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Integrating Alternative Data (Also Known as ESG Data) in Investment Decision Making

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  • Soh Young In
  • Dane Rook
  • Ashby Monk

Abstract

What is environmental, social, and governance (ESG) data and how do we evaluate its quality and effectiveness? This form of evaluation is important, as it is a precondition for investors trying to integrate ESG in investment decisions. Previous literature describes intrinsic properties of ESG data (e.g. multifaceted-ness and context dependence) and highlights a trade-off between the validity and reliability of ESG data, which is often tied to the lack of theoretical foundations and scarcity of high-quality ESG data. Encouragingly, new data technologies have improved the accessibility, availability, and transparency of ESG data, but an agreed theoretical framework to evaluate ESG data quality is still lacking. This paper seeks to fill that theoretical gap by proposing a ‘user-oriented’ approach to evaluate ESG data. In this framework, we consider ESG data to be a ‘continuous concept with limitless boundaries’ and characterise it in terms of its width and depth. The bearing of width and depth on ESG data quality is ultimately a function of the investment decisions in which such data is used: the approach we endorse is therefore user-centric. This study then shows how ESG data, when it is of high quality, maps onto the investment decision-making processes.

Suggested Citation

  • Soh Young In & Dane Rook & Ashby Monk, 2019. "Integrating Alternative Data (Also Known as ESG Data) in Investment Decision Making," Global Economic Review, Taylor & Francis Journals, vol. 48(3), pages 237-260, July.
    • Handle: RePEc:taf:glecrv:v:48:y:2019:i:3:p:237-260
    DOI: 10.1080/1226508X.2019.1643059
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    Citations

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    Cited by:

    1. Chao Li & Mian Wu & Wenli Huang, 2023. "Environmental, Social, and Governance Performance and Enterprise Dynamic Financial Behavior: Evidence from Panel Vector Autoregression," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(2), pages 281-295, January.
    2. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    3. Ilze Zumente & Nataļja Lāce, 2021. "ESG Rating—Necessity for the Investor or the Company?," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    4. Arthur Hughes & Michael A. Urban & Dariusz Wójcik, 2021. "Alternative ESG Ratings: How Technological Innovation Is Reshaping Sustainable Investment," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    5. Si, Minxing & Bai, Ling & Du, Ke, 2021. "Fuel consumption analysis and cap and trade system evaluation for Canadian in situ oil sands extraction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    6. Paola Demartini & Claudia Pagliei, 2023. "Can we trust ESG Ratings? Some insights based on a bibliometric analysis of ESG data quality and rating reliability," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2023(2 Suppl.), pages 161-187.
    7. Cong Zhang & Shanyue Jin, 2022. "What Drives Sustainable Development of Enterprises? Focusing on ESG Management and Green Technology Innovation," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    8. Reon Matemane & Tankiso Moloi & Michael Adelowotan, 2022. "Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques," JRFM, MDPI, vol. 15(10), pages 1-19, October.
    9. Chen, Lifeng & Khurram, Muhammad Usman & Gao, Yuying & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "ESG disclosure and technological innovation capabilities of the Chinese listed companies," Research in International Business and Finance, Elsevier, vol. 65(C).
    10. Bjorg Jonsdottir & Throstur Olaf Sigurjonsson & Lara Johannsdottir & Stefan Wendt, 2022. "Barriers to Using ESG Data for Investment Decisions," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    11. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    12. Magdalena Zioło & Iwona Bąk & Anna Spoz, 2023. "Incorporating ESG Risk in Companies’ Business Models: State of Research and Energy Sector Case Studies," Energies, MDPI, vol. 16(4), pages 1-25, February.
    13. Danny Zhao‐Xiang Huang, 2022. "Environmental, social and governance factors and assessing firm value: valuation, signalling and stakeholder perspectives," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1983-2010, April.
    14. Manyama, Mkama Thomas & Hepelwa, Aloyce Shaban & Nahonyo, Cuthbert Leonard, 2021. "GIS Based Environmental Cost−Benefit Analysis of Built Environment at Dar es Salaam Coastline Metropolitan," African Journal of Economic Review, African Journal of Economic Review, vol. 9(2), April.
    15. In, Soh Young & Weyant, John P. & Manav, Berk, 2022. "Pricing climate-related risks of energy investments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    16. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.
    17. Bhattacherjee, Purba & Mishra, Sibanjan & Kang, Sang Hoon, 2023. "Does market sentiment and global uncertainties influence ESG-oil nexus? A time-frequency analysis," Resources Policy, Elsevier, vol. 86(PA).

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