Nageswar Cherukupalli, VP of Financial Services of Infosys, discusses how in the wake of the COVID-19 pandemic, hedge funds are looking to AI to deal with rising trading volumes and market volatility. Nageswar reveals the prerequisites necessary in order for hedge fund managers to adopt AI platforms, as well as details of applications of AI in hedge funds.

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Despite having weathered a global pandemic, the markets have performed well this year. After 9 consecutive quarters of net outflows due to long-term relative underperformance, the US $3.7 trillion hedge fund industry saw a net inflow in the third quarter of 2020 according to Preqin. In fact, industry returns reached 16.6% this year, beating the S&P 500. This was the highest annual return since 2009!

Whether in a crisis or not, hedge fund managers are under relentless pressure to improve alpha or ‘active return on investment’ for their investors. In this scenario, it is vital to draw any advantage that can be gained through advanced technologies, particularly artificial intelligence (AI). AI is already extensively used in the financial services industry to reimagine investing and cost models and respond to real-time market changes. It has delivered significant competitive advantage to its adopters. Now, in the wake of the COVID-19 pandemic, hedge funds are looking to AI to deal with rising trading volumes and market volatility.

AI techniques can analyse vast amounts of data – real-time and historic, structured and unstructured – that cannot be easily comprehended by humans. In 2018, BarclayHedge reported that 56% of hedge funds were using AI in some way for their investment processes while some relied on AI to automate the entire lifecycle. More significantly, an index of hedge funds using AI outperformed the total hedge fund index by 72% in the 10-year period till 2020.

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Prerequisites for AI platforms

1. Data –

Data is the lifeblood of any AI system. With new data being created at high volume and velocity and alternate data becoming available across multiple internal and external sources, it is vital to identify and establish an ecosystem to manage and nourish information. Recently, Infosys applied AI techniques in data preparation and data wrangling for a US bank to equip them with the right set of data for AI and analytical modelling in the investment decision-making process.

2. Architecture –

AI platforms also require an open, flexible, API-based, microservices architecture with scalable processing, storage and compute power, preferably on cloud. AI models also need to be trained, which takes 3 to 6 months typically, but deliver superior outcomes compared to pre-trained models that are applied as-is.

3. People –

Building an AI talent pool is critical for hedge funds that want to compete with FinTechs and investment banks. AI models must be supervised by multi-functional teams with skills and experience in data science, technology and financial services. This is a serious challenge, though, as AI and data science capabilities are difficult to find.

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Applications of AI in hedge funds

Hedge funds and asset managers are discovering that AI has a deeper role to play beyond the investment process. Some common use cases include:

• Portfolio design: Using AI techniques, fund managers can choose the right set of data and, based on the prevailing market conditions, design the appropriate portfolio for their customers
• Trade execution: AI can automate trade execution for various customers and stocks at the same time, in large volumes. Man Group, a hedge fund manager, has been using AI to improve the efficiency of trade execution[v]
• Operations: AI can analyse text and voice communications and create reports from multiple documents. According to McKinsey, this type of automation can slash the time taken to produce a report by 60%[vi]
• Risk management: Through automation, AI can seamlessly monitor traders for compliance, update investment guidelines and regulations for traders and risk managers, ensure liquidity risk management, and execute regulatory reporting

Hedge fund managers looking to differentiate themselves and produce superior risk-adjusted returns for their investors should consider AI to build essential capabilities and access the right support and expertise. I recommend starting small by first choosing the right data and applying AI to a subset of this data in the existing investment models. This will build confidence, create challenger models to compare with traditional models, and build AI Ops that includes a reusable framework with proven use cases. Infosys AppliedAI solution provides an integrated offering for hedge fund managers to future-proof and efficiently scale AI across the whole enterprise while managing risk. Its solutions have already been deployed for financial services clients who are now reaping value through improved efficiency and return on investment.

Source [v] [vi] 

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