  Designing robust and accurate prediction models has been a viable research
area since a long time. While proponents of a well-functioning market
predictors believe that it is difficult to accurately predict market prices but
many scholars disagree. Robust and accurate prediction systems will not only be
helpful to the businesses but also to the individuals in making their financial
investments. This paper presents an LSTM model with two different input
approaches for predicting the short-term stock prices of two Indian companies,
Reliance Industries and Infosys Ltd. Ten years of historic data (2012-2021) is
taken from the yahoo finance website to carry out analysis of proposed
approaches. In the first approach, closing prices of two selected companies are
directly applied on univariate LSTM model. For the approach second, technical
indicators values are calculated from the closing prices and then collectively
applied on Multivariate LSTM model. Short term market behaviour for upcoming
days is evaluated. Experimental outcomes revel that approach one is useful to
determine the future trend but multivariate LSTM model with technical
indicators found to be useful in accurately predicting the future price
behaviours.
