Wealth Share Analysis with “Fundamentalist/Chartist” Heterogeneous Agents

Abstract

Abstract：We build a multi-assets heterogeneous agents model with fundamentalists and chartists, who make investment decisions by maximizing the constant relative risk aversion utility function.We verify that the model can reproduce the main stylized facts in real markets, such as fat-tailed return distribution, long-term memory in volatility, and so on. Based on the calibrated model, we study the impacts of the key strategies' parameters on investors' wealth shares. We find that, as chartists' exponential moving average periods increase, their wealth shares also show an increasing trend. This means that higher memory length can help to improve their wealth shares. This effect saturates when the exponential moving average periods are sufficiently long. On the other hand, the mean reversion parameter has no obvious impacts on wealth shares of either type of traders. It suggests that, no matter fundamentalists take moderate strategy or aggressive strategy on the mistake of stock prices, it will has no different impact on their wealth shares in the long run.

Agent-based model generating stylized facts of fixed income markets

“…The fundamentalist trader is defined in accordance with the descriptions provided in Chiarella et al (2009), Kaizoji et al (2015) and Xu et al (2014). The trader is granted a constant relative risk-aversion (CRRA) utility function required to be maximized w.r.t.…”

“…At the aggregated level, Kaizoji et al (2015)'s model proved able to reproduce fat-tail distributions of returns, slow decaying autocor-relations of absolute returns, fast decaying autocorrelations of signed returns, volatility clustering and transient faster-than-exponentially growing prices associated with bubbles. A market model characterized by the interactions of fundamentalist and chartist traders through the price-vector of dimension higher than two is provided in Xu et al (2014). Other models introducing heterogeneous agents investing in a multi-asset market can be found in Borghesi and Bouchaud (2007), Chiarella et al (2007), Fedyk et al (2013) and Eckrot et al (2016).…”

Multi-asset financial bubbles in an agent-based model with noise traders’ herding described by an n-vector Ising model

“…Expanding the CRRA utility function to quadratic order, the maximization problem (8) has been solved in Xu et al [29], and we report here the final solution. The fundamentalist portfolio allocation strategy is given by ⎛…”

“…Some ABMs do model several assets. Xu et al [29] developed an ABM in which both fundamentalists and noise traders maximize their expected utility differing only in the construction of the expected return. Similarly, Chiarella et al [30] introduced a model of heterogeneous agents that maximize a utility function and build their expectation of the future return based on past observations.…”

A detailed heterogeneous agent model for a single asset financial market with trading via an order book

“…latter type of models usually have prices adjusted in a stochastic manner [ 23 – 26 ]. Thus, while models with “intelligent” agents employing different strategies in realistic market environments have been proposed before [ 27 – 32 ], our model is motivated by the behavior of market participants following the rules of thumb employed by real life traders, while keeping the model as simple as possible. In particular, we do not dwell on whether these rules of thumb have solid microeconomical foundations.…”