An Alternative Framework for Time Series Decomposition and Forecasting and its Relevance for Portfolio Choice: A Comparative Study of the Indian Consumer Durable and Small Cap Sectors

TL;DR

This paper introduces a new framework for decomposing and forecasting stock market time series, focusing on Indian sectors, to improve portfolio decision-making through structural analysis and robust prediction methods.

Contribution

It proposes a novel decomposition approach tailored for sector-specific time series and evaluates multiple forecasting techniques for enhanced prediction accuracy.

Findings

Sector-specific time series exhibit distinct patterns.

The proposed methods improve forecasting accuracy.

Structural analysis aids better portfolio formation.

Abstract

One of the challenging research problems in the domain of time series analysis and forecasting is making efficient and robust prediction of stock market prices. With rapid development and evolution of sophisticated algorithms and with the availability of extremely fast computing platforms, it has now become possible to effectively extract, store, process and analyze high volume stock market time series data. Complex algorithms for forecasting are now available for speedy execution over parallel architecture leading to fairly accurate results. In this paper, we have used time series data of the two sectors of the Indian economy: Consumer Durables sector and the Small Cap sector for the period January 2010 to December 2015 and proposed a decomposition approach for better understanding of the behavior of each of the time series. Our contention is that various sectors reveal different time series patterns and understanding them is essential for portfolio formation. Further, based on this structural analysis, we have also proposed several robust forecasting techniques and analyzed their accuracy in prediction using suitably chosen training and test data sets. Extensive results are presented to demonstrate the effectiveness of our propositions.