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Exploring Trust and Beliefs in Market Efficiency: A Behavioral Finance Perspective

This study delves into the Efficient Markets Hypothesis (EMH) and its complexities by emphasizing the roles of trust, beliefs, and emotions in financial decision-making. It critiques traditional views by proposing an extended model that incorporates the subjective nature of information and its sources. By simulating varied methods of belief integration, the research reveals how trust manipulation can significantly influence stock valuations, suggesting implications for regulators and future research avenues in behavioral finance.

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Exploring Trust and Beliefs in Market Efficiency: A Behavioral Finance Perspective

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  1. Trust, News and the Efficient Markets Hypothesis BEHAVIOURAL RESEARCH Fairness, Trust and Emotions 1 July 2010 Behavioural Finance Working Group Cass Business School

  2. Efficient markets hypothesis • Bachelier (1900), Samuelson (1965), Fama (1970) • Prices in an efficient market reflect all publicly available information • In strong form, prices reflect all information • So what is information?

  3. What is information? • Is this information? • “IBM profits in 2011 will be $15.1 billion” • How about this: • “General Electric’s profits in 2006 were $20.9 billion” • That was “information” until profits were restated • It depends on the source...

  4. An extended model of information • We model beliefs instead of information • A belief: • is held by an agent • has a source • expresses the relative value of two goods (typically money and a financial instrument) • has a confidence level

  5. For instance • Jim Cramer told me that Intel stock is worth $28 • I place a confidence level of 5% in this belief

  6. On the other hand... • Google Finance tells me that Intel stock is worth $21.22 • I place a confidence level of 70% in this belief

  7. How do I integrate these beliefs? • I could just weight the confidence levels and produce an average valuation • Or I could go with the most trusted source • Other integration functions are available

  8. Confidence-weighted integration • Leads to smooth behaviour • Small changes in trust or value result in small changes in price

  9. Most-trusted integration • Leads to volatility • A small change in trust can result in a big jump in valuation

  10. Simulation 1 • Integration by weighted probability

  11. Simulation 2 • Integration by most trusted source

  12. Manipulation test • We specified one agent as a “promoter” of a higher price • We give them a one-off boost of 0.05 to trust, representing an investment in their reputation • Average price of stock is increased by 16% • In many plausible scenarios it is more worthwhile to invest in reputation than in fundamentals

  13. Other variations in model • Trust in some agents depends on trust in other agents • Leads to extreme volatility • Merged integration functions • Leads to more realistic medium volatility

  14. So what? • Regulators ought to be aware of the value of manipulating trust • Model implies that agents will overinvest in the structures of the market itself compared with investment in productive assets • Future research directions: • Multi-good markets • Inference of beliefs from market prices

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