In-depth Analysis of US Stock Market: Finding Certainty Amid Volatility

2026-07-10 15:51 133 views HK Stock Live

In-depth Analysis of US Stock Market: Finding Certainty Amid Volatility

Keywords: US stocks, macro economy, interest rate policy, sector rotation, investment strategy

Introduction

As a core barometer of global capital markets, every move of the US stock market captures investors' attention. After a strong rebound in 2023, the US market from 2024 to 2025 exhibited a more complex landscape: inflation stickiness exceeding expectations, wavering Fed policy path, recurring geopolitical risks, coupled with structural opportunities from the AI wave, repeatedly tugging market sentiment between optimism and caution. As of Q3 2025, while the S&P 500 remains near historical highs, volatility has risen significantly, sector rotation has accelerated, and the liquidity environment post-QE is reshaping asset pricing logic. This article systematically examines the core contradictions of the current US market from four dimensions: macro economy, valuation levels, industry trends, and risk factors, providing professional strategic reference for investors.

I. Macro Economy: Hidden Concerns Under Soft Landing Narrative

From a macro fundamental perspective, the US economy has shown surprising resilience. The labor market remains tight, with unemployment staying near 3.8%. Wage growth, though slowing, is still above pre-pandemic levels. Consumer spending remains robust driven by services, and corporate capex is rebounding thanks to AI infrastructure investment. However, the 'last mile' of inflation seems tougher than expected. Core PCE inflation hovers around 2.6%-2.8%, far from the Fed's 2% target. This has pushed back market expectations for rate cuts—from five at the start of the year to just one or two now, with the first cut possibly delayed to late 2025 or even 2026.

This 'higher for longer' environment is having side effects: rising delinquency rates on commercial real estate loans, stress on small banks, credit card and auto loan defaults hitting decade highs. While systemic risk is manageable, the lagged effect of credit tightening may gradually erode corporate earnings. Meanwhile, fiscal deficits widen, the yield curve inversion hasn't fully reversed, and term premium increases long-term funding costs. Overall, the US economy is walking a narrow path to soft landing, where any data surprise could trigger sharp repricing.

II. Market Performance: Index Prosperity and Structural Divergence

At the index level, major US benchmarks showed oscillating upward trends in 2025. The S&P 500 gained about 8% year-to-date, the Nasdaq rose about 11% driven by tech stocks, while the Dow Jones Industrial Average was relatively flat. Notably, this rally is highly concentrated in a few mega-cap tech stocks, the so-called 'Magnificent Seven'. AI beneficiaries like Nvidia, Microsoft, and Apple contributed the vast majority of the index gains, while the median gain of the remaining 493 stocks was only about 3%. This 'narrow width' rally historically signals increasing market fragility.

US stock market trend chart

The chart above shows daily price movement of the S&P 500 since 2023. After a correction of about 10% in H2 2024, the index stabilized and hit new highs in early 2025, but volume gradually shrank, indicating weakening bullish momentum. Technically, RSI remains in a strong range of 50-70, but MACD shows faint signs of bearish divergence. Although the VIX remains below 15, the implied volatility curve for short-term options suggests low pricing of tail risks, implying vulnerability to unexpected shocks.

On valuation, the S&P 500's forward P/E is around 22x, above the 90th percentile of the past decade. Excluding mega-cap stocks like FAANG, the median valuation of remaining stocks falls to about 18x, close to historical average. This means the current high valuation is not a broad bubble but a structural premium: investors are willing to pay more for disruptive technologies like AI, but if earnings growth disappoints, the correction space is significant.

III. Sector Rotation: From 'Embracing AI' to 'Wide Bloom'?

The most notable feature of the US stock market in 2025 is intensified sector rotation. In H1, capital continued to pour into tech and communication services, with AI-themed ETF assets surpassing $1 trillion. But entering Q3, the market began to diverge: energy stocks benefited from oil price spikes due to Middle East conflicts, defensive sectors like utilities and healthcare regained favor, and financials performed on steepening yield curve expectations. This rotation reflects investors shifting from pure growth seeking to balancing defense and value.

Looking ahead, AI infrastructure buildout remains a medium- to long-term theme. From chips to data centers, power equipment to software development, the industrial chain's dividends are expanding. However, investors should be cautious that the monetization path for AI applications is not yet fully clear, with some companies' valuations already discounting three to five years of future earnings. Meanwhile, traditional cyclical sectors like industrials and materials may benefit from US manufacturing reshoring and infrastructure bills, while consumer staples face cost pressures. Investors should focus on the match between earnings growth and valuation to avoid 'growth traps'.

IV. Risk Factors: Gray Rhinos and Black Swans Coexist

Key risks facing the US market can be summarized in three points. First, Fed policy error risk. If economic growth slows but inflation remains high, the Fed might face a stagflation dilemma, forced to choose between recession and inflation. Cutting rates too late could cause a hard landing; too early could reignite inflation. Second, geopolitical risks. Prolonged conflicts like Israel-Palestine and Russia-Ukraine disrupt supply chains, and tech decoupling amid great power competition could accelerate global industrial chain restructuring. US companies with high overseas revenue are more vulnerable. Third, liquidity risk. The Fed's balance sheet runoff continues, the Treasury General Account balance is rising, and bank reserves are decreasing, gradually tightening market liquidity. A credit event could trigger cascading effects.

Additionally, US listed companies' share buyback scale shrank in 2025, mainly because firms prioritized debt repayment over rewarding shareholders, weakening marginal buying support. Technical signals also warrant caution, including declining retail participation and high institutional positioning.

V. Investment Strategy: Building Resilience Amid Uncertainty

Given the complex environment, investors should adopt a 'barbell strategy': on one end, allocate to high-quality, strong cash flow, low-volatility defensive assets such as healthcare, consumer staples, and utilities; on the other end, selectively invest in tech and AI beneficiaries with long-term growth potential, but strictly control valuation safety margins. In the medium term, consider increasing weight in value stocks—financials, energy, and industrials are still at a discount in the large-cap value index, with clear earnings improvement trends.

At the index level, timing has become significantly harder. Consider dollar-cost averaging and use option strategies to hedge downside risks. Monitor the yield curve: if the 2-year/10-year spread turns positive and widens, it's often a signal of economic stabilization, favoring cyclical stocks; otherwise, tilt toward defense.

Conclusion

The US stock market stands at a crossroads. On one hand, the AI revolution wave hasn't peaked, corporate earnings remain healthy overall, and soft landing remains the base case. On the other hand, risks from high rates, limited valuation expansion, and liquidity tightening cannot be ignored. The future market will test investors' stock-picking ability and risk management more. As Howard Marks said, 'Investing is not about making precise predictions of future events, but about allocating probabilities.' In an era where uncertainty has become the new normal, maintaining patience and discipline, anchoring value amid volatility, is the way to long-term success.

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  1. CONNECT:[ UseTime:0.001086s ] mysql:host=127.0.0.1;port=3306;dbname=cjgpcs;charset=utf8mb4
  2. SHOW FULL COLUMNS FROM `g3ys_config` [ RunTime:0.002355s ]
  3. SELECT `value` FROM `g3ys_config` WHERE `name` = 'timezone' AND `subsite_id` = '35' LIMIT 1 [ RunTime:0.000610s ]
  4. SHOW FULL COLUMNS FROM `g3ys_route` [ RunTime:0.002666s ]
  5. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000731s ]
  6. SHOW FULL COLUMNS FROM `g3ys_theme` [ RunTime:0.002756s ]
  7. SELECT `paginator` FROM `g3ys_theme` WHERE `module` = 'home' AND `name` = 'etf' AND `subsite_id` = '35' LIMIT 1 [ RunTime:0.000631s ]
  8. SHOW FULL COLUMNS FROM `g3ys_lang` [ RunTime:0.002963s ]
  9. SELECT * FROM `g3ys_lang` WHERE `subsite_id` = 35 AND ( ( LOWER(name) = 'en-us' ) OR `subtag` = 'en-us' ) AND `status` = 1 ORDER BY `id` ASC LIMIT 1 [ RunTime:0.001018s ]
  10. SHOW FULL COLUMNS FROM `g3ys_member` [ RunTime:0.003843s ]
  11. SELECT * FROM `g3ys_member` WHERE `id` = 0 AND `subsite_id` = '35' LIMIT 1 [ RunTime:0.000832s ]
  12. SHOW FULL COLUMNS FROM `g3ys_document` [ RunTime:0.006247s ]
  13. SELECT `id`,`document_type` FROM `g3ys_document` WHERE `subsite_id` = 35 AND `name` = 'mei-gu-shi-chang-shen-du-po-xi-bo-dong-zhong-xun-zhao-que-ding-xing' AND `status` IN ('1','2') LIMIT 1 [ RunTime:0.001497s ]
  14. SELECT * FROM `g3ys_document` WHERE ( `name` = '5490' OR `id` = '5490' ) AND `subsite_id` = 35 LIMIT 1 [ RunTime:0.003026s ]
  15. SHOW TABLES LIKE 'g3ys_document_article' [ RunTime:0.003108s ]
  16. SHOW FULL COLUMNS FROM `g3ys_document_article` [ RunTime:0.001668s ]
  17. SELECT * FROM `g3ys_document_article` WHERE `document_id` = '5490' LIMIT 1 [ RunTime:0.000774s ]
  18. SHOW FULL COLUMNS FROM `g3ys_total` [ RunTime:0.002084s ]
  19. SELECT * FROM `g3ys_total` WHERE `target_id` = '5490' LIMIT 1 [ RunTime:0.000688s ]
  20. SHOW TABLES LIKE 'g3ys_document_article_lang' [ RunTime:0.002135s ]
  21. SHOW FULL COLUMNS FROM `g3ys_document_article_lang` [ RunTime:0.001872s ]
  22. SELECT * FROM `g3ys_document_article_lang` WHERE `subsite_id` = '35' AND `lang` = 'en-US' AND `document_id` = '5490' [ RunTime:0.008605s ]
  23. SHOW FULL COLUMNS FROM `g3ys_document_article_lang` [ RunTime:0.002363s ]
  24. SHOW TABLES LIKE 'g3ys_category_lang' [ RunTime:0.002214s ]
  25. SHOW FULL COLUMNS FROM `g3ys_category_lang` [ RunTime:0.001865s ]
  26. SELECT * FROM `g3ys_category_lang` WHERE `subsite_id` = '35' AND `category_id` = '1393' AND `lang` = 'en-US' LIMIT 1 [ RunTime:0.000785s ]
  27. SELECT * FROM `g3ys_total` WHERE `target_id` = '5490' LIMIT 1 [ RunTime:0.000607s ]
  28. UPDATE `g3ys_total` SET `view_total` = `view_total` + 1 WHERE `target_id` = '5490' [ RunTime:0.001967s ]
  29. SHOW FULL COLUMNS FROM `g3ys_record` [ RunTime:0.004722s ]
  30. SELECT `id` FROM `g3ys_record` WHERE `subsite_id` = '35' AND `type` = 'article' AND `url` = 'https://www.thaietf.com/mei-gu-shi-chang-shen-du-po-xi-bo-dong-zhong-xun-zhao-que-ding-xing' AND `create_time` >= 1787619119 AND ( `visitor_id` = 'add25ac9e5dd852b425ee290b93dd1ce' OR `session_id` = '6d18f879561287ac7c0e92102deadba2' ) LIMIT 1 [ RunTime:0.002048s ]
  31. INSERT INTO `g3ys_record` SET `subsite_id` = '35' , `uid` = '0' , `visitor_id` = 'add25ac9e5dd852b425ee290b93dd1ce' , `session_id` = '6d18f879561287ac7c0e92102deadba2' , `type` = 'article' , `target_id` = '5490' , `target_type` = 0 , `category_id` = '1393' , `title` = 'In-depth Analysis of US Stock Market: Finding Certainty Amid Volatility' , `url` = 'https://www.thaietf.com/mei-gu-shi-chang-shen-du-po-xi-bo-dong-zhong-xun-zhao-que-ding-xing' , `path` = '/mei-gu-shi-chang-shen-du-po-xi-bo-dong-zhong-xun-zhao-que-ding-xing' , `query_string` = '' , `referer` = '' , `referer_domain` = '' , `source_type` = 'direct' , `source_domain` = '' , `search_engine` = '' , `search_keyword` = '' , `utm_source` = '' , `utm_medium` = '' , `utm_campaign` = '' , `utm_term` = '' , `utm_content` = '' , `ip` = '216.73.216.179' , `user_agent` = 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)' , `device_type` = 'pc' , `browser` = '其他' , `browser_version` = '' , `os` = '其他' , `os_version` = '' , `language` = '' , `is_spider` = 0 , `spider_name` = '' , `status_code` = 200 , `country` = '' , `province` = '' , `city` = '' , `district` = '' , `isp` = '' , `region_code` = '' , `geo_status` = 0 , `geo_time` = 0 , `create_time` = 1787619179 , `update_time` = 1787619179 [ RunTime:0.003550s ]
  32. SHOW FULL COLUMNS FROM `g3ys_comment` [ RunTime:0.003449s ]
  33. SELECT COUNT(*) AS think_count FROM `g3ys_comment` WHERE `target_id` = '5490' AND `pid` = '0' [ RunTime:0.000732s ]
  34. SELECT * FROM `g3ys_document` WHERE `category_id` = 1393 AND `subsite_id` = 35 AND `pid` = '0' AND `status` = '1' AND ( ( sort > 0) or ( sort = 0 and id < 5490) ) ORDER BY `sort` ASC,`id` DESC LIMIT 1 [ RunTime:0.001561s ]
  35. SELECT * FROM `g3ys_document` WHERE `category_id` = 1393 AND `subsite_id` = 35 AND `pid` = '5489' AND `status` = '1' ORDER BY `sort` ASC,`id` DESC LIMIT 1 [ RunTime:0.001683s ]
  36. SELECT * FROM `g3ys_document_article_lang` WHERE `subsite_id` = '35' AND `lang` = 'en-US' AND `document_id` = '5489' [ RunTime:0.007280s ]
  37. SELECT * FROM `g3ys_document` WHERE `category_id` = 1393 AND `subsite_id` = 35 AND `pid` = '5490' AND `status` = '1' AND `document_type` = 0 ORDER BY `sort` DESC,`id` ASC LIMIT 1 [ RunTime:0.001535s ]
  38. SELECT * FROM `g3ys_document` WHERE `category_id` = 1393 AND `subsite_id` = 35 AND `pid` = '0' AND `status` = '1' AND `document_type` = 0 AND ( ( sort>0 and sort <= 0 and id!=5490) or ( sort = 0 and id > 5490) ) ORDER BY `sort` DESC,`id` ASC LIMIT 1 [ RunTime:0.001196s ]
  39. SELECT * FROM `g3ys_document_article_lang` WHERE `subsite_id` = '35' AND `lang` = 'en-US' AND `document_id` = '5580' [ RunTime:0.008211s ]
  40. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001295s ]
  41. SELECT * FROM `g3ys_total` WHERE `target_id` = '5490' LIMIT 1 [ RunTime:0.000809s ]
  42. SHOW FULL COLUMNS FROM `g3ys_ad_location` [ RunTime:0.002364s ]
  43. SELECT * FROM `g3ys_ad_location` WHERE `name` = 'ad-detail-full' AND `subsite_id` = '35' AND `status` = 1 LIMIT 1 [ RunTime:0.001008s ]
  44. SHOW FULL COLUMNS FROM `g3ys_ad` [ RunTime:0.003407s ]
  45. SELECT * FROM `g3ys_ad` WHERE `subsite_id` = '35' AND `ad_location_id` = '61' AND `status` = 1 AND `device` IN ('all','pc') AND ( `start_time` = 0 OR `start_time` <= 1787619179 ) AND ( `end_time` = 0 OR `end_time` >= 1787619179 ) ORDER BY `sort` DESC,`id` DESC [ RunTime:0.001123s ]
  46. SHOW FULL COLUMNS FROM `g3ys_ad_lang` [ RunTime:0.001993s ]
  47. SELECT * FROM `g3ys_ad_lang` WHERE `ad_id` = 61 AND `subsite_id` = '35' AND `lang` = 'en-US' [ RunTime:0.000762s ]
  48. SHOW FULL COLUMNS FROM `g3ys_ad_lang` [ RunTime:0.001975s ]
  49. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000802s ]
  50. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000681s ]
  51. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001107s ]
  52. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000715s ]
  53. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000782s ]
  54. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000802s ]
  55. SELECT * FROM `g3ys_ad_location` WHERE `name` = 'ad-right' AND `subsite_id` = '35' AND `status` = 1 LIMIT 1 [ RunTime:0.000862s ]
  56. SELECT * FROM `g3ys_ad` WHERE `subsite_id` = '35' AND `ad_location_id` = '63' AND `status` = 1 AND `device` IN ('all','pc') AND ( `start_time` = 0 OR `start_time` <= 1787619179 ) AND ( `end_time` = 0 OR `end_time` >= 1787619179 ) ORDER BY `sort` DESC,`id` DESC [ RunTime:0.000918s ]
  57. SELECT * FROM `g3ys_ad_lang` WHERE `ad_id` = 63 AND `subsite_id` = '35' AND `lang` = 'en-US' [ RunTime:0.000749s ]
  58. SHOW FULL COLUMNS FROM `g3ys_tags` [ RunTime:0.003486s ]
  59. SELECT * FROM `g3ys_tags` WHERE `subsite_id` = 35 AND `status` = 1 ORDER BY `sort` DESC,`id` DESC LIMIT 15 [ RunTime:0.001182s ]
  60. SHOW FULL COLUMNS FROM `g3ys_tags_lang` [ RunTime:0.001815s ]
  61. SELECT * FROM `g3ys_tags_lang` WHERE `tags_id` IN (777,776,775,774,773,772,771,770,769,768,767,766,765,764,763) AND `subsite_id` = 35 AND `lang` = 'en-US' [ RunTime:0.001464s ]
  62. SELECT * FROM `g3ys_category_lang` WHERE `subsite_id` = '35' AND `category_id` = '2075' AND `lang` = 'en-US' LIMIT 1 [ RunTime:0.000978s ]
  63. SELECT * FROM `g3ys_category_lang` WHERE `subsite_id` = '35' AND `category_id` = '2076' AND `lang` = 'en-US' LIMIT 1 [ RunTime:0.000698s ]
  64. SELECT * FROM `g3ys_category_lang` WHERE `subsite_id` = '35' AND `category_id` = '2077' AND `lang` = 'en-US' LIMIT 1 [ RunTime:0.000832s ]
  65. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000767s ]
  66. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '35' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.000705s ]
0.410737s