IDEAS home Printed from https://ideas.repec.org/a/sae/manlab/v46y2021i3p337-353.html
   My bibliography  Save this article

Selection of Variables in Data Envelopment Analysis for Evaluation of Stock Performance

Author

Listed:
  • B. Senthil Arasu
  • Desti Kannaiah
  • Nancy Christina J.
  • Malik Shahzad Shabbir

Abstract

This study deploys data envelopment analysis (DEA) to identify the appropriate variables for the performance valuation of stocks. For this purpose, sixty-nine non-financial stocks of the Nifty 100 index of The National Stock Exchange of India Ltd (NSE) were selected as a sample for this study. We segregated the selected stocks into three groups of inputs and outputs for DEA based on fundamental indicators (financial ratios); technical indicators (momentum indicators); and both, fundamental and technical indicators. The stock performance indicators are sourced from the ACE database from financial year 2014 to 2019. The results of the study suggest that all three sets of stock performance indicators help in the identification of efficient stocks. However, stocks identified under momentum indicators are seen to have been better performing in stock return compared to the other two groups. The outcome of this study may help academicians and investors construct an effective portfolio and analyse/study its performance evaluation

Suggested Citation

  • B. Senthil Arasu & Desti Kannaiah & Nancy Christina J. & Malik Shahzad Shabbir, 2021. "Selection of Variables in Data Envelopment Analysis for Evaluation of Stock Performance," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 46(3), pages 337-353, August.
    • Handle: RePEc:sae:manlab:v:46:y:2021:i:3:p:337-353
    DOI: 10.1177/0258042X211002511
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0258042X211002511
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0258042X211002511?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. D'Inverno, Giovanna & Carosi, Laura & Ravagli, Letizia, 2018. "Global public spending efficiency in Tuscan municipalities," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 102-113.
    2. Hong-Yi Chen & Cheng Few Lee & Wei-Kang Shih, 2020. "Technical, Fundamental, and Combined Information for Separating Winners from Losers," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 95, pages 3319-3365, World Scientific Publishing Co. Pte. Ltd..
    3. Lim, Sungmook & Oh, Kwang Wuk & Zhu, Joe, 2014. "Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market," European Journal of Operational Research, Elsevier, vol. 236(1), pages 361-368.
    4. Newton da Costa, Jr. & Marcus Lima & Edgar Lanzer & Ana Lopes, 2008. "DEA investment strategy in the Brazilian stock market," Economics Bulletin, AccessEcon, vol. 13(2), pages 1-10.
    5. Masoud Ahmadzade & Safar Fazli & Davod Khosroanjom & Reza Kiani Mavi, 2011. "Utilising data envelopment analysis for selecting stock and benchmark firms in Tehran stock exchange," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 12(4), pages 446-463.
    6. Jenni L. Bettman & Stephen J. Sault & Emma L. Schultz, 2009. "Fundamental and technical analysis: substitutes or complements?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(1), pages 21-36, March.
    7. repec:ebl:ecbull:v:13:y:2008:i:2:p:1-10 is not listed on IDEAS
    8. Treynor, Jack L & Ferguson, Robert, 1985. "In Defense of Technical Analysis," Journal of Finance, American Finance Association, vol. 40(3), pages 757-773, July.
    9. Joe Zhu, 2014. "DEA Cross Efficiency," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 4, pages 61-92, Springer.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    11. Cristina Abad & Sten A. Thore & Joaquina Laffarga, 2004. "Fundamental analysis of stocks by two-stage DEA," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 25(5), pages 231-241.
    12. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    14. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    15. Martin Eling, 2006. "Performance measurement of hedge funds using data envelopment analysis," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(4), pages 442-471, December.
    16. Hokey Min & Hyesung Min & Seong Jong Joo & Joungman Kim, 2008. "A Data Envelopment Analysis for establishing the financial benchmark of Korean hotels," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 4(2), pages 201-217.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    2. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
    3. Nuno Ferreira & Adriano Mendonça Souza, 2015. "Efficiency in Stock Markets with DEA: Evidence from PSI20," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 861-861.
    4. Mohammad Mehdi Hosseinzadeh & Sergio Ortobelli Lozza & Farhad Hosseinzadeh Lotfi & Vittorio Moriggia, 2023. "Portfolio optimization with asset preselection using data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 287-310, March.
    5. Jarmila Horváthová & Martina Mokrišová, 2018. "Risk of Bankruptcy, Its Determinants and Models," Risks, MDPI, vol. 6(4), pages 1-22, October.
    6. J. Francisco Rubio & Neal Maroney & M. Kabir Hassan, 2018. "Can Efficiency of Returns Be Considered as a Pricing Factor?," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 25-54, June.
    7. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
    8. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, December.
    9. David Vidal-Tomás & Ana M. Ibáñez & José E. Farinós, 2021. "The Effect of the Launch of Bitcoin Futures on the Cryptocurrency Market: An Economic Efficiency Approach," Mathematics, MDPI, vol. 9(4), pages 1-14, February.
    10. Alireza Namdari & Tariq S. Durrani, 2021. "A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.
    11. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    12. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    13. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    14. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    15. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    16. Poledrini Simone & Searing Elizabeth A. M. & Montrone Alessandro, 2022. "A Model for Directing and Modulating Public Interventions in Social Enterprises," Nonprofit Policy Forum, De Gruyter, vol. 13(4), pages 307-332, October.
    17. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.
    18. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    19. Saara Tamminen, 2017. "Regional effects or none? Firms' profitability during the Great Recession in Finland," Papers in Regional Science, Wiley Blackwell, vol. 96(1), pages 33-59, March.
    20. Trifan, Emanuela, 2004. "Entscheidungsregeln und ihr Einfluss auf den Aktienkurs," Darmstadt Discussion Papers in Economics 131, Darmstadt University of Technology, Department of Law and Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:manlab:v:46:y:2021:i:3:p:337-353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://www.xlri.ac.in/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.