5 Compliance Challenges that Your Algo Execution Model May be Creating

Algorithmic trading, or algo trading for short, is a recent technological development that’s helped to pave the way for revolutionary levels of market access and trading efficiency throughout virtually every financial market. But as with many cutting-edge technologies, could compliance be an issue for institutional investors to overcome? 

Algo execution models utilize the power of complex computer algorithms to execute trade orders at a pace that’s far quicker than human traders can keep up with. This can help to deliver unprecedented trading efficiency as well as brand-new challenges in terms of compliance. 

Artificial intelligence and machine learning (ML) have both come into the spotlight for driving trading efficiency, but the rapidly evolving technology has also caused regulators to become concerned about keeping up with the speed at which markets are changing due to AI. 

The lure of rapid trades, greater precision, and cost-effectiveness has made algo trading a much-desired technology for institutions and retail investors alike, but could there be compliance challenges on the horizon for the algorithms your institution is using? 

Regulators Struggle to Keep Up

Regulatory and compliance risk is a matter that should be a priority for institutions of all scales. The potential for losses due to matters relating to non-compliance with laws, regulations, and agreed industry standards risks undermining the effectiveness of advanced algorithmic trading tools. 

As regulators scramble to keep up with these rapidly evolving algo execution models, institutional traders will need to become increasingly aware of the compliance threats that their technology could pose both in the future and right now. 

This calls for greater compliance monitoring for the likes of hedge funds and broker-dealers to implement immediately if they’re using algo execution software. Deploying AI to monitor for international regulatory changes, legislation amendments, and recommended industry practices can be a great help in building a conducive and sustainable compliance framework. 

But what are the biggest compliance challenges that your algo execution model is creating? Let’s take a deeper look at five key issues that could draw regulatory scrutiny: 

1. CFTC Compliance

Algorithmic traders operating in the futures and derivatives markets are required to register with the Commodity Futures Trading Commission (CFTC) in the United States. 

The CFTC registration process focuses on providing detailed information about trading activities, risk management practices, and compliance procedures within trading strategies. 

Algo execution models have continually found themselves in the CFTC firing line, and in 2015, the commission initially planned to introduce a series of stringent rules to closely monitor algo activity. 

Although these rules were scrapped in 2017, the CFTC continues to place algo execution models under close scrutiny, meaning that institutions should always seek to maintain compliance with the commission when using algo models. 

2. SEC Reporting

Another key compliance issue for algo execution models involves SEC reporting. The Securities and Exchange Commission requires traders to frequently report their trading activities. 

This process typically requires submitting detailed reports regarding trading strategies, order types, and execution times. 

For algo execution models, these reporting requirements mean that trades need to be conducted and reported in a transparent manner, despite their autonomous nature.

It also requires institutions to utilize algorithmic trading platforms in a way that conforms to their specified trading strategy, or else it could uncover an inconsistency between SEC reporting and the firm’s trading patterns. 

3. Market Manipulation

Artificial intelligence plays a key role in algorithmic trading, and it’s the high-volume capabilities of AI trading that regulators fear could lead to market abuse. 

One core facet of algo execution is AI-powered high-frequency trading (HFT), and there are fears that this could lead to market manipulation by driving large volumes of trades in a way that drastically alters asset prices over a short period of time. 

Over the past decade, spoofing, an act that involves placing a large volume order on a single asset or derivative so that it shows up on the order book before canceling prior to fulfillment, led to charges in relation to flash market crashes. 

Spoofing can have an impact on the market because these phantom trades can be viewed by other traders and impact their investment decisions. 

In 2015, Navinder Singh Sarao was charged with market manipulation relating to a flash crash in 2010 that wiped nearly $1 trillion off of the market value of US stocks in a matter of minutes. 

Given the pace of algo execution models, there’s a greater threat that similar manipulation tactics could be utilized for underhanded market gains. 

4. Risks to Retail

Another key regulatory concern stems from the risks that algo execution models pose to retail investors. 

Although algorithmic trading models are widely available to retail investors today and are customizable to varying degrees, the flexibility offered to institutional traders is far greater than their retail counterparts, which could lead to weaker value for money and regulatory scrutiny over the true impact of algorithmic trading. 

These limitations and the proliferation of lower-cost, simplified algo execution models could present significant risks for retail traders, who may expose themselves to flawed software that leads to greater losses and possible future sanctions for the development of AI trading platforms. 

It’s for this reason that institutional investors are best served by utilizing tools provided by Tier 1 prime broker services that can manage the compliance implications of its models on their behalf. 

5. Risk Management Implications

Risk management can become a problem when institutions accommodate rapidly moving algo execution algorithms. 

The artificial intelligence framework of algorithmic trading models needs to be designed to handle countless market scenarios and adverse economic conditions to accurately manage risk. 

Risk management models need to be adaptable and offer sufficient oversight in order to detect anomalies or fundamental errors within the algorithms to prevent significant losses.

Again, this calls for around-the-clock compliance monitoring to not only appease regulators but ensure that adopted models are continuing to work in a manner that’s expected without unpredictable results compounding the misery by throwing money at a nonsensical investment strategy. 

Building a Sustainable Future

Although the future of compliance for algorithmic trading depends on the adaptability of global regulators, we’re sure to see more advancements in technology, AI, and ML enhance the ability of institutions to successfully trade at scale in an efficient and compliant manner. 

The future is full of opportunity and risks, and evolving regulations alongside market dynamics will heavily influence the role that algo trading will play in achieving efficiency within markets. 

In order to enjoy the benefits of capitalizing on opportunities at a rapid pace, more institutions can use prime brokers to provide transparent trade execution models alongside greater internal compliance protocols for the most sustainable use of autonomous algorithmic trading tools. When utilized responsibly and effectively, traders of all scales and ambitions can enjoy seeing their strategies made more efficient with the help of artificial intelligence.