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Aggregate excess demand on wall street.

Qingyuan Han1, Steve Keen2

  • 1University of Alabama in Huntsville, Department of Atmospheric Science, USA.

Heliyon
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Summary

Behavioral Finance Theory explains market prices better than the Efficient Market Hypothesis by measuring investor demand. This approach predicts market reversals and identifies crisis signals for S&P 500 returns.

Keywords:
Aggregate excess demandAsset market price changeBehavioral finance theoryEfficient market hypothesisFinancial market crisesMarket maker inventoryPerfect competitionPrediction of market directionsUtility maximizationWalrasian general equilibrium

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Area of Science:

  • Behavioral Finance
  • Market Microstructure
  • Quantitative Finance

Background:

  • The Efficient Market Hypothesis (EMH) struggles to explain asset price variances, necessitating alternative theories.
  • Behavioral Finance Theory (BFT) posits that psychological biases lead to irrational economic decisions, but identifying these behaviors has been challenging.

Purpose of the Study:

  • To demonstrate that price-taker behavior, measured by normalized excess demand (NED), can be quantified.
  • To show that NED and price-maker behavior explain significant S&P 500 daily return variances.
  • To establish a framework for predicting market fluctuations based on measurable behaviors.

Main Methods:

  • Quantifying price-taker behavior using normalized excess demand (NED).
  • Analyzing S&P 500 daily returns over eight years.
  • Incorporating Walrasian general equilibrium theory to abstract price-maker behavior.
  • Utilizing feedback analysis for long-term forecasting.

Main Results:

  • Measured NED explains a substantial portion of S&P 500 daily return variances.
  • Price-maker behavior accounts for the remaining variances.
  • Detected market maker inventory positions predict intraday and daily market reversals.
  • NED analysis identified signals for 2000, 2008, and 2020 market crises.

Conclusions:

  • The interaction between price-takers and price-makers drives market price fluctuations.
  • Quantifiable investor behavior (NED) offers predictive power for market movements.
  • This approach provides a more comprehensive explanation of asset price dynamics than traditional EMH.