Financial Marketing in E-Commerce: An Effective Approach to Intelligent Investment Strategies
Keywords:
Investors, Machine learning, Stock market, Forecasting, Investment strategiesAbstract
This study explores the application of machine learning in predicting stock market prices, focusing on how techniques like neural networks and regression improve forecasting accuracy. By analyzing market patterns and trends, the proposed model enhances investment decision-making. Experimental results indicate that machine learning outperforms traditional methods, offering investors better predictive insights [1],[2]. These advancements contribute to more effective risk management and strategic financial planning in volatile markets [3].
References
S. Gupta and R. Kumar, “Financial marketing strategies for e-commerce businesses,” Journal of E-Commerce and Financial Markets, vol. 28, no. 2, pp. 123-135, Apr. 2020.
A. Johnson and P. Sharma, "Leveraging machine learning in e-commerce investment decisions," in Proc. 12th Int. Conf. on Financial Technologies and Innovations, Berlin, Germany, 2021, pp. 198-205.
R. Lee, “Investment strategies in the digital economy: A financial marketing approach,” International Journal of Digital Marketing, vol. 15, no. 1, pp. 78-94, Jan. 2021.
J. White and L. Patel, “Artificial intelligence in investment strategies for e-commerce,” Journal of Financial Innovation, vol. 7, no. 3, pp. 45-59, Mar. 2022.
H. Zhang, “Data-driven investment strategies in e-commerce platforms,” International Journal of Business and Technology, vol. 22, pp. 110-125, Jun. 2021.
M. Brown and T. Davis, “The impact of financial marketing on e-commerce investment decisions,” Financial Markets Review, vol. 36, pp. 211-225, Oct. 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 2024
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
