Vignette: Value-oriented equity selection: Value and Momentum Everywhere!

[JustinMShea]

Per @braverock "Lets get a working example into a new vignette no later than tomorrow".
"Value and momentum everywhere". Asness, Moskowitz, Pedersen (2013).

https://www.aqr.com/Insights/Research/Journal-Article/Value-and-Momentum-Everywhere

Code

• We can ignore the trading parts for now, as previously discussed (Nathan's paper has a good share of data and prototype replication code)

• Then lets expand that to FactorAnalytics::fitFfm()

Data

AQR website

[vi-to]

For future reference, also related to comments under 1e75089.

We obtain factors and returns data from different sources. Sometimes they express returns (or excess returns) as a decimal number, other times as a percentage. It's a linear transformation so for regressions we run (and we run many), as long as all variables are expressed in the same "unit" there's no difference. Mixing up "units" will return rather silly results.

An idea can be to establish a preferred unit (keeping in mind potential instabilities) and then transform variables as we get them from the source (eg, in parsers and/or scrapers).

[braverock]

we should transform them to decimals. 0.05 for 5%.

This makes all the math work.

[jaymon0703]

Hi @vi-to are we meant to estimate factor betas for 48 equations, using equation 5? The plot datapoints in Figure 6 and this line could be interpreted this way - "Specifically, we estimate the time series regression for each of the 48 high, middle and low value and momentum portfolios across asset classes:

[vi-to]

Hey @jaymon0703, we are meant to do so and it's what we are already doing. Time-series regressions factor betas are the betas.

For figure 6 data points is a matter of calculating the actual (portfolios) average returns and the predicted (portfolios) expected returns plugging in estimates. I will add it shortly.