Neural Networks in Algorithmic Trading for Financial Markets

Authors

  • Raghunath Kashyap Karanam Senior Associate Consultant Cisco, Cisco Systems Inc., 300 East Tasman Dr. San Jose, CA 95134, USA
  • Vineel Mouli Natakam Independent Researcher, USA
  • Narasimha Rao Boinapalli Enterprise Architect, NBC Universal, 904 Sylvan Ave, Englewood Cliffs, NJ 07632, USA
  • Narayana Reddy Bommu Sridharlakshmi SAP Master Data Migration Consultant, BI LABS Inc., 517 US-1 S STE 3060, Iselin, NJ 08830, USA
  • Abhishekar Reddy Allam Sr. Informatica Developer, OPTUM, Minneapolis, MN, USA
  • Pavan Kumar Gade Informatica Developer, Advanced Knowledge Tech LLC, 2502 Crossroads Drive, Ardmore, OK, USA
  • SSMLG Gudimetla Naga Venkata IAM Engineer, HCL Global Systems Inc., 24543 Indoplex Circle, Farmington Hills. Michigan – 48335, USA
  • Hari Priya Kommineni Software Engineer, Hadiamondstar Software Solutions LLC, Fairfax, VA 22031, USA
  • Aditya Manikyala Java Developer, Dynamic Technology Inc., 4335 Premier Plaza, Ashburn, VA 20147

Keywords:

Neural Networks, Algorithmic Trading, Financial Markets, Machine Learning, Deep Learning, Predictive Analytics, Stock Market, Artificial Intelligence, Risk Management, Quantitative Finance

Abstract

This study focuses on how neural networks may enhance financial market algorithmic trading and decision-making. The primary objectives are to evaluate Feedforward, Convolutional, Recurrent, and Deep Reinforcement Learning neural networks in trading applications. The study uses a systematic secondary data evaluation of model literature and performance indicators. CNNs and RNNs excel in pattern recognition and time-series data prediction, improving trading signals and strategy optimization. More data, overfitting, and model interpretability help these models. The research recommends data pretreatment, regularization, and explainable AI to address these issues. Policy consequences include data quality, transparency, and computer resource allocation. To increase financial neural network application, these obstacles must be overcome and favorable rules created. Trading tactics improve and adapt.

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Published

2018-12-31

How to Cite

Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. Retrieved from https://4ajournal.com/article/view/95

Issue

Vol. 9 No. 1 (2018): January - December

Section

Articles

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Copyright (c) 2018 Asian Accounting and Auditing Advancement

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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