헤지 펀드가 위험을 헤징하면서 절대 수익을 추구해야하는데 AI쪽 펀더멘탈이 좋다보니 다들 한 방향으로 쏠려있었음.. 위 결과는 롱이랑 숏해놓은 포지션들의 청산 결과.. https://t.me/kkkontemp/2805
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Channel
@kkkontemp
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry
21,111subscribers
+3,415 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002098793993 |
|---|---|
| Type | Channel |
| Username | @kkkontemp |
| Created | Between 1 November 2023 and 31 May 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 5 November 2026 |
| Measurements held | 62 |
| Confirmed unchanged | 1 time, most recently 5 November 2026 |
| On Telegram | t.me/kkkontemp |
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 44% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 5 Nov 2026, 18:29 | 21,111 | +53 |
| 4 Nov 2026, 21:30 | 21,058 | +18 |
| 3 Nov 2026, 21:56 | 21,040 | +4 |
| 3 Nov 2026, 00:15 | 21,036 | +39 |
| 2 Nov 2026, 01:48 | 20,997 | +38 |
| 1 Nov 2026, 03:29 | 20,959 | +3 |
| 31 Oct 2026, 04:58 | 20,956 | -1 |
| 30 Oct 2026, 03:36 | 20,957 | +46 |
| 29 Oct 2026, 04:01 | 20,911 | +44 |
| 28 Oct 2026, 03:39 | 20,867 | +3 |
| 27 Oct 2026, 00:09 | 20,864 | +2 |
| 25 Oct 2026, 23:27 | 20,862 | +8 |
| 24 Oct 2026, 21:54 | 20,854 | -2 |
| 23 Oct 2026, 21:57 | 20,856 | +30 |
| 22 Oct 2026, 19:21 | 20,826 | +38 |
| 21 Oct 2026, 17:38 | 20,788 | +28 |
| 20 Oct 2026, 12:19 | 20,760 | +31 |
| 19 Oct 2026, 09:59 | 20,729 | +5 |
| 18 Oct 2026, 13:06 | 20,724 | +23 |
| 17 Oct 2026, 16:17 | 20,701 | first reading |
104 posts held, back to 29 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 63 pages of Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 6 November 2026 |
|---|---|
| Posts held | 104 (29 July 2026 – 6 November 2026) |
| Views total | 485,170 |
| Reactions total | 3,540 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Nov 2026, 14:59 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 6 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
13,278 reactions across 99 posts, in 13 distinct kinds. The most used accounts for 45.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 6,005 | 45.2% | |
| 👍 | 3,406 | 25.7% | |
| 👌 | 623 | 4.69% | |
| 😱 | 538 | 4.05% | |
| 😭 | 531 | 4.00% | |
| 🔥 | 510 | 3.84% | |
| 🤔 | 447 | 3.37% | |
| 🙏 | 358 | 2.70% | |
| 👏 | 302 | 2.27% | |
| 🤬 | 221 | 1.66% | |
| 💯 | 134 | 1.01% | |
| 🤯 | 125 | 0.941% | |
| ✍ | 78 | 0.587% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 104 of the 104 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 14,252 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 104 most recent posts we hold, published 29 July 2026 to 6 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram’s own ‘edited’ marker: 5 of 35 posts held from the last 30 days (14.3%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
헤지 펀드가 위험을 헤징하면서 절대 수익을 추구해야하는데 AI쪽 펀더멘탈이 좋다보니 다들 한 방향으로 쏠려있었음.. 위 결과는 롱이랑 숏해놓은 포지션들의 청산 결과.. https://t.me/kkkontemp/2805
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*ING, 5억불규모 데이터센터 대출 포트폴리오 매각 추진 주체:소식통 *TAIWAN NATIONAL STABILISATION FUND EXECUTIVE SECRETARY SAYS *TAIWAN URGES STATE-BACKED BANKS TO INVEST AT ATTRACTIVE VALUE
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크로스 애셋(자산군 간) 신호는 경기 침체가 아닌 ’위험 회피(risk-off)‘를 나타내고 있습니다. 국채와 금에는 매수세가 들어오는 반면 암호화폐는 약세를 보이고 있으나, 유가가 강세를 유지하고 금융주가 비교적 잘 버티고 있다는 점은 전반적인 성장 둔화로의 전환 가능성을 일축합니다. 이번 사태는 여전히 매크로 충격보다는 포지셔닝 충격에 가까워 보입니다. -- 이 부분이 시장 흐름 읽는데 어려웠네요 https://t.me/kkkontemp/2793
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UBS 장중 랩: (마감 아님) 항복(Capitulation) 국면의 역학이 나타나고 있습니다. 헤지펀드(HF) 중심의 매도와 개인 투자자의 반대매매(liquidation)가 시스템적 위험 축소(de-risking)와 맞물리면서, 거래량이 폭증하고 변동성의 변동성(vol-of-vol)이 급증했으며 쏠림이 심했던 포지션 전반에서 4~5시그마(σ) 수준의 극단적 변동이 발생하고 있습니다. 롱온리(LO) 자금의 유입은 상대적으로 견조하지만, 청산(unwind) 물량을 받아내기에는 역부족입니다. AI 섹터는 순환매에서 청산(liquidation) 단계로 전환되었습니다. 반도체, 메모리, 네오클라우드가 그 중심에 있으며, 약세 분위기는 이제 하이퍼스케일러와 AI 연계 산업재 섹터로 확산하고 있습니다. 이 같은 하락의 기폭제는 최종 수요의 둔화라기보…
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그저 레버리지 자금들의 전멸.. 큰 시그마 움직임들 미국 시장 대비 하락 폭이 컸던 테마: 전력망수혜주 -7.5σ 신용카드 발급사 -5.5σ 방산 업체 -5.4σ AI 인프라 -4.7σ 전력 슈퍼사이클 -4.6σ 데이터센터 구축 -4.4σ 우주 경쟁 -4.3σ 드론 생태계 -4.1σ 에너지 -3.7σ 상승 폭이 컸던 테마: 주택 유통 +5.6σ 임의소비재 +5.2σ 항공주 +4.7σ 헬스케어 유통 +4.5σ 의료기기/기술 +4.3σ 필수소비재 +4.1σ 병원 +4.0σ 팹5(Fab5 대형 기술주) +2.1σ 정리하자면, 모멘텀(Momentum) 스타일 팩터는 -4.6σ의 급락세를 보였으며, 롱(매수) 포지션이 숏(매도) 포지션 대비 5.4σ 하회함에 따라 소형주 숏퀴즈(Short Squeeze)가 발생했습…
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시사점: AI 관련주, 손절인가 버티기인가: 아시아 데이터센터와 CapEx 피크론 대한 관점 최근 AI 관련주 비중을 줄이고 다른 수혜 섹터로 갈아타야 할지, 아니면 엉덩이 무게로 버텨야 할지 - 실적이 제대로 찍히는 종목이 마땅치 않은 상황이지만, 반도체 단가가 50% 폭락 가정해도 PER이 5배 수준. PBR은 이미 다운사이클 반영중이라 현재가 Late 사이클이 아닌 Mid 미드 사이클이라고 판단되면 저평가. 이렇게 저평가된 실적주를 던지고, PER 수십 배짜리 ’스토리 기반 주식‘을 코어 롱으로 채워야 할지? 최근 텍사스 주지사 선거 여파 등으로 미국 내 전력망 제약이 커지면서 데이터센터 투자가 아시아로 옮겨가는 흐름에 주목 단기적인 선거여파로 심리적인 부분일수도 있지만 위 흐름 가정하에 아시아 이탈 수혜: 데이터센터 투자의 중심…
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한국은 미국보다 전력값이 2배나 비싸고 전력 인허가도 느려서 힘들다는게 아시아 네오클라우드 사업자들의 평 https://t.me/kkkontemp/2798
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뷰 꺾는 자들 등장
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MS: 텍사스 주지사 선거 결과로 인해 정책 불확실성이 커지고 전력 공급 추가 증설에 제약이 생길 수 있습니다. 당사는 이러한 상황이 전력 수요의 아시아 이탈(spillover) 현상을 확대하고, 하이퍼스케일러들이 아시아 지역 데이터센터 투자를 가속화하도록 유도하여 말레이시아, 태국, 인도, 싱가포르, 일본 내 신뢰할 수 있는 전력 공급 수요를 키울 것으로 보고 있습니다. 전반적으로 이는 당사가 아시아 민자 발전(merchant power) 가격을 한층 더 긍정적(bullish)으로 전망하게 만듭니다. 아시아로 넘어오는 15GW 규모의 이탈 수요는 지역 내 데이터센터 가동률이 당사의 긍정적 시나리오(bull case) 수준까지 올라감에 따라, 당사의 기존 예측치인 2030년보다 더 빠르게 흡수될 가능성이 있다고 추정합니다. 아시아 내 자가 …
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필자가 참고하는 2020년 이후 코리아 불/베어 지수 (1 이하 바닥권 구간) - 단기적인 타이밍은 몰라도 시장이 얼마나 빠졌는지 참고하는
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중간선거 관점 리프레시 https://t.me/kkkontemp/2780 https://t.me/kkkontemp/2781
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미국이 데센 건설에 차질이 생긴다는 가정하에 정책 결정자라면, 이 타이밍을 놓치지 않고 자국 내 인프라 유치를 위해 과감한 규제 철폐에 나서야하지 않을까 규제 완화와 파격적인 혜택으로 중동의 신흥 금융 허브로 부상했던 ‘두바이 모멘텀'을 지금 홍콩 싱가폴 도쿄가 가져가고 있는데 한국은 금융과 데이터센터 관련 규제를 과감히 풀고 글로벌 자본과 빅테크 투자를 적극 유치해야... 글로벌 거점 다변화라는 이 황금 같은 기회를...
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Showing the 12 most recent of 104 posts we hold for @kkkontemp. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
@kkkontemp edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republished by 91 registered channels. The 48 listed are the ones that have forwarded the most posts; the rest are counted here but not each listed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 18 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 30 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 17 seed channels' Telegram-generated recommendation lists in total, of which the 16 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 5 November 2026 — this entry's latest reading, not the date you are reading this.
“KK Kontemporaries” (@kkkontemp), 21,111 subscribers as measured 5 November 2026. Telegram Register, tgregister.com/channel/kkkontemp.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.