A Predictive Analysis of the Indian FMCG Sector using Time Series Decomposition - Based Approach

Abstract. Stock price movements being random in its nature, prediction of stock prices using time series analysis presents a very difficult and challenging problem to the research community. However, over the last decade, due to rapid development and evolution of sophisticated algorithms for complex statistical analysis of large volume of time series data, and availability of high-performance hardware and parallel computing architecture, it has become possible to efficiently process and effectively analyze voluminous and highly diverse stock market time series data effectively, in real-time. Robust predictive models are being built for accurate forecasting of values of highly random variables such as stock price movements. This paper has presented a highly reliable and accurate forecasting framework for predicting the time series index values of the fast moving consumer goods (FMCG) sector in India. A time series decomposition approach is followed to understand the behavior of the FMCG sector time series for the period January 2010 till December 2016. Based on the structural analysis of the time series, six methods of forecast are designed. These methods are applied to predict the time series index values for the months of 2016. Extensive results are presented to demonstrate the effectiveness ofthe proposed decomposition approaches of time series and the efficiency of the six forecasting methods.Keywords. Time series decomposition, Trend, Seasonal, Random, Holt Winters Forecasting model, Auto Regression (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA), Partial Auto Correlation Function (PACF), Auto Correlation Function (ACF).JEL. G11, G14, G17, C63

Can portfolio returns exceed market return? An examination of the efficient market hypothesis for the Indian stock market

Abctract. The paper explores the possibility of forming portfolio of stocks that can generate returns higher than the market over a time period. Various principles are used for portfolio formation in the year 2013, and it is examined whether such portfolios have been able to generate excess returns over the next five years. Data has been used for Indian companies which are listed in the National Stock Exchange and Bombay Stock Exchange. Further, our sample consist of companies that have in operation over this period, have earned profits each year, and have consistently paid dividends in each of the years. The period under consideration has seen upswings and downswings, and it is our interest to explore whether our portfolios have been able to generate excess returns. Our results provide interesting insight into portfolio formation and also structuring of mutual funds.Keywords. Portfolio, Price/earnings ratio, PEG ratio, Dividend yield, Net profit margin, Excess returns.JEL. G11, G14, G23, G24

A wavelet approach towards examining dynamic association, causality and spillovers

This paper presents an integrated granular framework of wavelet decomposition, DCC-GARCH, ADCC-GARCH, Diks-Panchenko nonlinear Granger’s causality and Diebold-Yilmaz spillover assessment techniques to understand temporal correlation, causal interplay and spillovers among volatile financial time series data exhibiting nonparametric behavior. The exercise has been carried out on daily closing observations of eight financial time series. Wavelet decomposition has been used to generate time varying components in which the other research models are applied to extract the interactive pattern of interaction to ascertain short and long run nexus. The findings rationalize the effectiveness of the presented research framework

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

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 - 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.Keywords. Time series, Decomposition, ARIMA, BSE Consumer Durables Index, BSE Small Cap Index.JEL. G11, G14, G17, C63