- Researchers warn that collective crypto trading strategies are vulnerable to exploitation.
- These programs promise to bring institutional-quality investment strategies within reach of retail investors.
A new academic paper warns that collective cryptocurrency trading systems – which pool users’ funds and trade with them automatically – are inherently vulnerable to loss of profits and exploitation by insiders, no matter how they are designed.
These findings come from research conducted by academics at Cornell Tech. In their research, they examined the fundamental trade-offs between profitability and economic fairness in such systems.
The team of eight researchers who co-authored the Jan. 2 paper found that these collective investment algorithms, or CoinAlgs, present unavoidable risks, whether they choose to keep their trading strategies private or opt for full transparency.
“CoinAlgs can either be transparent and risk losing profits, or be private and open the door to unfair value extraction by insiders,” the researchers said. “Even in scenarios where a CoinAlg may appear harmless, subtle backdoors can be inserted and generate profits for adversaries.”
Institutional grade software
Collective investment algorithms are not a new phenomenon. They have existed in the traditional financial world for years and are used at asset management firms like BlackRock and Renaissance Technologies to execute trades across multiple client portfolios.
But in cryptocurrency trading circles, they have grown in popularity in recent years, boosted by the proliferation of artificial intelligence.
They promise to put institutional-grade investment strategies or specialized trading intelligence within the reach of retail investors, even though these tools often lack the investor protections of regulated intermediaries.
Many CoinAlgs operating in DeFi lean heavily toward speculative assets, with a preference for emerging tokens and highly volatile memecoins.
Researchers used historical transaction data from Uniswap, the largest decentralized exchange, to simulate a profitable CoinAlg that correctly predicts future asset prices.
In this situation, they found that even a minimal leak of information from a private CoinAlg
Enabled significant value extraction by arbitrageurs, investors who exploit temporary price differences to make small but consistent profits.
At the same time, those using private CoinAlgs run the added risk of insiders using insider information to conduct similar arbitrages or early trades to extract profits.
Transparent CoinAlgs doesn’t fare much better.
They avoid insider trading issues by ensuring that all algorithmic trading models and data are open source and public. Yet this makes it even easier for arbitrageurs to profit from CoinAlgs trades, with little scope for recourse.
“Even moderate defensive strategies against arbitrage incur a significant transparency cost and thus (erode) profits,” the researchers said.
Yet despite the problems, CoinAlgs is likely to stick around. Many investors are attracted to them because they tout their ability to generate lucrative returns by outsourcing difficult investment decisions to AI algorithms.
Interest in such products will only increase as companies like OpenAI, Anthropic, and Google continue to invest money and resources into AI development.
“CoinAlgs are an inevitable part of the financial landscape,” the researchers said. “The search for principled safeguards remains an important avenue for future work. »
Tim Craig is DL News’ DeFi correspondent based in Edinburgh. Contact us with advice at tim@dlnews.com.
