The recent world events have driven quantitative analysts (also known as quants) to search for different ways to grow and diversify investments. 2022 was aligned with heavy federal policy-based rate increases to counterbalance the speedy COVID recovery. Because of this, experts are now assuming that inflationary pressures are going to fend off too much volatility in rates. As rate curves continue to steepen, quants are bound to focus more on medium and short trend strategies.
The current scenario is motivating many quants to look for modern data analytics solutions that will help them identify new strategies, scenarios and trends. In this blog, we’ll explore why quants should care about modern data and how the modern data stack fosters innovation and new business opportunities.
The Doctor Strange of the Finance World
In the last moments of Avengers Infinity War, Doctor Strange furiously looks through all possible scenarios in which the Avengers would come out victorious. The ability to look at different worlds at once and decide which of them will yield the most favorable outcome is the definition of a quant’s role.
Looking at different scenarios, however, requires sufficient compute power to recurse and define outcomes. This explains why quants have added Matlab, R, Java, Python and SQL to their already solid knowledge on calculus, linear algebra, differential models, probability and statistics. But what does this mean to a quant already working in the financial industry?
Quantitative analytics has historically used mathematical models to analyze investment portfolios. Harry Markowitz, a Nobel Prize-winning economist, is credited as a pioneer in the area of quantitative investment by introducing key concepts such as modern portfolio theory and using mathematics to quantify diversification. Back when Markowitz published the first papers on the topic, most of the mathematical work done was manual.
The current explosion of data volumes and the volatility of data processing and evaluation has added a technology overlay on a role that, historically, has been solely mathematical. The modern data stack has become the only way a quant can comply with the current pressures of the investment industry. This brings us to the next section…
Why Should Quantitative Investment Analytics Care About Modern Data?
We’ve talked about how modern data benefits organizations looking for new business opportunities. But what should quantitative analysts pivot to modern data? Well, first and foremost, modern data encourages data-driven decisions. It also provides quants access to new and broad-scoped data sources, which enables more attuned decision-making.
Moreover, data can be used to blend increasingly personalized and individualized patterns to investment strategies. Not only can data maximize unique outcomes and drive both satisfaction and retention, it can also help create the tailored investment strategies clients are constantly looking for.
Because quants work at the intersection of unique data sets and having to solve for unknown business problems, it’s best if they operate in a highly rapid and fluid environment. The modern data stack enables quants to easily use and define new features, while providing new opportunities to innovate.
Transforming Workflows in Quantitative Investment Analytics
The modern data stack can swiftly transform quant workflows in the following areas:
- Feature engineering: This is a machine learning technique that leverages data to produce new features for both supervised and unsupervised learning. Feature engineering can prove helpful for quants, especially when looking for new investment opportunities.
- Trade Cost Analysis (TCA): Instead of resorting to ad-hoc methods, portfolio managers can use the modern data stack, as well as machine learning models, to bring fund size determination to a new level.
- Model-drift management: Machine learning models can degrade and lose their predictive power. Modern data platforms such as Sigma and Snowflake can help data experts detect when their models start to deteriorate.
- Automation of stress testing: Stress testing is necessary in financial scenarios, where the data load increases can affect data processing times. Automating these stress tests (or even including them within an MLOps framework) can prove incredibly helpful.
- Solvency analysis: Predictive analytics is a strong point within a well-defined and mature modern data solution, making metrics such as solvency ratios easier to attain and discuss.
Always One Step Ahead
The more data the market gathers, the more it will require a fast response. Recent years have ushered in an explosion of technology solutions around machine learning, artificial intelligence and quantum processing capabilities, helping financial markets push the boundaries of what’s possible.
At Hakkoda, we leverage the current momentum in artificial intelligence and combine it with the deep domain expertise of SnowPro certified engineers to create new opportunities and elevate your current portfolios. Contact one of our experts today to learn more about how you can stay one step ahead of the competition.
If you’re looking to learn more about how to accelerate your financial services organization, don’t forget to register for Accelerate Financial Services, a virtual event hosted by Snowflake that will take place on May 17th and 18th. Anand Pandya, Hakkoda’s Global Head of Finance, will be attending as an expert speaker.
Anand will also appear as a panel member for “Transformation Data Experiences in Quantitative Research and Trading in Banking,” along with two others very experienced leaders in the financial sector. The latter is scheduled for June 15th at 10:00 AM PT/ 1:00 PM ET. Don’t forget to register to save your seat!
