How has the U.S. equity market’s Q2 earnings season performed so far?

As the U.S. equity market’s Q2 earnings season draws to a close, each earnings release is revealing the market’s true pulse. According to ZF Trading Desk, Goldman Sachs’ latest research report reveals that 327 companies in the S&P 500 have reported results—accounting for 69% of the index’s total market capitalization. Notably, 63% of these firms exceeded consensus earnings-per-share (EPS) expectations—a rare high proportion not seen in twenty-five years. Yet market reaction has been muted, primarily due to exceptionally low prior expectations driven by tariff-related pressures. Technology giants continued to lead in Q2, posting EPS growth rates over six times higher than those of other sectors. Under the shadow of tariffs, corporate adaptability has emerged as a critical variable. Though this earnings season appears strong on the surface, it conceals numerous intriguing contrasts and subtleties worth closer examination. Goldman Sachs cautions that tariff pressures may marginally intensify in the second half of the year, and sustained price hikes by companies to protect margins could further suppress real demand. Nonetheless, positive factors remain evident: corporations’ resilience against tariffs has surpassed expectations; fiscal policy is expected to provide modest support to 2026 earnings; and the growth momentum of mega-cap tech stocks may continue underpinning earnings resilience. The proportion of companies beating EPS estimates hit a 25-year high—but the ‘quality’ of those beats cannot be overlooked. Overall, the Q2 earnings report card looks quite impressive. As of August 1, 327 S&P 500 companies had reported Q2 results—representing 69% of the index’s total market capitalization. Among them, 63% posted EPS above analyst consensus forecasts—the highest level in twenty-five years, second only to the post-pandemic economic reopening phase. Earnings growth momentum also exceeded expectations: S&P 500 Q2 EPS rose 9% year-on-year—significantly higher than the 4% forecast at the start of the quarter. This growth was jointly driven by revenue expansion and margin improvement: average EPS beat was 8%, while average revenue beat stood at 3%, indicating solid performance both on the top line and in cost control. However, this high ‘beat rate’ reflects a special context: analysts substantially lowered their earnings expectations early this year, creating an unusually low benchmark. Goldman Sachs notes that this pre-emptive downward revision means the ‘substance’ behind the Q2 EPS beats is diluted—market reactions to positive surprises have notably weakened: stocks of companies beating EPS expectations outperformed the S&P 500 by only 55 basis points following their announcements, far below the historical median of 101 basis points; conversely, stocks of companies missing expectations underperformed by 362 basis points—a figure nearly double the historical average. Corporate guidance turns positive; earnings revisions reach a multi-year high In contrast to the caution expressed in Q1, corporate guidance for full-year EPS turned markedly optimistic during the Q2 reporting season. Data show that 56% of companies providing full-year EPS guidance raised their forecasts—a figure nearly double Q1’s 29%; meanwhile, the share of companies maintaining unchanged guidance dropped from 54% in Q1 to just 27%, signaling heightened confidence in future operational certainty.

EPS Forecasting and Risk Measurement Based on Time-Series Analysis and Monte Carlo Simulation

Introduction In corporate financial forecasting practice, deterministic point estimates often fail to adequately reflect the inherent volatility of future operating environments. For instance, a listed company—without accounting for risk and uncertainty—initially forecasts next fiscal year’s earnings per share (EPS) at RMB 1.80. However, given the relatively stringent penalty mechanism tied to performance evaluation, the chief accountant harbors significant concerns regarding the actual achievability of this target. Accordingly, quantitative risk analysis tools must be introduced to incorporate sales volatility and cost uncertainty into a unified framework—enabling assessment of the probability that EPS falls short of the target—and further supporting the design of cost-optimization scenarios to deliver actionable decision-making insights for management. Methodological Framework Sales Forecasting: Time-Series Analysis Leveraging Drisk software, the chief accountant first applies time-series analysis to the company’s historical sales data (comprising monthly sales records across multiple past accounting periods), building either an autoregressive or exponential smoothing model to extract trend, seasonality, and cyclical components. This yields a 12-month forward sales forecast series. This step transforms exogenous uncertainty on the sales side into quantifiable input variables, laying the groundwork for subsequent profit simulation. Costs and Expenses: Probabilistic Distribution Modeling Given the pronounced historical variability in cost of goods sold (COGS) and administrative expenses, the chief accountant assigns probabilistic distributions to both metrics based on past financial data: ・ COGS: modeled with a mean equal to 61% of current-period sales revenue, and a standard deviation equal to 10% of that mean; ・ Administrative expenses: modeled with a mean equal to 7.4% of current-period sales revenue, and a standard deviation likewise equal to 10% of that mean. These distributional assumptions reflect the enterprise’s actual cost-structure volatility—not rigid fixed ratios—thereby enabling more realistic modeling of potential future profit paths. Risk Simulation: Monte Carlo Method Building upon the sales forecast and cost distributions described above, the chief accountant runs Monte Carlo simulations using Drisk software (with over 10,000 iterations) to compute net income and EPS for each simulated scenario. Through large-scale random sampling, an empirical EPS distribution is generated, and the cumulative probability that EPS falls below the initial target of RMB 1.80 is calculated. Scenario Design and Simulation Results To reduce the risk of missing the EPS target, the chief accountant additionally defines two cost-control scenarios: lowering the COGS ratio from the baseline level (61%) to 56% and 51%, respectively.

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