AI in Finance: Applications and Infrastructure for Multi-Agent Learning
Prashant Reddy · Tucker Balch · Michael Wellman · Senthil Kumar · Ion Stoica · Edith Elkind

Fri Jun 14th 08:30 AM -- 06:00 PM @ 201
Event URL: »

Finance is a rich domain for AI and ML research. Model-driven strategies for stock trading and risk assessment models for loan approvals are quintessential financial applications that are reasonably well-understood. However, there are a number of other applications that call for attention as well.

In particular, many finance domains involve ecosystems of interacting and competing agents. Consider for instance the detection of financial fraud and money-laundering. This is a challenging multi-agent learning problem, especially because the real world agents involved evolve their strategies constantly. Similarly, in algorithmic trading of stocks, commodities, etc., the actions of any given trading agent affects, and is affected by, other trading agents -- many of these agents are constantly learning in order to adapt to evolving market scenarios. Further, such trading agents operate at such a speed and scale that they must be fully autonomous. They have grown in sophistication to employ advanced ML strategies including deep learning, reinforcement learning, and transfer learning.

Financial institutions have a long history of investing in technology as a differentiator and have been key drivers in advancing computing infrastructure (e.g., low-latency networking). As more financial applications employ deep learning and reinforcement learning, there is consensus now on the need for more advanced computing architectures--for training large machine learning models and simulating large multi-agent learning systems--that balance scale with the stringent privacy requirements of finance.

Historically, financial firms have been highly secretive about their proprietary technology developments. But now, there is also emerging consensus on the need for (1) deeper engagement with academia to advance a shared knowledge of the unique challenges faced in FinTech, and (2) more open collaboration with academic and technology partners through intellectually sophisticated fora such as this proposed workshop.

09:00 AM Opening Remarks (Talk) Prashant Reddy, Senthil Kumar
09:10 AM Invited Talk 1: Adaptive Tolling for Multiagent Traffic Optimization (Invited Talk) Peter Stone
09:30 AM Invited Talk 2: The Strategic Perils of Learning from Historical Data (Invited Talk) Jamie Morgenstern
09:50 AM Oral Paper Presentations 1 (Talk)
10:30 AM Coffee Break and Socialization (Break)
11:00 AM Invited Talk 3: Trend-Following Trading Strategies and Financial Market Stability (Invited Talk) Michael Wellman
11:20 AM Poster Highlights - Lightning Round (Talk)
12:00 PM Lunch
02:00 PM Invited Talk 4: Towards AI Innovation in the Financial Domain (Invited Talk) Manuela Veloso
02:20 PM Oral Paper Presentations 2 (Talk)
03:00 PM Coffee Break and Socialization (Break)
03:30 PM Invited Talk 5: Intra-day Stock Price Prediction as a Measure of Market Efficiency (Invited Talk) Tucker Balch
03:50 PM Invited Talk 6: RLlib: A Platform for Finance Research (Invited Talk) Ion Stoica
04:10 PM Poster Session - All Accepted Papers (Poster Session)
06:00 PM ICML Reception (Reception)

Author Information

Prashant Reddy (JPMorgan AI Research)
Tucker Balch (JPMorgan AI Research)
Michael Wellman (University of Michigan)
Senthil Kumar (Capital One)
Ion Stoica (UC Berkeley)
Edith Elkind (University of Oxford)

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