Peter Cotton

Peter Cotton

New York, New York, United States
40K followers 500+ connections

About

As a career quant I've contributed to a few areas in quantitative finance including the…

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Experience

  • Primary Commodity Fund Graphic

    Primary Commodity Fund

    Connecticut, United States

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    Connecticut, United States

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    New York, New York, United States

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    Princeton

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    Greater New York City Area

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Education

Publications

  • Microprediction: Building an Open AI Network

    MIT Press

    How a web-scale network of autonomous micromanagers can challenge the AI revolution and combat the high cost of quantitative business optimization.

    The artificial intelligence (AI) revolution is leaving behind small businesses and organizations that cannot afford in-house teams of data scientists. In Microprediction, Peter Cotton examines the repeated quantitative tasks that drive business optimization from the perspectives of economics, statistics, decision making under uncertainty, and…

    How a web-scale network of autonomous micromanagers can challenge the AI revolution and combat the high cost of quantitative business optimization.

    The artificial intelligence (AI) revolution is leaving behind small businesses and organizations that cannot afford in-house teams of data scientists. In Microprediction, Peter Cotton examines the repeated quantitative tasks that drive business optimization from the perspectives of economics, statistics, decision making under uncertainty, and privacy concerns. He asks what things currently described as AI are not “microprediction,” whether microprediction is an individual or collective activity, and how we can produce and distribute high-quality microprediction at low cost. The world is missing a public utility, he concludes, while companies are missing an important strategic approach that would enable them to benefit—and also give back.

    In an engaging, colloquial style, Cotton argues that market-inspired “superminds” are likely to be very effective compared with other orchestration mechanisms in the domain of microprediction. He presents an ambitious yet practical alternative to the expensive “artisan” data science that currently drains money from firms. Challenging the machine learning revolution and exposing a contradiction at its heart, he offers engineers a new liberty: no longer reliant on quantitative experts, they are free to create intelligent applications using general-purpose application programming interfaces (APIs) and libraries. He describes work underway to encourage this approach, one that he says might someday prove to be as valuable to businesses—and society at large—as the internet.

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  • Self-Organizing Supply Chains for Microprediction: Present and Future Uses of the ROAR Protocol

    Thirty-sixth International Conference on Machine Learning

    A multi-agent system is trialed as a means of crowd-sourcing inexpensive but high quality streams of predictions. Each agent is a microservice embodying statistical models and endowed with economic self-interest. The ability to fork and modify simple agents is granted to a large number of employees in a firm and empirical lessons are reported. We suggest that one plausible trajectory for this project is the creation of a Prediction Web.

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  • Stochastic Volatility Corrections for Interest Rate Derivatives

    Mathematical Finance

    Asymptotics of fast mean-reverting Vasicek and CIR models.

    Other authors
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  • Rapid Simulation of Correlated Defaults an the Valuation of Basket Default Swaps

    Probability, Finance and Insurance

    Basket default swaps are complex credit derivatives that are difficult to price analytically and are typically priced by using Monte Carlo simulations. The pricing and risk management of basket default swaps present challenging computational problems. We present a method for efficiently generating correlated default times whose marginal distributions are consistent with a reduced form stochastic hazard rate model.

    Other authors
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  • Contraction of an Adapted Functional Calculus

    Journal of Lie Theory

    We aim to show, using the example of a Riemannian symmetric
    pair (G;K) = (SL2(R); SO(2)), how contraction ideas may be applied
    to functional calculi constructed on coadjoint orbits of Lie groups

    See publication

Patents

Honors & Awards

  • Data Mind of the Year 2024

    Rebellion Research

  • Australian Junior Chess Champion

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    Winner of the national U/20 championship (Brisbane 1990).

  • Australian Mathematics Olympiad

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    Gold (1991). Bronze (1990).

  • Eric Hoffer Book Award Finalist

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    https://www.hofferaward.com/Eric-Hoffer-Award-category-finalists.html

  • International Physics Olympiad

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    Bronze (Cuba 1991).

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