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As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...
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We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
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Local and global analysis of a speculative housing market with production lag.

Chaos (Woodbury, N.Y.)·2018
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Investor sentiment and trading behavior.

Giovanni Campisi1, Silvia Muzzioli1

  • 1Marco Biagi Department of Economics, University of Modena and Reggio Emilia, Via Jacopo Berengario 51, 41121 Modena, Italy.

Chaos (Woodbury, N.Y.)
|October 2, 2020
PubMed
Summary

This study models investor trading decisions using a sentiment index within a dynamical system. The model incorporates differing fundamental value perceptions and risk asymmetry to capture market behavior.

Area of Science:

  • Economics
  • Financial Markets
  • Behavioral Finance

Background:

  • Financial markets exhibit complex dynamics influenced by investor sentiment.
  • Modeling trading decisions requires accounting for diverse investor perceptions and risk factors.

Purpose of the Study:

  • To develop a dynamical model of investor trading decisions incorporating a sentiment index.
  • To analyze how sentiment influences the proportion of fundamentalists with differing value perceptions.

Main Methods:

  • Utilized a discrete dynamical system to model agent trading behavior.
  • Integrated a sentiment index, linked to risk asymmetry, into the model.
  • Analyzed model equilibria and performed numerical simulations.

Main Results:

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  • Identified model equilibria under varying sentiment conditions.
  • Numerical analysis captured empirically observed stylized facts in financial markets.
  • Demonstrated the impact of sentiment and risk asymmetry on trading behavior.

Conclusions:

  • The sentiment index is a crucial factor in modeling financial investor trading decisions.
  • The dynamical model provides insights into market behavior driven by sentiment and risk.
  • Findings contribute to understanding financial market dynamics and investor psychology.