To The Moon

[Miser]Hi~ Tigers

The past two weeks gave AI/semiconductor investors a case study in whipsaw.

$NVIDIA(NVDA)$ delivered a blowout quarter that beat expectations across the board — and the stock still popped on the print — yet the move came against a backdrop where Fed Chair-designate Kevin Warsh used his Jackson Hole remarks to put inflation, not growth, back at the center of policy, and rate-hike odds for September jumped sharply overnight.

At the same time, long-end Treasury yields pushed toward multi-year highs before a Treasury buyback operation — nicknamed the "Bessent Put" — triggered a technical bond rally. None of this cancels the AI buildout story.

But it does mean the trade is no longer just "AI is strong, buy the chip stocks." It now runs through a longer chain: rates set the discount rate, earnings validate the demand, and only then does a technical setup tell you where to actually put on risk.

We're bringing in Dr. Franklin Wu, a University of Chicago-trained researcher specializing in macro rates, market microstructure, and U.S. equity trading strategies at a Shanghai financial institution, to walk through how to read this environment systematically — from Jackson Hole and long-end yields, to the AI compute chain, to a concrete framework for turning a market view into an executable, risk-defined trade.

📅 Date: 2 September 2026
⏰ Time: 20:00 SGT
🔗 Register: Quantitative Analysis Framework & Trading Logic for AI + Semiconductor Investing

Macro First: Why Jackson Hole and Long Yields Set the Tone for Tech — Warsh's message was unambiguous: the inflation target is fixed, financial conditions aren't restrictive enough to justify standing pat, and short-term rates remain the primary tool. For AI/semiconductor names, that's not background noise — higher discount rates compress the multiples that growth stocks rely on most, and the "Bessent Put" buyback-driven yield relief is a technical release valve, not a fix for the underlying deficit and inflation pressure. We'll separate what genuinely repairs valuations from what's a temporary reprieve.

The AI Compute Chain Is Bigger Than GPUs — Compute demand cascades into memory (HBM), networking and optics, power and cooling, and finally into enterprise monetization through cloud and software, with independent forecasts pointing to AI infrastructure claiming a steadily rising share of total semiconductor revenue through the rest of the decade. We'll map where names like NVDA, AMD, AVGO, and MRVL sit in that chain, why AMD's "second source" positioning matters as inference broadens across vendors, and why AVGO/MRVL's custom ASIC and networking business isn't a side story to the GPU narrative — it's where the profit pool is shifting.

From Chart to Trade: A Repeatable Execution Framework — Reading the macro and industry right only matters if it turns into an actual position. We'll walk through a three-question checklist — direction (are macro and industry aligned), location (is price near meaningful support), and risk (where does the loss stop if the thesis is wrong) — using moving averages, support/resistance, and volume as the entry tools, illustrated with real cases: a recent tech pullback to monthly support, and an event-driven bottom-fishing setup.

Leverage Is a Tool, Not a Bet — Leveraged ETFs like $Direxion Daily Semiconductors Bull 3x Shares(SOXL)$ and $ProShares UltraPro QQQ(TQQQ)$target a multiple of daily returns, which is not the same as that multiple over the long term: volatility decay means a sharp round trip up and down costs you more than the underlying's own move. We'll cover position sizing backward from a defined stop, and lay out three scenarios for September — a range-bound base case, a lower-yield bull case, and a yields-surge-again risk case — so you leave with a plan for more than one outcome, not a single forecast.

📅 Date: 2 September 2026
⏰ Time: 20:00 SGT
🔗 Register: Quantitative Analysis Framework & Trading Logic for AI + Semiconductor Investing
🏠 Dr. Franklin Wu: Researcher, Shanghai Financial Institution | U Chicago (Physics & Quantitative Research)