Experienced Trader Shares Strategy for Trading Market's Misplaced Expectations

Author: Benjamin Usachi

Shenchao Summary: This was a textbook example of a "misguided expectation" trade. The market saw weak CPI data and assumed everything was fine, pushing the Nasdaq to 30,060. However, the 30-year real yield surged to a 20-year high that same night—short-term rates eased, but long-term rates refused to follow. Tech stocks could no longer access cheap long-term funding, capping their valuation upside. Traders captured the decline from 30,060 to 28,768 with five rounds of short positions. The core strategy: Don’t just look at the data itself—look at how the market expected it to transmit, then assess whether that transmission mechanism is still functional.

• Case ID: 002
• Trading prototype: Transmission of incorrect expectations / Obsolescence of the old response function
• Market status: Duration pressure remains elevated with further tightening; short end eases, long end resists; credit stable; no liquidity crisis
• Market-implied causal chain: Weak CPI → Policy easing → Lower long-term funding costs → NQ valuation expansion
• Breakpoint: Between policy path and long-term funding price
• Veto variable: 10-year and 30-year real interest rates
• Cross-sectional confirmation: NQ weakened after becoming relatively strong compared to ES; ASML and TSM prices declined after strong earnings reports.
• Cleanest expression: Short NQ instead of indiscriminately shorting ES
• Entry structure: Fast variable fixes price, slow variable rejects price repair conditions
• Falsification conditions: Long-term real interest rates continue to decline; the U.S. dollar and funding conditions ease simultaneously; credit remains stable with broadening diffusion; NQ regains relative strength
• Conditions for redemption: The previously incorrect expectation has been corrected, but credit conditions have not deteriorated enough to justify a full risk-off stance.
• Execution flaw: External input did not alter the evidence but interfered with warehouse confirmation; target adjustments must be defined as new decisions.
• Case status: Interim archiving; final statistics will be compiled after closing the remaining position.
• One piece of advice: Trade against your expectations and wait for the market price to conflict with its own implied causal chain.

The trade that best demonstrated my strategy was shorting NQ from Tuesday through early Wednesday morning.

First batch of short positions entered at 30060. Second batch of short positions entered at 30040, with an expected price target of 29700. Within two hours, the price moved down to around 29880, then began to rebound following the PPI data release, rising all the way to 29990. Influenced by growing uncertainty, confidence wavered—fearing the position was too large and at risk of being trapped—so the short position at 30040 was closed at 29992 for profit. After observing for ten minutes and confirming the downtrend, a new short position was placed at 29950, with part allocated to NQ for long-term holding and part to MNQ to enable partial profit-taking, while setting an automatic stop-loss for MNQ at 29700. On Wednesday night, the outlook changed, anticipating a potential drop to around 29000; a new试探 (probe) MNQ short position was placed at 29000. In total, five batches of short positions were executed: the earliest batch at 30060 remains open; the batch at 30040 was closed due to loss of confidence; the NQ position at 29950 was manually closed at 28500; the MNQ position at 29950 was automatically closed at 29700; and the new MNQ short at 29000 remains open. On Friday, NQ closed at 28768.25.

To many experienced traders, this trade may seem messy and roundabout, but to me, it represents clear progress. First, it’s about timing. In the past, I only focused on direction and had terrible timing and entry points—I used to comfort myself by saying I should just play to my strengths and avoid my weaknesses. But this time, my entry timing, entry point, and take-profit level were all significantly more efficient and carried lower risk—that’s progress. Second, since this trade was based on a classic “valuation reset” and “misaligned expectations,” the market gave me ample time to gradually validate that my macro framework, indicators, and conditional assumptions were correct, allowing the price to move precisely as I anticipated.

Looking back, as I mentioned, this trade was a classic case of misplaced expectations. Some market participants formed positive expectations based on new data, causing the price to rise. However, the true price setters in the market did not share this expectation, leading to a sharp price decline. I’m reviewing and sharing this trade because I believe it serves as a textbook example of an expectation-based trading strategy—one that can be replicated—and I’m documenting it here for future reference.

The most common or frequently discussed expectations are data expectations, including event expectations—such as non-farm payrolls, CPI, or earnings per share (EPS) from financial reports. Or whether the Fed’s tone is dovish or hawkish, or if the U.S. and Iran will temporarily cease hostilities. After the actual data or event outcome is released, a discrepancy—known as a “surprise”—may emerge between the actual value and the consensus estimate. Much trading revolves around this dynamic, leading to intense debates over whether rates will be cut or AI spending reduced, in an attempt to anticipate a reversal. This is, in fact, the most basic yet most challenging area of trading.

However, the actual price is determined by the expectations of the last two layers.

Layer Two, I call it the transmission of expectations. After data is released, how will it alter policy paths, real interest rates, the dollar, credit, and risk premiums? Does a weak CPI only affect two-year yields, or is it sufficient to lower long-term funding costs for ten- and thirty-year maturities? When a company beats expectations, does it only boost quarterly profits, or does it also improve future cash flows and returns on capital? Data impacts different facets of the market in varying ways—some positive, some negative, and others unaffected. This is why we often see strong earnings reports accompanied by price declines, or weak data leading to price gains—because the transmission mechanism affects multiple dimensions simultaneously.

The third layer is asset expectations. After the first two layers change, what price should the market assign to a particular asset? Should valuation multiples rise, or only profit expectations? Should you buy NQ or ES? Should you buy long-term bonds or gold?

The so-called expected error either stems from a first-layer misestimation—such as last year’s widespread assumption that interest rates would continuously decline, only for cuts to suddenly stop—or, more commonly, from the second layer, where an expectation is not fully interpreted. The market fails to properly assess how this new event or data will propagate across different segments, skipping the second layer entirely and attempting to price in the third layer. As a result, the market arrives at a price that is completely misaligned with reality—creating an arbitrage opportunity.

Or, in a nutshell: traders correctly perceived a fact but wrongly assumed that the old transmission mechanism was still valid.

In last week’s report, I mentioned monitoring two key data points this week: Tuesday’s CPI will determine market pricing for rate hikes and whether interest rates may finally ease, reversing the trend of the past two weeks in which funding costs surged to their highest levels in two decades for four or five consecutive times; Thursday’s retail data will help explain the market’s revenue and sentiment outlook, showing whether consumers, after enduring recent price shocks, can sustain strong spending and continue providing companies with cash flow.

After the CPI data was released, interest rates indeed plummeted and prices rose, with the Nasdaq leading the gains. The market breathed a sigh of relief, and so did I. But for me, the observation window hadn’t ended—I knew the market still needed to genuinely navigate the transmission channel, and short-term price movements couldn’t determine the ultimate direction. Sure enough, that evening, just over an hour before I began my talk show, the 30-year real yield rebounded and once again breached a 20-year high. I knew the timing for shorting was approaching. The weak CPI in the morning had led the market to form a mistaken expectation that weak data would broadly support asset prices. But in reality, the market’s narrative had already shifted, and so had its transmission mechanism—making that day’s broad rally a mispricing.

The retracement of this indicator has two key implications. First, under a below-expectation CPI reading, the market typically prices in broad monetary easing, with interest rates expected to decline across the board. However, on that night, we saw short-term rates ease while longer-dated rates—10-year, 20-year, and 30-year—rebounded. This confirms that the market’s response to the weak CPI was limited to lowering near-term rate hike expectations, but showed no easing in longer-dated rates, which define the cost of capital and future risk. This validates the central thesis I’ve maintained over the past month: due to various macroeconomic factors, the cost of long-duration capital remains high and refuses to decline. The second implication concerns short-selling targets and levels. With long-duration capital costs still elevated, technology stocks are most negatively impacted, as tech companies rely heavily on borrowing funds for 10-, 20-, and 30-year horizons. If a weak CPI leads to lower short-term rates but persistently high long-term rates, Nasdaq will be hit first and hardest; by contrast, the S&P 500, with its broader sector diversification and fewer companies requiring massive long-term borrowing, benefits from lower short-term rates while being less severely affected by rising long-term rates. I articulated both of these conclusions live during that night’s broadcast: first, that Nasdaq’s outperformance had gone too far and warranted caution; second, that a new market dynamic had emerged—good news no longer provides the same level of support for tech stocks as it once did.

Regarding the selection of price levels, there are two verification layers. NQ’s previous high was at 30,060. Since the duration rate has once again broken above this level, it implies that the overall market valuation is contracting again. Therefore, I believe that unless there is positive news on the fundamental side, the price should not easily break above the previous high of 30,060. Accordingly, I placed two orders—at 30,060 and 30,040—with the latter serving as a backup in case the first order doesn’t fill.

The second layer of validation lies in ASML’s earnings report that night. A strong report but a price decline is a clear indication that the numerator (earnings) is being overwhelmed by the denominator (valuation multiples)—meaning that despite robust earnings expectations, high interest rates are forcibly pulling down the stock price. This secondary confirmation of the numerator’s strength gave me even greater confidence to take a short position.

There’s actually a slightly humorous element here. Logically, I’ve already confirmed this from multiple angles—the trading narrative, transmission mechanisms, indicator trends, and earnings performance—and the odds are heavily in my favor. Yet, for no clear reason, I developed a gut feeling: the PPI data simply won’t push the stock price higher anymore. First, yesterday’s weak CPI has already priced in much of the expected weakness in PPI; if even that was enough to push long-term rates to new highs, then the actual confirmation of weak PPI won’t bring any real upside. If the price spikes in the short term, it would actually be the ideal moment to short. Second, “a warrior isn’t defeated by the same move twice.” The market has already let retail investors make a day’s profit off this data—why would it let them do it again the very next day using the same tactic?

So I carried out the actions mentioned at the beginning of the article. Afterwards, TSM’s post-earnings decline further confirmed the downward trend, and following the same logic, I adjusted my take-profit level downward, moving the large position’s take-profit from 29,700 to 29,000. This level was determined based on observations of interest rates and the Nasdaq over the past month, correlating peaks in interest rates with recent lows in the Nasdaq, analyzing absolute levels and rate of change, ultimately deciding to initially target 29,000.

Probably yes. I’ve summarized five methods and approaches that can be carried forward.

I’ve previously written that I’ve suffered losses due to the relative relationship between the numerator and the denominator, causing me to miss out. In other words, to what extent does tightening on the denominator side slow or hinder the growth of the numerator, thereby preventing overall price appreciation? How "thick" is the valuation ceiling when the numerator is at a relatively high level? To what extent must the numerator grow to break through this valuation ceiling? Ultimately, what is the relative dynamic between these two factors in determining price behavior?

First, absolute position determines the valuation ceiling, the rate of change determines short-term shocks, and relative position determines who faces issues first. Absolute position and rate of change must be analyzed separately. A high but stable interest rate allows the market to adapt gradually; an interest rate that is not necessarily extreme in absolute terms but rises rapidly is more likely to cause short-term shocks. The former determines long-term constraints, while the latter determines whether immediate repricing is necessary.

At this moment of my short position, the absolute level of funding rates has locked in the valuation ceiling, and the slope is extremely steep. Meanwhile, NQ is approaching 30,000 points, and the market continues to price in strong AI earnings, recovering risk appetite, and valuation recovery driven by weak CPI. This has created a highly asymmetric price structure with substantial downside risk, strongly encouraging profit-taking and shorting.

Second, if a rapid decline is anticipated, consider both the previous high and low, as well as the position structure.

A mistaken expectation can persist for a long time. The market can be more optimistic than you imagine and continue moving in one direction driven by positioning, options, and sentiment. If you merely believe the market is wrong but lack a favorable price, a catalyst, or a clear condition for falsification, you may simply be right too early—and get crushed by the market first.

Let’s return to fast and slow variables. The optimal entry point occurs when the price has recovered, but the pricing conditions have not. In other words, the fast variable has pushed the price back up, while the constraints from the slow variable remain unchanged.

Return to the moment of the short position. For the bullish trend to continue at that time, two conditions needed to be met simultaneously: first, AI profitability and growth expectations had to continue rising; second, long-term funding costs could not continue to tighten. At that time, the first condition faced headwinds, while the second condition did not exist. The shorts did not need to prove that AI was a bubble or that the U.S. economy was entering a recession. The shorts only needed one of the two conditions to fail: long-term real interest rates remained elevated or continued to rise.

How do you identify mispricings in the transmission and pricing layers? My answer is the same as that of many major short sellers: determine whether expectations are built entirely on a single key assumption, then assess whether that key assumption is correctly priced.

First, articulate the implied causal chain currently embedded in the market. Do not simply state that the market is bullish or bearish; instead, clearly define: why the market believes that A leads to B, and why B leads to C. This time, the chain is: weak CPI → policy easing → lower long-term funding costs → expansion of tech valuations.

Second, identify the variables with veto power within this narrative. Every story has a market that must ultimately be confirmed. For long-duration tech stocks, the real long-term interest rate and the required rate of return hold veto power.

Third, observe whether this variable rejects confirmation. Market conflicts are not necessarily opportunities, as different assets may be trading on different themes. A contradiction is tradable only when an asset’s rise must depend on this variable, and the variable clearly refuses to cooperate.

Fourth, wait until the price continues to follow the old script. Once incorrect expectations have been fully corrected, there is no longer any profit to be made. The best opportunities arise when the underlying variables have changed, but the price still follows the old pattern due to inertia, short-term speculation, and outdated reaction functions.

Fifth, choose the expression that is most sensitive to this error and has the fewest impurities. Do not indiscriminately short all assets just because macroeconomic conditions suggest lower capital prices. Identify which asset relies most heavily on the now-defunct causal chain.

Sixth, predefine two exit conditions. One is falsification: the veto variable reconfirms the market narrative, indicating that you were wrong. The other is realization: your incorrect expectation has been corrected, and the price has completed its expected regression. Many people only exit when they’re wrong, but fail to exit when the logic has already been fulfilled.

Of these six steps, the hardest is distinguishing between "the market is truly wrong" and "the market is just not running the way I expected."

Information in the market is becoming increasingly abundant. CPI data, earnings reports, positions, and capital flows are all seen by everyone almost simultaneously. It is very difficult for individual investors to maintain a long-term advantage by learning facts earlier than large institutions.

But just because everyone sees the same fact doesn't mean everyone will understand it correctly.

Markets develop habits. Bad data equals rate cuts; rate cuts equal tech rallies; gold equals safe-haven demand; long-term bonds equal stock hedges; strong AI demand equals all AI assets should rise. Once these causal chains remain valid over the long term, they become automatic responses. But when macro conditions change, even if the data and assets remain the same, the old reaction functions may no longer hold.

This is where the mistaken expectation truly lies.

The biggest opportunity in the market doesn't necessarily come from information others don't know, but rather from the fact that others are still trading as if the old world remains, while you’ve already realized the world has changed.

This time, the market correctly interpreted the CPI but misunderstood its implications for long-term funding costs. The stock market rose according to its old reaction function, while the long-end bond market refused to confirm, and NQ priced in a nonexistent "denominator easing."

So, if this article leaves you with only one question, I hope it’s this one:

What causal chain did the market's initial reaction rely on, and does that same causal chain still hold today?