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US July Non-Farm Employment Plummets! Unemployment Rate Soars to 4.6%, Fed's 50-basis-point Rate Cut in September Expected to Trigger Market

2026-08-04 02:31

US July Non-Farm Employment Plummets! Unemployment Rate Soars to 4.6%, Fed's 50-basis-point Rate Cut in September Expected to Trigger Market

Non-Farm Data Disappoints! Job Market Cooling Faster Than Expected, Risk of US Hard Landing Emerges

On Monday, August 4, 2026, the global financial market welcomed the first heavyweight bomb of the week. The latest July non-farm employment data released by the US Bureau of Labor Statistics showed that new jobs were only 115,000, far below the market expectation of 175,000, and the previous value was also significantly revised downward to 143,000. At the same time, the closely watched July unemployment rate unexpectedly rose from 4.3% in the previous month to 4.6%, reaching the highest level in two and a half years. As soon as this 'disappointing' data was released, it immediately caused a stir on Wall Street, and investors' concerns about a US 'hard landing' quickly spread.

As a professional platform focusing on financial information and market analysis, Mazhu Investment has, for the first time, broken down the deep meaning behind this non-farm report. This data is not just a monthly fluctuation, but reveals a structural cooling of the US labor market under a high-interest rate environment. Looking at the sub-data, employment growth in the service and construction sectors has significantly slowed, while the manufacturing sector has even shown negative growth, which is consistent with the recent decline in manufacturing PMI data. In addition, the average hourly wage growth rate fell to 3.8%, showing that the momentum of wage growth is weakening. Although this helps alleviate core inflation pressure, it also indicates that the consumption capacity of the American public is facing a test, and consumption is the core engine supporting US economic growth.

Market Reaction Intense: Safe-Haven Sentiment Soars, US Stock Futures Fall

After the non-farm data was released, the global financial market reacted violently. Investors sold off risk assets and turned to traditional safe harbors. Specific market performance is as follows:

  • In the stock market: US Dow Jones Industrial Average futures fell more than 450 points before the market opened, and S&P 500 and Nasdaq 100 futures both fell more than 1.5%. Technology and growth stocks were the first to bear the pressure.
  • In the bond market: Capital frantically flowed into the US Treasury market. The interest rate-sensitive 2-year US Treasury yield plummeted 15 basis points to 4.25%, the largest single-day drop in nearly three months. The 10-year US Treasury yield also fell below the 4.0% psychological level to 3.95%.
  • Foreign exchange and commodities: The US Dollar Index (DXY) fell sharply by 0.8%, falling to around the 103 level. At the same time, spot gold prices rose short-term to above $2,450 per ounce, with a daily increase of more than 1.2%; international oil prices were under pressure due to demand concerns.

This typical 'stocks fall, bonds rise, dollar weakens, gold strengthens' safe-haven combination clearly reflects the rapid shift in market sentiment. Capital flow data shows that a large amount of institutional capital is withdrawing from high-yield bond funds and stock ETFs, and instead flowing into money market funds and gold ETFs, which shows that the risk preference of main capital is rapidly cooling.

Fed's 50-basis-point Rate Cut in September Expected to Soar, Monetary Policy Turning Point Entering Countdown

In the face of such weak non-farm employment data, market expectations for the Fed's future monetary policy have undergone a dramatic change. According to data from the Chicago Mercantile Exchange's (CME) FedWatch tool, after the non-farm data was released, the interest rate futures market priced the probability of the Fed 'cutting rates by 50 basis points' at its September monetary policy meeting instantly soared to 82%, while a week ago this probability was less than 30%. In addition, the market even began to expect that the Fed might cut rates by a total of 4 to 5 basis points within this year to prevent the economy from falling into a deep recession.

Looking back at the first half of 2026, the Fed has maintained a 'cautious wait-and-see' attitude. Although inflation data has fallen from high levels, policymakers have always worried that early rate cuts might trigger a rebound in inflation. However, the cliff-like cooling of the July non-farm data has undoubtedly sounded the alarm for the Fed. Economists point out that the rapid rise in the unemployment rate has triggered the famous 'Sahm rule'—when the 3-month moving average of the unemployment rate is 0.5 percentage points higher than the low point of the past 12 months, the economy is usually already in recession. Currently, this indicator is on the verge of being triggered, which means that the Fed must quickly shift from the 'fighting inflation' mode to the 'protecting employment and preventing recession' mode.

The Mazhu Investment analysis team believes that the Fed's decision in September will face a difficult balance. On the one hand, the deterioration of the job market requires it to take aggressive easing actions; on the other hand, geopolitical risks and potential supply chain issues may still cause disturbances to inflation. However, looking at the current macroeconomic data, the start of the rate cut cycle has become an irreversible trend, and investors need to prepare for the change in the liquidity environment in advance.

Investment Strategy Adjustment: How to Find Opportunities in Economic Cooling and Rate Cut Cycle?

In the face of the dual changes of US economic cooling and rising expectations of Fed rate cuts, investors should promptly adjust asset allocation to respond to market fluctuations and capture potential investment opportunities. The Mazhu Investment team has compiled three major layout directions for you:

  • Lock in high-quality US Treasuries and high-dividend assets: As the Fed starts the rate cut cycle, the US Treasury yield center will further decline, which will directly push up bond prices. Investors can focus on medium and long-term US Treasury ETFs to lock in current higher coupon income and earn capital gains. At the same time, against the background of increasing stock market volatility, high-dividend blue chips with stable cash flow and strong fundamentals will become a safe haven for capital.
  • Long-term allocation of gold and safe-haven assets: The dual drive of a weaker dollar and falling real interest rates will provide solid support for gold prices. In the current environment of increasing global economic uncertainty, incorporating gold and gold-related stocks into the investment portfolio can not only hedge geopolitical risks but also obtain excess returns in the rate cut cycle. It is recommended that investors focus on physical gold ETFs and stocks of large gold mining companies.
  • Both defensive sectors and innovative technology: Under economic downward pressure, defensive sectors such as healthcare and consumer staples often outperform the broader market. However, the interest rate cut environment is also beneficial to growth-sensitive sectors. Technology leaders with AI technology applications and robust profitability will face long-term layout opportunities after a short-term pullback. Investors should avoid blindly selling off, but use market fluctuations to optimize their position structure.

Conclusion: Stay Calm and Dynamically Adjust the Portfolio

The disappointing July non-farm data has undoubtedly set the tone for the second half of the global financial market. The slowdown in US economic growth is an indisputable fact, and the Fed's policy shift will become the core variable dominating asset prices. In this period full of variables, investors should remain calm and avoid emotional trading.

Mazhu Investment will continue to track subsequent changes in US economic data and speeches by Fed officials to provide you with the most timely financial hot spot analysis and market dynamics. In a volatile market, only through professional analysis and rational asset allocation can one find certainty in uncertainty and achieve long-term stable wealth growth. Visit Mazhu Investment now to get more professional investment and financial information and market analysis, let us help you make wise decisions in the complex financial market!

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  1. CONNECT:[ UseTime:0.001189s ] mysql:host=127.0.0.1;port=3306;dbname=cjgpcs;charset=utf8mb4
  2. SHOW FULL COLUMNS FROM `g3ys_config` [ RunTime:0.003917s ]
  3. SELECT `value` FROM `g3ys_config` WHERE `name` = 'timezone' AND `subsite_id` = '37' LIMIT 1 [ RunTime:0.000959s ]
  4. SHOW FULL COLUMNS FROM `g3ys_route` [ RunTime:0.003770s ]
  5. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001233s ]
  6. SHOW FULL COLUMNS FROM `g3ys_theme` [ RunTime:0.010025s ]
  7. SELECT `paginator` FROM `g3ys_theme` WHERE `module` = 'home' AND `name` = 'maju' AND `subsite_id` = '37' LIMIT 1 [ RunTime:0.005814s ]
  8. SHOW FULL COLUMNS FROM `g3ys_lang` [ RunTime:0.010792s ]
  9. SELECT * FROM `g3ys_lang` WHERE `subsite_id` = 37 AND ( ( LOWER(name) = 'en' ) OR `subtag` = 'en' ) AND `status` = 1 ORDER BY `id` ASC LIMIT 1 [ RunTime:0.003417s ]
  10. SHOW FULL COLUMNS FROM `g3ys_member` [ RunTime:0.006886s ]
  11. SELECT * FROM `g3ys_member` WHERE `id` = 0 AND `subsite_id` = '37' LIMIT 1 [ RunTime:0.001816s ]
  12. SHOW FULL COLUMNS FROM `g3ys_document` [ RunTime:0.004876s ]
  13. SELECT `id`,`document_type` FROM `g3ys_document` WHERE `subsite_id` = 37 AND `name` = 'us-july-nonfarm-payrolls-cooling-fed-rate-cut-expectations-2026' AND `status` IN ('1','2') LIMIT 1 [ RunTime:0.002270s ]
  14. SELECT * FROM `g3ys_document` WHERE ( `name` = '6128' OR `id` = '6128' ) AND `subsite_id` = 37 LIMIT 1 [ RunTime:0.005973s ]
  15. SHOW TABLES LIKE 'g3ys_document_article' [ RunTime:0.002904s ]
  16. SHOW FULL COLUMNS FROM `g3ys_document_article` [ RunTime:0.002164s ]
  17. SELECT * FROM `g3ys_document_article` WHERE `document_id` = '6128' LIMIT 1 [ RunTime:0.000951s ]
  18. SHOW FULL COLUMNS FROM `g3ys_total` [ RunTime:0.002254s ]
  19. SELECT * FROM `g3ys_total` WHERE `target_id` = '6128' LIMIT 1 [ RunTime:0.000791s ]
  20. SHOW TABLES LIKE 'g3ys_document_article_lang' [ RunTime:0.002853s ]
  21. SHOW FULL COLUMNS FROM `g3ys_document_article_lang` [ RunTime:0.002102s ]
  22. SELECT * FROM `g3ys_document_article_lang` WHERE `subsite_id` = '37' AND `lang` = 'en' AND `document_id` = '6128' [ RunTime:0.009780s ]
  23. SHOW FULL COLUMNS FROM `g3ys_document_article_lang` [ RunTime:0.002429s ]
  24. SHOW TABLES LIKE 'g3ys_category_lang' [ RunTime:0.002172s ]
  25. SHOW FULL COLUMNS FROM `g3ys_category_lang` [ RunTime:0.001890s ]
  26. SELECT * FROM `g3ys_category_lang` WHERE `subsite_id` = '37' AND `category_id` = '1434' AND `lang` = 'en' LIMIT 1 [ RunTime:0.000774s ]
  27. SELECT * FROM `g3ys_total` WHERE `target_id` = '6128' LIMIT 1 [ RunTime:0.001030s ]
  28. UPDATE `g3ys_total` SET `view_total` = `view_total` + 1 WHERE `target_id` = '6128' [ RunTime:0.001717s ]
  29. SHOW FULL COLUMNS FROM `g3ys_record` [ RunTime:0.004226s ]
  30. SELECT `id` FROM `g3ys_record` WHERE `subsite_id` = '37' AND `type` = 'article' AND `url` = 'https://www.setmal.asia/us-july-nonfarm-payrolls-cooling-fed-rate-cut-expectations-2026' AND `create_time` >= 1790490873 AND ( `visitor_id` = 'e0e643e0c628278e1559a9343852100b' OR `session_id` = 'abe24a7d6d18ec8caf6730ccddd8888c' ) LIMIT 1 [ RunTime:0.001352s ]
  31. INSERT INTO `g3ys_record` SET `subsite_id` = '37' , `uid` = '0' , `visitor_id` = 'e0e643e0c628278e1559a9343852100b' , `session_id` = 'abe24a7d6d18ec8caf6730ccddd8888c' , `type` = 'article' , `target_id` = '6128' , `target_type` = 0 , `category_id` = '1434' , `title` = 'US July Non-Farm Employment Plummets! Unemployment Rate Soars to 4.6%, Fed\'s 50-basis-point Rate Cut in September Expected to Trigger Market' , `url` = 'https://www.setmal.asia/us-july-nonfarm-payrolls-cooling-fed-rate-cut-expectations-2026' , `path` = '/us-july-nonfarm-payrolls-cooling-fed-rate-cut-expectations-2026' , `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` = '216.73.217.117' , `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` = 1790490933 , `update_time` = 1790490933 [ RunTime:0.003336s ]
  32. SHOW FULL COLUMNS FROM `g3ys_comment` [ RunTime:0.004306s ]
  33. SELECT COUNT(*) AS think_count FROM `g3ys_comment` WHERE `target_id` = '6128' AND `pid` = '0' [ RunTime:0.001194s ]
  34. SELECT * FROM `g3ys_document` WHERE `category_id` = 1434 AND `subsite_id` = 37 AND `pid` = '0' AND `status` = '1' AND ( ( sort > 0) or ( sort = 0 and id < 6128) ) ORDER BY `sort` ASC,`id` DESC LIMIT 1 [ RunTime:0.001829s ]
  35. SELECT * FROM `g3ys_document` WHERE `category_id` = 1434 AND `subsite_id` = 37 AND `pid` = '6050' AND `status` = '1' ORDER BY `sort` ASC,`id` DESC LIMIT 1 [ RunTime:0.001313s ]
  36. SELECT * FROM `g3ys_document_article_lang` WHERE `subsite_id` = '37' AND `lang` = 'en' AND `document_id` = '6050' [ RunTime:0.009989s ]
  37. SELECT * FROM `g3ys_document` WHERE `category_id` = 1434 AND `subsite_id` = 37 AND `pid` = '6128' AND `status` = '1' AND `document_type` = 0 ORDER BY `sort` DESC,`id` ASC LIMIT 1 [ RunTime:0.001821s ]
  38. SELECT * FROM `g3ys_document` WHERE `category_id` = 1434 AND `subsite_id` = 37 AND `pid` = '0' AND `status` = '1' AND `document_type` = 0 AND ( ( sort>0 and sort <= 0 and id!=6128) or ( sort = 0 and id > 6128) ) ORDER BY `sort` DESC,`id` ASC LIMIT 1 [ RunTime:0.001485s ]
  39. SELECT * FROM `g3ys_document_article_lang` WHERE `subsite_id` = '37' AND `lang` = 'en' AND `document_id` = '6164' [ RunTime:0.009070s ]
  40. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001294s ]
  41. SHOW FULL COLUMNS FROM `g3ys_ad_location` [ RunTime:0.003745s ]
  42. SELECT * FROM `g3ys_ad_location` WHERE `name` = 'ad-detail-full' AND `subsite_id` = '37' AND `status` = 1 LIMIT 1 [ RunTime:0.001313s ]
  43. SHOW FULL COLUMNS FROM `g3ys_ad` [ RunTime:0.003710s ]
  44. SELECT * FROM `g3ys_ad` WHERE `subsite_id` = '37' AND `ad_location_id` = '70' AND `status` = 1 AND `device` IN ('all','pc') AND ( `start_time` = 0 OR `start_time` <= 1790490933 ) AND ( `end_time` = 0 OR `end_time` >= 1790490933 ) ORDER BY `sort` DESC,`id` DESC [ RunTime:0.001314s ]
  45. SHOW FULL COLUMNS FROM `g3ys_ad_lang` [ RunTime:0.007204s ]
  46. SELECT * FROM `g3ys_ad_lang` WHERE `ad_id` = 70 AND `subsite_id` = '37' AND `lang` = 'en' [ RunTime:0.001499s ]
  47. SHOW FULL COLUMNS FROM `g3ys_ad_lang` [ RunTime:0.004612s ]
  48. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001604s ]
  49. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001091s ]
  50. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001453s ]
  51. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001265s ]
  52. SELECT * FROM `g3ys_route` WHERE ( `subsite_id` = '37' OR `subsite_id` = '0' ) AND `module` = 'home' AND `status` = 1 ORDER BY `sort` DESC,`id` ASC [ RunTime:0.001490s ]
0.736163s