英文标题：
《Market Self-Learning of Signals, Impact and Optimal Trading: Invisible
  Hand Inference with Free Energy》
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作者：
Igor Halperin and Ilya Feldshteyn
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最新提交年份：
2018
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英文摘要：
  We present a simple model of a non-equilibrium self-organizing market where asset prices are partially driven by investment decisions of a bounded-rational agent. The agent acts in a stochastic market environment driven by various exogenous \"alpha\" signals, agent\'s own actions (via market impact), and noise. Unlike traditional agent-based models, our agent aggregates all traders in the market, rather than being a representative agent. Therefore, it can be identified with a bounded-rational component of the market itself, providing a particular implementation of an Invisible Hand market mechanism. In such setting, market dynamics are modeled as a fictitious self-play of such bounded-rational market-agent in its adversarial stochastic environment. As rewards obtained by such self-playing market agent are not observed from market data, we formulate and solve a simple model of such market dynamics based on a neuroscience-inspired Bounded Rational Information Theoretic Inverse Reinforcement Learning (BRIT-IRL). This results in effective asset price dynamics with a non-linear mean reversion - which in our model is generated dynamically, rather than being postulated. We argue that our model can be used in a similar way to the Black-Litterman model. In particular, it represents, in a simple modeling framework, market views of common predictive signals, market impacts and implied optimal dynamic portfolio allocations, and can be used to assess values of private signals. Moreover, it allows one to quantify a \"market-implied\" optimal investment strategy, along with a measure of market rationality. Our approach is numerically light, and can be implemented using standard off-the-shelf software such as TensorFlow.
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中文摘要：
我们提出了一个非均衡自组织市场的简单模型，其中资产价格部分由有限理性主体的投资决策驱动。代理人在由各种外部“阿尔法”信号、代理人自身行为（通过市场影响）和噪音驱动的随机市场环境中行事。与传统的基于代理的模型不同，我们的代理聚合了市场中的所有交易者，而不是代表性代理。因此，它可以被识别为市场本身的有限理性成分，提供了一种特殊的隐形手市场机制的实现。在这种背景下，市场动态被建模为这种有限理性市场主体在其对抗性随机环境中的虚拟自我游戏。由于这种自我博弈的市场主体所获得的回报无法从市场数据中观察到，我们基于神经科学启发的有界理性信息论逆强化学习（BRIT-IRL），建立并求解了一个简单的市场动力学模型。这导致了具有非线性均值回归的有效资产价格动态，在我们的模型中，这是动态生成的，而不是假设的。我们认为，我们的模型可以以与黑人同窝人模型类似的方式使用。特别是，它在一个简单的建模框架中表示常见预测信号的市场观点、市场影响和隐含的最佳动态投资组合分配，并可用于评估私人信号的价值。此外，它还可以量化“市场隐含”的最佳投资策略，以及市场合理性的衡量标准。我们的方法在数值上很轻，可以使用标准的现成软件（如TensorFlow）来实现。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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