• 한국융합학회논문지
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• 제11권8호
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• pp.165-171
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• 2020

텍스트 마이닝을 활용한 주가 방향 예측 연구에서는 대부분 뉴스, SNS 데이터를 사용하고 있다. 하지만 뉴스, SNS 데이터로부터 기업에 대한 솔직하고 생생한 정보는 얻기 어렵다는 약점이 존재한다. 본 논문에서는 실제 근무 경험이 있는 내부 직원의 기업 리뷰를 반영하여, 종업원 만족도를 활용한 주가의 방향성을 예측하는 문제를 다룬다. 머신러닝 모델별 성능평가를 통해 예측 정확도를 비교, 분석한 결과 종업원의 기업 리뷰 데이터를 추가로 이용한 주가 방향 예측 모델은 그렇지 않은 모델 대비 뛰어난 분류 성과를 보였다. 본 연구는 금융 공학에 자연어처리기술을 활용한 융합 연구로서 주가 예측 분야에서 종업원 만족도를 활용한 기존에 없던 새로운 방법론을 추구하였다. 실무적으로 주가 방향 예측 분야에 유용한 정보를 제공할 것으로 기대된다.

• 한국IT서비스학회지
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• 제13권3호
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• 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.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권3호
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• pp.123-128
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• 2019

본 논문에서는 RCNN (recurrent convolution neural network) 계층 모델을 채택한 인공 지능에 기반을 둔 주가 예측을 제안한다. LSTM (long-term memory model) 기반 신경망은 시계열 데이터의 예측에 사용된다. 다른 한편, 컨볼루션 신경망은 데이터 필터링, 평균화 및 데이터 확장을 제공한다. 제안된 주가 예측에서는 위에서 언급 한 장점들을 RCNN 모델에서 결합하여 적용함으로써 다음날의 주가 종가를 예측한다. 그리고 최근의 시계열의 데이터를 강조하기 위해 커스텀 가중치 손실 함수가 채택되었다. 또한 시장의 상황을 반영하기 위해 주가 인덱스에 관련된 데이터를 입력으로 포함하였다. 제안된 주가 예측 방식은 실제 주가를 대상으로 한 실험에서 3.19%로 테스트 오차를 줄였으며, 다른 방법보다 약 19%의 성능 향상을 거둘 수 있었다.

• 한국정보시스템학회지:정보시스템연구
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• 제32권1호
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• pp.179-199
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• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.

• 한국융합학회논문지
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• 제12권7호
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• pp.31-36
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• 2021

중국 우한발 코로나 19 바이러스로 인하여 세계 경제가 침체하여, 미국연방준비제도를 비롯한 대부분 국가에서는 통화량을 늘려 경기를 부양하는 정책을 내놓았다. 주식 투자자들 대부분은 기업에 대한 재무제표 분석이 없이 유명 유튜버의 추천종목이나 지인의 말만 듣고 투자하는 경향이 있어서 주식투자의 손실 가능성이 크다. 따라서, 본 연구에서는 기존 자동매매 조건에서 발전된 인공지능 딥러닝 기법을 이용하여 주가에 영향을 미치는 거시지표를 분석하고 예측하여 주가에 미치는 상관관계를 통한 개별주가예측에 가중치를 부여하고 주가를 예측한다. 또한, 주가는 실시간 증시뉴스에 민감하게 반응하기 때문에 증시뉴스 텍스트 마이닝을 통하여 인공지능으로 예측된 주가에 가중치를 반영하여 더 정확한 주가 예측을 하여 주식 투자자에게 매매의 판단 근거를 제공하여 건전한 주식투자가 되도록 이바지하였다.

• 재무관리연구
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• 제12권2호
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• pp.221-237
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• 1995

When we apply parametric models to the movement of stock prices, we don't know whether they are really correct specifications. In the paper, any prior conditional mean structure is not assumed. By applying the nonparametric model, we see if it better performs (than the random walk model) in terms of out-of-sample prediction. An interesting finding is that the random walk model is still the best. There doesn't seem to exist any form of nonlinearity (not to mention linearity) in stock prices that can be exploitable in terms of point prediction.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.

• 한국융합학회논문지
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• 제11권10호
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• pp.19-24
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• 2020

4차산업혁명의 핵심인 인공지능 기술은 인간의 능력을 뛰어넘어 주식예측에도 적용하고 있으면 예측이 불가능한 것을 딥러닝 기법과 머신러닝을 통하여 지능화된 판단을 내리고 있는 실정이다. 미국의 펀드매니지먼트 회사에서는 증시 에널리스트의 역할을 인공지능이 대신하고 있으며, 이 분야의 연구가 활발히 진행 중에 있다. 본 연구에서는 BLSTM을 이용하여 기존의 LSTM방식의 단방향 예측에서 발생하는 오류를 줄이고, 양방향으로 예측하여 예측에 대한 오류를 줄이고, 주식 가격에 영향을 미치는 거시 지표, 즉 경제성장률, 경제지표, 이자율, 무역수지, 환율, 통화량을 분석한다. 거시 지표 분석 후에 개별 주식에 대한 PBR, BPS, ROE 예측과 가장 주식 가격에 영향을 미치는 외국인, 기관, 연기금 등 매수와 매도 물량을 분석하여 주식의 목표주가를 정확히 예측하여 주식 투자에 도움을 주기 위해 본 연구를 수행했다.