Skip to content
All library documents

Beta-Neutral Long–Short Strategies and Beta Estimation

Article Quant Q&A · Author: sparkle
Read the original

Summary

The document introduces a beta-based long–short idea: short assets with higher market beta and buy assets with lower beta, with the aim of profiting across bullish and bearish markets. The response connects this concept to the betting-against-beta literature and notes that some exchange-traded funds seek to capture the associated premium. For estimating beta, it suggests linear regression of an asset’s returns against market returns, with or without an intercept, and mentions built-in functionality in R performance-analysis tools and Python data libraries.

The exchange provides pointers rather than a full strategy specification. It does not explain portfolio weighting, risk controls, rebalancing, transaction costs, estimation windows, or the scenarios in which the strategy could lose money. It also offers no backtest or performance evidence, so the claim of performance across market regimes is not established here. The practical takeaway is that beta estimation is a regression task, while assessing the strategy requires additional research into implementation and risk.

Key ideas

  • A beta-based long–short portfolio shorts higher-beta assets and buys lower-beta assets.
  • The idea is related to the betting-against-beta literature.
  • Beta can be estimated by regressing asset returns on market returns, with or without an intercept.
  • R and Python statistical libraries provide regression tools for beta estimation.
  • The document does not establish profitability or detail implementation risks and portfolio construction.

Tags

EquitiesFactor investingStatistics
Full text
# Beta arbitrage in CAPM


# Beta arbitrage in CAPM












i'm following the "Computational Investing 1" course at Coursera.org, I was affascinated by the Beta arbitrage of CAPM

Video: https://class.coursera.org/compinvesting1-002/lecture/view?lecture_id=119

It shows, that if I found Betas and weights, I could make profit in Bearish and bullish markets. Strategy: SHORT for the Higher Beta, and LONG for Lower beta.

Is that really works? What are the bad scenarios? What he didn't say?

Do you know some code in R or Python, that calculate Betas? Someone has already applied this theory?

## Answer by user1234440 (score 4)

https://quant.stackexchange.com/a/11287

This is in essence the idea behind Andrea Frazzini's paper 'Betting Against Beta'. There are various ETFs that aim to exploit the premium.

In R, you can do just do a linear regression using the `lm(Y~X)` which includes an intercept or using `lm(Y~X+0)` which regresses without an intercept. Assuming you've saved the model in variable `lm.r`, then to get the coefficients, you simply `coef(lm.r)`.

In package `PerformanceAnalytics`, there are built in functions whereby you can just plug in the parameters, independent and dependent and risk free rate to give out the coefficients.

For python, you should take a look in the `pandas` package for regression analysis. Last time I checked its inside the package but was being moved to `stats-model` package for reasons beyond me.

Hope that helps

## Answer by user8056 (score -1)

https://quant.stackexchange.com/a/11276

you can simply use excel using "single index model"

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.