A Virtual Wallet Product Recommender System Based on Collaborative Filtering

Prieto-Torres, David Rodolfo; Galpin, Ixent

doi:10.1007/978-3-030-61702-8_16

Cited by 2 publications

(1 citation statement)

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“…This study showed that ensemble classifier-based recommender systems surpass single classifiers in predicting stock price movements. Prieto-Torres and Galpin [24] used a CF model to build a virtual wallet recommender system for a FinTech company to enhance the user experience on their mobile application. However, this method worked best using a non-personalized approach where the top-N most popular stocks were recommended to each user.…”

Section: Literature Surveymentioning

confidence: 99%

Applications of Machine Learning in Predictive Analysis and Risk Management in Trading

2023

IJIRCST

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The stock market is considered the primary domain of importance in the financial sector where Artificial Intelligence combined with various algorithmic practices empowers investors with data-driven insights, enhancing decision-making, predicting trends, and optimizing risk management for more informed and strategic financial outcomes. This research paper delves into the real-world applications of machine learning and algorithmic trading, observing their historical evolution together and how both of these can go hand in hand to control risk and forecast the movement of a stock or an index and its future. The research is structured to provide comprehensive insights into two major subdomains in the application of AI in algorithmic trading: risk management in equity markets and predictive analysis of stock trends through the application of machine learning models and training the current existing data which is feasible and training them with respect to historical scenarios of various market trends along with various fundamental and technical analysis techniques with the help of various deep learning algorithms. For risk management of a portfolio in finance, various machine learning models can be employed, depending on the specific needs and goals of the portfolio manager or risk analyst and implementing various value-at-risk algorithms along with deep learning techniques in order to assess risk at particular trade position and to manage volatile trades at unprecedented situations. The significance of this research paper lies in its practical applicability, offering real-world solutions to enhance trading strategies and decision-making processes with a focus on mitigating risk and capitalizing on market opportunities and also giving clear insights with respect to the current practical limitations of application of the provided solution and future scope to overcome the same.

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Section: Literature Surveymentioning

confidence: 99%