Link between HML factor and future GDP growth

The previous post explored the cyclic behaviour of the Fama French HML factor.  I proposed further work to link HML to the economy.  In fact, a literature search discovers that Liew and Vassilou (1999), document this effect:


The table contains past 12 month return of HML versus next year’s GDP growth.

The difference between ‘Bad States’ (GDP growth in bottom quartile) and other states is large in several countries.  For example, in the US, average HML growth of 2.81% led to Bad States compared to about 10% ahead of other states.

This agrees with the time series plot in the previous post showing that extreme growth (low HML) typically leads to economic recessions.


Further to the last post I ran some ‘4 year cycle’ analysis on pseudo factors.

Pseudo factors can be constructed by subtracting various Fama French portfolios:


‘High minus Up’ (Value minus Momentum) shows momentum outperforms value about 0.5% per month in the second half of the cycle, accelerating towards the end.


‘Up minus Market’ shows momentum outperforms the market at least 1% per month throughout the cycle.  Note that exits from recessions favor value due to the ‘momentum crash’ phenomena documented by Daniel and Moskowitz.


9 thoughts on “Link between HML factor and future GDP growth

  1. Very interesting (and hopeful) relationship! The doubt comes from the messiness of the data in the table: the fit is not universal across countries (too many exceptions?), the drop from good to normal to bad is rarely monotonic (US: HML is higher in normal than in Good states?!)… Also in your trend lines the R^2 seems to be very small… (<.05)–do you consider this a problem? Your posts are very inspiring–thanks for the great work.

    • Thanks for the comment! Bear in mind, HML is calculated from US stock market data so the economies in other countries are less likely to be linked. Agreed, the trend is not monotonic but HML return is much lower before ‘bad states’, which are the ones to avoid for investing.

      The R^2s are low due to the scatter in the data. I look at the relative values. A typical example: if I change the period from 47 to 48 months and R^2 triples, the 4 year cycle seems significant.

  2. I found for you a very interesting anomaly/factor to test along the lines of your work, but I am not sure if you are still maintaining (interest in) this blog or not… Let me know and I will send you the links…

  3. Liquidity (proxied by share turnover, defined on p. 5, last paragraph) is a weighty and independent factor of returns, combining nicely with others (momentum, value) to created more pointed clusters of promising future performers ( See Tables 3 and 4 and Figure 2 (A, B, C), Tables 5-6). Can one access easily a public database with such a liquidity categorization?!

  4. Pingback: Estimating Fama-French HML factor in real-time | RRSP Strategy

  5. Pingback: US recessions, the Value Factor (HML) and current status | RRSP Strategy

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