Quantitative investment firms such as Two Sigma, AQR, Dimensional, etc. have built their success on a foundation of data and analytics. Asset owners, financial advisors, and fundamental asset managers now have an opportunity to use similar analytics and approaches to improve their decision making and better meet their investment objectives.
The barriers to using data analytics to make investment decisions have been greatly lowered over the last few years, with increased availability of data, low cost computing power, and tools for analysis and visualization. However, tools alone cannot provide results, and the critical element to generating results from data analytics is understanding how decision makers psychologically and physically want to interact with data and analytics. This makes data analytics less of rocket science and more like fishing.
As part of a recent presentation I made at a Moody’s Analytics conference, I created the graphic above comparing data analytics to fishing:
In an organization, there are folks who
- Like to fish on their own – i.e. they just want access to data and then will perform their own analysis and come to their own conclusions
- Prefer to buy fish from the market – i.e., they like to have the data gathered and cleaned for them, so that they can then interpret the results
- Like to eat their fish in a restaurant, prepared by a chef, and served on a platter – i.e., they want the results delivered to them
Investment decisions can be improved using analytics by identifying the specific preferences of the person making decisions. Note that I intentionally use the word person, and not organization or team. This is to emphasise that effective solutions need to take into account the individual decision maker’s approach and biases.
Several asset managers and financial advisors that I have spoken to see the value that analytics can bring to their decision making process but struggle with application. One potential reason is that decision makers might be expecting customized recommendations specific to their situation. For example, a portfolio manager at a hedge fund might want investment recommendations based on cross referencing their personal investment approach with social chatter, but is provided with expensive tools that only get them the raw data, and its up to the portfolio manager or their analyst to make sense of the relationships and put it into practice.
Similarly, an investment advisor’s goal might be to identify the portfolio of assets that best meet their client’s behavioral and economic objectives. While they might have access to several tools and databases, they might prefer being served the portfolios based on some objective and subjective criteria on a regular basis.
In these examples, human experience can ensure that we don’t end up providing the customer in the restaurant with a fishing pole.
Further reading: