陳大強

Soulded out

[陳大強 @chasechen99]

@macro_guru You really think that is ideal origination when you look at the trustoilot reviews?

[陳大強 @chasechen99]

@Dirkyyyy guess that’s good news for now. LOL thanks bro. 🙏

[陳大強 @chasechen99]

+104 Upsatart trustpilot reviews
We’ve broken the daily record three times in just one week. Anyone know what’s going on?@Upstart

[陳大強 @chasechen99]

@Ironic_Ape @JacksonMatysik Yes plz

[陳大強 @chasechen99]

@WinnerInvestor @balkanstocks Lollll People don’t know that he actually has a private trust managing it. The trust helps him sell stocks and then uses a small portion of the money to buy them back. Yet everyone keeps saying this shows confidence 😂🤣🤣

[陳大強 @chasechen99]

@Tax__Accountant @zhangtaoSG @Upstart I think regardless of the stock price, buy back on a regular schedule

[Nexus @GMN_watch]

🚨 $OPEN Acquisition Contract Update:

Week ending June 20:

• 529 acquisition contracts
• 87 last year
• +508% YoY

Despite a modest 2% weekly dip, acquisition volumes remain near multi-year highs and are running at more than 6x last year’s pace.

[陳大強 @chasechen99]

@StockAnalystPro @anthonynoto People don’t know that he actually has a private trust managing it. The trust helps him sell stocks and then uses a small portion of the money to buy them back. Yet everyone keeps saying this shows confidence lol

[陳大強 @chasechen99]

@rounder74339385 @davegirouard @HenryInvests @RonAMacro Their model can now access repayment data that isn’t from corporate loans. Let’s check out their earnings report 25q4

[Morgan Brown @morganb]

This is inaccurate. That program was removed 9 months ago. We replaced it with Cash Now, More Later and flipped how agents get paid in the process.

Under the old model, agents paid us out of their listing commission. Under the new one, we pay agents when they work with us, and we increased that comp further with our recent agent program reboot.

Agents keep the listing, their clients get cash upfront plus upside participation in the sale.

Plenty of companies sell leads to agents. We think flipping the comp dynamic and giving sellers more options is a better answer for everyone.

[Niko @lngspt]

$APP Nice write up from Mr. DBC, and I agree with all of it. I really think the company is at an inflection point, and compensation tied to a $1T MC target speaks for itself.

I've written some long form AppLovin pieces in the past, but those have been centered on valuation and metrics. With this post, I want to talk briefly about what the business actually is, at a high level. Us App bulls have a tendency to focus on the financials because they are so attractive, but for many investors, AppLovin is, much like its proprietary recommendation model, a black box.

*For the cult, if you see anything that merits correcting or clarifying, please do so.*

With the above said, I want to talk about what the company actually does. In simple terms, the company is an ad matching engine…

On that point, AppLovin sits on both sides (supply and demand) of the ad marketplace.

Well, what does that mean? On the supply side, AppLovin will run auctions on behalf of companies selling ad slots via MAX.

On the demand side, AppLovin represents advertisers, AKA purchasers of ad slots (historically, these have been advertisers that advertise mobile games). This means that AppLovin effectively bids in auctions against other bidders via AXON.

(Importantly, in auctions where AppLovin is both auction house and bidder, AppLovin doesn't self-preference, and it doesn't ingest other bidders' bid information. But MAX is still critical for AppLovin in that AppLovin can access impression context, auction clearing prices, demand depth, and downstream outcomes (installs, purchases, engagements) via AppLovin's Adjust. Max also charges a take rate to advertisers that participate in auctions, but AppLovin does not have to pay the take rate when it represents an advertiser on the demand side, so there's a cost-reduction benefit to owning the supply side as well. I should note that MAX has market share around 60-70%, if I'm not mistaken.)

Before moving forward, I should note that this trips a lot of people up. In operating MAX and AXON, AppLovin has insight into both ends of an auction (as bidder and as auctioneer), which is not intuitive. For a mental model, it is as if Sotheby's is running an auction – blind bid, not waterfall – but then also representing bidders in other auctions (or potentially its own).

Ok. So AppLovin is a bidder and an auction house.

But how does AppLovin make money? It's (primarily) off of the ad performance of the advertisers that outsource bidding and placement to AppLovin, which makes the business model entirely grounded in expected value.

If that's confusing, here's an easy example where AppLovin gets paid for installs.

Step 1. An advertiser approaches AppLovin with a mobile gaming ad that it wants to show in a mobile gaming app ad slot. It's paying AppLovin on an install basis, agreeing to pay AppLovin $30 each time a user installs.

Step 2. This is where expected value comes in. AppLovin will calculate the likelihood of install for each impression. If the install probability per impression is 0.05%, then the expected value of an impression is $.015, or $15 CPM (that's cost per mille, or thousand, for the uninformed).

Step 3. After running that calculation, AppLovin thus knows it can bid anything less than $15 CPM for an ad slot and net the spread.

(That's an example with one advertiser. In reality, AppLovin is running many auctions simultaneously and matching ads to auctions where they think the ad will perform best and return the most to each advertiser.)

From the mental model, it is easy to see why AppLovin's mobile gaming user data is so important. With the unique data set that AppLovin acquired as through its former ownership of mobile games, AppLovin can more effectively target and monetize users, as it knows what those users are most likely to respond to. In the example above, the data behind the recommendation model may have helped AppLovin forecast additional .01% probability of install, and that either translates to higher bids relative to competitors, or higher spreads that contribute directly to top line growth.

The mental model is also important because you can see why ecommerce has the potential to be huge for AppLovin. If a mobile gamer is playing a game and gets an advertisement for another mobile game, that gamer may not be incentivized to install the other mobile game. They already have a game, and they might be satisfied with that game already. But as AppLovin expands AXON to ecommerce, gamers can instead be shown items like clothing and household goods that they are more inclined to purchase. As a result, adertiser ROAS has the potential to grow significantly, and AppLovin receives a performance-based chunk of all incremental ROAS (or in some cases impressions or installs).

That's AppLovin in a nutshell. I hope that clears things up for people that have been on the fence about the company or those who like the financials but don't understand the business.

[Jeffrey Ye @jeffye888]

https://t.co/OWlcXdwyuD

[陳大強 @chasechen99]

Dave’s first post after the earnings report didn’t mention the earnings at all — he only talked about how great his former company Google was which upset me a bit

[陳大強 @chasechen99]

@StockZombie222 What percentage of your portfolio is in UPST?
And What would you rate Dave on a scale of 1 to 10?

[陳大強 @chasechen99]

@deemahdee1 @HenryInvests @teowk2020 Why does Dave keep staying silent at this point🥲🥲

[陳大強 @chasechen99]

Does anyone know why the personal loans on the balance sheet increased from 284M to 338M?

[陳大強 @chasechen99]

@zoozai_invest If the main reason is marketing
No year gathering
Why did Pau mentioned it（mistake?
The good news is that cost control isn't as bad as expected. The bad news is that Paul didn't seem to perform very well on this question