AI Advances for Hedge Funds to Gain the Competitive Advantage

on August 13, 2020

Skills Gap/Human Capital Shortage

The hedge fund industry is increasingly heading towards a more data-driven future. Whether the hedge fund is quantitative or not, data-based processes and skills are important for many aspects of operations, such as reporting and portfolio management functions. When employees lack data-driven skills in programs such as Python, necessary compliance, performance, and risk reporting can take significant time and energy away from other projects that are more central to the success of the firm. Furthermore, data can be used to build predictive models to give further insights when it comes to alpha generation.

Hedge Funds and Artificial Intelligence

Even though hedge funds’ demand for technological talent is high and accelerating, there is a serious human capital shortage and a skills gap in these data-centric positions. AIMA, or the Alternative Investment Management Association, has noted a trend where many hedge funds now hire data scientists and other quantitative experts rather than those from traditional fields of finance and banking. According to Bloomberg, listings for AI-based jobs within the financial sector increased by approximately 60% from just 2018 to 2019. The job market has not had time to respond to this steep increase in demand, and a lack of technology-centric applicants to fill these jobs has resulted in a shortage of talent.

Hedge funds have a recognized need for employees with high technological proficiencies, but hiring these in-demand individuals could be costly. It is also time-consuming and expensive for current employees to return to a university and learn skills in classes that are often broader than their jobs’ function. A better option is often to teach current employees the targeted skills necessary to meet the fund’s objectives. On-the-job training can be a lower-cost alternative to hiring new workers or sending employees back to school for university training. With technically-trained employees, the hedge funds’ procedures can move into the future of data-driven processes and insights, gaining an edge over their competitors.

Also Read:Hiring Skilled Employees VS Training Employees Internally

Uses of Data-Driven Technology in Hedge Funds

I. Hedge funds need to gather and clean pricing data from multiple jurisdictions and across various asset classes before it can be properly used by researchers, traders, and compliance officers. With the right training, data-gathering and cleaning skills can lead to significant improvements within a firm. For example, if a hedge fund upskills or reskills its employees to retrieve data, they can access more data than their competitors as hedge funds often purchase the same data from the same sources. Expanding the data used by the fund can lead to more innovative and unique portfolio strategies and higher returns. Additionally, the firm can organize its data into one place, eliminating the need to gather data from different sources every time it is needed.

Hedge Funds Data Cleaning and Gathering

When a company learns good practices for data cleaning, the reports and predictions generated from the data will also be higher quality. Clean data underlying the analytics is crucial, or else the analytics is likely to produce incorrect or misleading conclusions.

Finally, a fund with superior data organization and cleaning capabilities will have more time for research, trading, and capital raising responsibilities. With greater efficiency and higher quality insights, the hedge fund is likely to see significant improvements in its performance.

II. Researchers and traders can use Python to perform statistical research on the obtained data in order to identify and test new trading opportunities and strategies. Python, when used with practical machine-learning skills can analyze the data and pick up patterns that humans may not be able to discern, providing additional insights to hedge funds. The fund can then use these insights to examine and test new trading strategies. Even a traditional, or non-quantitative, hedge fund can crosscheck some of their opinions with a more quantitative approach using Python and the most relevant AI-skills.

Test new trading opportunities

III. Portfolio management and reconciliation tools should be fully automated with the ability to generate an array of on-demand snapshot reports, such as performance, regulatory and compliance, and risk management reports. Using Python, these reports can be generated efficiently and accurately. As the number and magnitude of compliance requirements for hedge funds grows, it is important to have a team that can address these requirements efficiently so that the firm can focus on its primary objectives — raising capital and generating alpha. Additionally, the ability to do this task in-house eliminates significant expenses for the firm and allows the firm more control over the quality of the reports than if the reports were constructed by an external consultant. Therefore, Hedge funds who provide Python training to their employees will have a competitive advantage.

Automated Portfolio Management

IV. All data should be stored and remain retrievable at future dates. After employees are taught basic technological skills in Python, hedge funds will be able to access Python’s data storage systems. Python allows employees to access stored data more efficiently than Excel. Additionally, the quantity of data storage that can be held in Python is also much greater than that of Excel. Upskilling employees to use Python allows the firm to easily access large amounts of data and save it for future use.

Database and Server Room


The number of hedge funds is multiplying, making the competition for superior returns stiffer. According to Hedge Fund Research (HFR), in 2002, there were approximately 2,000 hedge funds, while by 2019, there were approximately 10,000. Furthermore, regulations are growing more complicated and time-consuming, while investors are simultaneously demanding more reports and up-to-date insights about the hedge fund’s performance. In light of these industry changes, the effective use of data is imperative for firms to gain an advantage, distinguish themselves, and dominate their competition. According to AIMA: “[h]edge fund principals see a time when trying to compete without machine-learning capabilities will be akin to trying to compete without a Bloomberg terminal.” With data skills, a hedge fund can lower costs significantly and gain a competitive advantage. During this transition, providing hedge fund employees with the necessary, data-driven skills will decrease their time on tasks unrelated to profit generation, better their performance through higher quality data, and give funds the competitive advantage over other hedge funds who fail to adapt to the industry’s new frontier.

Also Read:Should I Learn Python? Finance Professionals

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