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#Variance Reel by @quantguild (verified account) - 🚀 Master Quantitative Skills with Quant Guild: https://quantguild.com Join the Quant Guild Discord server here: https://discord.com/invite/MJ4FU2c6c
54.8K
QU
@quantguild
🚀 Master Quantitative Skills with Quant Guild: https://quantguild.com Join the Quant Guild Discord server here: https://discord.com/invite/MJ4FU2c6c3 @QuantGuild Video Title: Black-Litterman vs. Mean-Variance Portfolio Optimization #shorts #short #finance #statistics #maths #trading #investing #stocks #finance #fyp #finance #foryoupage
#Variance Reel by @vince.quant - Most people think quants predict market direction. We usually don't Here's what they do predict: volatility, how much prices move. Key points: Vola
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VI
@vince.quant
Most people think quants predict market direction. We usually don’t Here’s what they do predict: volatility, how much prices move. Key points: Volatility clusters: big moves follow big moves, small moves follow small moves. GARCH(1,1) captures it with three terms: baseline (long-term variance) shock (recent squared return) memory (previous variance) Persistence is high: α + β ≈ 0.9 - 0.99 -> volatility can stay elevated for weeks or months. Constant-vol models fail: they underestimate risk after shocks VaR illustrates the impact: same portfolio, same confidence level, but risk estimates can change dramatically Takeaway: predicting risk, not returns, is where quants find a signal #quant #garch #finance #algotrading #trading
#Variance Reel by @aibutsimple - Principal Component Analysis (PCA) is a dimensionality reduction technique for simplifying data by projecting it onto a smaller set of orthogonal dire
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AI
@aibutsimple
Principal Component Analysis (PCA) is a dimensionality reduction technique for simplifying data by projecting it onto a smaller set of orthogonal directions called principal components. These components capture the maximum possible variance in the data, meaning they preserve the most important patterns while discarding noise and redundancy. By keeping only the top components, high-dimensional data can be compressed into a lower-dimensional representation with minimal information loss. Struggling to Understand Machine Learning? Join 7000+ Others in our Weekly AI Newsletter—educational, easy to understand, math included, and completely free (link in bio 🔗). C: deepia Join our AI community for more posts like this @aibutsimple 🤖
#Variance Reel by @exceldictionary (verified account) - How to create variance in-cell bar charts. 📊 Download this free step-by-step guide I created using the link in my profile. . #scribe #scribehow #exc
91.8K
EX
@exceldictionary
How to create variance in-cell bar charts. 📊 Download this free step-by-step guide I created using the link in my profile. . #scribe #scribehow #excel #exceltips #exceltricks spreadsheets
#Variance Reel by @finance.thomas (verified account) - 1. They do not pick stocks, they manage a covariance matrix Every position is defined by its contribution to portfolio variance, not expected return a
179.9K
FI
@finance.thomas
1. They do not pick stocks, they manage a covariance matrix Every position is defined by its contribution to portfolio variance, not expected return alone. When the matrix shifts, the portfolio rebalances. Automatically. No discretion. No opinion. Pure math. 2. PCA, isolating real risk factors from noise Principal Component Analysis decomposes the covariance matrix into independent risk factors. Component 1: market beta. Component 2: sector tilt. Component 3: style exposure. Quants build positions explicitly neutral to components 1 and 2. That is how you isolate alpha from beta. 👨‍💻Drop “Quant” in comments to go deeper on how factor models drive real market dynamics every day.​​​​​​​​​​​​​​​​ 3. Factor models, the signal architecture Every security gets expressed as a vector of factor exposures: momentum, quality, low vol, liquidity. A cross-sectional regression runs daily across thousands of securities. Output: a ranked signal vector. Top decile bought. Bottom decile shorted. That is the trade. 4. Portfolio construction = optimization as execution The fund solves a quadratic optimization problem daily. Maximize expected factor return subject to vol, turnover, and concentration constraints. Output is not a trade idea. It is an exact position vector across 500 to 2000 securities. 5. Orthogonalization = the final edge The portfolio vector gets projected onto unwanted risk factors and stripped clean. Market beta hedged. Sector tilts neutralized. Pure factor exposure remains. This runs every night. By open, the portfolio is mathematically realigned.
#Variance Reel by @askanujj (verified account) - The finance world is in a frenzy because Claude and other LLMs can now build a DCF, a P&L forecast, or a variance analysis in seconds. But here's the
71.6K
AS
@askanujj
The finance world is in a frenzy because Claude and other LLMs can now build a DCF, a P&L forecast, or a variance analysis in seconds. But here’s the truth that every Financial Analyst in India needs to hear: Data is a commodity. Insights are the premium. 🚀 Here is the 3-Pillar Framework to stay irreplaceable in 2026: 1. From ‘Data Processor’ to ‘Strategic Architect’ 🏗️ AI is a “Data Processor”—it follows instructions. You must become a “Strategic Architect.” The Shift: AI can calculate a 15% IRR, but it can’t tell you if that IRR is realistic given the current GST fluctuations or rural demand shifts in India. The Action: Stop spending 4 hours cleaning data. Use AI to do it in 4 minutes. Spend the remaining 3 hours and 56 minutes asking: “What does this number actually mean for our bottom line?” 2. The ‘Assumptions’ Alpha 📉 A model is only as good as its inputs. In the AI era, the “Assumptions” are the only thing that matters. The Nuance: AI doesn’t understand “Street Knowledge.” It doesn’t know that a competitor just poached a key sales team or that a local supply chain is breaking. The Action: Your job is to feed the AI the right context. You are the “Filter” that ensures the AI doesn’t produce “Garbage In, Garbage Out.” 3. The ‘Decision-Support’ Edge 🤝 In a world of automated reports, the person who can communicate the ‘So What?’ wins. The Reality: A CFO doesn’t want a 50-tab Excel sheet; they want a 3-bullet recommendation on whether to Invest, Divest, or Wait. The Action: Master the art of Financial Storytelling. Use the time AI saves you to build relationships with department heads and understand the business behind the numbers. The Bottom Line: AI isn’t taking your job. An Analyst using AI to handle the grunt work while they focus on High-Value Strategy is taking your job. I’m using these tools to handle the boring 80%, so I can spend my time on the 20% that actually gets me promoted: Strategic Decision Making. What’s your take on AI taking away jobs? Let’s debate in the comments? #financialanalyst #claude #finance #financecareers #ai
#Variance Reel by @cloud_x_berry (verified account) - Machine Learning Roadmap 2026… Follow @cloud_x_berry for more info #MachineLearning #ML #ArtificialIntelligence #DataScience #LearnML supervised le
112.6K
CL
@cloud_x_berry
Machine Learning Roadmap 2026… Follow @cloud_x_berry for more info #MachineLearning #ML #ArtificialIntelligence #DataScience #LearnML supervised learning, unsupervised learning, reinforcement learning, classification, regression, clustering, dimensionality reduction, feature engineering, model training, model evaluation, overfitting, underfitting, bias variance tradeoff, cross validation, hyperparameter tuning, neural networks, deep learning, ML algorithms, real world ML applications
#Variance Reel by @propmarketintern - Over time, losing money starts to provoke less of an emotional reaction because the brain stops interpreting each loss as a personal failure and start
259.5K
PR
@propmarketintern
Over time, losing money starts to provoke less of an emotional reaction because the brain stops interpreting each loss as a personal failure and starts categorizing it as a normal business expense of participation. As you accumulate screen time, you internalize that variance is unavoidable, that even good trades can lose, and that the only meaningful question is whether you executed your process correctly. That shift reduces shock: the nervous system learns the pattern, your position sizing becomes more calibrated, and the outcome stops feeling like an emergency because it no longer threatens your identity or your ability to continue trading. The result is a quieter relationship with losses—still attentive, still respectful, but less dramatic—where you can take the hit, log the lesson, and move on without needing to immediately repair the emotion with another trade. // follow @propmarketintern for daily memes! #tradingmemes #gambling #daytrading #parlay #trading
#Variance Reel by @deeprag.ai - The math behind PCA is pure linear algebra. 📐🧠 Principal Component Analysis works by re-expressing data in a new coordinate system where the axes a
108.3K
DE
@deeprag.ai
The math behind PCA is pure linear algebra. 📐🧠 Principal Component Analysis works by re-expressing data in a new coordinate system where the axes are chosen mathematically, not intuitively. First, the data is mean-centered so variance is measured correctly. Next, PCA computes the covariance matrix, which captures how features vary together. From there, PCA performs an eigenvalue decomposition (or Singular Value Decomposition) to find: • Eigenvectors → the principal directions • Eigenvalues → how much variance each direction explains Projecting the data onto the top-k eigenvectors is just a matrix multiplication, producing a lower-dimensional representation that minimizes reconstruction error in the least-squares sense. Nothing heuristic. Nothing learned. Just geometry, projections, and optimal variance preservation. This is why PCA is foundational to machine learning, statistics, and numerical methods. Credit: deepia Follow @deeprag.ai for math-driven explanations behind modern AI. . . . . . . #PCA #LinearAlgebra #Eigenvectors #Eigenvalues #MatrixDecomposition SVD MathBehindAI MachineLearningMath Statistics DataScience DimensionalityReduction MLTheory STEM
#Variance Reel by @hustleuphoney - Day 21/21 - SQL Challenge (Final Day) Wrapped up the challenge with a mix of ranking, retention, distribution, and business logic problems. • SS 200
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HU
@hustleuphoney
Day 21/21 – SQL Challenge (Final Day) Wrapped up the challenge with a mix of ranking, retention, distribution, and business logic problems. • SS 2007 – Rank Variance Per Country: Used window functions to rank records within each country and calculated the difference between rankings to measure variance. • DL – Histogram of Users and Purchases: Grouped users by purchase count and calculated frequency distribution to generate histogram-style output. • DL – Active User Retention: Compared user activity across consecutive days to calculate retention rate using date logic and aggregation. • DL – Well Paid Employees: Joined employee and department data and filtered employees earning more than department average. • LC SQL50 (2 Questions): Practiced core patterns like joins, filtering, and aggregation from the SQL50 set to strengthen fundamentals. • DL – SuperCloud Customer: Used grouping and conditional aggregation to identify customers meeting product usage criteria. Key learning from 21 Days: Most SQL problems repeat the same core ideas — joins, window functions, aggregation, filtering, and clear thinking. Consistency > complexity. 21 days done. On to bigger goals 🚀
#Variance Reel by @goodmovie_greatmovie - When Loki realised. Following his escape with the Tesseract in Avengers: Endgame, a "variant" version of Loki is arrested by the Time Variance Autho
28.5M
GO
@goodmovie_greatmovie
When Loki realised. Following his escape with the Tesseract in Avengers: Endgame, a "variant" version of Loki is arrested by the Time Variance Authority (TVA)—a bureaucratic organization that exists outside of space and time to protect the "Sacred Timeline." ​To avoid being deleted from existence, Loki is recruited by Agent Mobius to help track down an even more dangerous variant of himself that is attacking TVA agents across history. ​The series is a mind-bending sci-fi thriller that follows Loki as he travels through different eras and apocalypses, questioning his own nature, the concept of free will, and whether a "villain" can ever truly change his stripes. #tomhiddleston #loki #marvel #explore #movies Follow: @goodmovie_greatmovie for daily movie🍿content.
#Variance Reel by @adorama (verified account) - Want more control over your colors? 🎨 @austin.james.jackson shows how the new @lightroom's Variance slider lets you fine-tune how similar colors int
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AD
@adorama
Want more control over your colors? 🎨 @austin.james.jackson shows how the new @lightroom’s Variance slider lets you fine-tune how similar colors interact. Bring them together for harmony or push them apart for more contrast and impact!🤯 Your edit, your call! Would you try this? 👀✨ #adorama #createnomatterwhat #lightroom #photoediting #photographersofinstagram

✨ #Variance発見ガイド

Instagramには#Varianceの下に135K件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。

Instagramの膨大な#Varianceコレクションには、今日最も魅力的な動画が掲載されています。@goodmovie_greatmovie, @propmarketintern and @finance.thomasや他のクリエイティブなプロデューサーからのコンテンツは、世界中で135K件の投稿に達しました。

#Varianceで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。

人気カテゴリー

📹 ビデオトレンド: 最新のReelsとバイラル動画を発見

📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索

🌟 注目のクリエイター: @goodmovie_greatmovie, @propmarketintern, @finance.thomasなどがコミュニティをリード

#Varianceについてのよくある質問

Pictameを使用すれば、Instagramにログインせずに#Varianceのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。

パフォーマンス分析

12リールの分析

✅ 中程度の競争

💡 トップ投稿は平均7.3M回の再生（平均の2.9倍）

週3-5回、活動時間に定期的に投稿

コンテンツ作成のヒントと戦略

🔥 #Varianceは高いエンゲージメント可能性を示す - ピーク時に戦略的に投稿

✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長890文字

📹 #Varianceには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用

✨ 多くの認証済みクリエイターが活動中（50%） - コンテンツスタイルを研究

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