A current focal point is the trading concentration ratio of the top 5% of stocks: U.S. markets versus A-shares. From a U.S. market perspective, the trading volume share of the top 5% of U.S. stocks has historically exceeded 70% on three occasions. First, during the 1995-2000 dot-com boom. Second, during the 2007-2008 subprime mortgage crisis, which triggered safe-haven flows. Third, from 2017 to the present, driven by capital clustering into large technology firms, where the current share stands at approximately 70%. Insights from the U.S. experience suggest: First, when major industrial trends emerge, trading concentration can break through long-standing historical thresholds. Relying solely on the volatility range of the past 10-20 years as a threshold may lead to missing significant opportunities. Second, the current level of trading concentration in U.S. stocks is not at a peak; it is slightly below the September 2020 high and remains far below the levels seen during the 2000 dot-com bubble. From an A-share perspective, the trading volume share of the top 5% of stocks has risen rapidly but has not yet breached 50% or reached historical highs. As external transformative changes occur, there remains room for further increases in the trading concentration of relevant A-share sectors. First, even compared to historical peaks, current concentration levels are lower. Second, in the face of major industrial trends, trading concentration itself has the potential to reach new highs. Third, the accelerated selling of traditional assets in this cycle may be difficult to sustain, and the steep ascent in trading concentration is likely to moderate. Technology sector trading share/market cap share: U.S. markets versus A-shares. From the U.S. market perspective, during the dot-com era, the trading and market cap shares of the internet industry repeatedly hit new highs. As shown in a referenced chart, the trading share of the U.S. hardware equipment sector maintained a stable threshold (around 17%) for most periods, such as the 1970s-80s and 2000s-10s. However, precisely during the 1990s dot-com cycle surge and the post-2023 AI cycle surge, this share significantly and persistently broke through the two-decade threshold. From an A-share perspective: First, A-shares have experienced four waves of technology industry booms, leading to a systematic rise in the median trading volume and market cap shares of the TMT sector. Second, sectors like new energy vehicles in 2021 and optical modules around 2025-26 have also seen a systematic increase in their median trading volume share. Therefore, in the face of accelerating industry prosperity and earnings, congestion indicators represented by trading concentration, trading volume share, and market cap share can easily become ineffective. Such indicators are more applicable to thematic sectors where earnings have not yet materialized, such as humanoid robots, commercial aerospace, and AI applications. During phases where industrial trends translate into actual earnings, the indicative power of fund positioning structures and the magnitude of position increases for stock prices has also declined, having been ineffective for nearly two years. Historical patterns that have recently failed include: First, historically, a single sector holding over 20% might face short-term pressure, but the electronics sector has broken this pattern. Second, from a "bull market mindset" perspective: the sector with the largest quarterly increase in positioning does not necessarily underperform in the next quarter; the current AI industry trend has broken market patterns typical of存量 (stock) markets. For industrial trends representing major inflection points, the aforementioned timing indicators have become ineffective. For short-term judgments, what other indicators can be referenced? Drawing on U.S. and Chinese cases, historically, major inflection points brought by industrial revolutions have rendered some timing indicators obsolete. In this context, fundamentals and industrial trends remain the most fundamental indicators for identifying the sustainability of market movements. For seeking better risk-reward ratios in smaller market waves, some short-term indicators can be considered: at the sector level, the deviation from the moving average (e.g., EMA20 deviation) is a more suitable indicator with a historically more stable median. At the index level, sentiment diffusion indicators show modest effectiveness for short-term timing. Risk warnings include geopolitical risks, overseas inflation risks, and potential underperformance of domestic growth stabilization policies.
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