Generating Alpha with Digital Receipts from High-Frequency Shoppers

This post was originally published on Big Data Protocol

Using Alternative Data to Capture Retail Trends Before Earnings Calls


Following the August 11 earnings call, Weight Watchers’ ($WW) stock fell 40%. HFS model data predicted this drop before earnings came out. The simple metric generated by the HFS model can be used by large funds a retail investors alike to capture alpha.


Amass Insight’s new proprietary data product, called HFS Stream, incentivizes a panel of high-frequency shoppers from the U.S., U.K. and Germany, covering 26 direct-to-consumer (DTC ) publicly-traded companies, to analyze their purchases via digital and physical receipt uploads. With this data, investors can gain an edge on expected ordering volumes and revenue prior to earnings announcements. For institutions highly familiar with other credit card transactional datasets, this is a good complement given the hyper-focused panel across two continents. Whether you are quant-based or fundamental, a multi-billion dollar fund manager or Robinhood trader — anyone can comprehend and use this dataset to generate alpha.

Looking at data going back to 2014, it’s worth highlighting a specific use-case for Weight Watchers International ($WW) in Q2 2021, and Wayfair ($W) in Q2 2020 around how a user would leverage this dataset for alpha generation.

Let’s take a look at the dataset:

Source: Digital and physical receipts from high-frequency shoppers at major retailers and subscription-based companies

History: Dating back to Q1 2014


Target Investment Style: Tactical (1–3 month trading horizon), event-based

Use-Case: Understand gross sales volume prior to earnings announcements

As depicted in the below graphic for Weight Watchers International ($WW), the data model predicted a large year-over-year decrease in sales volume during Q2 2021, updated on July 7th. Subsequently, the announcement by Weight Watchers on August 11th aligned with this model’s decrease and the stock experienced a ~40% price decrease over the 24 hours following the announcement.

As an investor, one has visibility into sales activity at the end of each quarter with a 10–45 day trading window until announcement.

In the below example for $WW, an investor could see a 39% decrease in sales QoQ, and 21% decrease YoY posted July 1st 2021. Presumably, the investor could create a short or put option position on $WW before the August 11th announcement during that 42 day window.

Weight Watchers International ($WW) Data Model Outputs:

Looking at the combination of 39% decline QoQ (adjusting for seasonality) and a 21% decline YoY, an investor would consider an underwhelming quarterly announcement in the weeks following.

This data product covers 26 publicly traded tickers, with 6 additional DTC companies going live in the coming months.

We’ll be rolling these out slowly on the BDP Marketplace in the near future. Join the alpha generation 🌙

About Big Data Protocol

Big Data Protocol is democratizing commercially viable datasets for the masses through a Web3 data marketplace. The Protocol tokenizes commercially valuable data through a network of +18,000 professional data providers and makes the data token liquid on Uniswap — thus making data into its own asset class. Users earn data by providing liquidity to data tokens.

The Protocol has a vast existing ecosystem of professional data providers by leveraging Amass Insights, founded in 2015 by our co-founders, Jordan Hauer & Mark Donovick. Amass Insights connects +18,000 professional data providers with over 10,000 consumers of data, who are primarily investment managers.

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Generating Alpha with Digital Receipts from High-Frequency Shoppers was originally published in Big Data Protocol on Medium, where people are continuing the conversation by highlighting and responding to this story.

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