Abstract Starting from the information uncertainty in the financial market, by applying the discussion of investor irrational behavior in behavioral finance, we analyze the irrational behavior of groups and its impact on market price instability, which is of great significance to the health and stability of the financial market.
It has certain practical significance.
Keywords Uncertain conditions Group cognitive behavioral bias 1 The connotation of group cognitive behavioral bias was proposed. In 1970, Fama defined an efficient financial market as an asset in his paper "Efficient Capital Market: A Review of Empirical Research" on the efficient market hypothesis.
A market in which prices fully reflect available information.
The establishment of the efficient market hypothesis relies on the assumption of investor "rationality", a perfectly competitive market model based on complete rationality.
This is contradictory to the actual situation and has certain flaws.
Regarding the efficient market hypothesis, research on behavioral finance can be summarized into three levels: bounded rational individuals, group behavior and inefficient markets.
Based on the incompleteness and asymmetry of information, market traders can be divided into informed traders and uninformed traders.
Kahneman and Riepe (1998) believe that people deviate from standard decision-making models in many ways, and economists refer to irrational investors as "noise traders".
In behavioral finance, cognitive behavioral biases in financial markets include overconfidence, information response bias, loss aversion, regret aversion, mental accounting, confirmation bias, time preference, herding behavior and feedback mechanisms.
Since the effect of investor group behavior is significant and interactive, here we mainly explore the impact of group cognitive behavioral bias from the perspective of herd behavior and feedback mechanisms that interact and bring significant effects in the financial market.
2 Pricing model analysis of group cognitive behavioral bias 2.1 Positive feedback trading strategy model The structure of the positive feedback trading strategy model is shown in Table 1.
In the table, α and β are the slopes of the demand curves of passive investors and positive feedback investors respectively, and pj (j=0,1,2,3) is the price in each period.
Conclusion: (1) When the information is noise-free.
When the information is positive, that is, Φ=?, p1=p2 (μ>0) and p1=0 (μ=0) are established. According to the market equilibrium conditions of period 1 and period 2, it can be solved: when μ>
0, p1= p2=α?quasi/(α-β); when μ=0, p1=0, p2=?quasi.
When β>α/2, the addition of arbitrageurs makes the price in any period deviate further from the true value than if it did not exist.
Therefore, under the condition of no noise information, the existence of arbitrageurs causes the price to deviate from the true value.
2.2 Herd behavior model (1) Information overlay and herd behavior.
The information-based herd behavior model was originally proposed by Banerjee (1992).
When the m-th investor makes an investment choice (m>2), his behavioral choices are shown in Table 2.
It can be seen that the characteristic of the decision-making rule that reaches equilibrium in the model is the externality of herd behavior.
When investors make decisions, they are unable to determine whether other people's choices are correct or not, and they still ignore their own information and follow others.
In this model, herd behavior exhibits positive feedback, bringing volatility and variability to asset prices.
(2) Imitation contagion and herd behavior.
Without access to basic value information, traders can only rely on the behavior observed in the market as the basis for decision-making, and choose their own investment decisions by imitating the behavior of others. The resulting herd behavior leads to asset prices.
changes.
According to the lux (1995) model, there are 2N speculative traders who are optimistic or pessimistic about market expectations. Assuming that there are no investors with intermediate attitudes, the speculators’ average concept index x∈, x=0, the optimistic attitude and the pessimistic attitude are the same
, x>0 means optimistic investors dominate, x<0 means pessimistic investors dominate.
Attitude contagion means that when the number of optimistic investors is dominant, pessimistic investors will buy stocks; when the number of pessimistic investors is dominant, optimistic investors will sell stocks, that is, attitude
A transfer has occurred.
According to the contagion mechanism: dx/dt=2υ[tanh(ax)-x]cosh(ax) where a is the coefficient of herd behavior or contagion intensity, and v is the speed of change.
There is a unique stable equilibrium when a≤1 and x=0.
a>1, the equilibrium is unstable, there is x+>0 or x-<0, that is, a≤1, the herd effect is weak, the price deviation will gradually disappear, and the system will return to equilibrium after being disturbed; a>1, the herd effect
Stronger, once deviation occurs, the equilibrium will be unstable through mutual contagion.
Contains contagion and price dynamics: x=2υ[tanh(a1p/υ+a2x)-x]cosh(a1p/υ+a2x) p=β[xTN+TF(pf-p)] Investment behavior depends on price momentum, enhanced
infection effect.
If a2 (the weight of information obtained from other people's behavior) ≤1, there is a unique equilibrium. If a2>1, there are two equilibria: optimistic market E+ and pessimistic market E-.