Summary of Neuro-symbolic Traders: Assessing the Wisdom Of Ai Crowds in Markets, by Namid R. Stillman and Rory Baggott
Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets
by Namid R. Stillman, Rory Baggott
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the impact of deep generative models on financial markets, particularly when these models make semi-autonomous buy/sell decisions. The authors develop “neuro-symbolic traders” that utilize vision-language models to estimate asset value, calibrated using gradient descent and market data. They test these agents on synthetic and real-world financial data, including stocks, commodities, and foreign exchange rates. In a virtual market environment, the agents’ beliefs about underlying values influence observed price dynamics, leading to price suppression compared to historical data, highlighting potential risks to market stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep generative models are being used more in finance. This paper looks at how these models might affect financial markets when they make their own decisions. The authors created “neuro-symbolic traders” that use computer vision and language models to figure out the value of an asset. They tested these agents on real-world financial data and found that when they traded, prices went down compared to what happened in the past. This could be a problem for market stability. |
Keywords
» Artificial intelligence » Gradient descent
