Financial Knowledge Base

In the previous article The end point of this semiconductor cycle is probably not in 2026, I presented my conclusion first, but deliberately did not dive too deep into the specific details of the financial reports.

What we are covering this time is the part that is most easily obscured by market sentiment: When semiconductors rise, everyone knows they are profitable; but what truly determines whether a cycle can be extended or which company can capitalize on high growth more thoroughly is often not the stock price, but rather the profit and loss statement, capital expenditure, and product investment direction during the trough.

If I must make a more specific judgment, as of May 13, 2026, I still do not pinpoint 2026 as the end of this upcycle. However, if I have to pick just one major player among the giants that is most worth watching, it would be SK hynix. Not because it hasn’t gone through a downturn—quite the opposite—but because it made the most representative strategic choices when things looked their worst in 2023.

My previous article covered the semiconductor cycle, and I feel like there’s a piece of background/context missing.

Your judgment/conclusion regarding this point—the overall direction is correct. Furthermore, I believe it is a prerequisite that is easiest to overlook when trying to understand this current semiconductor boom.

A more accurate way to put it is not that “all internet giants are fighting in the same field,” but rather: Large Models have, for the first time, brought together major players previously scattered across different domains—such as search, advertising, social media, e-commerce, office productivity, cloud computing, and content distribution—into direct competition within the same technical stack.

This technology stack includes models, computational power, inference, cloud, Agents, distribution gateways, and commercialization closed loops. Everyone’s original “moat” is different, but now we must all fill the same gap. Those who fail to do so will see their future search entry points, ad pricing, office suites, e-commerce conversion, and social traffic distribution rewritten by others.

Regarding this round of semiconductor trends, I temporarily do not see a peak in 2026.

If forced to give an initial judgment, as of May 12, 2026, I am more inclined to place the truly critical period between the second half of 2027 and the first half of 2028, rather than now. The core driver of this current uptrend—particularly in US listed storage and Korean semiconductors—is not a general recovery, but rather AI pulling HBM, DDR5, and enterprise SSD up simultaneously. If supply expansion fails, both prices and profits will rise together.

This also explains why companies like Micron, SK hynix, and Samsung seem to be “printing money” lately. The semiconductor cycle hasn’t vanished, but this time it is unlikely to collapse when demand first kicks in; rather, it is more likely to crash when capacity expansion finally catches up, and the market has already front-loaded two or three years’ worth of profit.

Regarding this problem/issue from Futu, I will state the conclusion first.

Futu Securities currently defaults to displaying cost-averaging, which is not what many people understand as simply calculating an average based only on buys and ignoring sells. Extending this concept further, Hong Kong brokers do not have a unified default methodology. If it is a holdings page aimed at Chinese retail investors, common display methods include cost-averaging, average cost, and breakeven price (or principal protection price). However, if the broker is an international firm like IBKR that places more emphasis on tax lots and statement consistency, FIFO (First-In, First-Out) is generally the default method.

When discussing domestic brokerage firms, if we refer to the most common field in A-share trading clients—the “Cost Price/Holding Cost Price”—it is more commonly focused on the diluted cost basis or break-even price, rather than simply the average purchase price. This is just because different brokerages do not have standardized naming conventions; some call it “holding cost,” others call it “diluted cost,” and some provide a separate figure for the “average purchase price.”

Wuliangye’s performance fluctuation this time was not merely an ordinary dip; it directly disrupted the long-held unspoken norms within the baijiu industry. According to its 2025 annual report, the company reported full-year revenue of 40.529 billion yuan and net profit attributable to owners of 8.954 billion yuan. What is even more striking is that the company also conducted prior accounting error corrections for the first quarter, half year, and third quarter of 2025. Simply put, many figures from 2025 that initially looked impressive were later recalculated.

My judgment on this matter is straightforward: It’s not simply an “earnings crash,” but rather Wuliangye telling the market that their past approach of relying on channel pressure and reporting through future reserves can no longer be sustained.

A few days ago, someone asked me using the pre-adjusted prices of very early Kweichow Moutai stock. Honestly, I was taken aback at first glance: Looking at the “pre-adjustment” values, Yahoo had no negative numbers, while East Money showed negative numbers. When I later reviewed my article from June, titled Detailed Explanation of ‘Adjustment’ and Data Acquisition in Backtesting, I realized that I had mixed up several things. At the time, I presented the “ratio method” as if it were the single standard, but pre-adjustment in the domestic A-share context and the commonly used adjusted close for Hong Kong/US stocks are fundamentally different metrics.

This article only does one thing: separate these two metrics/standards. I will put my judgment first so that you don’t get confused later: The pre-adjustment (or forward adjustment) of leading domestic apps is more like leveling the candlesticks by following the exchange’s ex-rights/ex-dividend reference price; the commonly used international adjusted close is more like using a cumulative multiplier to express the total return from “reinvesting dividends.” They are both called pre-adjustment, but they answer different questions.

This matter deserves separate discussion because it directly impacts how we view Maotai. Previously, many people treated Maotai as an eternally rising consumption myth, and whether young people drank it or not was just a minor factor. Now, that is no longer the case. It is true that young people naturally have little interest in Baijiu (Chinese liquor), but this is more like a slow-moving variable. The sudden turnaround reported in the annual report appears to be driven by the contraction of an entire old system: obsolete business demands, traditional wealth distribution methods, and established status-driven consumption patterns.

In mature markets like the US stock market, the closing price (Closing Price) holds significant reference value. It’s not only a summary of daily trading sentiment but also a benchmark for index calculations, fund net asset valuation, and portfolio valuations. Consequently, trading demand focused on closing prices has emerged. Within the Interactive Brokers (IB) trading platform, MOC (Market-on-Close, Closing Price) and LOC (Limit-on-Close, Limit Price) are important order types that allow investors to execute trades at the close.