Given the complexity and multifaceted nature of the financial markets, effective aggregation of financial time series data underscores the optimization of predictive modeling in the finance industry. This research presents an innovative approach to clustering using auto encoders designed to distill informative representations out of S&P 500 financial time series data. Our particular methodology is horizontal (stock averages) and vertical (1-hour intraday frequency) dual-dimensional, which enables us to capture temporal patterns along with contextual richness. Comparative research demonstrates auto encoder-driven clustering enhances data quality and granularity, providing actionable understanding of market behavior. These implications applied to predictive modeling would also be considered under risk management, incipient investment strategies, or just pure advanced financial analyses.
Fama EF. Efficient capital markets: A review of theory and empirical work. The journal of Finance. 1970 May 1;25(2):383-417.
2.
Thomas KP, Rajini DG. Evolution of Sustainable Finance and its Opportunities: A Bibliometric Analysis. Indian Journal of Information Sources and Services. 2024;14(2):126-32.
3.
Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18.
4.
Jamithireddy NS. Integrating Decentralized Finance (DeFi) Protocols into SAP Systems for Automated Payment Processing. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2025;16(2):346–66.
5.
Kingma D, Welling M. Auto-encoding variational bayes. 2013 Dec 20.
6.
Bamal S, Singh L. Detecting Conjunctival Hyperemia Using an Effective Machine Learning based Method. Journal of Internet Services and Information Security. 2024;14(4):499-510.
7.
Bessembinder H, Chan K. The profitability of technical trading rules in the Asian stock markets. Pacific-basin finance journal. 1995 Jul 1;3(2-3):257-84.
8.
Mokoena G, Nilsson J. A sophisticated cybersecurity intrusion identification model using deep learning. International Academic Journal of Science and Engineering. 2023;10(3):17-21.
9.
Khandani AE, Kim AJ, Lo AW. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance. 2010 Nov 1;34(11):2767-87.
10.
Almaliki OJ, Al-saedi MO. The Impact of the Qualitative Peculiarities of Accounting Information Based on the Financial Reports of Commercial Banks. International Academic Journal of Social Sciences. 2023;10(1):49-56.
11.
Zhou L, Li F. A survey of clustering techniques in financial time series analysis. Journal of Financial Technology. 2028;5(3):123–140.
12.
Kapoor SI, Menon R. Assessing the Impact of Microfinance on Entrepreneurship in Developing Economies. International Academic Journal of Innovative Research. 2025;12(2):20-5.
13.
Maaten L, Hinton G. Visualizing data using t-SNE. Journal of machine learning research. 2008;9(Nov):2579-605.
14.
Shlens J. A tutorial on principal component analysis. 2014 Apr 3.
15.
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. science. 2006 Jul 28;313(5786):504-7.
16.
Cheng W, Zhang Z. Deep learning-based clustering for financial market data analysis. Financial Engineering and Risk Management. 2020;6(1):31-45.
17.
Xie L, Li Y. Using deep learning for anomaly detection in financial transactions. Journal of Financial Risk Management. 2017;15(4):219-234.
18.
Yao L, Zhang X. A hybrid clustering approach to portfolio optimization using temporal and contextual data. Computational Finance Journal. 2019;3(2):59-75.
19.
Liu Z, Li J. Enhancing stock market prediction with multi-dimensional clustering and deep learning. Journal of Quantitative Finance. 2021;7(2):150-165.
20.
Chen L, Tang L. Exploring the role of volatility and volume in clustering financial time series. International Journal of Financial Studies. 2018;6(3):92-10.
21.
Zhang X, Li Y. A comprehensive review of clustering techniques in financial time series analysis. Journal of Financial Analytics. 2022;34(2):123-140.
22.
Aggarwal CC. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter. 2013 Apr 30;14(2):49-58.
23.
Bengio Y. Learning deep architectures for AI. Foundations and trends® in Machine Learning. 2009 Nov 14;2(1):1-27.
24.
Jain A k, Dubes R c. Algorithms for clustering data. Prentice-Hall, Inc.; 1988 Jul 1.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
