Optimization Strategy for Cross-Market Asset Allocation Under the Sino-US Debt Cycle

Currently, China and the US are at a critical turning point in their debt cycles, with明显 divergent policy orientations. On the Chinese side, fiscal and monetary policies tend to be conservative, focusing on bottom-line support rather than large-scale stimulus, with priority given to resolving structural contradictions. The US faces policy uncertainty amid the mid-term election context; the path of dual fiscal and monetary easing is uncertain, but AI-driven economic growth remains the core engine.

From the market performance perspective, US stock indices have shown divergent trends recently: the Dow Jones Industrial Average performed best (up 3.27% in 3 months), the S&P 500 rose slightly by 0.77%, while the Nasdaq Index only increased by 0.16% [0], reflecting adjustments in market expectations for tech stock growth.

Significant divergence has emerged within the TMT sector. According to the latest data:

• Alphabet (GOOGL)
  : Up 56.64% year-to-date, with the strongest performance; P/E ratio is 28.86x, relatively reasonable valuation [0]
• Apple (AAPL)
  : Up 11.48% year-to-date; P/E ratio is as high as 36.28x, overvalued but with strong profitability (net profit margin of 26.92%) [0]
• Microsoft (MSFT)
  : Up 13.75% year-to-date; cloud business grows steadily, P/E ratio is 33.73x [0]

As the core driver of US growth, AI is reshaping the competitive landscape of the TMT sector. Microsoft maintains a leading position in the cloud computing field with its partnership with OpenAI; Google continues to invest in AI search and infrastructure; Apple seeks breakthroughs in edge AI. This AI-driven structural growth provides differentiated investment opportunities for the TMT sector.

In a potential inflationary debt environment, commodity allocation strategies need to be more refined. Recent data shows:

• Crude Oil
  : Up 12.72% in 3 months, with the best performance, benefiting from tight supply-demand balance and geopolitical risks [0]
• Copper
  : Down 14.65% in 3 months; weak industrial demand drags down prices, but long-term demand from new energy transformation provides support [0]
• Silver
  : Plunged 36.55% in 3 months; stronger industrial attributes than precious metal attributes, most impacted by economic slowdown [0]

Upstream industries highly correlated with the global economy, such as copper and coal, have cross-cycle holding value. The key lies in cost curve transparency:

1. Copper
   : Long-term demand from new energy transformation is certain; short-term price corrections provide allocation opportunities
2. Energy Sector
   : XLE Energy ETF rose 2.21% recently, with a P/E ratio of only 17.61x, showing obvious valuation advantages [0]
3. Basic Materials
   : XLB Materials ETF is relatively stable, with a P/E ratio of 23.30x [0]

Based on the current debt cycle and macro environment, the following allocation principles are recommended:

1. Avoid Assets with High Macroeconomic Exposure
   : Reduce allocation to assets highly sensitive to the macro economy
2. Choose Structural Growth Opportunities
   : Focus on structural drivers such as AI and new energy transformation
3. Emphasize Cross-Market Diversification
   : Diversified allocation across Chinese and US markets, stocks and bonds, commodities and currencies

• US Tech Stocks (25%): Focus on AI infrastructure leaders (MSFT, GOOGL)
• US Energy Stocks (15%): Traditional energy and new energy benefiting from inflation expectations
• Chinese Consumer Staples (10%): Such as leading liquor companies, with strong anti-cyclical properties
• Global Healthcare (10%): Defensive sector, benefiting from population aging

• Crude Oil Related (8%): Allocate directly or through XLE
• Gold and Silver (7%): Inflation hedge; currently GLD is up 0.86%, SLV up 4.38% [0]
• Agricultural Products (5%): Allocate through ETFs like DBA, currently relatively stable [0]

• US Treasury Bonds (10%): Relatively stable yields
• High-Yield Bonds (5%): Yield enhancement under controllable credit risk
• Cash and Equivalents (5%): Maintain liquidity and wait for allocation opportunities

Based on correlation analysis, the current correlation between TMT and commodities is low, providing a good opportunity for diversified allocation. Consider rotation when the following signals appear:

1. Rapid Rise in US Treasury Yields
   : Increase allocation to defensive assets
2. Unexpected Easing of Chinese Policies
   : Increase allocation to structural growth opportunities in China
3. Breakthrough Application of AI Technology
   : Increase allocation to related tech stocks
4. Significant Changes in Inflation Expectations
   : Adjust the proportion of commodity allocation

1. Policy Risk
   : Unexpected adjustments in Chinese and US policies
2. Technology Risk
   : Uncertainty in the development path of AI
3. Liquidity Risk
   : Liquidity exhaustion amid intensified market volatility
4. Exchange Rate Risk
   : Impact of US dollar exchange rate fluctuations on cross-market allocation returns

It is recommended to establish a quarterly adjustment mechanism and conduct dynamic rebalancing based on the following indicators:

1. Changes in Macro Indicators
   : Key indicators such as inflation, employment, and PMI
2. Valuation Levels
   : Comparison of relative valuations of various asset classes
3. Technical Signals
   : Breakthrough or失守 of key technical levels
4. Capital Flows
   : Trends in institutional capital allocation changes

Through this systematic cross-market asset allocation strategy, in the complex environment of the Sino-US debt cycle, we can not only grasp structural opportunities brought by technological revolutions such as AI but also hedge inflation risks through physical assets like commodities, achieving stable returns after risk adjustment.

[0] Gilin API Data