The Top 5 Most Valuable Sources of Alternative Data for Investors in 2023

Alternative data is transforming investing, but with so many data types now available, it can be hard to know where to focus your efforts. Based on my experience as a data analyst, I recommend concentrating on these 5 alternative data sources for unlocking unique insights in 2023:

  1. Web Data
  2. Mobile Location Data
  3. Social Media Data
  4. Financial Transaction Data
  5. Sensor Data

In this article, we‘ll explore compelling examples of each data type, along with expert analysis of how you as an investor can turn these data streams into an informational edge. Let‘s dive in!

1. Web Data: Tapping Online Breadcrumbs

The web is filled with digital breadcrumbs that offer clues into a company‘s performance and outlook, if you know where to look. As an investor, focus your web data collection on these high-signal categories:

  • Job Listings: Watch for key skills in demand, which indicate strategic priorities. For example, a spike in AI roles could signal a bigger push into machine learning.
  • Company Review Sites: Employee sentiment on Glassdoor offers workforce insights that can impact retention, productivity, and more.
  • Product Review Sentiment: Positive vibes on Amazon and elsewhere suggest strong customer satisfaction and loyalty.
  • Search Volume: Look to Google Trends for demand signals for key products. Surging search volumes could precede sales spikes.

According to a Stanford and University of Chicago study, hedge funds using web scraping to gather alternative data generate excess returns of up to 21% annually. That‘s a stellar payoff for those putting in the work to mine web data.

Hotel Occupancy Rates

One especially useful web metric is hotel occupancy rates. Scraped daily from brand websites, occupancy data offers visibility into hospitality revenue performance.

For example, back in 2016 a hedge fund named Sunstone Partners Capital used web-scraped occupancy data to foresee trouble brewing for Hilton Worldwide Holdings. Sunstone saw Hilton‘s occupancy rates stalling months before management lowered guidance. This early signal allowed Sunstone to trim positions, sidestepping losses from Hilton‘s 20% stock dip after the cut to earnings estimates.

The Takeaway

With web scraping and targeted analysis, digital breadcrumbs can deliver investment alpha. Stay focused on high-signal web metrics like job postings, reviews, search trends, and occupancy data.

2. Location Data: Mapping Competitive Advantages

Location data from millions of mobile devices offers hard-to-find insights, revealing visit patterns and site activity analytics. Investors skilled at translating location signals will reap rewards.

Store and Plant Traffic

By monitoring mobile pings at store and plant locations, investors can estimate traffic volumes. More visits and longer dwell times typically suggest higher sales and production.

For example, analysis by Thasos Group of mobile location data found shopper visits to Best Buy stores jumped 21% in November 2018 vs. the previous year. This pointed to strong holiday sales months before Q4 results came in, allowing informed investors to profit.

Occupancy and Operations

Location data also provides visibility into occupancy rates and operational activity across sites. Higher hotel occupancy percentages generally equate to greater revenue, while surging late night plant activity could flag increased manufacturing output.

Back in 2018, using location data Thasos Group projected accelerated Model 3 production at Tesla‘s California plant weeks before Tesla confirmed it publicly, capturing a 9.1% gain for clients. The timeseries view provided unique insights.

Tesla Plant Activity from Location Data

Thasos Group tracked mobile location pings at Tesla‘s plant to forecast growing Model 3 output ahead of Tesla‘s announcement

The Takeaway

Harness location intelligence to map competitive dynamics and operational activity. The bird‘s eye view provides hard evidence to complement traditional financial metrics.

3. Social Media Data: Taking the Collective Pulse

Billions of social media users provide a real-time barometer for brand sentiment, if you tune in to the right signals. Focus social listening on these high-value metrics:

  • Sentiment – Analyze brand mentions and conversations across social platforms to gauge consumer perceptions. More positive sentiment bodes well for sales.
  • Volume – Track hashtag volumes and keyword mentions for visibility into brand awareness and interest levels.
  • Engagement – Measure likes, shares, and reactions to score content resonance and virality. Higher engagement signifies greater consumer enthusiasm.
  • Demographics – Study audience growth and category followers to spot emerging opportunities in key cohorts.

Research suggests social media analytics can boost investment returns. A University of Manchester study found tweets predicting earnings surprises yielded excess returns of nearly 7% annually. Meanwhile, J.P. Morgan mined Reddit to gain an early read on GoPro‘s declining popularity, prompting them to downgrade the stock months before GoPro announced falling sales and layoffs.

Peloton‘s Pandemic Surge

When pandemic lockdowns hit, social listening provided early signals of surging demand for at-home fitness company Peloton. Retail investors armed with insights from Peloton‘s soaring social engagement were able to ride the stock up over 400% in 2020.

Peloton Social Media Volume During Pandemic

Analyzing Peloton‘s social media volume and sentiment revealed surging interest in 2020

The Takeaway

Social analytics provide real-time awareness of brand traction. Combine social data with traditional metrics to take the market‘s pulse.

4. Financial Transaction Data: Visibility Into Spending

By tapping data on credit card swipes, wire transfers, and other transactions, investors gain an unprecedented view into consumer and B2B spending patterns.

Card transaction data reveals sales volumes across sectors, while cash flow information sheds light on everything from supply chain payments to M&A deals. According to McKinsey, analysis of credit card data improves sales forecast accuracy by 20-50%.

Major financial data providers like BBVA, Mastercard, and SWIFT offer aggregated transaction analytics to investors, while startups like RavenPack provide tools to extract cash flow insights from wire transfer information.

Let‘s explore two compelling transaction data use cases:

Earnings Forecasting

By analyzing merchant transaction volumes, investors can predict earnings surprises. Back in 2015, Ancerno analysts mined Mastercard data to forecast eBay‘s earnings weeks early, allowing profitable trades around earnings announcements.

M&A Tracking

Wire transfer data reveals M&A deal payments, enabling investors to detect acquisitions before public announcements. A study by Greene and Sorescu found analyzing cash flow statements provides advanced warning of 53% of deals.

The Takeaway

Follow the money to unlock spending insights. Transaction analytics shine a light into consumer behavior and business activities.

5. Sensor Data: Eyes in the Sky

Finally, leveraging data from internet-connected sensors opens up new visibility. Focus sensor analytics on:

  • Satellite Imagery – Gain activity insights for construction, mining, farms, and more by studying imagery. Higher activity levels can impact markets.
  • Vehicle Sensors – Traffic detection sensors allow analysis of car volumes, wait times, and drive-thru activity. More traffic generally increases sales.
  • Supply Chain Sensors – IoT sensors tracking location, temperature, delays and other logistics data provide operational visibility.

A study by J.P. Morgan found hedge funds using satellite imagery generate returns exceeding 10%. And Waydev analyzed drive-thru wait times using computer vision, allowing more accurate forecasts of fast food earnings.

McDonald‘s Drive-Thru Activity

One compelling sensor data use case comes from analyzing drive-thru activity at fast food chains. Computer vision company Waydev mounted cameras to effectively count cars in drive-thru lanes across hundreds of McDonald‘s locations. More cars in line pointed to higher sales.

This image processing data allowed Waydev to accurately predict McDonald‘s earnings, including a Q1 2019 sales beat, demonstrating the power of sensor analytics.

McDonald's Earnings Prediction with Drive-Thru Data

Waydev used camera data to count cars in McDonald‘s drive-thrus, predicting earnings (Source: Forbes)

The Takeaway

Sensors massively expand data inputs. Evaluate opportunities to track activity via satellite imagery, cameras, IoT sensors and more.

Alternative data opens new doors to gain an investing edge. While some data types like web and social metrics are more easily accessible, don‘t overlook the potential in location, sensor, and transaction data.

The key is determining the right question you need answered, then utilizing both alternative and traditional data sources in tandem to derive those insights. With the tools and techniques outlined here, you‘re equipped to tap these valuable data streams and unlock informational advantages.

The data revolution is accelerating. Will you lead, or be left behind? With the right strategies, alternative data offers a path to boost performance. The time to start is now!

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