Nowadays, Artificial Intelligence agents are using deep learning techniques to train large neural networks to recognize patterns in data. Unstructured data learning from social media posts and news articles can provide unparalleled insight for developing trading strategies.
Even though past performance does not predict future returns, AI agents use historical data to learn how the market reacted in past events. Based on these reactions, AI agents can learn to behave in more productive ways in future market conditions.
Challenges with the proliferation of AI agents in the Market
Using AI in the market can also lead to some problems that need to be identified and managed:
- Decision Transparency — the “Black Box” of algorithms, particularly deep learning algorithms make grasping how decisions are made virtually impossible. These include trading decisions, investment decisions, and risk management decisions. The communication mechanisms inside the AI agent is not transparent. When money is lost, it’s difficult for the hedge fund or the regulatory body to reconcile that loss to any foul-play. If the AI agent is at the center, then it is the AI agent that is responsible. But, who is responsible for the AI agent? If a third party AI agent is used, is it the financial management firm that’s responsible or the company that created the AI agent that’s responsible?
- Systematic Risk — The problem is further complicated when multiple intermediaries experience rapid loss in a short span of time. Then, it leads to a new kind of systematic risk. Volatility in the market begets more volatility in the AI world. As volatility inputs are fed into AI agents, it accounts for that volatility by making new trading decisions that can potentially increase volatility.
- Market Concentration — AI’s reliance on third-party providers of AI and machine learning as well as its reliance on data providers can create pockets of market concentrations with limited competition in each area of specialty. When there are vulnerabilities in one of these pockets of concentration, then the whole system can potentially experience an escalating effect that creates systematic risk.
- More Volatility and Greater Diversity — Due to the sophisticated mechanisms of AI to adapt to individual client’s risk profiles for trading, trading recommendations, and trading strategies will become more diverse. In this diversity, a new kind of volatility is emerging. The problem with this new kind of volatility is that it’s hard to pinpoint the reason. With diversity, the volatility may be observed in the market but the reasoning behind the volatility may be difficult to discern.
- Historical Data — Historically, we didn’t have many great financial crises that rocked the market. The data that we have on catastrophe is limited at best. This limits the AI agent’s ability to function in catastrophic scenarios. AI agents may function well during normal market conditions. But, as soon as outliers arise that move the market into territories with large systematic risk, human intervention is paramount to manage such risks.
With the proliferation of AI agents for trading, we, as humans have to understand that when the market is saturated with AI traders, there will be no advantage. Thus, there will be no profits or losses. Markets will be at equilibrium.
What makes the markets functional is the ups and downs of prices, the differences in price determinations and the varieties of insights that are generated at any given moment.
The optimal scenario is to have AI agents work with humans side by side to create “Intelligent Money Managers” who pay attention to client’s needs as well as understanding both the advantages and the limitations of using AI agents.
When humans can intervene in such a way as to regulate AI agent’s activities for optimal insight (combining human intuition and AI insights), then AI agents can function as it should: provide liquidity to the market, optimize information flows, make efficient execution and enrich the decision flow.
Team-work between humans and AI agents are really the key to having a market that is regulated for efficiency while providing adequate transparency.