“Alternative data” is polarizing. You either hate it or you love it.
What is alternative data?
In the financial industry, “alternative data” refers is information about a particular company that is published by sources outside of the company, which can provide unique and timely insights into investment opportunities. Actually, for anyone outside of the industry, “alternative data” is just data, often categorized as big data, which means that they may be very large, complex and often cannot be handled by software traditionally used for storing or handling data.
Examples of alternative data sets would be foot traffic, web scraped data, web traffic, app data, satellite imagery, manufacturing data, NLP analysis, credit card transactions, product reviews, social media and more. AIMA estimates that there will be 5,000 different alternative datasets available by 2024. They will vary in depth, quality, breadth, uniqueness, history and many more.
Driving success with alternative data or why is alternative data important
To simplify this very complex process, there are three main ways an investor can outperform: Selection, timing and leverage. In order to accomplish that, they need to have the top talent, a solid and consistent strategy and the tools for execution. Based on the global hedge fund benchmark study of 2021 done by AIMA, Simmons & Simmons and Seward & Kissel LLP, more than 88% of hedge funds (below $1B in AUM) struggle to deliver performance.
Asset managers who succeed to drive higher performance and increase their competitive edge, are consistently leveraging new technology and data. The best can leverage hundreds of datasets with a team of over 20 data scientists and spend over $30M dollars annually. 76% of hedge funds above $1B in AUM are increasing their investments in alternative data and machine learning as they seek to gain an information edge to meet their client’s demand in both efficiently managing risks and generating alpha. While smaller asset managers are struggling to adopt new technologies, mainly due to time and money constraints.
New data users will know and understand its value but have a very hard time implementing it into their daily processes and due diligence. While this is no easy task, there are some guidelines that can help us with this process.
How to build a data realistic strategy with alternative data:
At Lagoon we advise our clients to consider a simple 5 layer strategy when incorporating new data in their investment strategy:
The 5 layers to help build an alternative data strategy:
- Strategy and Vision — Understand how alternative data can help you better understand the investment strategy and assumptions. Start with the fundamentals, what is the company reporting and highlighting in their earnings. What are the main KPI you’re tracking? What does the company do that influences them? What data could help me understand those KPIs better? Where are my blindspots and what risks do they pose? This is the starting point, don’t buy any data if you don’t have this figured out.
- Identify Datasets — Identify the relevant data sources that could be of value for your investment goals, starting with your strategy. Are you investing long term or short term? Ask yourself – if you were the CEO of the company what would be the most important KPIs to track? What is the data that can be a good proxy to that KPI? What is the data frequency needed? For example, if you’re investing in e-commerce companies, understanding their web traffic, app usage and advertisement metrics would be highly valuable to assess their overall strategy and performance. Only than, start looking for this data. You can use the help of companies like Neudata, Eagle Alpha or Datarade to find the most relevant datasets for you.
- Assess Value — Evaluate the expected ROI of buying the data. If you plan to invest $100M in a company or $1M there’s a big difference in what kind (and how much) data you should buy. If you’re going to do a one time, mega-buy, or day trading has a huge impact as well. Ask yourself – If I had this information, how would I act differently? Would I take more risk on a specific name or allocate capital differently?
- Implementation — Implementing the right technological infrastructure in order to retrieve value from the data is key. You can’t really analyze 1B data points in excel. Your team needs the right tools and expertise to evaluate the dataset, incorporate it and take it to production, being top notch, all the time. You can use simple Python notebooks, AWS, snowflake, databricks etc. Make sure you build the right infrastructure that fits your needs or use third party
- Insights and Evaluation — The last and crucial step is to decide which insights should be pursued and drive decision. You’ll be missing a big part of the value if you’re using the data in order to just confirm what you already know or think. The whole point is to uncover knowledge that you (and the market) don’t already know and that requires a level of risk-tolerance and fearlessness.
There is no doubt that successfully incorporating alternative data into your decision-making process requires a lot of effort, but the rewards far outweigh the costs. Using data is a must in order to gain a competitive edge. Having the right technological capacity to collect, store, and analyze data requires effort and time.
Lagoon is an intuitive data tool for professional investor, enabling them to seamlessly focus on the relevant and reliable data sources, to build and monitor specific data KPIs critical for investment analysis and risk hedging.
With over 50 data sources such as web traffic, web scraping, credit cards, foot traffic, social media, app data and more…and built as an easy-to-use solution to generate insights and signals from the data. Investment professionals can efficiently zero in on opportunities and risks by using a combination of tools & expertise to analyze overwhelming volumes & varieties of data.
Schedule a demo, today.