Dynamical spin-glass-like behavior in an evolutionary game

Slanina, František; Zhang, Yicheng

doi:10.1016/s0378-4371(00)00500-8

Cited by 17 publications

(13 citation statements)

References 30 publications

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“…One therefore has to resort to a Monte-Carlo integration of Eq. (15), and as shown in Fig. 5 we find nearly perfect agreement with simulations of the original batch process.…”

Section: And Response Functionsupporting

confidence: 83%

“…The Minority Game [14] imposes to trade at each time step, and hence focuses on price impact. The first paper to apply the minority game framework to a two time-step payoff is [15], where the payoff at time t is a[N i n(t) − N i n(t − 1)], N i n(t) being the number of people with a long position at time t (no short position is allowed) and is similar to our price p(t); therefore, this payoff can be translated into a(t)A(t + 1). The idea is to reward people for anticipating the crowd.…”

Section: Price Evolution Minority and Majority Gamesmentioning

confidence: 99%

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Price return autocorrelation and predictability in agent-based models of financial markets

Challet¹,

Galla²

2005

Quantitative Finance

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We demonstrate that minority mechanisms arise in the dynamics of markets because of price impact; accordingly the relative importance of minority and delayed majority mechanisms depends on the frequency of trading. We then use mixed majority/minority games to illustrate that a vanishing price return auto-correlation function does not necessarily imply market efficiency. On the contrary, we stress the difference between correlations measured conditionally and unconditionally on external patterns.Whether financial markets are predictable or not is a debate whose origin can be traced back to Bachelier's hypothesis that prices follow a random walk [1]. Later work, in particular by Fama [2] and Samuelson [3] aimed at proving mathematically that markets are efficient, i.e. unpredictable, grounding their theories on perfect rationality. This is indeed the simplest view of a financial market, and a very convenient one. There are however good reasons to believe that markets are not perfectly efficient. The well-known January effect is a systematic deviation from efficiency and has been observed in real markets for many years, but is reportedly disappearing; other examples include the predictive power of moving averages [4]. According to more recent theoretical studies, the cost of acquiring information [5] or the presence of noise traders [6] prohibits perfect efficiency, as perfect information can only be achieved at an infinite cost, or by accepting a considerable risk; this leads to the hypothesis of marginally efficient markets, where most of the easily detectable predictability is removed by traders.Market efficiency is often illustrated by the fact that the price return auto-correlation function is essentially zero. Denoting the log-price at time t by p(t), the price return is defined as r(t) = p(t + 1) − p(t), and assuming that r(t) is a stationary process, its auto-correlation readsHere · · · stands for averages over time; note that we normalise by the average magnitude of returns. The units of time are arbitrary in this paper. If C(τ ) = 0, statistically significant predictions of future price changes are possible on time-scales τ , that is, the knowledge of r(t) allows to make probabilistic statements about r(t + τ ). The analysis of real market data shows that C(τ ) ≈ 0 typically for τ larger than a few minutes [7,8,9]. However, the existing correlations on shorter time-scales are not exploitable in reality because of transaction costs [9]. This apparent efficiency however does not deter some practitioners from making statistically abnormal profits. In this paper we argue that the correlation function as defined in Eq. (1) is a poor measure of predictability. It is indeed an unconditional measure of correlation and does not differentiate between technical analysis patterns, states of the market or of the economy. Finding and exploiting relevant information patterns generates arbitrage opportunities. As a consequence, correlation functions conditional on these patterns have to be used.In the following we will...

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“…One therefore has to resort to a Monte-Carlo integration of Eq. (15), and as shown in Fig. 5 we find nearly perfect agreement with simulations of the original batch process.…”

Section: And Response Functionsupporting

confidence: 83%

Section: Price Evolution Minority and Majority Gamesmentioning

confidence: 99%

Price return autocorrelation and predictability in agent-based models of financial markets

Challet¹,

Galla²

2005

Quantitative Finance

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show abstract

“…On the other hand bubbles do not exist in Minority Games, and seem to be a consequence of a kind of majority game. Markets are rather escape games (Slanina and Zhang 2001), where one wishes to enter the market before the majority enters and to withdraw before the majority withdraws, i.e. anticipate to the crowd twice; $-games reduce the escape game by only asking to anticipate the majority of the next time step Bouchaud 2002, Andersen andSornette 2003).…”

mentioning

confidence: 99%

Inter-pattern speculation: Beyond minority, majority and $-games

Challet

2008

Journal of Economic Dynamics and Control

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“…First, the diversity of agents is limited, since agents all have the same historical memory and time-horizon. Second, in real markets, some agents are trend-followers who effectively play a majority game [6][7][8][9][10][11][12][13]; while the others are fundamentalist who effectively play a minority game. In order to create an agent-based model that more closely mimics a real financial market, Gou proposed 'mix-game' model which is an extended version of MG model by dividing agents into two groups: each group has different historical memories and time-horizons; one group plays the minority game and the other plays the majority game [14].…”

Section: Introductionmentioning

confidence: 99%