Channel
@Hedge_Capital
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
2,882subscribers
-374 since we began measuring on 11 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1002095562483 |
|---|---|
| Type | Channel |
| Usernames | @Hedge_Capital @No_Google |
| Description | long when it makes sense, short when it doesn’t stocks, events, momentum, mispricing Any questions: @net_admin_global |
| Created | Between 1 November 2023 and 31 May 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 1 October 2026 |
| Measurements held | 16 |
| On Telegram | t.me/Hedge_Capital |
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 15 September 2026 and assigned it the closest of 31 fixed categories, at 72% 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 why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 1 Oct 2026, 10:22 | 2,882 | no change |
| 1 Oct 2026, 09:47 | 2,882 | -48 |
| 25 Sept 2026, 22:59 | 2,930 | -19 |
| 17 Sept 2026, 02:55 | 2,949 | -23 |
| 13 Sept 2026, 13:36 | 2,972 | -34 |
| 10 Sept 2026, 04:21 | 3,006 | -21 |
| 4 Sept 2026, 19:00 | 3,027 | -21 |
| 31 Aug 2026, 18:36 | 3,048 | -43 |
| 28 Aug 2026, 20:56 | 3,091 | -6 |
| 26 Aug 2026, 07:25 | 3,097 | -38 |
| 22 Aug 2026, 23:08 | 3,135 | -27 |
| 19 Aug 2026, 06:15 | 3,162 | -56 |
| 15 Aug 2026, 20:15 | 3,218 | -29 |
| 12 Aug 2026, 05:25 | 3,247 | -9 |
| 11 Aug 2026, 20:00 | 3,256 | no change |
| 11 Aug 2026, 19:47 | 3,256 | first reading |
40 posts held, back to 24 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 2 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. It is computed over the 15 of 20 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 29 September 2026 |
|---|---|
| Posts held | 40 (24 July 2026 – 29 September 2026) |
| Views total | 23,270 |
| Reactions total | 204 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 1 Oct 2026, 10:22 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 1 October 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.
837 reactions across 33 posts, in 15 distinct kinds. The most used accounts for 14.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 117 | 14.0% | |
| ❤ | 107 | 12.8% | |
| 🤩 | 104 | 12.4% | |
| 🎉 | 100 | 11.9% | |
| 🔥 | 99 | 11.8% | |
| ❤🔥 | 77 | 9.20% | |
| 💯 | 72 | 8.60% | |
| 😍 | 66 | 7.89% | |
| 🥰 | 66 | 7.89% | |
| 😁 | 23 | 2.75% | |
| 🤣 | 2 | 0.239% | |
| 👀 | 1 | 0.119% | |
| 😱 | 1 | 0.119% | |
| 🤨 | 1 | 0.119% | |
| 🤯 | 1 | 0.119% |
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 33 of the 40 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 837 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 40 most recent posts we hold, published 24 July 2026 to 29 September 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.
Winners have a tendency to keep winning — at least for a while. That’s the practical takeaway I’m using for $AMD here. Jegadeesh and Titman’s momentum research found persistence in stocks that had already performed strongly. The important point is that strength alone doesn’t make a stock “too expensive” to own. Sometimes the market is still processing the information that created the move in the first place. AMD f…
Not every trade needs a 15% target to be worth taking Sometimes the edge is in probability rather than distance. $NVDA is sitting near the top of its range and, importantly, it isn’t showing the type of distribution I’d expect before a larger reversal. Momentum research gives me a framework for accepting that. Recent winners don’t have to immediately mean-revert simply because they are trading near highs. So this…
Markets often move before the story feels complete. That’s one reason I pay attention when selling pressure repeatedly fails to break a trend. Hong and Stein argued that information can diffuse gradually through a market. When that happens, prices can underreact first and trend later as additional investors respond. $PLTR looks like that type of tape to me. Weakness keeps getting absorbed. Buyers don’t need to do…
A tight trade can still be a good trade The mistake is judging every setup by the size of the target rather than by the relationship between probability, risk and capital committed. $NVDA is consolidating near its highs instead of immediately rejecting them. Momentum research gives me no reason to assume that strength must reverse purely because the stock has already performed well. So I’m not asking for much. I’…
Prices don’t only trend because fundamentals change Sometimes price itself changes investor behavior. That feedback loop is what interests me in $TSLA. Daniel, Hirshleifer and Subrahmanyam’s work on investor overconfidence describes how successful prior beliefs can reinforce confidence and push prices further before the process eventually reverses. TSLA has already priced in a lot of optimism, but the stock is no…
❤2
Overreaction doesn’t always end with a crash Sometimes it ends with a boring pullback. De Bondt and Thaler’s classic work found evidence consistent with investors overreacting to dramatic information and prior price performance. That’s the framework I’m using for $AMZN. The stock looks stretched, but I’m not building some grand bearish thesis around Amazon. I simply think expectations have moved far enough ahead …
The second failed push often interests me more than the first one $TSLA has already had the opportunity to turn higher prices into a clean continuation. It hasn’t. Behavioral models of overconfidence offer one explanation for why these moves can overshoot: investors become more confident as prior price action appears to validate their beliefs. Eventually, the marginal buyer becomes harder to find. That’s where I t…
❤4🎉4👍2🔥2😁2🤩1
Momentum is useful right up until you start confusing it with certainty $MU is still giving me a continuation setup, but it’s also far enough into the move that I want less exposure, not more. Daniel, Hirshleifer and Subrahmanyam modeled how overconfidence and self-attribution can reinforce short-run momentum before eventually contributing to overreaction. That distinction is exactly what I’m managing here. I sti…
🔥4❤3🎉3👍3🤩1
The market can move from underreaction to extrapolation faster than people expect Barberis, Shleifer and Vishny modeled both behaviors: investors may initially react too little to new information, then eventually place too much weight on a perceived streak. $META is starting to look like it has crossed that line. The upside story is already well understood. What I’m watching now is whether price can continue rewar…
👍3❤2🎉2🔥2🤩1😁1
Mean reversion trades work best when you don’t ask them to become something bigger That’s exactly how I’m treating $AMZN. De Bondt and Thaler found evidence that markets can overreact, creating subsequent reversal effects. But I don’t need a full-blown behavioral anomaly here. I need a much smaller version of the same mechanism. Momentum has weakened and my target is deliberately close. I’m fading the last part o…
🎉4👍4❤2🔥2😁2🤩1
The more obvious a trend becomes, the more careful I get about joining it late $META has moved far enough that I’m now interested in the other side. Barberis, Shleifer and Vishny describe a behavioral process where investors can extrapolate a sequence of good news and eventually overreact. The practical implication is simple: a good company and an attractive short can exist at the same time. I’m not shorting META…
❤4🎉3🤩3🔥2👍1😁1
A crowded trade doesn’t reverse just because it’s crowded That’s an important distinction. Shleifer and Vishny’s work on the limits of arbitrage explains why mispricing can persist even when sophisticated investors recognize it: taking the opposite side requires capital, timing and the ability to survive being early. That’s why I’m not dramatically oversizing this $TSLA short. I think the stock is struggling to j…
🎉5❤3👍2🔥2🤩2
Showing the 12 most recent of 40 posts we hold for @Hedge_Capital. 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.
Named by 13 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.
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 1 October 2026 — this entry's latest reading, not the date you are reading this.
“Hedge Capital” (@Hedge_Capital), 2,882 subscribers as measured 1 October 2026. Telegram Register, tgregister.com/channel/Hedge_Capital.
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.