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Modern methods of data mining: Applicability in the securities market

Vol. 28, Iss. 5, MAY 2022

PDF  Article PDF Version

Received: 9 March 2022

Received in revised form: 23 March 2022

Accepted: 6 April 2022

Available online: 30 May 2022

Subject Heading: Securities market

JEL Classification: C45, C53, C88, G11, G12

Pages: 1178–1196

https://doi.org/10.24891/fc.28.5.1178

Aleksei S. DMITRIEV Financial University under Government of Russian Federation, Moscow, Russian Federation
alex.inc2015@yandex.ru

ORCID id: not available

Subject. The article addresses data mining methods, their applicability in the securities market.
Objectives. The aim is to identify the specifics of using modern data mining methods in the stock market, outline basic tenets for building a combined data mining model.
Methods. The study rests on logical and systems approaches, general scientific methods of analysis and synthesis, and comparative analysis.
Results. Based on the analysis of existing works and models, the paper unveils the specifics of using modern methods of data mining in the stock market, defines the main postulates for building a data mining model. The findings can be used by financial market participants, State authorities, and research and educational organizations.
Conclusions. Today, data mining methods are an alternative to the traditional portfolio analysis and management methods, being a logical continuation of them due to the ability to work with a large amount of diverse information and use approaches that overcome the shortcomings and limitations of other methods. To build a model of data mining to evaluate assets and portfolio management in the securities market, it is necessary to combine several methods of different models, combine the fundamental and technical analysis, and create systems for identifying asset volatility.

Keywords: intelligent data analysis, securities market, econometric model, portfolio management, machine learning

References:

  1. López-Robles J.R., Rodríguez-Salvador M., Gamboa-Rosales N.K. et al. The Last Five Years of Big Data Research in Economics, Econometrics and Finance: Identification and Conceptual Analysis. Procedia Computer Science, 2019, vol. 162, pp. 729–736. URL: Link
  2. Hayakawa K. Recent development of covariance structure analysis in economics. Econometrics and Statistics, 2021. URL: Link
  3. Henrique B.M., Sobreiro V.A., Kimura H. Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 2019, vol. 124, pp. 226–251. URL: Link
  4. Berzon N.I., Buyanova E.A., Gazman V.D. et al. Innovatsii na finansovykh rynkakh [Innovations in financial markets]. Moscow, HSE Publ., 2013, 424 p.
  5. Semenkova E.V., Andrianova L.N., Krinichansky K.V. The Concept of Fair Pricing in the Regulation Framework of the Russian Securities Market. Journal of Reviews on Global Economics, 2018, vol. 7, pp. 562–571.
  6. Krinichanskii K.V., Bezrukov A.V. [Some Practical Problems of the Portfolio Optimization Model]. Zhurnal ekonomicheskoi teorii = Journal of Economic Theory, 2012, no. 3, pp. 142–147. URL: Link (In Russ.)
  7. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 2019, vol. 1, pp. 206–215. URL: Link
  8. Zolfaghari M., Gholami S. A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction. Expert Systems with Applications, 2021, vol. 182. URL: Link
  9. Pagach D.P., Warr R.S. Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited. Advances in Accounting, 2020, vol. 51, no. 100497. URL: Link
  10. Floros C., Gkillas K., Konstantatos C., Tsagkanos A. Realized Measures to Explain Volatility Changes over Time. Journal of Risk and Financial Management, 2020, vol. 13, iss. 6, p. 125. URL: Link
  11. Christina Dan Wang, Zhao Chen, Yimin Lian, Min Chen. Asset selection based on high frequency Sharpe ratio. Journal of Econometrics, 2022, vol. 227, iss. 1, pp. 168–188. URL: Link
  12. Zheng J., Wang Y., Li S., Chen H. The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm. Algorithms, 2021, vol. 14, iss. 10, p. 299. URL: Link
  13. Barak S., Arjmand A., Ortobelli S. Fusion of multiple diverse predictors in stock market. Information Fusion, 2017, vol. 36, pp. 90–102. URL: Link
  14. Vijh M., Chandola D., Tikkiwal V.A., Kumar A. Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 2020, vol. 167, pp. 599–606. URL: Link
  15. Ghosh P., Neufeld A., Sahoo J.K. Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Finance Research Letters, 2022, vol. 46, part A, no. 102280. URL: Link
  16. Chen Y., Liu K., Xie Y., Hu M. Financial Trading Strategy System Based on Machine Learning. Mathematical Problems in Engineering, 2020, pp. 10–23. URL: Link
  17. Kumar A., Lei Z., Zhang C. Dividend sentiment, catering incentives, and return predictability. Journal of Corporate Finance, 2022, vol. 72, no. 102128. URL: Link
  18. Chang J., Tu W., Yu C., Qin C. Assessing dynamic qualities of investor sentiments for stock recommendation. Information Processing &Management, 2021, vol. 58, iss. 2, no. 102452. URL: Link
  19. Hung H.-C., Chuang Y.-J., Wu M.-C. Customizable and committee data mining framework for stock trading. Applied Soft Computing, 2021, vol. 105, no. 102277. URL: Link
  20. Koratamaddi P., Wadhwani K., Gupta M., Sanjeevi S. Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Engineering Science and Technology, an International Journal, 2021, vol. 24, iss. 4, pp. 848–859. URL: Link
  21. Ahmadi E., Jasemi M., Monplaisir L. et al. New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic. Expert Systems with Applications, 2018, vol. 94, pp. 21–31. URL: Link

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