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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

Author

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  • Jaydip SEN

    (Calcutta Business School, India)

  • Tamal DATTA CHAUDHURI

    (Calcutta Business School, India)

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.

Suggested Citation

  • Jaydip SEN & Tamal DATTA CHAUDHURI, 2016. "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," Journal of Economics Library, KSP Journals, vol. 3(2), pages 303-326, June.
    • Handle: RePEc:ksp:journ5:v:3:y:2016:i:2:p:303-326
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    References listed on IDEAS

    as
    1. Jaydip Sen & Tamal Datta Chaudhuri, 2016. "Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector," Papers 1601.02407, arXiv.org.
    2. repec:arx:papers:1604.04044 is not listed on IDEAS
    3. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
    4. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
    5. N/A, 2004. "Index for 2004," European Union Politics, , vol. 5(4), pages 511-512, December.
    6. M. Hanias & P. Curtis & E. Thalassinos, 2012. "Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 23-32.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    2. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    3. Sidra Mehtab & Jaydip Sen & Subhasis Dasgupta, 2020. "Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models," Papers 2011.08011, arXiv.org, revised Jan 2021.
    4. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    5. Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020. "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers 2009.10819, arXiv.org.
    6. Jaydip Sen, 2018. "Stock composition of mutual funds and fund style: a time series decomposition approach towards testing for consistency," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 4(3), pages 235-292.
    7. Jaydip Sen & Aditya Jaiswal & Anshuman Pathak & Atish Kumar Majee & Kushagra Kumar & Manas Kumar Sarkar & Soubhik Maji, 2023. "A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market," Papers 2305.17523, arXiv.org.
    8. Jaydip Sen & Hetvi Waghela & Sneha Rakshit, 2024. "Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning," Papers 2407.01572, arXiv.org.
    9. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries," Papers 2001.09769, arXiv.org.
    10. Tran Phuoc & Pham Thi Kim Anh & Phan Huy Tam & Chien V. Nguyen, 2024. "Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    11. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector," Papers 1705.01144, arXiv.org.
    12. Abhiraj Sen & Jaydip Sen, 2023. "Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks," Papers 2309.13696, arXiv.org.
    13. Ananda Chatterjee & Hrisav Bhowmick & Jaydip Sen, 2021. "Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models," Papers 2111.01137, arXiv.org.
    14. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector: An Application of the R Programming in Time Series Decomposition and Forecasting," Papers 1706.07821, arXiv.org.
    15. Sidra Mehtab & Jaydip Sen, 2020. "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers 2004.11697, arXiv.org, revised May 2021.
    16. Tasnim Uddin Chowdhury & Md. Shahidul Islam, 2021. "ARIMA Time Series Analysis in Forecasting Daily Stock Price of Chittagong Stock Exchange (CSE)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(6), pages 214-233, June.
    17. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers 2104.06259, arXiv.org.
    18. Sidra Mehtab & Jaydip Sen, 2019. "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing," Papers 1912.07700, arXiv.org.
    19. Jaydip Sen & Arup Dasgupta & Partha Pratim Sengupta & Sayantani Roy Choudhury, 2023. "A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market," Papers 2310.14748, arXiv.org.
    20. Jaydip Sen & Saikat Mondal & Sidra Mehtab, 2021. "Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model," Papers 2111.04976, arXiv.org.

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    More about this item

    Keywords

    Time series; Decomposition; ARIMA; BSE Consumer Durables Index; BSE Small Cap Index.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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