Counting Wal-Mart cars to forecast earnings | Hacker News Reader

Counting Wal-Mart cars to forecast earnings

classic.cnbc.com

50 pointsduck12 comments

I don't think you should be so quick to dismiss this. While one variable alone is obviously not a good predictor, don't forget that these firms often have many other sources of data. If they can figure out what the average spend per customer is and combine that will some satellite traffic analysis, that should be enough to predict a trend.

Also, some analysts don't necessarily need an exact figure for a quarter. Knowing that Wal-Mart traffic is trending up or down might be enough enough information to make a profit. Information is valuable to Wall Street, even if that information seems insignificant at first glance.

Most importantly, the analysts using the satellite images can look look at historical earnings reports to see whether the tool has any utility before they use it to make a prediction.

In fact, I'm almost positive they did (in part because the article says so, more or less).

EDIT: In particular, the regression mentioned was no doubt based on the relationship between historical earning reports and the sat imagery (once controlled for daily, weekly, monthly, and seasonal variation, etc.)

When American Express was indirectly involved in fraud (salad oil IIRC), the stock tanked, because analysts sensibly realized that this public litigation would shake consumer confidence in them - arguably, trust is the very thing that a credit card company sells. But Warren Buffett went down to the supermarket to observe what consumers were actually doing - and saw that they were using them as normal. So he put in most (IIRC) of his wealth into it and more than doubled it (IIRC).

This satellite imagery analysis is doing quantitatively what he did qualitatively.

This is a perfect example of why you need to be super wary when investing on your own -- the technology, resources, and knowledge in the hands of these big firms is simply unattainable by individuals.

On the other hand, you can exploit much smaller pockets of profitability as a small time investor.

I agree.

Real profit from investment comes from finding something that is below "true" price and buying it, or finding something that's above "true" price and selling it.

When small investors try to evaluate stocks and bonds, they compete with just about every hedge fund, fund manager, investment bank prop trading group.

On the other hand, small investors can attempt to profit by looking at investments that allow for relatively small dollar profits. For example, small real estate investment properties (e.g. houses with rental apartments) with values of, say, $300K, provide potential profits that are too small to be worth a large investor's time. In contrast, smaller investors should find the time spent vs. potential upside quite reasonable, and will have to compete only with other small investors -- which makes it more likely they can find investments that are a good deal.

I think that people who play with unmanned aerial vehicles (drones) and image analysis just found themselves a business model.

They're not really counting cars, they do "parking lot fill rate analysis". Much easier to do - you just subtract the percentage of the parking lot that has the same color as the ground.

I see two main issues with this analysis. First, the amount of profit per customer visit is not necessarily constant, and can depend on things such as gas prices (e.g., cheap gas encourages two shopping trips per week, even though the total amount spent on groceries remains the same; expensive gas makes some people take the bus).

Second, is even the amount of visits well estimated? The article mentions things such as 0.7% accuracy in profit estimation. Random fluctuations in the number of car passengers, time of visit etc. (how often are these images taken anyways: once a month, or ten times an hour? the article doesn't mention) can lead to much greater variations than that.

> First, the amount of profit per customer visit is not necessarily constant, and can depend on things such as gas prices

Given the scope of the company doing this kind of analysis, I don’t think variance is going to be significant. Given a large enough sample or pool of data, some kind of “regression to the mean” effect is going to kick in. Remember it is all “estimation” in the end anyways, and I can see the value in having some kind of observable data as opposed to data based on “honest” reporting by those you are trying to study.

I don't get how counting cars in Wal-Mart and comparing rate of change over time can really help predict profit margin.

The average size of basket (retail sort-of-equivalent of ARPU), profit margin on goods, etc are all equally important but easily fluctuating factors which also make up profit projection.

Even in the article says that the 4% increase in cars parked in June this year over last was due to extra cost-slashing WalMart was doing... well that can lead to lower profits per customer so even with more customers coming in the store it could lead to a net 0 profit gain (but more merchandise shifted)

It's like saying I can guess the value of a in a = x * y * z by just monitoring the change in z, when all 3 are variables.

If you know something about the probability distribution of x and y, that might work out better than you expect.

Next they are going to tell us the amount of bandwidth that amazon uses directly reflects how much they make.. no kidding.

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