A Stock Recommendation System

Jashanjit Singh, John Jenq

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)164-171
Number of pages8
JournalEPiC Series in Computing
StatePublished - 2023
Event38th International Conference on Computers and Their Applications, CATA 2023 - Virtual, Online
Duration: 20 Mar 202322 Mar 2023


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