New Models Re-Root Finance in the Real Economy

从过去继承的“机构投资的食谱”似乎不再与当今金融市场的成分相匹配。例如，嵌入在现代产品组合理论中的许多真实性，如正常分布，标准偏差，作为风险的预测因子，理性投资者，无风险率，稳定的相关性等，现在似乎......我如何精确地提出这个... ‘overly optimistic.’ In fact, in the wake of the Asian financial crisis, the Long-Term Capital debacle, the Internet bubble and bust, the ‘perfect storm’ in 2002-'03, the global financial and sub-prime crisis, the European debt crisis... and whatever’s about to happen next... you’d be forgiven for chucking your old finance textbooks out a moving car’s window. Many investors have come to see the abstractions in the Capital Asset Pricing Model, Modern Portfolio Theory, Efficient Markets Hypothesis, Mean-Variance Optimization, Value-at-Risk, or any of the traditional investment and risk models as potentially dangerous.

你为什么危险，你问？好吧，戈登克拉克和我有argued这些自上而下的模型是金融服务业在实体经济上建立的一层抽象和推导的主要驱动因素。我们一直导致认为，这种抽象层以金融产品和服务的形式表现出来，是为了简化群众的财务和投资的“高度复杂”业务。因此，例如，在评估资产课程和产品的风险概况的评估中取代了是否投资于特定安全（公司股票或政府债券）的决定。与企业的关系已被产品提供商和消费者之间的关系所取代。具有讽刺意味的是，这种简化的尝试实际上导致了巨大的过度复杂性。最后，我们看到了更多和更大的不透明度，甚至在金融中保密。并且，令人沮丧的是，金融服务抽象也让大型机构投资者支付了比他们所知的更高的费用和成本。亚博赞助欧冠（亲爱的巨人州：如果你认为你知道银行和经理如何从你身上赚钱，那么你就会少了解银行和资产管理而不是我想象的。相信我。你觉得你知道吗？你。不知道。知道。）

Anyway, the abstract finance layer sitting atop the real economy is what has allowed the financial services industry to be so successful. And this is why I think we need new models that remove some (albeit not all) of the abstractions and re-inject a healthy dose of ‘reality’ into everything investors do. To make this more concrete, I’d like to direct you to agent-based models (ABMs), as I think they could be quite profound. I encourage everybody to read Richard Bookstaber’s paper entitled,“Using Agent-Based Models for Analyzing Threats to Financial Stability.”It really opened my eyes to the power of these bottom-up approaches to modeling. Here’s some blurbage directly from the paper:

First, here’s Bookstaber’s take down of the top-down approach:

“动态随机一般均衡模型的简化的假设(动态圣ochastic general equilibrium] model, which are typical for traditional, top-down analytical models, facilitate mathematical tractability but at the same time limit their appeal for modeling market crises. For example, in a DSGE model, the risk is introduced through well-specified exogenous shocks that do not change through the actions taken by the agents, whereas in a real crisis, the risk tends to come from the actions of the agents themselves, such as the pulling away of liquidity, the fire sales due to forced liquidation, and the withdrawal of sources of funding. Agents react, adapt in their behavior, and in doing so create endogenous uncertainty... Furthermore, the assumption of rationality and optimization—and with it the ability of the actors to solve a multi-period decision—is not realistic during a crisis (if it is ever realistic). During crises, historical relationships no longer hold, the course of events depends on feedbacks among agents, and the key determinants of those feedbacks are unknown—who is leveraged, what positions they might have to liquidate, how the banks might alter their funding with shifts in the value of their collateral. The very notions of a representative agent and of an equilibrium model itself are inherently inappropriate for modeling a crisis.”

第二,这就是Bookstaber认为是更好的approach: ABMs.

“[In ABMs], the system is not directly modeled as a globally integrated entity. Systemic patterns emerge from the bottom up, coordinated not by centralized authorities or institutions (although these may exist as environmental constraints) but by local interactions among autonomous decision-makers. This process is known as ‘self-organization’... An ABM need not use a representative agent. The agents can have different rules and heuristics, endowments, and objectives. This heterogeneity readily allows models to incorporate gaming behavior and informational asymmetries... The decisions undertaken today by an agent directly depend on the past choices made by other agents in the population, thus creating strong path dependence... Interdependence may involve iterative processes through which agents influence other agents, who in turn influence others. An ABM thus describes a system from the perspective of its constituent units. A priori constraints on agent interactions can be dictated by the realities of the problem being addressed rather than being imposed based on equilibrium conditions, homogeneity assumptions, or mathematical regularity conditions that are required by analytical frameworks.”

Fascinating. So ABMs are the future, right? Well, not so fast. ABMs are apparently quite threatening to mainstream economists:

“虽然可能是应对金融脆弱性和危机的有希望的方法，但ABMS仍然是经济和金融研究的主流。一个原因是他们对新古典主义学校周围建立的许多方法和机器提供了明确的挑战。另一个是，因为基于代理的建模并不代表一组干净和优雅的方程式的世界，因此它不会借给学术出版的标准模式。AXELROD（2006）已注意到主流经济学中ABMS的问题：“ABM可能是一个坚硬的卖出。由于大多数正式的理论家等同于数学模型的模型，因此他们中的一些人难以说服基于代理的模拟的适当性和价值并不令人惊讶。“

I myself have faced off against the ‘mainstream’ economists for years due to my belief that case studies and fieldwork offer fruitful lessons and insights for academic research. (Yeah, that’s crazy, I know). So I’m obviously very interested in ABMs as well. These are tools that really focus on the idiosyncrasy and local complexity of economic life, and then, on top of that layer, build more differentiation and irrationality in dynamic ways. That’s far more appealing than a model that seeks to impose a general equilibrium on top of people that are quite obviously irrational and idiosyncratic. Anyway, in my view, this approach could be very useful in removing some of the layers of abstraction in finance (that seem to most serve finance professionals) and, in turn, allow us to re-root finance in the real economy. At the very least, ABMs can create a layer of abstraction that is better than the current one. And I think that’d be a very cool thing to do...