Entropy and sustainable investment beliefs: A simple metric for analysing belief organisation Dane Rook University of Oxford D.Phil. Researcher, Geography & the Environment dane.p.rook@ouce.ox.ac.uk
[How] can investors make long ‐ term decisions that are more sustainable, more responsible, and maximise risk ‐ adjusted returns?
Expectations drive decisions… • Financial decisions (sustainable or otherwise) stem from expectations • Expectations blend the mathematical and psychological ( homo economicus meets ‘animal spirits’); balance shifts with: – Individual preference – Context – Timeframe – Degree of uncertainty/know ‐ ability • Should desire that expectations be: – Accurate – Precise*
…and beliefs [partly] drive expectations… • Investment expectations emanate (at least partly) from how investors believe markets function and price paths are generated • Investment beliefs are : “…conjectures and working assumptions about the investment world (including the economy, the workings of the financial system, and social, environmental and other risks) that underlie and inform investment decision ‐ making” (Woods and Urwin, 2010: p. 7) • Any investment decision is therefore an (explicit or implicit) expression of (possible combination of) investment belief(s)
How to judge investment belief(s) • Beliefs + Context Expectations Decisions • How to judge the quality of beliefs? Clues from Foxes and Hedgehogs (Tetlock, 2005): – Accuracy (Correspondence) – Precision (Coherence)* • The plural: (eco)systems and networks of beliefs: – Dynamism – Interaction*
How to judge investment belief(s) • Predictive validity – Robustness across contexts – Change in composition • Coherence measures – Subjective/qualitative • Gedankenexperiment • Scenario analysis – Objective/quantitative* • Beyond correlation?
Sustainable investment beliefs sampler A : Environmental, social, and corporate governance (ESG) factors will impact short ‐ term investment risks and returns (i.e. less than one year) C : The benefits of incorporating sustainable investment principles into the investment process are unlikely to outweigh the costs of doing so B : ESG factors will impact long ‐ term investment risks and returns (i.e. more than three years) E : Investors are over ‐ sensitive to short ‐ term factors and not sensitive to long ‐ term factors K : Companies can gain significant competitive advantage through their strategic response to climate change L : Investors can successfully incorporate carbon risk into portfolio management P : Countries (and governments) can gain significant competitive advantage in their economies through their strategic response to resource scarcity Q : Companies can gain significant competitive advantage through their strategic response to climate change
Belief interactions
Organising principles of beliefs • Converse’s (1964) constraint : “…the success we would have in predicting, given initial knowledge that an individual holds a specific attitude, that he holds certain further ideas and attitudes.” • Informational content (order versus entropy) • The internal (logical) and the external (social)
Organising principles of beliefs • Martin’s (2000) constraint: “…the inverse of the degree of arbitrary movement in the space of all possible beliefs” • The negative of entropy (amount of order) • Overall constraint = Tightness (logical constraint) + Consensus (social constraint) • Social constraints imply that belief holders: “…frame their understanding…in the same way” (Baldassarri and Goldberg, 2010: p. 5)
Belief interactions redux Low Consensus, High Tightness High Consensus, High Tightness Low Consensus, Low Tightness High Consensus, Low Tightness
The pith of it all… • Tightness: – How one’s beliefs fit together • Consensus: – Homogeneity of conceptualisation of ESG topics in financial markets? – Market distribution of beliefs • Belief ‘densities’ • Implications for investors and policymakers! – If only we could measure it…
Measuring belief networks • Informational entropy (Shannon’s entropy): – And the sign said [small samples] need not apply! [with apologies to the 5 man electrical band] • Thermodynamic entropy from statistical physics suits smaller samples and can provide some preliminary intuitions: LN(W / (M^N)) where: W = N! * (N1! * N2! * … * NM!)^ ‐ 1 M is possible belief positions N is the number of overall respondents/believers Ni is the count of believers assuming a belief position
Entropy in action: A preliminary test ‐ drive • Unique dataset of asset manager beliefs • Sample size of 22 respondents, with 18 beliefs examined across 3 categories: – Sustainable Investment – Climate Science – Resource Scarcity • Test of the entropy (thermodynamic version) measure on various belief combinations
Entropy in action: Some early findings • Belief networks become more ‘disorganised’ (random) with higher dimensionality • Some beliefs appear to dominate others and exhibit an enduring influence as other beliefs are incorporated/removed – ‘Core’ beliefs? • Possibility of ‘gestalt’ or ‘emergent’ properties of belief configurations
Extensions: Why this matters + where to go next • Help practitioners understand their own beliefs on financial markets and ESG factors through more objective and ‘periscopic’ methods of analysis – Particularly CORE beliefs • Strategies for change and opportunity • Large ‐ sample analysis and tightness/consensus decompositions… • WE NEED MORE (GOOD) BELIEF DATA!!!
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