Global Valuation
AI and Finance
Esther in the broader AI context
GV Esther complements Generative AI and Machine/Deep Learning techniques with stochastic simulation and optimization capabilities.
These are two areas where the GenAI and ML tools are ineffectual, as illustrated by Gartner Group in When Not to Use Generative AI. This is particularly true for derivatives pricing and portfolio risk management if these tools are used alone. Both ML and LLMs can be used enrich and fine-tune Esther models and vice versa.
All Large Language Models have one main blind spot: Solving mathematical problems.
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Bill Gates: “Math is a very abstract type of reasoning. Right now, I’d say that’s the greatest weakness [of GPT4]. Weirdly, it can solve lots of math problems. There are some maths problems where if you ask it, to explain it in an abstract form, make essentially an equation or a program that matches the math problem, it does that perfectly and you could pass that off to a normal solver.”
[From: Bill Gates on AI and the rapidly evolving future of computing]
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Dr Stephen Wolfram: “People say, what’s going to happen to all the programmers,” said Wolfram at an event for the launch of the Alpha GPT extension. “It’s like, what’s going to happen to everybody who does boilerplate […] documents of various kinds? That’s kind of going away. And similarly, people have rushed into […] going into computer science school and learning how to write Java code, Python code, whatever else it is. And it’s like, a lot of that is just going to go away.”
[From: CHATGPT + WOLFRAM - THE FUTURE OF AI! - YouTube]
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Global Valuation Esther is the first and only solver for all mathematical problems in the risk and pricing domain.
The Esther compiler is based on domain-specific language extensions to leading programming languages that allow to express math problems in risk and pricing and factor out in full generality the complexities involved in orchestrating a high performance execution strategy.
Adding Strategy Optimisation to LLMs
GPTs are incredibly capable when it comes to most AI tasks, with the one exception being planning skills. To master these skills, they must learn decision theory under uncertainty. This theory relies on constructing probabilistic models to represent the problem at hand, collecting data to calibrate model parameters, generating scenarios for the possible future evolutions of the observable system, and optimizing actions to this scenario set. Strategies can then be refined by back-testing them in time.
Unfortunately, creating such models traditionally requires PhD-level mathematicians to deploy a range of applied math tools, and the hardware compute costs are also high. This has made mathematical finance, the foundation of pricing theory and risk management, the most advanced area of decision theory under uncertainty.
Global Valuation has developed a generic compiler-solver combination for a vast array of problems in this domain. The technology employs a novel mathematical framework that avoids the need for mathematical shortcuts, greatly simplifying the modelling language. Plus, Esther's vectorisation technique utilises operator algebras and matrix operations, which are optimised for AI servers, resulting in unparalleled levels of performance.
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Combining GPTs with Esther can dramatically reduce hardware costs and simplify model building to the point that it can be entirely automated. This, in combination with the cost of data access that GPTs can enable, can trigger a quantum leap in the use cases of decision theory under uncertainty. The primary and immediate application domain is risk and pricing for banks, clearing houses, investment buy sides, and regulators. However, the range of applications will be vast and far broader than the Finance domain once costs of access are drastically reduced.
​Integrating Esther with Large Language Models
The integration of Large Language Model solutions with Esther promises to raise the quality level of strategy design and implementation to new levels.
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Esther adds maths intelligence to LLMs, a cross-industry and cross-asset capability to add the use of stochastic control theory into LLMs. This capability is useful in Finance problems from valuation to strategy optimisation, resource allocation, risk management and model risk.
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LLM naturally interface with the Esther Compiler since the Esther language extensions are isomorphic to natural languages apart for boiler plate code decorations. LLMs excel at defining mathematical problems but require a generic mathematical solver such as Esther Solver to calculate
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The Esther Solver was designed from the ground up to execute on the same GPU platforms on which LLMs run.
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Esther is based on models in the same HMM class which Machine Learning algorithms are designed to around. Esther calibrates HMM models based on forward looking information such as asset valuations while ML algorithms estimate HMM models based on historical time series. The two approaches are complementary and mutually reinforce and correct one another.
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Both Esther and LLM models are designed to scale, aggregate large amounts of information and project out scenarios at the legal entity level of aggregation. This promises to remove inefficiencies due to a management silos in organisations.
