China Galaxy Securities: Strong Non-Farm Data Does Not Mean Fed Rate Hike This Year 中国银河证券：非农走强并不意味着美联储年内加息

Galaxy Macro's core argument is actually quite clever: employment data is strong, so there is no reason to cut rates; yet the structure is not robust enough to drive an inflationary spiral, so there is also no reason to raise rates. In this way, the Federal Reserve is portrayed as a passive, hesitant bystander who can only be "data-dependent," while market panic is depicted as a self-fulfilling game of crying wolf. The elegance of this framework lies in its ability to hedge both sides—it acknowledges the current data reality (which does not support rate cuts) while soothing long-term fears (no need to fear rate hikes). However, it cleverly sidesteps a more fundamental and agonizing question: within the ambiguous realm of "data dependence," what exactly is the real risk?

Is the market truly "excessively pricing in" rate hikes? I believe the market is, in fact, pricing in the Fed's "unpredictability" itself. When Powell and his colleagues elevate "data dependence" to a guiding principle, they essentially turn the path of monetary policy into a highly challenging real-time data interpretation game. May's non-farm payroll was strong, but what about April's? Or next month's? Any fluctuation in a key data point could cause the narrative to shift instantly from "rate cuts are distant" to "rate hikes are imminent." The high-frequency volatility and uncertainty of this narrative are, in themselves, the most expensive trading costs. The market is not foolish; labeling this uncertainty with a simple tag of "rate hike risk" and trading accordingly is, in fact, a highly rational risk management behavior. Accusing the market of "excessive pricing" is no different from mocking someone walking a tightrope for being overly sensitive to the quality of their safety rope.

What's even more interesting is the carefree footnote at the end of the report: "After extreme pricing in AI, the market faces a period of correction risk, and the recovery of liquidity expectations will appear within the year, but it will take time." This sentence exposes another kind of "excess"—the ambition to "over-explain" everything with macroeconomic narratives. After rigorously discussing non-farm payroll data and inflationary spirals, it abruptly jumps to the ups and downs of the AI sector and liquidity turning points, with a logical leap more jarring than the Fed's policy shifts. Crudely attributing the euphoria and correction of AI tech stocks to macroeconomic liquidity tides is, at best, a simplification of the innovation cycles in the tech industry and the dynamics of market sentiment. The AI rally has its own logic of technological breakthroughs, application deployments, and capital narratives. Tying it entirely to the Fed's rate expectations is like trying to use a tide chart to predict every move of a surfer—clumsy and amateurish. This analytical framework is less an insight than a symptom of an academic obsession with "unified explanations," ultimately resulting in an attempt to explain everything while explaining nothing thoroughly.

What truly makes me uncomfortable about this report is the institutionally detached composure hidden between the lines. It's as if someone is observing a frantic ant migration from above and calmly remarking, "You're going the wrong way; there's no need to panic." But traders caught in the market's torrent face real financial fluctuations and career pressures—they lack the hindsight and ivory-tower composure of the report's authors. Their "excessive" reactions are survival instincts for navigating a chaotic system. Simplifying complex market sentiments into a conclusion of "excessive pricing" might itself be a form of "analysis overreach"—an excessive pursuit of neat conclusions that sacrifices the noisy, contradictory yet vibrant texture of the market's true temperature.

So, rather than confidently telling us "there is no need to excessively price in rate hike risks," a more honest approach might be to admit: in the current fog of "data dependence," any single deterministic conclusion is suspect. The market seeks anchors in anxiety, but the true anchors may not exist within the simple, binary framework provided by this report. The real risk might lie in the very moment when we all try to soothe our unease with a clear narrative. In the face of the financial market's complex, massive system, maintaining humility and acknowledging the unknown is far more valuable than rushing to deliver a diagnosis of "excessive pricing."

“市场对加息的担忧成为了交易的主线。” 这句话本身没错，但紧接着那句“过度定价了加息风险”，就显得既狡猾又居高临下。当一份来自金融机构的研报，轻描淡写地将市场的集体焦虑定义为一种“过度”反应时，我们恐怕得先看看它自己的逻辑链条是不是真的那么天衣无缝。

银河宏观的核心论证其实很讨巧：就业数据强，所以没有降息理由；但结构又没强到能推升通胀螺旋，所以也没加息理由。于是，美联储就被塑造成一个被动、犹豫、只能“数据依赖”的旁观者，而市场的恐慌则被描绘成一场自导自演的狼来了游戏。这个框架的漂亮之处在于它左右逢源，既承认了当下的数据现实（不支持降息），又安抚了长期的情绪（不用怕加息）。但它巧妙地回避了一个更根本、也更折磨人的问题：在“数据依赖”的模糊地带里，真正的风险到底是什么？

市场真的是在“过度定价”加息吗？我倒觉得，市场是在为美联储的“不可预测性”本身定价。当鲍威尔和他的同事们把“数据依赖”奉为圭臬时，他们实际上把货币政策的路径变成了一项高难度的实时数据解读游戏。五月的非农是强的，但四月的呢？下个月的呢？任何一个关键数据的风吹草动，都可能让叙事从“降息遥远”瞬间切换到“加息临近”。这种叙事的高频波动和不确定性，本身就是最昂贵的交易成本。市场不是傻子，它为这种不确定性套上一个“加息风险”的简单标签并进行交易，恰恰是高度理性的风险管理行为。指责市场“过度定价”，无异于嘲笑一个在钢丝上行走的人对安全绳的质量过于敏感。

更有趣的是研报最后那句轻飘飘的附笔：“市场在AI的极致定价后面临一段时间的回调风险，流动性预期的恢复年内将会出现，但需要等待。” 这句话暴露了另一种“过度”——试图用宏观叙事“过度解释”一切的野心。前脚还在严谨讨论非农数据与通胀螺旋，后脚就突然跳到AI板块的涨跌和流动性拐点，这之间的逻辑跳跃比美联储的政策转向还要突兀。把AI科技股的狂欢与回调，粗暴地归因于宏观流动性的潮汐，这恐怕是对科技产业创新周期和市场情绪动力学的一种简化。AI行情有其自身的技术突破、应用落地和资本叙事逻辑，将其完全捆绑在美联储的利率预期上，就像试图用潮汐表来预测冲浪高手的每一个动作，显得笨拙而外行。这种分析框架，与其说是洞察，不如说是一种追求“大一统解释”的学术洁癖在作祟，最终导致什么都想解释，却什么都没解释透彻。

这份研报真正让我感到一丝不适的，是那种隐藏在字里行间的、来自机构的从容。它像是在俯瞰一场慌乱的蚁群迁徙，然后冷静地指出：“你们走错方向了，不必如此惊慌。” 但身处市场洪流中的交易者，面对的是真金白银的波动和职业生涯的压力，他们没有研报撰写人那种事后的、书斋里的从容。他们的“过度”反应，恰恰是应对混沌系统的生存本能。将复杂市场情绪简化为一个“定价过度”的结论，这本身可能就是一种“分析过度”——过度追求简洁明了的结论，而牺牲了市场真实温度中那种嘈杂、矛盾却又充满活力的质感。

所以，与其信誓旦旦地告诉我们“无需过度定价加息风险”，或许更诚实的态度是承认：在当前“数据依赖”的迷雾中，任何单一的确定性结论都显得可疑。市场在焦虑中寻找锚点，而真正的锚点，可能根本就不存在于这份研报所提供的那个非此即彼的简单框架里。真正的风险，或许就藏在我们所有人都试图用一个清晰故事来安抚自己不安的那一刻。在金融市场的复杂巨系统面前，保持谦逊，承认未知，比急于给出一个“过度定价”的诊断，要珍贵得多。