Realtime stock prediction using Transformations and Modeling - PRAGATHI, DR Y.V.S.SAI; NARASIMHAM, M.V.S PHANI (The journal of contemporary issues in business and government, 2021)
Real time stock prediction is growing demand with dynamic changing markets. Especially sentiments of the market are going to change the stock price dynamically, giving fund managers challenging scenario when they are investing. This paper proposes stock prediction model aiding stock agents to predict closing prices and current price of the stock. The algorithm models the stock tick and balance data parameters using KPCA Trans-formation and Artificial neural network model. Our stock predictor is cost optimized and end to end delay optimized to achieve real time stock prediction. Stock predictor is architected using Clean architecture and GRPC based microservices which gives the advantage of real time modifications, pluggability with minimum validation costs. Secure and cost-effective data backup features of the cloud will give additional cost advantage to the companies implementing the new stock predictor services.