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A Comparison of Competing Asset Pricing Models: Empirical Evidence from Pakistan

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  • Eleftherios Thalassinos

    (Department of Maritime Studies, Faculty of Maritime and Industrial Studies, University of Piraeus, 185-33 Piraeus, Greece
    Department of Insurance and Risk Management, Faculty of Economics, Management and Accountancy, University of Malta, 2080 Msida, Malta)

  • Naveed Khan

    (Department of Management Sciences, Hitec University, Taxila 47080, Pakistan)

  • Shakeel Ahmed

    (Department of Management Sciences, Hitec University, Taxila 47080, Pakistan)

  • Hassan Zada

    (Department of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan)

  • Anjum Ihsan

    (Department of Management Sciences, Islamia College University, Peshawar 25120, Pakistan)

Abstract

In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan’s stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth’s regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions.

Suggested Citation

  • Eleftherios Thalassinos & Naveed Khan & Shakeel Ahmed & Hassan Zada & Anjum Ihsan, 2023. "A Comparison of Competing Asset Pricing Models: Empirical Evidence from Pakistan," Risks, MDPI, vol. 11(4), pages 1-24, March.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:4:p:65-:d:1106286
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    References listed on IDEAS

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    1. Jagannathan, Ravi & Wang, Zhenyu, 1996. "The Conditional CAPM and the Cross-Section of Expected Returns," Journal of Finance, American Finance Association, vol. 51(1), pages 3-53, March.
    2. Barillas, Francisco & Kan, Raymond & Robotti, Cesare & Shanken, Jay, 2020. "Model Comparison with Sharpe Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(6), pages 1840-1874, September.
    3. Konstantin B. Kostin & Philippe Runge & Michel Charifzadeh, 2022. "An Analysis and Comparison of Multi-Factor Asset Pricing Model Performance during Pandemic Situations in Developed and Emerging Markets," Mathematics, MDPI, vol. 10(1), pages 1-16, January.
    4. John Lintner, 1965. "Security Prices, Risk, And Maximal Gains From Diversification," Journal of Finance, American Finance Association, vol. 20(4), pages 587-615, December.
    5. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    6. Fletcher, Jonathan, 2018. "Bayesian tests of global factor models," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 279-289.
    7. Hou, Kewei & Xue, Chen & Zhang, Lu, 2015. "A Comparison of New Factor Models," Working Paper Series 2015-05, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    8. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    9. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 25(2), pages 65-86.
    10. He, Zhiguo & Kelly, Bryan & Manela, Asaf, 2017. "Intermediary asset pricing: New evidence from many asset classes," Journal of Financial Economics, Elsevier, vol. 126(1), pages 1-35.
    11. Adam Zaremba & Alina Maydybura & Anna Czapkiewicz & Marina Arnaut, 2021. "Explaining Equity Anomalies in Frontier Markets: A Horserace of Factor Pricing Models," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(13), pages 3604-3633, October.
    12. Santhakumar Shijin & Arun Kumar Gopalaswamy & Debashis Acharya, 2012. "Dynamic risk‐return relation with human capital: a study on Indian markets," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 7(2), pages 146-159, April.
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    2. Karol Sikora, 2023. "Profit and Loss Account Variant Selection by Companies Listed on the Warsaw Stock Exchange:An Empirical Perspective," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 839-854.

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