Category Archives: Greg Robin Random Walk

Random Walk Email Intelligence has never identified more unusual promotional activity than over the past 30 days.   In June alone our systems have captured nearly 100 never seen before campaigns as consumer preferences shift.

HOME FURNISHING PROMOTIONS ACCELERATE TO NEW HIGHS

BIG, LOVE, W, WSM

In home furnishings our data indicates COVID pulled forward, not just several quarters but year’s worth of demand.  Now, with the backyard, garage space and wallets maxed out, consumers are shifting away from creating the perfect Instagram worthy at-home backyard oasis.

Random Walk Email Intelligence indicates record growth the discount campaigns in Q2 from Pottery Barn.

DEMAND FOR CASUAL ‘AT HOME’ FOOTWEAR FADING?

CROX, FL, SCVL

During COVID, Americans gobbled up footwear for the outdoors or comfort around the house casual footwear retailers benefitted and throttled back promotions.  However, this summer with consumers more spartan due to gas prices and $20 hamburgers they don’t appear to need so many sandals.

Crocs and Crocs retailers such as Foot Locker and Shoe Carnival have been blasting an all-time high volume of Croc’s specific discounts.

GET OUTDOORS ON THE ROAD MEETS $6 GAS

CWH, WGO, YETI

Without COVID checks, a free pass from work, and with $6 gas it appears road trip RV, van and mega SUV trips could be stalling.  Our email intelligence has tracked growth in promotional volumes for outdoor camping accessory related businesses such as Camping World, Winnebago, and Yeti.

Into summer camping season Camping World has blasted out an all-time high volume of significant and steep discounts.

Our data visualizations allow quick identification of outliers within a specific sector.  Find out which brands are losing organic demand and responding with increased “push” discounting campaigns to their leads

Download the pdfhttps://ranwalk.com

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.

Our data ensemble detects Blue Apron giving away food to attempt to bring back past customers.