Japanese equity markets are showing concentration in AI and semiconductor stocks comparable to the dot-com bubble, but analysis by Nomura Securities reveals that beneath the AI narrative, systematic factor-based selection—across value, momentum, and beta metrics—is driving stock performance in both surging and stable names. Rather than thematic buying alone, investors can identify overlooked stocks within the same factor groups that are lagging but may follow as the factor-driven rally matures, though the persistence of such follow-through effects has narrowed to under two months.
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Nomura Securities' quantitative analyst Norinobu Nishioka analyzed Japanese stock market concentration using factor analysis (stock price momentum, historical beta, valuation metrics, and others across TOPIX 500 constituents). The analysis shows that while AI and semiconductor stocks like Kioxia and Fujikura have surged sharply—reaching concentration levels comparable to the dot-com bubble era of around 2000 in April 2026—the underlying driver is not purely thematic: multiple factors (value, momentum, beta) are being selected across both high-volatility and low-volatility stocks in parallel, indicating systematic factor-based stock selection is occurring alongside the AI narrative.
Why it matters
During periods when a specific theme (like AI) dominates headlines, investors face a challenge in finding outperformers outside that theme. The research demonstrates that factor-driven selection is a measurable, repeatable mechanism independent of thematic concentration. By identifying which factors are showing the largest divergence between high- and low-volatility stocks in a given month, investors can target overlooked stocks within the same factor group—offering a potential strategy to capture upside when factor-driven rallies extend beyond AI stocks. This approach may be particularly relevant for Japanese institutional and retail investors seeking diversification beyond the AI consensus.
What to watch
The sustainability of factor-led outperformance is shortening: the observed follow-through effect from leading high-volatility stocks to lagging peers in the same factor now lasts less than two months, down from longer persistence in prior years. A "factor-led strategy" that rotates monthly exposure based on factor volatility and the magnitude of return divergence has shown generally upward performance over the long term, with renewed strength as of early 2026. The June 2026 analysis identified low B/P (price-to-book inverse, a value metric) as the factor differentiating high-volatility stocks; candidates for following that factor were stocks with low prior-month momentum and low B/P within TOPIX 500.
The concentration of Japanese equity returns in AI and semiconductor stocks has reached levels not seen since the dot-com bubble era, with April 2026 showing extreme skewness in single-month returns. However, Nomura's quantitative decomposition reveals that this concentration masks an equally important mechanism: systematic selection across fundamental and momentum factors is occurring in parallel. Both high-volatility (sharply rising or falling) and low-volatility stocks are being sorted by the same factors—value metrics (B/P and expected E/P), momentum, beta, and profitability expectations—suggesting that the market is not monolithically chasing an AI theme but rather executing layered selection criteria.
This finding is significant because it implies opportunity for stock-pickers willing to look beyond headline themes. When a factor group (such as low price-to-book stocks) is driving performance in high-volatility names, other stocks within that same factor group that have underperformed in the prior month may be candidates for follow-through gains. Nomura's historical backtest of a "factor-led strategy"—which rotates monthly through factors showing the highest divergence between high- and low-volatility subsets, then selects lagging stocks within those factors—demonstrates long-term outperformance, suggesting the mechanism has held through different market regimes.
The critical limitation, however, is that the persistence of factor-driven rallies has deteriorated: what once provided multi-month upside now lasts less than two months. This compression means the strategy requires disciplined monthly rebalancing and rapid execution, making it less accessible to passive or lower-turnover investors. For those with the infrastructure and mandate to rebalance frequently, the analysis offers a quantifiable method to identify undervalued stocks in a market that appears monolithic but is in fact being sorted by measurable structural factors.
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