Category Archives: Greg Robin Random Walk

OCTOBER 2021 UPDATE

Bed Bath & Beyond (BBBY) Our system uncovers steep discounts, increased promotional campaigns, revealingly tepid demand foretelling, reduced earnings, guidance.

Dicks Sporting Goods (DKS) Promotional ensemble captured reduced significant incentive emails, steep discounts, translating into continued robust revenue growth, improved margins.

Promotional Index Our first data product has launched providing the most robust and quantitative view of nearly 300 brands promotional cadence dating back 4 years.

While most “home” related retailers were reducing their promotional campaigns and email sending volumes due to strong demand and supply chain constraints BBBY’s was doing the opposite.  For much of 2021 Bed Bath and Beyond crafted the narrative of a revamped, growing e-commerce platform and a turnaround with new in-house products.  While management sold the turnaround tale, our email intelligence detected they were ramping up “push” email offers to all-time highs in the face of stalling organic demand.   With a long history of blasting out never ending 20% off coupons, our systems tracked an increase in the maximum discount implied and more frequent pushing of the same offers to leads.

The Random Walk Promotional Ensemble enables investors to quickly compare email discount campaign cadence on weekly basis with prior periods.  As seen below both total promotional emails as well as our significant incentives category have been tracking at higher levels than any period over the past 3 years.

For Dick’s Sporting Goods (DKS) our promotional ensemble revealed the opposite situation as Bed Bath.  As demand for sporting goods and a return to youth team sports accelerated this summer, DKS dramatically reduced nearly every form of promotion.  Our systems tracked a 65% reduction in incentive promotions. 

In both BBBY and DKS. investors focused on proxying revenues with expensive transactional failed to capture the extent of the inflections.

BBBY missed revenues by 3% with revenue guidance just 2% below expectations.  The explosive 300% growth in promotional activity provided a clearer picture of the dramatic decline in organic demand consistent with the 25% drop in share price that ensued.

For DKS, while revenues did blast by consensus, our ensemble correctly tracked higher merchandize margins translating to bottom line improvements with a blowout $2 earnings beat and a 25% increase in earnings guidance for the year.

The newly launched Random Walk promotional index data product provides the most robust quantitative view of promotional cadence available.  Uncover demand changes as management responds to them weekly.

With more than 6 years of historical data tracking email promotional activity from nearly 300 consumer brands we are excited to launch our first data product!

This fall select clients can access structured weekly data for this first time.

Investors will be able to quantify and help answer the following:

• Changes in campaign frequency
  • Did a brand run more marketing campaigns in order to drive sales?
  • Did a brand reduce campaigns as demand increases?
• Growth in customer and lead lists
  • Has a business grown its lead list?
• Intensity of promotional offers and discounts
  • Did the size of discount increase as a seasonal sale progressed?
  • Did the coupon specify a higher discount than prior periods?
• Changes in promoted products
  • Are there changes in which products are being discounted?
• Changes in targeting strategy
  • Is a brand struggling to hit sales quotas engaging in more “blasts” to its entire list?

Robust 5 year returns with no correlations to the major averages

Superior Performance from 2017-2021

Walking Earnings Signals, performance is as follows for entering a trade on the day before the earnings event and exiting on the earnings event date. In practice, the signal would be available shortly after the end of the fiscal quarter and an investor would have a few weeks between the end of the quarter and the earnings announcement date to build this position

Promotional Activity Predicts Share Performance

In this study we’ve illustrated a use case in which a company’s promotional activity data can be used to successfully predict how that company’s stock will move at its next earnings announcement. With strong standalone performance, this study demonstrates that the promotional data can be a valuable input into the decision making process of both discretionary and systematic investors who wish to adjust positions, confirm positions, or discover new trading opportunities heading into earnings events.

Discretionary investors may also wish to use this data to identify inflection points (both positive and negative) in consumer stocks for opportunistic strategies or use this data to identify downside risk for existing positions. Systematic investors may wish to derive ranks with monthly or weekly frequency based on these inputs to drive a long/short decile portfolio.

contact us to inquire about adding the promotional ensemble as a data feed

Download the PDF

Why Credit Card Data Fails

Big data in investing is here and we are a part of it. However, some institutional investors are getting confused as to the end zone.

There are several expensive credit card transaction products that correlate well with coincident revenues some of the time. With Wall Street investors trained to drool at regressions, error bands and correlations, the allure that these products can do the decision making is appealing. While often accurate in predicting some component of revenues, this “data” approach has herded investors into a series of grossly inaccurate conclusions on consumer stocks in 2017.

To be specific, the largest big-data transaction vendors led investors right to slaughter in a wide range of mall based retailers this spring. Ironically, the data generally projected somewhat accurate revenues in names including JC Penny (JCP), Macy’s (M),  Foot Locker (FL), Michael Kors (KORS), Vitamin Shoppe(VSI) and several others. In summary, the transactional data indicated results were to be inline, an expensive and grossly wrong conclusion.

Why did this expensive credit card data fail miserably? Look how tight those error bands are! The transactional data provided no context into organic demand of these products. Credit card receipts did not factor in the desperation among retailers engaged in their steepest discounting in history. Share prices collapsed and investors overly focused on transactional data were left with a classic Pyrrhic victory, attempting to take solace with the mantra of “but we were right on revenues”.

Other times, the transactional data is correct most of the time when its business as usual, then misses the largest move because something unusual has occurred the sample did not detect. So investors are correct, when there is no money to be made, and wrong right when a business is about to be dramatically revalued due to a massive inflection in demand. Vitamin Shoppe (VSI) comes to mind in this scenario. The Random Walk ensemble captured the 50% plus collapse in business as customers migrated to Amazon and stopped by placebo pills altogether. Through a combination of click stream data, review volumes, email responses our more robust, less precise approach generated alpha for our clients.

In terms of risk reward, compounding the problem is the herding mechanism related to this mass consumption transactional data. Every highly transactional, well resourced hedge fund is viewing the identical data sets, yielding the same conclusion. This now incorrect herd exacerbates the share price collapse as panicked analysts- now uncommitted shareholders all attempt to rush through a tiny exit door at once.

In contrast, our more robust and diverse data ensemble can be less precise for coincident revenues, but more ACCURATE in predicting changes in consumer behavior that ultimately influence share price. Our view is that new customer acquisition volumes, organic demand for products and repeat customer frequency drive share price. These are the metrics we focus on predicting because the relate future growth. We don’t want to predict the present or past.