금융 분야의 기계학습 모형 활용 추이
Trends of Machine Learning Application in Finance
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초록
본 연구는 금융 분야에서 활용되는 기계학습 모형을 설명하고, 기존연구를 활용 분야별로 체계적으로 정리한다. 금융자산의 가격예측, 시장 이상현상의 탐지, 자산투자, 포트폴리오 관리에 기계학습 모형을 활용한 다양한 연구가 이루어지고 있다. 기계학습 모형은 거시경제변수, 금융자산의 가격정보 등 기존 금융 분야에서 주로 사용한 데이터뿐만 아니라, 이미지와 텍스트 데이터 등 새로운 형태의 데이터를 이용한 연구가 가능하게 했다. 하지만, 기계학습 모형의 빠른 발전에 비해 금융경제학과 금융공학 분야에서 기계학습 최신모형을 활용하는 연구는 적극적으로 이루어지지 않고 있다. 반면 인공지능 기계학습 분야에서는 최신모형의 도입은 빠르게 이루어지나, 경제학과 금융시장에 대한 깊은 이해 없이 기계학습 모형을 적용한 연구가 많다. 기계학습 모형을 활용한 연구는 계속 증가하고 있으며, 양 분야에 대한 폭넓은 이해를 바탕으로 한 통합연구가 필요하다. 본 문헌조사 연구는 전통적인 계량경제학 모형이 아닌 기계학습 모형을 활용해 새로운 연구를 진행하는 재무금융 연구자에게 참고자료가 될 것이다.
Abstract
This study mainly explains the use of machine learning (ML) models in financial research and systematically categorizes literature based on their application domains. Diverse research utilizing ML models covers areas such as price prediction, market anomaly detection, and portfolio management. ML enables researchers to analyze not only traditional financial data like macroeconomic variables and historical asset prices but also new types of data such as images and text data. Despite the rapid advancements observed in the ML field, the application of state-of-the-art ML models is not actively pursued in the traditional finance community. Other academic communities, such as applied mathematics, statistics, and computer science, demonstrate a rapid adoption of cutting-edge ML models without considering economic implications. As the field of ML continues to grow, there is a demand for research that provides a comprehensive understanding of both ML and the financial sector. Consequently, this research can serve as a reference for interdisciplinary researchers applying ML models in the field of finance.
참고문헌 (115)
- 김이환, "기계학습 방법론을 활용한 아파트 매매가격지수 연구", 부동산분석, 8(3), pp.1~29 (2022), 한국부동산원[DOI]
- 김태영, "기계학습과 설명가능한 인공지능 모형을 이용한 강남의 전세가격 결정요인 분석", 주택연구, 31(3), pp.127~154 (2023), 한국주택학회[DOI]
- 김형준, "기업부도예측과 기계학습", 金融工學硏究, 18(3), pp.131~152 (2019), 한국금융공학회[DOI]
- 방정석, "ESG 논란과 주식 수익률: 자연어 처리의 활용", 경영학연구, 51(6), pp.1739~1764 (2022), 한국경영학회[DOI]
- 류두진, "기계학습 기반 기업신용정보 분석을 통한 채무불이행 예측", 재무연구, 34(4), pp.199~234 (2021), 한국재무학회
- 송민찬, "중소기업 신용보강을 위한 딥러닝 기업 건전성 분류", 金融工學硏究, 21(2), pp.103~122 (2022), 한국금융공학회[DOI]
- 이주화, "뉴스", 소셜 미디어 텍스트와 투자자 기대, 2021, pp.50(2), 533~555[DOI]
- Aggarwal, A., "Deep learning approach to determine the impact of socio economic factors on bitcoin price prediction", 2019 Twelfth International Conference on Contemporary Computing (IC3), pp.1~5 (2019)
- Ahmed, S., "Artificial intelligence and machine learning in finance : A bibliometric review", Research in International Business and Finance, 61, pp.101646~ (2022)
- Aljawazneh, H., "Comparing the performance of deep learning methods to predict companies’ financial failure", IEEE Access, 9, pp.97010~97038 (2021)
- Avramov, D., "Machine learning vs. economic restrictions : Evidence from stock return predictability", Management Science, 69(5), pp.2587~2619 (2022)
- Bao, W., "A deep learning framework for financial time series using stacked autoencoders and long-short term memory", PloS one, 12(7), pp.e0180944~ (2017)
- Bezerra, P. C. S., "Volatility forecasting via SVR-GARCH with mixture of Gaussian kernels", Computational Management Science, 14, pp.179~196 (2017)
- Breiman, L., "Random forests", Machine Learning, 45, pp.5~32 (2001)
- Brown, T. B., "Language models are few-shot learners", Advances in Neural Information P rocessing Systems, 33, pp.1877~1901 (2020)
- Butler, K. T., "Machine learning for molecular and materials science", Nature, 559, pp.547~555 (2018)
- Business news and business cycles, "Bybee, L.", National Bureau of Economic Research, ~, pp.National Bureau of Economic Research (No. w29344) (2021)
- Cao, J., "A neural network approach to understanding implied volatility movements", Quantitative Finance, 20(9), pp.1405~1413 (2020)
- Cao, Q., "A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market", Computers & Operations Research, 32(10), pp.2499~2512 (2005)
- Carleo, G., "Machine learning and the physical sciences", Reviews of Modern Physics, 91(4), pp.045002~ (2019)
- Chatzis, S. P., "Forecasting stock market crisis events using deep and statistical machine learning techniques", Expert Systems with Applications, 112, pp.353~371 (2018)
- Chen, T., "XGboost: A scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785~794 (2016)
- Cho, K., "Learning phrase representations using RNN encoder-decoder for statistical machine translation", Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1724~1734 (2014)
- Cochrane, J. H., "Presidential address: Discount rates", Journal of Finance, 66(4), pp.1047~1108 (2011)
- Cortes, C., "Support-vector networks", Machine Learning, 20, pp.273~297 (1995)
- Craja, P., "Deep learning for detecting financial statement fraud", Decision Support Systems, 139, pp.113421~ (2020)
- Cui, T., "Portfolio constructions in cryptocurrency market : A CVaR-based deep reinforcement learning approach", Economic Modelling, 119, pp.106078~ (2023)
- Cutler, A., "Ensemble Machine Learning" (2012), Springer
- Danel, T., "Spatial graph convolutional networks", International Conference on Neural Information Processing, 5, pp.668~675 (2020)
- Deng, H., "Ensemble learning for the early prediction of neonatal jaundice with genetic features", BMC Medical Informatics and Decision Making, 21, pp.338~ (2021)
- Ding, Q., "Hierarchical multi-scale gaussian transformer for stock movement prediction", IJCAI, pp.4640~4646 (2020)
- Feng, F., "Temporal relational ranking for stock prediction", ACM Transactions on Information Systems(TOIS), 37(2), pp.1~30 (2019)
- Göçken, M., "Integrating metaheuristics and artificial neural networks for improved stock price prediction", Expert Systems with Applications, 44, pp.320~331 (2016)
- Gómez, J. A., "End-to-end neural network architecture for fraud scoring in card payments", Pattern Recognition Letters, 105, pp.175~181 (2018)
- Goodell, J. W., "Artificial intelligence and machine learning in finance : Identifying foundations, themes, and research clusters from bibliometric analysis", Journal of Behavioral and Experimental Finance, 32, pp.100577~ (2021)
- Goodfellow, I., "Generative adversarial networks", Communications of The ACM, 63(11), pp.139~144 (2020)
- Goodfellow, I., "Deep learning" (2016), MIT press
- Gu, S., "Empirical asset pricing via machine learning", Review of Financial Studies, 33(5), pp.2223~2273 (2020)
- Heaton, J. B., "Deep learning for finance : Deep portfolios", Applied Stochastic Models in Business and Industry, 33(1), pp.3~12 (2017)
- Henrique, B. M., "Building direct citation networks", Scientometrics, 115, pp.817~832 (2018)
- Henrique, B. M., "Literature review : Machine learning techniques applied to financial market prediction", Expert Systems with Applications, 124, pp.226~251 (2019)
- Hinton, G. E., "Neural Networks: Tricks of The Trade Second", pp.599~619 (2012)
- Hinton, G. E., "A fast learning algorithm for deep belief nets", Neural computation, 18(7), pp.1527~1554 (2006)
- Ho, T. K., "Random decision forests", Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, pp.278~282 (1995)
- Hochreiter, S., "Long short-term memory", Neural Computation, 9(8), pp.1735~1780 (1997)
- Hosaka, T., "Bankruptcy prediction using imaged financial ratios and convolutional neural networks", Expert Systems with Applications, 117, pp.287~299 (2019)
- Hull, J., "Optimal delta hedging for options", Journal of Banking & Finance, 82, pp.180~190 (2017)
- Hutchinson, J. M., "A nonparametric approach to pricing and hedging derivative securities via learning networks", Journal of Finance, 49(3), pp.851~889 (1994)
- Israel, R., "Can machines ‘learn’ finance?", Journal of Investment Management, 18(2) (2020)
- Janiesch, C., "Machine learning and deep learning", Electronic Markets, 31(3), pp.685~695 (2021)
- ( e-) Imag (in) ing price trends, "Jiang, J.", Chicago Booth, ~, pp.Chicago Booth Research Paper, (21-01) (2020)
- Jiang, Z., "Cryptocurrency portfolio management with deep reinforcement learning", 2017 Intelligent Systems Conference (IntelliSys), pp.905~913 (2017)
- Kajtazi, A., "The role of bitcoin in well diversified portfolios : A comparative global study", International Review of Financial Analysis, 61, pp.143~157 (2019)
- Kara, Y., "Predicting direction of stock price index movement using artificial neural networks and support vector machines : The sample of the Istanbul Stock Exchange", Expert Systems with Applications, 38(5), pp.5311~5319 (2011)
- Ke, G., "LightGBM: A highly efficient gradient boosting decision tree", Advances in Neural Information P rocessing Systems, 30 (2017)
- Financial machine learning, "Kelly, B. T.", National Bureau of Economic Research, ~, pp.National Bureau of Economic Research, (No. w31502) (2023)
- Kim, H. Y., "Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models", Expert Systems with Applications, 103, pp.25~37 (2018)
- Kim, H., "Corporate bankruptcy prediction using machine learning methodologies with a focus on sequential data", Computational Economics, 59(3), pp.1231~1249 (2022)
- Kim, K., "Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index", Expert systems with Applications, 19(2), pp.125~132 (2000)
- Kim, K. H., "Predicting the success of bank telemarketing using deep convolutional neural network", 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCP aR), pp.314~317 (2015)
- Kim, T., "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data", PloS one, 14(2), pp.e0212320~ (2019)
- Kipf, T. N., "Semi-supervised classification with graph convolutional networks" (2016)
- Krizhevsky, A., "Imagenet classification with deep convolutional neural networks", Advances in Neural Information Processing Systems, 25 (2012)
- Lahmiri, S., "Cryptocurrency forecasting with deep learning chaotic neural networks", Chaos, Solitons & Fractals, 118, pp.35~40 (2019)
- LeCun, Y., "Deep learning", Nature, 521(7553), pp.436~444 (2015)
- Lee, S. I., "Threshold-based portfolio : The role of the threshold and its applications", Journal of Supercomputing, 76(10), pp.8040~8057 (2020)
- Liang, Z., "Adversarial deep reinforcement learning in portfolio management" (2018)
- Libbrecht, M. W., "Machine learning applications in genetics and genomics", Nature Reviews Genetics, 16(6), pp.321~332 (2015)
- Lin, C. M., "Recurrent neural network for dynamic portfolio selection", Applied Mathematics and Computation, 175(2), pp.1139~1146 (2006)
- Liu, W., "Portfolio diversification across cryptocurrencies", Finance Research Letters, 29, pp.200~205 (2019)
- López de Prado, M., "Machine learning for econometricians : The readme manual", Journal of Financial Data Science, 4(3), pp.10~30 (2022)
- Lu, W., "A CNN-LSTM-based model to forecast stock prices", Complexity, 2020, pp.1~10 (2020)
- Ma, X., "Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning", Electronic Commerce Research and Applications, 31, pp.24~39 (2018)
- MacKinlay, A. C., "Event studies in economics and finance", Journal of Economic Literature, 35(1), pp.13~39 (1997)
- Man, X., "Financial sentiment analysis(fsa): A survey", 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), pp.617~622 (2019)
- McCulloch, W. S., "A logical calculus of the ideas immanent in nervous activity", Bulletin of Mathematical Biophysics, 5, pp.115~133 (1943)
- McNally, S., "Predicting the price of bitcoin using machine learning", 2018 26th Euromicro Conference on Parallel, Distributed and Network-Based P rocessing (PDP ), pp.339~343 (2018)
- Meng, T. L., "Reinforcement learning in financial markets", Data, 4(3), pp.110~ (2019)
- Moghar, A., "Stock market prediction using LSTM recurrent neural network", Procedia Computer Science, 170, pp.1168~1173 (2020)
- Moro, S., "A data-driven approach to predict the success of bank telemarketing", Decision Support Systems, 62, pp.22~31 (2014)
- Murphy, K. P., "Machine learning: A probabilistic perspective" (2012), MIT press
- Ozbayoglu, A. M., "Deep learning for financial applications : A survey", Applied Soft Computing, 93, pp.106384~ (2020)
- Park, D., "A machine learning-based early warning system for the housing and stock markets", IEEE Access, 9, pp.85566~85572 (2021)
- Parmar, N., "Image transformer", International Conference on Machine Learning, pp.4055~4064 (2018)
- Patel, M. M., "A deep learning-based cryptocurrency price prediction scheme for financial institutions", Journal of Information Security and Applications, 55, pp.102583~ (2020)
- Peng, K., "A survey on deep learning for financial risk prediction", Quantitative Finance and Economics, 5(4), pp.716~737 (2021)
- Pricope, T. V., "Deep reinforcement learning in quantitative algorithmic trading : A review" (2021)
- Pritchard, A., "Statistical bibliography or bibliometrics", Journal of Documentation, 25, pp.348~ (1969)
- Prokhorenkova, L., "CatBoost:Unbiased boosting with categorical features", Advances in Neural Information Processing Systems, 31 (2018)
- Pumsirirat, A., "Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine", International Journal of Advanced Computer Science and Applications, 9(1) (2018)
- Qu, Y., "Review of bankruptcy prediction using machine learning and deep learning techniques", Procedia Computer Science, 16, pp.895~899 (2019)[DOI]
- Quinlan, J. R., "Induction of decision trees", Machine Learning, 1, pp.81~106 (1986)
- Radford, A., "Improving language understanding by generative pre-training", OpenAI, ~ (2018)
- Radford, A., "Unsupervised representation learning with deep convolutional generative adversarial networks" (2015)
- Reed, R., "Pruning algorithms-a survey", IEEE Transactions on Neural Networks, 4(5), pp.740~747 (1993)
- Reiser, P., "Graph neural networks for materials science and chemistry", Communications Materials, 3(1), pp.93~ (2022)
- Rumelhart, D. E., "Learning representations by back-propagating errors", Nature, 323(6088), pp.533~536 (1986)
- Rundo, F., "Machine Learning for Quantitative Finance Applications : A Survey", Applied Sciences, 9(24), pp.5574~ (2019)
- Sagi, O., "Ensemble learning: A survey", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), pp.e1249~ (2018)
- Sarker, I. H., "Deep learning : A comprehensive overview on techniques, taxonomy, applications and research directions", SN Computer Science, 2, pp.420~ (2021)
- Scarselli, F., "The graph neural network model", IEEE Transactions on Neural Networks, 20(1), pp.61~80 (2008)
- Selvin, S., "Stock price prediction using LSTM, RNN and CNN-sliding window model", 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.1643~1647 (2017)
- Shahvaroughi Farahani, M., "Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models", Soft Computing, 25(13), pp.8483~8513 (2021)
- Silver, D., "Mastering the game of Go without human knowledge", Nature, 550(7676), pp.354~359 (2017)
- Song, Y. Y., "Decision tree methods : Applications for classification and prediction", Shanghai Archives of Psychiatry, 27(2), pp.130~135 (2015)
- Applying deep learning to enhance momentumtrading strategies in stocks, "Takeuchi, L.", Stanford University, ~ (2013)
- Vaswani, A., "Attention is all you need", Proceedings of The 31st International Conference on Neural Information Processing System, 6000-6010 (2017)
- Vo, N. N. Y., "Deep learning for decision making and the optimization of socially responsible investments and portfolio", Decision Support Systems, 124, pp.113097~ (2019)
- Wang, J., "A review on graph neural network methods in financial applications", 15367 (2021)
- Wang, Y., "Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud", Decision Support Systems, 105, pp.87~95 (2018)
- Xu, B., "Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional randomfield", Proceedings of The AAAI Conference on Artificial Intelligence, 35(5), pp.4537~4545 (2021)
- Ying, J. J. C., "A preliminary study on deep learning for predicting social insurance payment behavior", 2017 IEEE International Conference on Big Data (Big Data), pp.1866~1875 (2017)
- Zhou, L., "Performance of corporate bankruptcy prediction models on imbalanced dataset : The effect of sampling methods", Knowledge-Based Systems, 41, pp.16~25 (2013)
- Zhu, B., "A hybrid deep learning model for consumer credit scoring", 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pp.205~208 (2018)
- Zoabi, Y., "Machine learning-based prediction of COVID-19 diagnosis based on symptoms", npj Digital Medicine, 4, pp.3~ (2021)
