Journal Browser
Open Access Journal Article

Predictive Analytics in Financial Markets

by James Smith 1,*
1
James Smith
*
Author to whom correspondence should be addressed.
Received: 29 October 2021 / Accepted: 24 November 2020 / Published Online: 30 December 2021

Abstract

Predictive analytics has revolutionized various industries, and the financial market is no exception. This paper explores the application of predictive analytics in the context of financial markets, focusing on the use of advanced statistical models and data mining techniques to forecast market trends, assess investment opportunities, and mitigate potential risks. The study delves into the methodologies employed, such as time-series analysis, machine learning algorithms, and neural networks, highlighting their effectiveness in enhancing decision-making processes. Furthermore, the paper discusses the challenges faced by practitioners, including data quality, model interpretability, and the ethical considerations of algorithmic trading. By examining case studies and empirical evidence, the paper provides insights into the current state of predictive analytics in financial markets and suggests potential future directions for research.


Copyright: © 2021 by Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Cite This Paper
APA Style
Smith, J. (2021). Predictive Analytics in Financial Markets. Management Analytics and Decision, 3(2), 26. doi:10.69610/j.mad.20211230
ACS Style
Smith, J. Predictive Analytics in Financial Markets. Management Analytics and Decision, 2021, 3, 26. doi:10.69610/j.mad.20211230
AMA Style
Smith J. Predictive Analytics in Financial Markets. Management Analytics and Decision; 2021, 3(2):26. doi:10.69610/j.mad.20211230
Chicago/Turabian Style
Smith, James 2021. "Predictive Analytics in Financial Markets" Management Analytics and Decision 3, no.2:26. doi:10.69610/j.mad.20211230

Share and Cite

ACS Style
Smith, J. Predictive Analytics in Financial Markets. Management Analytics and Decision, 2021, 3, 26. doi:10.69610/j.mad.20211230
AMA Style
Smith J. Predictive Analytics in Financial Markets. Management Analytics and Decision; 2021, 3(2):26. doi:10.69610/j.mad.20211230
Chicago/Turabian Style
Smith, James 2021. "Predictive Analytics in Financial Markets" Management Analytics and Decision 3, no.2:26. doi:10.69610/j.mad.20211230
APA style
Smith, J. (2021). Predictive Analytics in Financial Markets. Management Analytics and Decision, 3(2), 26. doi:10.69610/j.mad.20211230

Article Metrics

Article Access Statistics

References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Karimi, H., Kiani, A. A., & Seyed Hosseini, S. M. (2015). A review of predictive analytics in financial markets. Expert Systems with Applications, 42(11), 6255-6273.
  3. Wang, S., Wang, Y., & Wang, Q. (2014). A review of financial market forecasting using machine learning algorithms. Expert Systems with Applications, 41(6), 2985-3001.
  4. Brockwell, P. J., & Davis, R. A. (1996). Time Series Analysis and Its Applications: With R Examples. Springer Science & Business Media.
  5. Granger, C. W. J., & Newbold, P. (1986). Forecasting Economic Time Series. Academic Press.
  6. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (4th ed.). Wiley.
  7. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and Regression Trees. CRC Press.
  8. Python, L., & Skiena, S. (2008). The Algorithm Design Manual. Addison-Wesley.
  9. Bertsekas, D. P. (1995). Neural Network Learning: Theoretical Foundations (Vol. 3). Athena Scientific.
  10. Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Prentice Hall.
  11. Goodfellow, I. J., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  12. Katsuyama, T., Hida, J., & Sone, Y. (2012). High-frequency trading: A review. Journal of Banking & Finance, 36(11), 2890-2905.
  13. Jegadeesh, N., & Titman, S. (1993). Returns to trading and asset pricing biases. The Journal of Finance, 48(1), 3-27.