RL
Ralph Lauren Corporation
stock NYSE

As a researcher, the papers and techniques used in the most recent release are the big shifts here. They created a super memory-efficient method of dealing with long context alongside a bunch of other optimizations that allow them to serve the model at a highly reduced cost. V4 is an excellent foundation model technology-wise, and MoonshotAI and ZAI will RL it into state-of-the-art — similar to what they did with V3/R1. This, and US AI companies are trying to shift into profitability by increasing the cost to consumers — which they’ll find doesn’t work when there’s a Chinese product for a fraction of the price.

# Consumer
|**Ticker**|**Dec 22, 2025 Price**|**Apr 28, 2026 Price**|**% Change**|
|:-|:-|:-|:-|
|**AZO**|$3,391.50|$3,577.91|\+5.50%|
|**CVNA**|$155.00|$210.40|\+35.74%|
|**CELH**|$48.20|$55.60|\+15.35%|
|**SBUX**|$92.40|$98.67|\+6.79%|
|**DKNG**|$42.10|$49.30|\+17.10%|
|**UAL**|$65.20|$78.45|\+20.32%|
|**RL**|$185.00|$212.30|\+14.76%|
|**VIK**|$35.40|$42.10|\+18.93%|
|**Category Average**|||**+16.81%**|

Wouldn’t be shocked if RL materially benefits from more than which currently exist.

Four reports in 24 hours on Wednesday is not a "pick the winner" setup — it's a "find the divergence" setup. Here's how I'd frame what to actually watch:
1. Capex guide vs. cloud rev growth. MSFT, GOOGL, and META all guided $80-100B+ capex for FY26. If any of them lifts the capex number AND keeps cloud accelerating, that's the bull case re-stamping itself. If capex ticks up but cloud growth flattens, that's the first crack — they're spending into a slowing demand curve.
2. AWS operating margin direction. AWS margin has been the swing factor for AMZN three quarters running. If margin is flat or down with rev growth above 18%, that's actually fine — they're investing. If margin is up but rev decelerates, that's cost-cutting masking demand softness, which is the worse setup.
3. Meta's Reality Labs loss vs. ad rev growth ratio. Reality Labs lost \~$17B last year. If ad rev growth is decelerating AND RL losses are widening, the stock is going to take a different reaction than the "bulls love everything" setup the last few prints have produced.
4. Apple is the loneliest report. AAPL doesn't have an AI capex story to ride, so it has to win on services margin and iPhone unit cycles. The bar is highest there because AAPL has been flat-to-down YTD while the rest ripped.
Honest take: the easy money on this print cycle was already made in the run-up. The interesting trades come Friday morning, after we see which of these four diverges from the others. Sector beta gets you to the print; idiosyncratic moves come after it.

About a year ago I published a [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6501822) applying ensemble Q-learning to pairs trading on NIFTY 50 stocks (hourly data, 128,400+ data points). The model reported a 105.2% six-month return with a Sharpe ratio of 2.08, which I was pretty excited about at the time.
I have since gone back through the entire pipeline, the code and the paper together, and written a formal [critical review](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6501822) identifying a chain of compounding methodological problems that invalidate those numbers. There were numerous errors including lookahead bias, selection leakage, spread construction that contradicts theory, Markov property violations, financially meaningless PnL calculations, incorrect benchmarks, and inefficient computational design
The research question itself, whether ensemble Q-learning can genuinely outperform rule-based pairs trading, is still worth answering. A clean version of this would address all the flaws identified in the review paper.
I am a high school student based in Mumbai and I am looking to collaborate with anyone who has experience in RL, statistical arbitrage, or quantitative backtesting to build this out properly.
If you have worked on similar problems or can spot anything I missed in the critique, I would genuinely like to hear from you. Do check out the papers, drop a comment, or DM me.