Dynamic Valuation of Memory Semiconductor Stocks

 Recently (July 2026), financial markets have exhibited behavior that appears irrational over short time horizons. Leading memory semiconductor companies such as Samsung Electronics, SK hynix, and Micron have traded at approximately 5-7 times forward PER, despite reporting exceptionally strong earnings. The current low valuation of memory semiconductor companies reflects skepticism about the persistence of future earnings rather than weakness in current profitability. Consequently, the key investment question is not:"How large are today's earnings?". Instead, the market asks:"Can today's earnings be sustained over many years?" Equivalently, investors focus less on the level of earnings than on their dynamics. Rather than asking, "How large are earnings?" the market effectively asks,"Are earnings growth rates accelerating or decelerating?". Although global AI infrastructure is expected to continue expanding across North America, Europe, Asia, and other regions, investors remain concerned that aggressive capital expenditures could eventually lead to excess capacity and oversupply. As a result, current valuations reflect not only strong present fundamentals but also uncertainty regarding the long-term equilibrium between AI-driven demand and future supply.

The purpose of this blog is to present a brief mathematical perspective on the mechanisms underlying the recent behavior of memory semiconductor stocks. I am not an expert in financial markets; rather, I am a learner who enjoys applying mathematical tools to understand complex real-world phenomena. This blog was developed with the assistance of ChatGPT.

The 12-month forward price-to-earnings ratio  is defined as 12MF P/E=$\frac{P}{E_{12m}}$, where $P$ is the current stock price and $E_{12m}$ is the expected earnings over the next twelve months. Many investors implicitly assume that a low forward P/E indicates undervaluation. However, the market does not price today's earnings alone. Instead, it prices the entire expected trajectory of future earnings. More generally, the stock price may be regarded as a function $P=f(E(t),R(t),S(t),\sigma(t)),$where $E(t)$ denotes the expected earnings path, $R(t)$ the discount rate, $S(t)$ market supply--demand dynamics, and $\sigma(t)$ the uncertainty associated with future earnings and the macroeconomic environment. From this perspective, valuation is only one component of a multidimensional dynamic system.

The first derivative, $\frac{dE}{dt}$, represents the earnings growth rate, whereas the second derivative, $\frac{d^2E}{dt^2}$, measures the acceleration or deceleration of earnings growth. In the second quarter of 2026, leading memory semiconductor companies such as Micron, SK hynix, and Samsung Electronics reported record earnings and historically high profitability. Nevertheless, the market appeared to assume that $\frac{d^2E}{dt^2}\lesssim0$, implying that earnings growth was approaching its peak or beginning to decelerate even though the absolute level of earnings remained exceptionally high. Consequently, investors assigned lower valuation multiples despite outstanding current results.

Mathematically, this means that the market discounts not only the expected earnings path $E(t)$, but also its derivatives. The value of a company is therefore influenced by the level, slope, and curvature of expected earnings. In other words, the market often prices the future evolution of earnings rather than today's earnings themselves. At the same time, market microstructure amplified the price decline. Leveraged ETF rebalancing, margin-related selling, profit-taking, algorithmic trading, and negative market sentiment generated additional selling pressure. These factors can temporarily move prices away from their fundamental values. A simple representation is $P(t)=V(t)+\varepsilon(t),$ where $V(t)$ denotes the intrinsic value implied by long-term fundamentals and $\varepsilon(t)$ represents short-term deviations caused by liquidity, market microstructure, and investor behavior. During periods of market stress, the noise term $\varepsilon(t)$ can become large enough to dominate price movements.

Recently, considerable attention has been paid to leveraged ETFs on Samsung Electronics and SK hynix, as many investors believe that their daily rebalancing amplifies short-term price volatility. Let us briefly explain the underlying mechanism.

Let $X_t$ denote the target exposure at time $t$, $L$ the leverage ratio ($L=2$ for a 2$\times$ ETF), and $A_t$ the ETF's net asset value. The target exposure is $X_t=LA_t.$ If the underlying stock moves by a daily return $r$, the ETF must rebalance its position approximately by $\Delta X\approx L A_t r,$ where $\Delta X$ is the amount of exposure that must be bought ($\Delta X>0$) or sold ($\Delta X<0$) in order to maintain the target leverage.

For a 2$\times$ leveraged ETF, $\Delta X\approx 2A_t r.$ If the stock price falls by $a\%$, then $r=-a$, and therefore $\Delta X\approx -2A_ta.$ Hence, to maintain a leverage ratio of $L=2$, the ETF must sell approximately $2a\times A_t$ worth of the exposure.Thus, a 2$\times$ leveraged ETF is structurally forced to buy when the underlying stock rises ( $r>0 \rightarrow \Delta X>0$),  and sell when it falls ($r<0  \rightarrow \Delta X<0$).  This procyclical trading mechanism can amplify short-term market movements, particularly when leveraged ETFs become sufficiently large relative to the underlying stock's daily trading volume. Similar feedback mechanisms also arise from margin calls, forced liquidations, and algorithmic execution, all of which may reinforce price movements independently of changes in fundamental value.

The recent correction in memory semiconductor stocks therefore appears to reflect the interaction of two distinct mechanisms. The first is a fundamental revaluation, driven by concerns that the acceleration of future earnings may be slowing. The second is a market microstructure effect, in which leveraged ETFs and other systematic trading strategies amplify short-term price movements. Distinguishing these two mechanisms is essential for understanding why stock prices can deviate substantially from intrinsic value, even when current corporate earnings remain exceptionally strong.