Document Type
Conference Proceeding
Publication Date
1-1-2023
Journal / Book Title
Epic Series in Computing
Abstract
One of the most difficult analyses of all time is stock market predictions. Expert analysts and software engineers are collaborating to create a stable and reliable platform for predicting future stock value. The fundamental difficulty is that a variety of factors will influence the price fluctuations. Stock recommendations are vital for investment firms and individuals. However, no unique stock selection approach can capture the dynamics of all stocks without adequate analysts. Nonetheless, the majority of extant recommendation techniques are built on prediction algorithms ANN (Artificial Neural Network) to buy and keep high-yielding companies. We offer a unique strategy in this paper that uses reinforcement learning to recommend a stock portfolio based on the Yfinance data sets. We will present an ARIMA framework for recommendation systems, as well as a foundation for determining the system's value. Within this paradigm, we do probabilistic studies of algorithmic approaches. These studies illustrate the value of recalling earlier activities and examines how this recollection may be used.
DOI
10.29007/p27x
Journal ISSN / Book ISBN
85152617818 (Scopus)
Montclair State University Digital Commons Citation
Singh, Jashanjit and Jenq, John, "A Stock Recommendation System" (2023). School of Computing Faculty Scholarship and Creative Works. 63.
https://digitalcommons.montclair.edu/computing-facpubs/63