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Hello,

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welcome back to Papers with Backtest podcast.

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Today,

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we dive into another algo trading research paper.

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We're looking at the growing world of alternative data and specifically,

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yeah,

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the insights we can glean from information scraped directly from the web.

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It's a fascinating area,

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definitely.

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And it really is.

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And,

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you know,

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recent research indicates just how significant it's becoming.

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I saw something like a

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full 50%

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of institutional investors.

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Yeah,

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that's right.

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Half of them are planning to ramp up their use of alternative data pretty soon.

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Wow.

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It's a striking statistic,

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isn't it?

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It really underscores the shift we're seeing in how investment decisions are getting informed.

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Absolutely.

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And,

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you know,

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all the different types of alternative data out there,

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web scrape data,

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well,

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it seems to stand out as particularly popular.

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Exactly.

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So,

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okay,

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we know it's popular,

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but when we talk about web scrape data.

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What exactly are we referring to?

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Like,

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what does that actually entail in practice?

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Right.

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Let's unpack that.

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Essentially,

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it's data that's automatically collected from publicly available websites.

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Right.

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Just out there on the internet.

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Exactly.

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Specialized companies use programs,

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bots basically,

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to periodically harvest this information.

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Okay.

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So they're just constantly scanning and pulling data?

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Pretty much.

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Or sometimes they can get it more directly through public APIs.

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Ah,

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application programming interfaces.

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Yeah,

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exactly.

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Which let computer systems talk to each other,

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share info in a structured way.

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Makes it a bit cleaner sometimes.

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And there are companies that specialize in this.

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Oh,

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yeah.

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You've got vendors like

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Quandl,

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Saver,

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Thinknum,

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Yipit.

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Quite a few operating in this space now.

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Gotcha.

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What's really fascinating here,

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though,

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is just the sheer volume of information we're talking about.

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Right.

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The internet is just vast.

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It's incredible.

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Something like,

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what,

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4 billion web pages containing around 1.2 million terabytes of data.

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That's hard to even comprehend.

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It is.

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And hidden within all that is potentially,

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well,

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a treasure trove of signals for investors,

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if you know where to look and how to interpret it.

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Absolutely.

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So let's think about the kinds of data you could pull.

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Job listings,

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maybe.

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That's a great example.

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Like a company that's aggressively hiring.

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That often means they're growing,

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right?

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It just makes intuitive sense.

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It does make sense,

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generally.

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More hiring often signals.

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Expansion,

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investment,

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good things.

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But I guess,

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could it also signal something else?

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Like maybe problems?

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High turnover or restructuring?

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That's a really crucial point.

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Context is absolutely key with any data,

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but especially alternative data.

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An uptick in job postings alone,

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well,

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it might not tell the whole story.

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But if you start to layer in other information,

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then the picture can become much clearer.

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Okay,

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like what kind of other info?

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Well,

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take company ratings,

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you know,

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on sites like Glassdoor.

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Ah,

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right,

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employee reviews and ratings.

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Exactly.

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So if you see a company's ratings going up,

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getting better,

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especially if that's happening at the same time they're increasing their job postings.

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Oh,

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okay,

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so hiring and people are happier there or getting happier.

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Precisely.

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That could be a really compelling indicator,

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positive momentum,

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maybe a sign of a healthy,

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expanding organization.

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That makes a lot of sense.

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It's about connecting the dots,

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looking at multiple signals.

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You got it.

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And it's not just about like internal company stuff like hiring,

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is it?

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You mentioned online retail data earlier.

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Yeah,

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that can be incredibly telling too.

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Think about e-commerce sites.

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Okay.

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High product rankings,

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you know,

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best sellers.

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Yeah.

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That often suggests strong sales.

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Sure.

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If something is flying off the virtual shelves.

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Exactly.

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But conversely,

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if you suddenly see a lot of heavy discounting on products.

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Ah,

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yeah.

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Big sales,

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price cuts.

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That could be a red flag.

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Might point to weaker demand,

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maybe inventory buildup they need to clear.

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Okay,

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this is starting to paint a picture.

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But maybe,

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maybe a concrete example would really drive it home.

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This is where I started to see the real potential.

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Yeah,

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definitely.

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There's a really good example focusing on Amazon back in late 2018.

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Amazon,

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okay.

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What happened?

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So picture this.

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It's December 26th,

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right after Christmas.

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Amazon puts out a press release.

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Announcing?

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A record-breaking holiday season.

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They were quite specific,

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too.

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Oh,

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yeah.

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They said the Echo Dot was their number one top-selling product across all categories on Amazon.

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Wow.

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Number one overall.

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Not just devices.

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Exactly.

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And that the Echo Dot and the Fire TV Stick weren't just the top Amazon devices,

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but they were the best-selling products,

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period,

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across all of Amazon for the holidays.

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That's huge.

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And they also mentioned millions of Prime members using Alexa for voice shopping,

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GIFs,

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devices,

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everyday stuff.

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Okay,

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so a big announcement.

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What happened next?

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Well,

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here's the kicker.

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Amazon stock price,

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it surged massively by about 23%.

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23%

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just from that announcement?

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Seems like it.

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And crucially,

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during that same period,

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the S&P 500 was essentially flat.

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Whoa,

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so the broad market wasn't doing much,

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but Amazon shot up.

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Right,

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which strongly suggests that the market hadn't fully priced in just how successful Amazon's holiday season had really been.

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It was news.

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Exactly.

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It sounds like news.

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Maybe not to everyone.

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This is where the alternative data comes in.

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This is exactly where it comes in.

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For those tracking it,

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this surge might have been,

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well,

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less of a surprise.

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How so?

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Think at them.

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One of those vendors we mentioned.

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Yeah.

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They were using web scrape data,

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specifically from Best Buy's website.

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Okay.

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Tracking Amazon products sold through Best Buy.

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Precisely.

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And their data actually showed the increasing sales strength of Amazon products,

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like the Echo devices,

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throughout the holiday period.

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Starting way back on Black Friday.

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So weeks before the official Amazon announcement.

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Weeks before the data was already indicating strong performance.

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That's the information advantage right there.

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It really can be.

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And it wasn't just some vague signal about Amazon products.

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No.

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Best Buy's website data also seemed to corroborate Amazon's specific claims.

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It showed the Echo Dot and the Fire TV Stick as top sellers in their respective categories on Best Buy's site too.

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So independent validation from a major retailer's own data,

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that adds a lot of weight.

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It really does.

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Makes the signal much more robust.

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Okay,

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that's compelling.

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But you mentioned job listings earlier,

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too.

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Did that play a role here?

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It potentially adds another layer,

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yes.

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If you look back throughout 2018,

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before the holidays,

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the number of open job positions related to Amazon Alexa on Amazon's own corporate careers website,

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it saw a remarkable increase.

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How much?

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About 53%.

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It went from around

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500 postings.

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to over 750 by year end.

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Wow,

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a more than 50%

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jump in Alexa related jobs?

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Right.

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So for investors tracking that data point throughout the year,

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it suggests a major strategic push,

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significant investment in the whole Alexa ecosystem.

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Which might make you think,

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hmm,

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they're betting big on Alexa.

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Maybe the related hardware sales will be strong too.

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Yeah.

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Especially coming into the holidays.

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Exactly.

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It could indicate an expectation internally of future growth,

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product expansion.

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Making those strong holiday sales for Echo devices seem more plausible,

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maybe even predictable to some extent.

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That makes me wonder,

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though.

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OK,

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we see this this link potentially between more Alexa hiring and strong Echo dot sales.

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But does that automatically mean it's,

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you know,

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great for Amazon's overall profit?

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And that that brings us to a really important point about the limitations.

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Right.

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Because Amazon is huge,

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diversified.

294
00:07:31.604 --> 00:07:31.995
Exactly.

295
00:07:32.604 --> 00:07:37.370
So while the sales data gives us great insight into how specific products are doing.

296
00:07:37.868 --> 00:07:40.991
and the hiring data suggests investment in a specific area,

297
00:07:41.612 --> 00:07:46.319
it doesn't instantly tell you the whole story about the bottom line for a giant like Amazon.

298
00:07:46.397 --> 00:07:47.640
A hit product line is great,

299
00:07:47.718 --> 00:07:51.726
but maybe it's still a small fraction of overall revenue or profit.

300
00:07:51.819 --> 00:07:52.382
It could be.

301
00:07:53.202 --> 00:07:53.929
And similarly,

302
00:07:54.366 --> 00:07:56.007
those increased job listings,

303
00:07:56.913 --> 00:07:57.241
yes,

304
00:07:57.444 --> 00:07:59.194
they can signal growth and investment,

305
00:08:00.210 --> 00:08:04.194
but they also point directly to potentially higher costs,

306
00:08:04.397 --> 00:08:04.601
right?

307
00:08:04.757 --> 00:08:04.897
Yeah.

308
00:08:04.960 --> 00:08:05.710
More salaries,

309
00:08:05.819 --> 00:08:06.554
more overhead.

310
00:08:06.748 --> 00:08:08.369
Which could actually hurt profitability,

311
00:08:08.388 --> 00:08:09.249
at least in the short term.

312
00:08:09.268 --> 00:08:10.868
It could absolutely affect profitability.

313
00:08:11.110 --> 00:08:12.668
So you need to consider that side,

314
00:08:12.731 --> 00:08:12.848
too.

315
00:08:12.930 --> 00:08:15.313
So it's definitely not a magic crystal ball,

316
00:08:15.508 --> 00:08:16.633
this web-scraped data.

317
00:08:16.750 --> 00:08:18.868
It doesn't just give you all the answers on a plate.

318
00:08:18.954 --> 00:08:19.071
No,

319
00:08:19.172 --> 00:08:19.774
absolutely not.

320
00:08:19.993 --> 00:08:20.954
It has its limitations,

321
00:08:21.211 --> 00:08:22.469
just like any data source.

322
00:08:22.571 --> 00:08:23.657
You have to be aware of them.

323
00:08:23.750 --> 00:08:24.579
So what's the takeaway,

324
00:08:24.610 --> 00:08:24.750
then?

325
00:08:24.829 --> 00:08:25.797
Is it still valuable?

326
00:08:26.219 --> 00:08:26.329
Oh,

327
00:08:26.516 --> 00:08:27.735
I think it's incredibly valuable.

328
00:08:27.750 --> 00:08:28.391
The real power,

329
00:08:28.454 --> 00:08:28.657
though,

330
00:08:29.188 --> 00:08:30.516
often comes when you combine it.

331
00:08:30.547 --> 00:08:31.079
Combine it with?

332
00:08:31.219 --> 00:08:32.579
With traditional financial data,

333
00:08:33.016 --> 00:08:34.032
the earnings reports,

334
00:08:34.047 --> 00:08:34.938
the balance sheets,

335
00:08:35.094 --> 00:08:35.875
And also with,

336
00:08:36.047 --> 00:08:36.313
you know.

337
00:08:36.684 --> 00:08:38.747
Good old fashioned qualitative analysis,

338
00:08:39.686 --> 00:08:40.667
understanding the business,

339
00:08:40.729 --> 00:08:41.428
the management,

340
00:08:41.467 --> 00:08:42.768
the competitive landscape.

341
00:08:42.870 --> 00:08:44.553
So it's another piece of the puzzle,

342
00:08:44.612 --> 00:08:45.596
not the whole picture.

343
00:08:45.713 --> 00:08:46.252
Exactly.

344
00:08:46.698 --> 00:08:48.096
It's about building a more complete,

345
00:08:48.354 --> 00:08:51.057
more holistic picture of the company and its prospects.

346
00:08:51.182 --> 00:08:51.377
Okay.

347
00:08:52.018 --> 00:08:53.557
But even with those limitations,

348
00:08:53.776 --> 00:09:01.088
it seems clear that web scrape data can provide really valuable and maybe crucially timely insights.

349
00:09:01.542 --> 00:09:02.417
That's a key word.

350
00:09:02.932 --> 00:09:03.417
Timely.

351
00:09:04.108 --> 00:09:05.370
It can offer a different lens,

352
00:09:05.429 --> 00:09:06.370
maybe a quicker lens,

353
00:09:06.850 --> 00:09:10.434
to view a company's performance and what might be coming down the pike.

354
00:09:10.512 --> 00:09:12.958
Giving you potentially an early peek behind the curtain.

355
00:09:13.216 --> 00:09:13.380
Yeah,

356
00:09:13.575 --> 00:09:14.161
something like that.

357
00:09:14.622 --> 00:09:15.458
Identifying trends,

358
00:09:15.520 --> 00:09:21.809
maybe potential turning points before they become widely apparent in the traditional financial reports that come out quarterly.

359
00:09:21.919 --> 00:09:23.809
So it encourages a more dynamic,

360
00:09:23.903 --> 00:09:26.372
more real-time understanding of what's happening.

361
00:09:26.731 --> 00:09:27.778
I think that's a great way to put it.

362
00:09:28.028 --> 00:09:28.747
It's another tool,

363
00:09:28.919 --> 00:09:29.669
a powerful one,

364
00:09:30.028 --> 00:09:32.231
for staying informed in a fast-moving market.

365
00:09:32.460 --> 00:09:35.105
Thank you for tuning in to Papers with Backtests podcast.

366
00:09:35.425 --> 00:09:37.669
We hope today's episode gave you useful insights.

367
00:09:38.028 --> 00:09:40.134
Join us next time as we break down more research.

368
00:09:40.372 --> 00:09:41.857
And for more papers and backtests,

369
00:09:41.919 --> 00:09:44.919
find us at https.paperswithbacktests.com.

370
00:09:45.325 --> 00:09:45.880
Happy trading!