Buy-side and sell-side analysts are faced with increasingly vast amounts of large, complex and unusual data sets. How can analysts use cutting-edge tools and improve their coding skills to extract value and generate insights faster and more efficiently?
- Buy-side analysts must explore ways of finding alpha in the most cost-effective manner and beat benchmarks to compete with passive investment strategies.
- Sell-side analysts are under pressure to lower the cost of producing high quality and differentiated research to gain the attention and access of the buy-side.
- The relationship between data, technology and human talent is key to the successful integration of these trends along the research continuum.
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A new generation of analyst
Historically, if an analyst ever wanted to prove their worth, Excel was where they predominately operated. Today, it’s all about Python.
Research firms are already hiring analysts with strong coding skills. Those who work confidently across data programming languages, building flexible yet robust tools enabling them to focus on value, making better use of the data, analytics and connectivity they need.
Today’s top research analysts already have much a more universal skill set than seen in the past, and this new breed is looking to make a tangible impact from the outset.
Escaping the confines of the spreadsheet
The notion of rapid evolution has always existed in investment research, but is executed through Excel, which is slower and less efficient than Python. As Python becomes more widespread, analysts will be able to innovate more quickly.
MIFID II is just one of several reasons why analysts are under more pressure to provide insight and that their research is worth the investment.
Couple this with the challenge of combining data sets to create differentiated insight, and analysts must be able to seamlessly integrate disparate data sets, for example, from third-party sources with their proprietary content through a flexible framework and single connected workflow.
Python has the advantage over Excel in its ability to handle large data sets as well as incorporate machine learning and modelling.
Research is no longer undertaken in a vacuum
Many analysts still spend hours a week completing routine tasks like cleaning data or jumping between different systems and know-how automation can help lessen repetitive tasks.
Analysts are actively seeking ways to reduce the number of clicks for everyday tasks or remove copy-and-paste errors that can creep in when moving data between apps.
Naturally, time saved can be used for creative thinking or deeper financial and valuation analysis to build robust company models, or more time spent on higher value-add tasks such as talking with investors and clients.
While we are seeing coding as an asset to an analyst, it may not be for everyone. It’s use-case driven, and not all will need to program code in Python.
However, it’s important for analysts to understand how it enables much deeper research.
There is a fine balance between the art of conducting investment research and applying coding to it. Those firms and analysts alike that successfully embrace coding will undoubtedly reap the benefits.
May your code be with you
Those who want to gain a competitive advantage are realising the value of programming, whether in automating repetitive tasks to improving research quality and financial modelling accuracy.
Ultimately, the aim is to provide a competitive edge for alpha generation for the buy-side and differentiated research among the sell-side.
Even for those not yet coding, the appetite for adoption is high.
This will only increase as analysts become more curious about seeing coding in action and show more enthusiasm to start programming as a consequence.
Download our Workspace Without Limits report and find out how financial institutions have shifted their mindset from asking themselves ‘why we are doing this?’ to ‘how can I adopt this?’
Learn how analysts work with data for company screening and relative valuation analysis; understand the output benefits of efficiency and improved quality derived from building models using programming; and explore how sustainable investing is the biggest area of opportunity when it comes to new workflow tools.
