The Rise of Data-Driven Investing

Because educated guess has a market price, in the form of advisory, textbooks, and the formulae you put inside the cell of a spreadsheet.

By conceptualizing economic interactions under uncertainty, there are 2 ways how it might be accomplished.

One is through a set of assumptions, the other is by using simulations through which we can clearly isolate classifications and patterns.

For decades, we have relied on the former approach due to having limited computational resources at our disposal.

But as these resources become cheaper, along with data storage and the variety of data, we can arrive at a better understanding of reality by using the latter approach.

The rise of data-driven investing“Your kings of the universe are no longer the folks wearing suits and going to galas.

It’s the folks that are crunching Python and going to meet-ups.

These are becoming the new masters of the universe.

” Gene Ekster, CFA; originator of the term “alternative data”.

Data-Driven investing builds on what models can achieve by enabling investors to achieve significantly more granularity from their analyses.

Through increasingly sophisticated techniques that can capture huge quantities of data, we now have the means to reveal behavior, trends, and patterns of enormous relevance when gauging the appeal of a potential investment.

Known as alternative data when applied to investing, there’s seemingly no limit to what kinds of information can be extracted.

Whether it’s credit card data allowing us to verify what consumers are purchasing; geolocation data that can track cell phones, or data scraped from airline websites that can tell us whether or not to invest in the travel industry, these non-traditionally sourced datasets are facilitating much greater insights into potential investment targets.

Data-driven vs.

Model-drivenSimply put, the more relevant data we have to hand, the more informed our decision-making becomes.

Narang’s Inside the Black Box identifies 2 distinct advantages of using a data-driven approach:Data mining is much less widely practiced than theory-based investing, so there is a far lower number of competitors.

It is also more technically complex which creates more rigid barriers to entry into this field.

Data-driven strategies can identify trends, patterns and behaviors that may not necessarily “fly under the flag” of an existing theory.

This expands the universe of potential outcomes.

Model-driven strategies, in contrast, are limited to capturing results that must be explained by existing equations and formulae, and thus generates a comparatively narrow set of conclusions.

Today, practitioners of data-driven investing are now identifying a range of new investment themes directly as a result of this new and improved accessibility to big data.

Takashi Suwabe a portfolio manager of Quantitative Investment Strategies at Goldman Sachs Asset Management, observes three themes in particular: Momentum, Value, and Profitability.

But it’s no use simply having more data at one’s disposal — the ability to harness this data and generate useful insights must similarly evolve…Data and technology: a match made in heavenGiven such a vast degree of data proliferation — more than any one investor can reasonably handle — the power to process all this data becomes crucial.

Thankfully, that processing power is increasing all the time.

Computing techniques are allowing seemingly disparate, and often somewhat abstract data to be collated, analyzed and structured into easier-to-interpret formats.

And data storage technology is also on the rise, allowing highly-scalable processes to be applied to enormous quantities of data.

According to data analytics platform Novus, three core structural fundamentals are needed to analyze data effectively:Data Foundation — an infrastructure that effortlessly collects, stores, aggregates, and normalizes all of your data sources automatically.

Analytics Engine — an intelligent analytics engine that allows you to pull, combine, screen, and analyze all your data in various ways.

Technology — the technology necessary to extract insight from your data and analytics by simply logging into a system.

But data-driven methodology remains far from perfect…The notion of “garbage in — garbage out” remains a pertinent one.

There’s no point in having lots of data to hand for the sake of it.

Investors still have to decide whether it is ultimately relevant and whether useful insights can be gleaned from it.

If data is fed into the system that has no connection to what we are attempting to predict, then spurious results will follow.

Furthermore, evidence suggests that not everyone is correctly utilizing the massive amounts of data available.

As former Hewlett-Packard CEO Carly Fiorina once succinctly observed, “The goal is to turn data into information, and information into insight.

” A lot of data may end up being ultimately meaningless, while even valuable data will require laborious treatment to ensure it is free of errors.

It is also worth verifying whether computers are processing data quickly enough to ensure it remains meaningful.

According to Narang, data mining strategies require “nearly constant adjustment to keep up with the changes going on in markets, an activity that has many risks in itself”.

As hardware continues to evolve, however, one would expect this concern to be more ably managed.

In the search for yield, data is proving to offer a competitive edge to investors, transforming the way in which decisions are being reached.

In response, asset managers are redesigning their operating models, hiring data specialists by the bucketload, and overhauling their data-handling infrastructure, including the industry’s biggest hitters such as Winton and Two Sigma.

It seems only a matter of time before the entire cross-section of investors wakes up to the enormous potential of data for helping them make smarter investment decisions.

But is data-driven investing rendering financial models obsolete?.Not quite.

At this stage, it would seem the two are augmenting each other to provide more robust analysis.

As observed by Third Point’s founder Dan Loeb, “When we add in the use of data sets and ”quantamental” techniques [a hybrid strategy involving fundamental analysis with quantitative investment models] that are increasingly important to remain competitive while investing in single-name equities, it is clear that our business is rapidly evolving”.

Originally published at www.


com on February 28, 2019.

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