DHF Capital

BAS KOOIJMAN CEO and Asset Manager of DHF Capital

Q How is artificial intelligence being used in trading today, and what are some of the most common applications within the use of this technology?

Artificial Intelligence (AI) has made significant headway in the trading industry, enhancing speed, efficiency, and precision. The global algo-trading market size was valued at $2.03 billion in 2022; it is projected to grow from $2.19 billion in 2023 to $3.56 billion in 2030. The technology is an already critical component of algorithmic trading which has opened a new dimension for investing and speculative trading alike. According to Wall Street, algo-trading accounts for more than 60% of overall US equity trading. Furthermore, the global algo-trading sector is forecasted to expand by 10.5% annually through 2028, with the Asia Pacific region experiencing the fastest growth and North America occupying the largest market size.

At DHF Capital, we use AI and algo-trading to help us analyse multiple markets simultaneously, assess risk levels, forecast price movements, and execute trades at high speed. AI has allowed us to use large quantities of data to put trading and risk management strategies in place that ensure more efficient and precise decision-making. Overall, our improved computational infrastructure has helped in developing better financial services and trading capabilities such as better portfolio management and trade execution. In this regard, AI-powered tools help us construct and manage investment portfolios based on an individual’s financial goals, risk tolerance, asset class preference and time zone.

Q Can you describe some of the benefits that AI offers to traders?

AI offers several benefits, including improving speed and efficiency and providing the capacity to handle larger volumes of data. These capabilities can be used to improve risk management, deliver better predictions, and maximise automation.

Algorithms used by artificial intelligence can analyse vast amounts of data at high speed, making real-time trading decisions more efficient and helping to level the playing field between large asset managers and smaller investors. This way traders can benefit from the data available to enhance their trading experience and get a more comprehensive view of the market.

Predictive capabilities are an interesting feature that helps us at DHF Capital to better approach the market thanks to suggestions derived from historical data. Since implementing AI more comprehensively, it has led to us using robo-advisors that have helped us manage portfolios better based on us being able to analyse specific investment goals, manage our risk tolerance better, and measure other parameters more accurately. These AI-powered advisors can monitor a vast array of assets, make predictions about future performance, and rebalance portfolios as needed.

Q What are some of the key challenges associated with using AI in trading?

While AI offers the ability to handle large volumes of data, its outcome depends on the quality of the input provided and on the strength of the models used. The use of AI involves some technical challenges and various other risks that must be considered.

In this regard, AI models are heavily dependent on the quality and quantity of data available for training. Poor quality data or lack of sufficient data can lead to inaccurate predictions and poor trading decisions. In addition, the models’ reliance on historical data could result in overfitting where models could underperform when confronted with real-time data. This can affect predictions and trading performances.

Traders and firms using AI need to make sure that they truly understand the models they use to have a grasp of the predictions and trading decisions that are being made to be able to act on and improve on them.

Q What are some of the ethical concerns that arise with the use of AI in trading, and how can these be addressed to ensure fairness and transparency in the market?

The use of AI in trading can raise several ethical concerns as it can potentially create an uneven playing field, where traders with more advanced AI capabilities have a significant advantage over others. This could exacerbate wealth inequality. AI systems can perpetuate or amplify existing biases in the market if they are trained on biassed data - which can then in turn lead to unfair trading practices and outcomes.

In addition to this, the vast amount of personal data used by AI systems for personalised trading decisions raises privacy concerns. Often traders and clients are unaware of the extent to which their personal information is being used. This lack of transparency can be problematic for traders and regulators alike who are trying to understand why certain trading decisions were made or personal data used.

Regulators have a critical role in ensuring the responsible use of AI in financial markets. Their primary goal should be to protect investors, ensure fairness, and maintain market integrity while enabling innovation. Overall, the goal of regulation should be to strike a balance between allowing for innovation and protecting market participants and the integrity of financial markets.

AI is a huge topic and I’ve only touched upon a few points in this interview - as for all industries involved the financial services industry will require ongoing efforts as AI technology continues to evolve.

Q How do you see AI evolving in the trading industry in the next 5-10 years, and what new applications or use cases do you anticipate emerging?

With the public data available being taken into consideration we expect to continue evolving and expanding its influence in the trading industry over the next 5-10 years. We expect to see even more personalised robo-advisors, better predictive capabilities in financial forecasting and the evolution of regulatory technology. Blockchain technology could also be integrated with AI to enhance transparency in trading and alongside this we anticipate smart contracts to automate transactions based on predefined conditions and AI algorithms.

While these trends are promising, they also underscore the need for robust oversight, regulation, and ethical considerations as AI becomes more integral to the trading industry.

Q What new skills will be required for traders to succeed in a world increasingly driven by algorithms

and machine learning?

AI cannot replace humans but will rather change the way we work. Relying on algo-trading instead of human emotion should allow traders to make more accurate and consistent returns. AI allows finance professionals to focus less on manual and repetitive tasks and more on strategic decision-making, risk management, and oversight. In an AI-driven trading world, the most successful traders will be those who can effectively leverage AI tools while also applying their human judgement and intuition.

As trading becomes more data-driven, understanding how to work with vast amounts of data and develop machine-learning models will become increasingly important. In fact, a recent study found that trading algorithms are currently responsible for 92% of Forex trades. Finance professionals will need to understand how to train, validate, and interpret models, and how to avoid common pitfalls. In line with this, knowing how to code will become increasingly valuable in a more tech-driven world.

Finally, traders need to understand the limitations of AI and machine learning, including issues like bias in data and the challenges with interpreting some types of models. They also need to understand that AI models are tools that assist decision-making and not infallible solutions.

Q What are some of the potential risks associated with relying too heavily on AI in trading, and how can these risks be mitigated?

Reliance on AI in trading indeed comes with potential risks. Overfitting is such a risk - this occurs when a machine learning model is too complex and learns the training data too well, including its noise and outliers. Overfitted models perform poorly on unseen data, which can lead to inaccurate predictions and trading decisions in the real market.

AI relies heavily on the quality of data it is provided with. Incorrect or biassed data can lead to inaccurate predictions. There are also privacy concerns when handling sensitive data, and regulations like GDPR require strict compliance. Trading systems that manage vast amounts of data could fail due to software bugs, hardware failures, or cybersecurity attacks. Such failures could lead to significant losses if not detected and addressed promptly.

As mentioned, a few times in this interview, non-compliance can lead to legal issues and fines. Compliance with financial regulations can become more complex with AI systems. These risks can be mitigated but it’s important to remember that while AI can greatly enhance trading capabilities, it’s essential to manage these risks to reap the benefits while minimising potential downsides.

Q How does the use of AI impact market efficiency and the broader economy?

AI has the potential to improve market efficiency by rapidly processing large amounts of information and making informed decisions quickly. This allows for faster price adjustments in response to new information - which is of course a key aspect of market efficiency.

Increased liquidity and price discovery are also benefits of implementing AI. By analysing vast amounts of data and identifying complex patterns, AI can contribute to more accurate price discovery (determining the price of an asset in the marketplace through the interactions of buyers and sellers).

Regulators and policymakers will need to carefully manage these potential downsides to ensure that the benefits of AI in trading are broadly shared and that financial stability is maintained.