capm_model_posts.txt

# Social Media Posts: Capital Asset Pricing Model (CAPM)

================================================================================
## SHORT-FORM POSTS
================================================================================

### Twitter/X (280 chars max)

Just built a CAPM calculator in Python! The formula: E(Rᵢ) = Rf + βᵢ[E(Rm) - Rf]

Beta measures market risk exposure - higher β = higher expected returns but more volatility.

#Python #Finance #DataScience #QuantFinance #CAPM

---

### Bluesky (300 chars max)

Implemented the Capital Asset Pricing Model (CAPM) from scratch in Python.

Key insight: Expected return = Risk-free rate + β × Market risk premium

Simulated 10 assets, estimated betas via OLS regression, and visualized the Security Market Line. R² shows how much return variance comes from market movements.

---

### Threads (500 chars max)

Ever wonder how Wall Street prices risk?

The CAPM model says: E(Rᵢ) = Rf + βᵢ[E(Rm) - Rf]

Translation: Your expected return equals the risk-free rate PLUS your beta times the market premium.

Beta (β) measures how much an asset moves with the market. β > 1 means more volatile than market, β < 1 means less.

Built this in Python - simulated 10 assets, ran OLS regression to estimate betas, plotted the Security Market Line. Clean way to see risk vs reward!

#Finance #Python

---

### Mastodon (500 chars max)

Implemented CAPM (Capital Asset Pricing Model) in Python with full visualization.

Core equation: E(Rᵢ) = Rf + βᵢ[E(Rm) - Rf]

Where β = Cov(Rᵢ, Rm) / Var(Rm)

Key findings from simulation:
- Estimated betas via OLS regression
- Avg R² shows systematic risk proportion
- Plotted Security Market Line (SML)
- Calculated Jensen's Alpha for abnormal returns

Limitations: single-factor model ignores size/value/momentum. See Fama-French for extensions.

#QuantFinance #Python #DataScience

================================================================================
## LONG-FORM POSTS
================================================================================

### Reddit (r/learnpython or r/finance)

**Title:** Built a Capital Asset Pricing Model (CAPM) Implementation in Python - Here's What I Learned

**Body:**

Just finished building a complete CAPM implementation and wanted to share what I learned!

**What is CAPM?**

CAPM answers: "What return should I expect given the risk I'm taking?" The formula is:

E(Rᵢ) = Rf + βᵢ × [E(Rm) - Rf]

In plain English: Expected return = Risk-free rate + (Beta × Market risk premium)

**What is Beta?**

Beta (β) measures how sensitive an asset is to market movements:

β = Cov(Rᵢ, Rm) / Var(Rm)

- β = 1: Moves exactly with market
- β > 1: More volatile than market (aggressive)
- β < 1: Less volatile than market (defensive)

**What I Built:**

1. Simulated market returns and 10 assets with different betas (0.5 to 1.8)
2. Estimated betas using OLS regression (scipy.stats.linregress)
3. Compared expected vs realized returns
4. Visualized the Security Market Line (SML)
5. Calculated Jensen's Alpha (abnormal returns)

**Key Takeaways:**

- R² tells you what proportion of return variance comes from market movements (systematic risk)
- Assets above the SML are "undervalued" - they earned more than CAPM predicted
- Portfolio beta is just the weighted average of individual betas
- CAPM has limitations - it's a single-factor model. Fama-French adds size, value, and momentum factors

**Libraries used:** numpy, pandas, matplotlib, scipy

Check out the full interactive notebook here:
https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/capm_model.ipynb

Happy to answer questions!

---

### Facebook (500 chars max)

Ever wondered how investors calculate expected returns?

The Capital Asset Pricing Model (CAPM) has a simple but powerful idea: the return you expect should be based on how much risk you take.

The formula: Expected Return = Risk-free rate + Beta × Market premium

Beta measures how much an asset moves with the market. Higher beta = higher expected returns, but also more volatility.

Built an interactive Python notebook exploring this - check it out!

https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/capm_model.ipynb

---

### LinkedIn (1000 chars max)

Just completed a Capital Asset Pricing Model (CAPM) implementation that demonstrates key quantitative finance concepts.

**The Core Insight**

CAPM provides a framework for pricing risk:
E(Rᵢ) = Rf + βᵢ × [E(Rm) - Rf]

Where beta (β) measures systematic risk - the covariance of asset returns with market returns, normalized by market variance.

**Technical Implementation**

- Simulated market and asset returns using factor model structure
- Estimated beta coefficients via OLS regression with scipy.stats
- Calculated Jensen's Alpha to identify risk-adjusted abnormal returns
- Visualized the Security Market Line showing risk-return tradeoffs

**Key Skills Demonstrated**

- Statistical modeling and regression analysis
- Financial theory application
- Data visualization (matplotlib)
- Quantitative analysis with NumPy/Pandas

**Limitations Acknowledged**

CAPM assumes single-factor exposure. Modern extensions (Fama-French 3/5 factor models) capture size, value, profitability, and investment factors.

View the full interactive analysis:
https://cocalc.com/github/Ok-landscape/computational-pipeline/blob/main/notebooks/published/capm_model.ipynb

#QuantitativeFinance #Python #DataScience #PortfolioManagement

---

### Instagram (500 chars max)

Risk vs Reward: Visualized

This is the Security Market Line - the foundation of modern portfolio theory.

The CAPM formula:
Expected Return = Risk-free rate + β × Market premium

Beta tells you how volatile an asset is compared to the market:
• β < 1 = defensive (utilities, bonds)
• β = 1 = moves with market
• β > 1 = aggressive (tech, small caps)

Red dots above the line? Those assets outperformed expectations.

Built this analysis in Python to understand how Wall Street prices risk.

.
.
.
#Finance #DataScience #Python #QuantFinance #Investing #DataVisualization #StockMarket #PortfolioManagement