• Asia pacific journal of information systems
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• 제24권2호
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• 응용통계연구
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• 제33권6호
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• pp.753-762
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• 2020

본 연구는 대한민국 육군이 선도적으로 도입하고자 노력하고 있는 AI 면접체계의 자료를 통합 비교차 다중 분위수 회귀나무 모형(unified non-crossing multiple quantile tree; UNQRT)을 활용하여 분석한 것이다. 분위수 회귀가 일반적인 선형회귀에 비하여 많은 장점을 가지지만, 선형성 가정은 여전히 많은 현실 문제해결에 있어 지나치게 강한 가정이다. 선형성을 완화한 모형의 하나인 기존 나무모형 기반의 분위수 회귀는 추정된 분위수 함수별로 교차하는 문제와 분위수별로 나무모형을 제시하여 해석력을 저하시키는 문제가 있다. 통합 비교차 다중 분위수회귀나무 모형은 비교차 제약식을 부여한 상태로 다중 분위수 함수를 동시에 추정함으로서 분위수 함수의 교차 문제를 해결하며, 극단 분위수에서 안정된 결과를 기대할 수 있고, 하나의 통합된 나무모형을 제시하여 우수한 해석력이 있다. 본 연구에서는 통합 비교차 다중 분위수회귀나무 모형을 활용하여 육군 AI 면접체계의 결과와 기존 인사자료간 관계를 충분히 탐색하여 의미있는 다양한 결과를 도출하였다.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제37권2호
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• pp.233-256
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• 2023

이 연구는 수학교육에서의 ChatGPT의 활용 방안 도출을 위한 기초 연구로서 국가수준 학업성취도 평가 및 대학수학능력시험 문제에 대한 ChatGPT의 응답을 분석하였다. ChatGPT는 생성형 인공지능 모델로서 여러 분야에서 주목 받고 있으며, 교육계에서도 ChatGPT 활용 방안에 대한 요구의 목소리가 높아지고 있다. 이에 이 연구에서는 3개년 국가수준 학업성취도 평가 및 대학수학능력시험 문제에 대한 ChatGPT 3.5의 응답에 대해서 정답률, 풀이 과정의 정확도, 오류 유형을 분류하여 분석하였다. ChatGPT의 국가수준 학업성취도 평가 문제 및 대학수학능력시험 문제의 정답률은 각각 37.1%, 15.97%로 나타났다. ChatGPT의 풀이 과정의 정확도는 5점 만점으로 산출하였을 때, 국가수준 학업성취도 평가는 3.44점, 대학수학능력시험은 2.49점으로 산출되었다. ChatGPT의 수학 문제를 풀이하는 데 나타나는 오류 유형은 절차적 오류와 기능적 오류로 나뉘었다. 절차적 오류는 다음 단계로의 식을 연결 짓는 과정이나 계산상의 오류를 가리키며, 기능적 오류는 ChatGPT가 텍스트를 인식, 판단, 출력하는 과정에서 발생하는 오류였다. 이러한 분석은 정답률만이 ChatGPT의 수학적 성능을 판단하는 기준이 되어서는 안 되며, 풀이 과정의 정확도나 오류유형까지도 복합적으로 고려해야 함을 시사한다.