A study on stock price prediction through analysis of sales growth performance and macro-indicators using artificial intelligence
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Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.
Abstract
Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.
참고문헌 (16)
- P. D. Easton, "PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital", The accounting review, 79(1), pp.73~95 (2004)
- 김민수, "인터넷 검색추세를 활용한 빅데이터 기반의 주식투자전략에 대한 연구", 한국경영과학회지, 38(4), pp.53~63 (2013), 한국경영과학회[DOI]
- Y. S. Kim, "News and stock prices : Intelligent investment decision-making model through sentiment analysis of big data", Intelligence Information Research, 18(2), pp.143~156 (2012)[DOI]
- T. N. Sahu, "An empirical study on the dynamic relationship between oil prices and Indian stock market", Managerial Finance (2014)
- H. Bessembinder, "A comparison of trade execution costs for NYSE and NASDAQ-listed stocks", Journal of Financial and Quantitative Analysis, pp.287~310 (1997)
- 곽재현, "빅데이터를 활용한 욜로(YOLO) 현상 분석: 여행소비를 중심으로", 관광연구저널, 32(2), pp.21~34 (2018), 한국관광연구학회[DOI]
- 정지선, "온라인 언급이 기업 성과에 미치는 영향 분석 : 뉴스 감성분석을 통한 기업별 주가 예측", 지능정보연구, 21(4), pp.37~51 (2015), 한국지능정보시스템학회
- J. C. Jeon, "Analysis of development and use cases of expert systems for stock investment", Journal of the Korean Intelligent Information Systems Society, pp.163~175 (1995)
- S. H. Jang, "A.I Design Strategy for Investment Judgment Support Aid", 62~65 (2018)
- 기정훈, "빅데이터 분석을 활용한 도시통계 표현 연구 : 체르노프 얼굴을 활용한 우리나라 광역자치단체의 지역사회건강지표의 표현을 중심으로", 공간과 사회, 26(1), pp.336~358 (2016), 한국공간환경학회[DOI]
- 최현철, "빅 데이터 환경과 인문학적 플랫폼", 문화와 융합, 39(5), pp.177~202 (2017), 한국문화융합학회[DOI]
- S. H. Lee, "Sentiment analysis in big data", Financial Engineering Research, 12(2), pp.79~96 (2013)
- 송성환, "트레이딩을 위한 소셜 빅데이터 분석 모델", 예술인문사회 융합 멀티미디어 논문지, 6(3), pp.91~100 (2016), 사단법인 인문사회과학기술융합학회[DOI]
- J. M. Perkel, "Why Jupyter is data scientists'computational notebook of choice", Nature, 563(7732), pp.145~147 (2018)
- M. Abadi, "Tensorflow: A system for large-scale machine learning", 12th {USENIX}symposium on operating systems design and implementation ({OSDI} 16), pp.265~283 (2016)
- 홍성혁, "Research on Stock price prediction system based on BLSTM", 한국융합학회논문지, 11(10), pp.19~24 (2020), 한국융합학회[DOI]