timely to financial analyst. In this study, we propose a method that real-time data is converted to digitalized data by combining MD&A disclosure with IT information technology and provided to financial analyst's information environment in real-time. The real-time information provided by MD&A can help the financial analysts' activities and reduce information asymmetry.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.45-54
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• 2019

This study examines whether auditors restrain the analysts' opportunistic behavior as reviewing the companies' interim reports. Analysts' forecasts show a walkdown pattern in which their optimism has decreased as the earnings announcement date has approached. At the beginning of the year, there is a lack of high-quality benchmark information that enables information users to judge the accuracy of analyst's earnings forecasts. Thus, early in the year, analysts are highly inspired to disseminate optimistic forecasts in order to gain manager's favor. In this study, we examine adequate benchmarks prevent analysts from disclosing optimistically biased forecasts. We conjecture that auditors' efforts might mitigate analysts' walkdown pattern. To test this hypothesis, we use data from Korea, where it is mandatory to disclose auditor's review hours. We find that the analyst forecast's walkdown decreases with the ratio as well as the number of audit hours. It implies that an auditor's effort in reviewing interim financial information has a monitoring function that reduces analysts' opportunistic optimism at the beginning of the year. We conjecture that the tendency will be more pronounced when BIG4 auditors review the interim reports. Consistent with the prediction, BIG4 auditors' interim review effort is more effective in suppressing the analysts' walkdown.

• International journal of advanced smart convergence
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• v.7 no.3
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• pp.73-78
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• 2018

This study focuses on presenting the IT program module provided by BKLS measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. Barron at al(1998) set up a BKLS measure to guide the market by intermediate analysts. The BKLS measure was measured by using the changes in the analyst forecast dispersion and analyst mean forecast error squared. This study suggests a model of the algorithm that the BKLS measure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market as measured. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine. Because BKLS measure is not carried out in a concrete method, it is practically very difficult to estimate the BKLS measure. It is expected that the BKLS measure of Barron at al(1998) introduced in this study and the model of IT module provided in real time will be the starting point for the follow-up study for the introduction and realization of IT technology in the future.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.86-94
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• 2018

This study focuses on presenting the IT program module provided by real - time forecasting and database of the voluntary disclosure quality measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. This study suggests a model of the algorithm that the quality of real - time voluntary disclosure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.193-200
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• 2019

Accruals-Quality(AQ) is an important proxy for evaluating the quality of accounting information disclosures. High-quality accounting information will provide high predictability and precision in the disclosure of earnings and will increase the response to stock prices. And high Accruals-Quality, such as mitigating heterogeneity in accounting information interpretation, provides information usefulness in capital markets. The purpose of this study is to suggest how AQ, which represents the quality of accounting information disclosure, is transformed into digitized data in real-time in combination with IT information technology and provided to financial analyst's information environment in real-time. And AQ is a framework for predictive analysis through big data log analysis system. This real-time information from AQ will help financial analysts to increase their activity and reduce information asymmetry. In addition, AQ, which is provided in real time through IT information technology, can be used as an important basis for decision-making by users of capital market information, and is expected to contribute in providing companies with incentives to voluntarily improve the quality of accounting information disclosure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.19-24
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• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.