The Volume and Behaviour of Crowds

Shi, Leilei

doi:10.2139/ssrn.3532838

Cited by 4 publications

(3 citation statements)

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“…"Using price and volume information in the volume-price differential equation as well as data from Chinese stock markets, Shi et al (2011) provide a theoretical model based on conditioning to explain investor behavior (Shi, 2013). Their model shows that investors can be either overconfident or panicked based on price momentum.…”

Section: Note(s)mentioning

confidence: 99%

The underlying coherent behavior in intraday dynamic market equilibrium

Shi

Guo

Fenu

et al. 2023

CFRI

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PurposeThis paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.Design/methodology/approachThe authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.FindingsThe authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).Research limitations/implicationsThe authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.Practical implicationsThe authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.Social implicationsUnderstanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.Originality/valueIt uncovers subjects' intelligent interactively trading behaviors.

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Section: Note(s)mentioning

confidence: 99%

The underlying coherent behavior in intraday dynamic market equilibrium

Shi

Guo

Fenu

et al. 2023

CFRI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Now, we assume the existence of a liquidity utility U(p,v tt ) expressed in terms of trading wealth 29 . It is the rate of liquidity, similar to power or the rate of work in physics, which we term liquidity energy E(p,v tt ) (Shi, 2006 and2013). It is defined as follows:…”

Section: Hypothesis Three-the Liquidity Utility Hypothesismentioning

confidence: 99%

“…Seventh, it would be better to invest index if one could not predict the outcome of individual stocks because people adapt to outcome efficiently on a daily basis. Eighth, it might be possible for us to develop a feasible automated trading strategy and run automated, algorithmic, and high frequency trading in financial market in terms of Shi's price-volume probability wave equation and market crowd's trading behaviors if outcome arbitrage could cover the cost involved, because stationary equilibrium exists widely on a trading day in financial market, particularly in bond market (Shi, 2013).…”

Section: Potential Applications and Practicesmentioning

confidence: 99%