结构动态自旋市场网络
Structurally dynamic spin market networks
摘要：
提出了一种基于智能体的有向演化复杂网络股价动态模型，并通过直接仿真进行了研究。稳定的政权保持作为极端动态，战略变量的自适应性和重联规则之间的平衡的结果。节点代理“大脑”的固有结构由具有局部和全局输入以及反馈连接的递归神经网络建模。对于特定的参数组合，复杂网络表现出小世界现象和无标度行为。本地领导者（网络集线器，其策略经常被其邻居所适应的代理）的识别通过网络中的重复随机游走过程来进行。模拟结果表明，经验相关的动态价格回报和波动聚类。程式化市场统计的其他新兴方面是适合度的齐普菲分布。
英文摘要：
The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.
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