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An agent based early warning indicator for financial market instability
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An agent based early warning indicator for financial market instability

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  • Vidal-Tomás, David
  • Alfarano, Simone

Abstract

Inspired by the Bank of America Merrill Lynch Global Breath Rule, we propose an investor sentiment index based on the collective movement of stock prices in a given market. We show that the time evolution of the sentiment index can be reasonably described by the herding model proposed by Kirman on his seminal paper "Ants, rationality and recruitment" (Kirman, 1993). The correspondence between the index and the model allows us to easily estimate its parameters. Based on the model and the empirical evolution of the sentiment index, we propose an early warning indicator able to identify optimistic and pessimistic phases of the market. As a result, investors and policymakers can set different strategies anticipating financial market instability. The former, reducing the risk of their portfolio, and the latter, setting more efficient policies to avoid the effect of financial crashes on the real economy. The validity of our results is supported by means of a robustness analysis showing the application of the early warning indicator in eight different stock markets.

Suggested Citation

  • Vidal-Tomás, David & Alfarano, Simone, 2018. "An agent based early warning indicator for financial market instability," MPRA Paper 89693, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:89693
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    More about this item

    Keywords

    Herding behaviour; Kirman model; Financial market;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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