Abstract
Prediction of future movement of stock prices has been the subject
matter of many research work. On one hand, we have proponents of the
Efficient Market Hypothesis who claim that stock prices cannot be
predicted accurately. On the other hand, there are propositions that
have shown that, if appropriately modelled, stock prices can be
predicted fairly accurately. The latter have focused on choice of
variables, appropriate functional forms and techniques of forecasting.
This work proposes a granular approach to stock price prediction by
combining statistical and machine learning methods with some concepts
that have been advanced in the literature on technical analysis. The
objective of our work is to take 5 minute daily data on stock prices
from the National Stock Exchange (NSE) in India and develop a
forecasting framework for stock prices. Our contention is that such a
granular approach can model the inherent dynamics and can be fine-tuned
for immediate forecasting. Six different techniques including three
regression-based approaches and three classification-based approaches
are applied to model and predict stock price movement of two stocks
listed in NSE - Tata Steel and Hero Moto. Extensive results have been
provided on the performance of these forecasting techniques for both the
stocks.