Alternative data investing

Seeking alpha from crowdsourcing and collective intelligence investing

Alternative data has become a valuable tool for investment management firms seeking alpha. Collective intelligence investing and the use of crowdsourcing platforms is growing in popularity, creating new growth opportunities and new risks. Our two reports explore sources of alternative data, its impact on investment strategies, and the challenges it presents in firms’ quest for alpha.

Tapping the collective wisdom of the crowd

Can investment managers benefit from the information provided by online communities and crowdsourcing platforms that focus on investing and trading-related discussions? As investment managers increase their use of these alternative data sources, they are looking to the wisdom of the crowd for potential alpha advantage. We refer to this process of generating market insights from online communities and crowdsourcing platforms as collective intelligence investing.

Two key phenomena—content-creation web 2.0 technologies and advanced computing power—appear to be the key enablers behind the collective investment movement. Web 2.0 has enabled online users to generate and share content on a diverse array of platforms. And the rise of advanced computing and analytics capabilities have allowed investment managers and information support vendors to generate real-time market insights from vast quantities of data.

Collective intelligence investing: The quest for alpha generation via alternative data brings new risks

Evaluating collective intelligence investment sources

The insights gleaned from collective intelligence investing covers a range of information—from trading signals, investment themes, and investment research to earnings estimates, quantitative algorithms, and asset allocation strategies—with each dataset contributing to an investment-decision mosaic.

But not all investment-related chat rooms, online communities, and crowdsourcing platforms are created equal. Research indicates that smaller closed communities (compared to open communities), could be better positioned to provide alpha-generating ideas, as they tend to boast a better signal-to-noise ratio.1,2 The challenge for investment managers planning to use collective intelligence and crowdsourcing is to identify and sort the useful and dependable signals from the noise across different platform types.

Balancing risks and rewards

While mature online communities and crowdsourcing platforms offer the potential for increased alpha rewards, they also present risks that should be carefully negotiated, including:

  • Community engagement
  • Data integrity
  • Material nonpublic information
  • Model outputs
  • Information security

The impact and priority of each risk type tend to vary with the investment manager and the unique processes and scale of the online communities and crowdsourcing platform type.

Download the full report to learn more about collective intelligence investing.

Larry Cao, CFA, “Crowdsourcing investment insights: How and why it works,” CFA Institute, May 9, 2014.
Len Costa, “Do Good Investment Managers Give Away Great Ideas? It’s Entirely Rational According to New Study,” CFA institute, June 25, 2012.

A potential agent for change

Alternative data will likely transform active investment management over the next five years. Hedge fund management, long-only mutual funds, and even private equity managers will be impacted. Firms that don’t update their investment processes to incorporate alternative data could face the strategic risk of being outmaneuvered by competitors that effectively incorporate big data investment into their securities valuation and trading signal process.

Seeking an information advantage

The lure of alternative data is largely the potential for an information advantage over the market regarding investment management decisions. True information advantage has occurred at various times in the history of securities markets, and alternative data seem to be its most recent manifestation. Where carrier pigeons once afforded an information advantage, today's fortunes may rest on successfully accessing and analyzing vast volumes of big data.

Speed and knowledge are advancing with the use of advanced analytics, and there will be no waiting for laggards, nor turning back.

Beware—and be ready for—the risks

An organization's inability to spot, assess, manage, and respond to strategic risks may affect its critical assets, financial performance, or reputation. Estimating the risk and reward equation seems more of a challenge for alternative data than for other types. The risks could be higher; however, the rewards may also be greater.

Information advantage can be hard to come by in current markets—and any edge, even a narrow timing advantage, may yield a more effective trading signal, algorithm, or investment model. Any thoughtful new strategic direction should consider four potential risks—data, model, regulatory and talent—all related to incorporating these new sources of data in investment selection processes.

Looking ahead

The signals for the future adoption of alternative data seem both positive and mixed. Research on the adoption of alternative data finance by investment firm managers indicates that the question to ask may not be whether, but how quickly alternative data—and its collective intelligence and crowdsourcing counterparts—could become mainstream. While there are certainly risks associated with incorporating these emerging and evolving sources into investment-decision processes, there may also be strategic risks associated with not doing so. With improvements in advanced data analytics and increasing availability of data to analyze—along with greater risk awareness and management—the stage seems set for investment management firms to supplement their decision processes with alternative sources of data.

Download our reports to learn more about the rewards, risks, and adoption of alternative data and collective intelligence in investment management.

What is alternative data?

Alternative data consists of unstructured, big data sets drawn from sources such as:

  • News feeds
  • Social media
  • Online communities
  • Communications metadata
  • Satellite imagery
  • Geospatial information

A well-known application of alternative data is satellite imagery analysis of parking lots, replacing the old-school approach of physical foot-traffic counts with clickers. In this case, alternative data approaches are faster and more comprehensive than physical counts, leading to an information advantage—even though the data sets were measuring similar consumer activities.

Investment management firms considering alternative data to augment investment selection and portfolio construction processes find that this one change can be highly complex because it cuts across the operating model. Deloitte professionals have the necessary experience and are dedicated to finding innovative solutions for emerging business tools such as AI and alternative data.

Learn more about Deloitte’s expertise and capabilities.

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