付鹏的财经世界 engagement report
@fupenglondon - 66K followers on X
Measured over 10 original posts from a 30-day window, last computed on October 1, 2026.
Engagement
A typical post picks up 114 interactions against 66K followers, an engagement rate of 0.19%. Measured over 10 original posts, its engagement rate beats 58% of 20,445 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 23K times each, and 0.505% of those impressions turn into an interaction. That is about 34.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.33 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 01:00 UTC, and Monday is the busiest day of the week. Of the 10 posts sampled, 50% carry an image or video and 40% link out. The account's strongest tracked post pulled 448 interactions, about 3.9x its own typical post. Recurring topics include #付鹏, #付鹏的财经世界, #宏观经济.
Measured over 10 original posts from a 30-day window, last computed on October 1, 2026. Recurring tags: #付鹏, #付鹏的财经世界, #宏观经济.
Compared with accounts its own size
付鹏的财经世界's engagement rate beats 58% of the tracked X accounts closest to it in follower count (20,445 accounts, accounts of similar size (decile 5 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 21% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.19%, 付鹏的财经世界 sits above the 50th percentile of the 205,048 accounts in this comparison. That places it in the above the median band, which runs 0.143% to 0.678%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 01:00 UTC, and Monday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 50% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 40% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 50% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 40% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 280 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Aug 29, 20263.9x their median
我前面提到的有“英伟达是全球最贵的久期资产之一”，就有朋友问是不是说英伟达的估值很贵，这里的贵不是指大家讲的传统估值，而是久期，怎么理解呢？价格对贴现率有多敏感，取决于现金流有多靠后。一只每年稳定吐出现金、分红回购很大的公司，久期短；一只市值成立的前提是“未来好几年还要继续超常增长”的公司，久期长。 现在市场给英伟达的可不是“成熟现金牛”的价格，而是2028年远期盈利更高的增速，未来主营需求，毛利率都维持下的情况，在这种情况下远期X18看起来不贵，但若是贴现率高一点，18 倍立刻变成贵。而目前英伟达股息率大约 0.45%。 5 万亿回报几乎全部来自终值重估，不来自票息。贴现率升 50 个基点，少掉的是后面那些年的现值，不是今年少发的红利。 周四黄仁勋和CFO的叙事把久期溢价加上去，周五沃什jackson Hold把短端利率拉高，久期溢价被抽走一半——基本面一夜没变，变的是 r。这就是长久期资产的定义：对 r 的弹性远大于对当季利润的弹性。 英伟达是全球体量最大、增长假设最长、对贴现率最敏感的权益资产之一。
- Jul 31, 20263.7x their median
前两天在新火 @_BitfireGroup月度VIP私享会上和一些老朋友交流，大家最关心的依然是 AI 赛道的下一阶段。在金融市场上，同一事情放在不同阶段，答案可能完全相反。前两年只要大厂愿意加码资本性支出（CapEx）砸钱做基建，市场就给高估值；但到了今年二季度，市场开始严苛拷问“自由现金流”（FCF）。 从大厂财报看得很清楚，当巨头们的自由现金流趋向归零，靠自己账上现金无成本做资本支出的时代就结束了。接下来如果要继续扩 CapEx，就必须向外部借钱或融资，而现在的利率成本是 6%-7%。资本市场不再盲目认可“左手倒右手”的资本循环，而是开始逼问：砸了这么多基础设施，下游到底有没有业务流量和真实利润来回本？ 这就像当年传统大基建：2003年修路、建桥，上游的水泥、钢筋、工程机械订单爆表、业绩大涨；但路修好之后，必须得有“车”跑。现在的 AI 赛道正处在这个关键窗口期：基建层（GPU/HBM/算力）第一阶段高潮走完，但真正的终端应用（比如医疗AI、无人驾驶、企业级垂直模型等）还没全面兑现。 很多人做交易最大的误区就是“把可能性当成确定性”，且“在极致确定性里盲目加杠杆”。存储（DRAM/HBM）归根结底也是商品，逃不出商品周期律。当行业基本面极其确定、波动率降至极低时，场外金融杠杆的过度堆积往往才是引发暴跌和闪崩的隐形杀手。去杠杆发生时，即使公司基本面没问题，价格也会剧烈波动。 在流动性收紧与信用分化的宏观大环境下，市场必然走“缩圈”逻辑：资金会率先清掉外围、高弹性、缺乏现金流支撑的投机资产，持续向最核心、确定性最高的标的集中。这也是为什么比特币等纯“分母端”（纯流动性资产）资产，往往会先于传统股票市场消化完估值压力。 AI产业当前处于"中游到下游的过渡期"——以英伟达为代表的基建层已进入成熟阶段（"老登股"特征），但下游应用层尚未出现类似ChatGPT的里程碑式突破。未来 10-18 个月是产业周期的切换过渡期。AI 的下半场绝不是再重复讲一遍“烧钱基建”的故事，而是看 AI 作为效率工具进入真实行业后，能否产生稳定的“付费、利润与扩散”。通用大模型最终会成为普适性工具，真正能筑起护城河的，必然是深耕垂直领域的应用与商业化闭环。
- Jul 23, 20263.0x their median
今天很荣幸作为新火集团首席经济学家受邀参与由 @Wikiexpo_global举办的 #WikiEXPOHongKong2026 ，我将今天讲的几个核心观点归纳发布如下： 一、流动性：全球资产的底层逻辑 首先第一块，先跟大家讲流动性层面。不管是哪一类资产，到今天为止，包括主流加密资产，本质全都和全球核心流动性挂钩。 2008年金融危机之后，全球流动性在2008到2021年走到阶段性顶峰。 流动性不能只看新闻里的加息、降息，它分三个维度，大家一定要记牢。加息降息只是利率曲线端的变化，不能代表完整流动性环境。我这边简单放了一张图表。 观察流动性可以拆成三件事：水池里的水量、水池水温、池内资金压力分布。底层逻辑可以简单理解成P/Q×G。 这点很关键，专业视角看流动性，可以从利率端、利率曲线、美联储资产负债表、公开市场操作等角度跟踪。 但还有更简易的观察方式：当前传统交易里，大家普遍把比特币这类主流加密资产，当作流动性强弱的先行指标。
- Jul 28, 20262.9x their median
6月24日开始不断飙升的英伟达的信用违约掉期CDS开始被市场注意到，去年年底大家都在讨论的“左手倒右手”的玩法开始被质疑，犹如上周谷歌的资本支出背后关注FCF一样：前几年市场信心十足的时候，花钱（自由现金流下降）意味着抢占未来，而现在反而越是烧钱花钱越是意味着担忧；在前几年市场信心十足的时候，“左手倒右手”大法带来越高度绑定大家越认为规模会迅速扩大，而现在则是越是高度绑定，反倒开始意味着将来可能火烧赤壁，祸及连船；
- Aug 20, 20262.7x their median
BTC作为无现金流资产，对长端贴现率（10Y）中隐含的长期增长预期不敏感，但对短端无风险套利收益率（美元现金收益、银行间流动性与杠杆资金成本）极其敏感，短端利率的见顶或走平，往往意味着市场对“流动性边际收紧最剧烈阶段”的定价告一段落，从而直接释放高 Beta 风险资产的估值修复空间。 图表展示了美债收益率（1Y和10Y）、长短端利差与比特币（BTC）的关系，左侧图是7月底当时我们注意到滞后与利差的情况，右侧图示目前的情况。
- Sep 7, 20262.6x their median
https://t.co/v8gq4Cpogv
- Apr 27, 20262.3x their median
The Clarity Act (to be passed by the House of Representatives in 2025, with the Senate still pushing forward) further categorizes digital assets into three types: Digital Commodities (such as Bitcoin and Ethereum), which are essentially tied to the functions of the blockchain system and are regulated by the CFTC; Investment Contract Assets (similar to securities), regulated by the SEC; and Permitted Payment Stablecoins, regulated by banks. This act will separate the previously attempted unified definition of attributes: stablecoins will primarily handle application-level payment/settlement functions (subject to strict reserve and redemption rules), while retaining the value store function as a crucial element of Bitcoin's value maintenance role, assuming the attributes of speculative assets and commodities, rather than everyday currency functions.
- Aug 24, 20262.3x their median
前面两个月我们一直提示，现在市场处在 AI 的（完整可行商业闭环）的证明证伪的真空期，市场的亢奋情绪降温，从资本支出军备竞赛的奖励（谁 CapEx 加大谁未来牛逼现在就先给估值）的逻辑切换到了资本支出军备竞赛的惩罚（资本支出蚕食 FCF 同时未来的不确定性 ROI）。 在这种环境下，之前美股上谷歌的自由现金流转负的惩罚和转到苹果上就是很典型的切换，现在中国的互联网巨头（资本支出的金主爸爸们）之间也正在上演一样的事情。阿里这季度资本开支（CapEx）大幅飙升、导致 FCF 连续净流出，而当庞大的 AI 算力与基础设施投入短期内难以形成等体量的利润闭环时，高强度的 CapEx 消耗了离岸现金，原本投资者对边际资本回报率已经开始产生了严重怀疑，现在大的这个窗口期，全球市场对于 AI 的完整闭环叙事正好在半信半疑，结果还宣布巨额配售/可转债融资，这相当于现有股东股权给稀释了，一边回购注销、一边融资稀释，就变成了做了个套。 大空头 Michael Burry 减持甚至清仓阿里、转而重仓或加仓京东，其实和投资者从谷歌切换到苹果的逻辑一样，谁保持相对克制的资产负债表扩张，谁没有为了追赶 AI 军备竞赛而消耗 FCF，甚至不惜进一步融资去投，谁回购计划执行确定性高，市场现在就把谁当做是安全的。 当然你会依旧说不投不花钱基建被甩下来就会在未来失去身位，这话过去三年讲一直是OK的，但是到了当前这个窗口期市场就是需要一个证明能够闭环的点才能回到正向反馈，不然大叙事已经无法让资本冲动了。
- Sep 17, 20262.2x their median
凯文沃什对于通胀的关注排在了首位，态度非常的坚决，加息被他表述成“去掉一剂宽松”，而不是承认金融条件本来就已经紧——这句话很关键，等于提前堵住了“现在已经够紧、不该再动”的反对意见。 市场给出的可能年内还有一次加息（12月），以及明年可能还有一次加息（季柴油/取暖油和裂解价差会不会把那29%的“再加两次”打满），才可能结束。 凯文沃什不认为会带来就业的问题，刻意切断菲利普斯曲线式的权衡，他连续用了否定句：不认为必须伤害劳动力市场才能完成目标；不认为双重使命在中期相互打架，这不是“就业不重要”，而是“就业眼下不是约束”，他倾向于认为生产率的提升（包括新技术应用）能够在一定程度上吸收名义成本冲击。如果放任能源端推高通胀预期，企业长期资本开支被侵蚀，最终带来的全面衰退对就业的破坏，远比短期加息降温要惨烈得多。 凯文沃什对于通胀前景提及到了能源与裂解价差这个变量，这也是我们之前特别关注的环节之一，市场往往紧盯原油（WTI/布伦特）价格，但终端经济真正消耗的是成品油。当炼化产能出现瓶颈或受地缘、检修制约时，裂解价差（Crack Spread）暴涨意味着炼厂毛利扩大的同时，成品油价格严重脱离原油基准。 即将到来的冬季取暖季，超低库存的柴油取暖油可能会出现异常的风险，柴油不仅是冬季取暖油（Heating Oil），更是重卡物流、航运、农机与工矿机械的直接动力源。超低库存叠加取暖旺季，柴油若出现非线性挤仓或价格脉冲，会以最快速度渗透到物流运费、生鲜食品和终端零售，演变为难以压制的二次通胀风险。 沃什将“能源裂解”这一原本偏向大宗商品微观交易的指标，直接上升为货币政策的前瞻锚，说明美联储在考量通胀粘性时，已将供给端结构性脆弱视作不可忽视的风险敞口。美联储能做的，是防止相对价格冲击扩散成二轮、三轮通胀。 值得注意的沃什提到了关于分配的话题，加息稳定物价对于最不富裕的人可以从中受益，这点他提及了两次；低收入群体的边际消费倾向最高，支出中绝大部分直接锁定在能源、食品和房租等刚性开支上。富裕阶层拥有资产对冲通胀，而低收入家庭只能承受实际购买力的硬性缩水。传统鸽派常指责“加息是牺牲底层工人的工作来保物价”。沃什强调分配效应，正是为了逆转这一叙事——将稳定物价定义为保护中低收入阶层财富底线的最直接手段，从而为坚决紧缩争取更大的政策独立性与社会共识。 美债曲线熊平基本可以确定，短端领涨、长端跟不动，熊平的利率曲线对于黄金，BTC等高流动性敏感资产的挤压，真实利率和美元一起抬头，而且抬的是流动性P水温最敏感的那一段——2年到5年。
- Jul 16, 20262.2x their median
做过大宗商品的老法师否都会对周期有着深刻的理解，价格不是结果，而是激励机制的起点。“永远短缺”叙事在周期上半场如此有力，却总在下半场被自我修正。 市场远期结构super back ， 相关市场的高波动率，对基本面的钝化反应，通常领先物理供需转折数月至数年。 短缺叙事 + 价格领先信号 → 供给响应滞后 + 需求抑制/下游挤压 → 市场（价格与相关资产估值）提前定价转折 + 波动放大 在商品与周期资产的定价机制中，价格与估值往往领先于物理供需的实际出清。 当公司交出“爆表”业绩后，股价却不再显著上涨甚至出现小幅回调，同时波动率明显放大，这通常标志着近期的强业绩已被市场充分定价，而参与者开始对未来路径产生实质性分歧。 业绩本身成为“确认事件”，而非新的催化剂；分歧则集中在增长可持续性、利润率峰值、供给响应速度与需求弹性等维度，最终通过股价反应与波动率变化提前反映出来。 任何商品周期都会反复出现的结构性特征，高价/高业绩本身会诱发供给响应与需求调整。 存储（DRAM与HBM）说到底也是商品，是逃不过商品周期性的这种特征的。
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 114 interactions against 66K followers, an engagement rate of 0.19%. Measured over 10 original posts, its engagement rate beats 58% of 20,445 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 23K times each, and 0.505% of those impressions turn into an interaction. That is about 34.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.33 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 01:00 UTC, and Monday is the busiest day of the week. Of the 10 posts sampled, 50% carry an image or video and 40% link out. The account's strongest tracked post pulled 448 interactions, about 3.9x its own typical post. Recurring topics include #付鹏, #付鹏的财经世界, #宏观经济.
- What is 付鹏的财经世界's engagement rate on X?
- 付鹏的财经世界 (@fupenglondon) has an engagement rate of 0.19%, based on the median interactions across 10 original posts from the last 30 days against 66,345 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.19%, 付鹏的财经世界 sits above the 50th percentile of the 205,048 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @fupenglondon have real engagement?
- Its engagement rate beats 58% of the tracked X accounts closest to it in follower count (20,445 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @fupenglondon post?
- Most posts go out around 01:00 UTC, and Monday is its busiest day, at roughly 0.33 posts per day across the measured window.