  Designing robust frameworks for precise prediction of future prices of stocks
has always been considered a very challenging research problem. The advocates
of the classical efficient market hypothesis affirm that it is impossible to
accurately predict the future prices in an efficiently operating market due to
the stochastic nature of the stock price variables. However, numerous
propositions exist in the literature with varying degrees of sophistication and
complexity that illustrate how algorithms and models can be designed for making
efficient, accurate, and robust predictions of stock prices. We present a gamut
of ten deep learning models of regression for precise and robust prediction of
the future prices of the stock of a critical company in the auto sector of
India. Using a very granular stock price collected at 5 minutes intervals, we
train the models based on the records from 31st Dec, 2012 to 27th Dec, 2013.
The testing of the models is done using records from 30th Dec, 2013 to 9th Jan
2015. We explain the design principles of the models and analyze the results of
their performance based on accuracy in forecasting and speed of execution.
