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Ole Jakob Wold

Ole Jakob Wold

Ole Jakob joined DNB Asset Management in 2017 as a Portfolio Manager and Head of our Systematic Active Equity team.

Prior to joining DNB Asset Management, Ole Jakob was a manager of Guggenheim Partners for seven years. Before that he was the CEO of Alfred Berg Aktiv Forvaltning for many years.

Ole Jakob is a Civil Economist from the Norwegian School of Economics (NHH).

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In DNB Systematic Active Equity Team, we use regression analysis with more than 60 variables to find attractive stocks. Some insights from that model are easily relatable. Since the model is designed to find what stock characteristics drive prices in equity markets NOW, you will see results that correlate well with the general discussion in media.

This allows us to rank stocks according to the current market environment. It is a dynamic approach that surprisingly few people try to follow. Most quantitative models rely on persistent risk premiums rather than dynamic premiums. That to side, our model is ideal for checking if “something is different”.

The 60 factors are a problem. 60 factor payoffs are too complex to evaluate, but this is where Principal Component Analysis (PCA) comes to the rescue.

The set of 60 factor payoffs tells us what ‘view’, what ‘preferences’ that influence market pricing in the period we focus on. We have this analysis since 1973 for the US. PCA analysis can take these data series and tell us what ‘set of preferences’ are most common in this period.

We do PCA analysis on data since 1973 to find out what two general sets of preferences dominate the pricing the most. By design, these ‘views’ will be the most opposing views you can find. They are orthogonal.

It is like reducing the very complex market pricing into two camps. Think of it as one camp of investors that favor this view and another camp that favors that view. –Like the value/growth battle, only much more complex. Remember that we combine more than 60 factor payoffs here.

With the PCA analysis done, we can plot how the battle goes. We plot what camp has the most influence on stock prices. The following graph places the general market performance of every month since 1973 in these two dimensions.

Market preferences reduced from 63 to 2 dimensions in USA

The obvious takeaway here is that there are two very distinct clusters. The two left hand RED dots mark two market states that seem to occur very often, while some observations are ‘far out’. The observation at the top left is the peak of the IT bubble. The observations out to the right are all observations between 2000 and 2001. All equity managers remember those two years, and these dots are far from the mean for good reasons.

PCA analysis gives us what is called eigenvectors. In our case, the eigenvectors are interesting because their weights tell us what is important in the most dominating camps of investors. The most important characteristics in these first two eigenvectors are:

Eigenvectors

The first eigenvector describes a value, low-risk approach to stock pricing. The 2nd looks like a high-risk growth approach.

For some, these plots look random and unorganized but now consider the below plot. Here the observations after the debt crisis are plotted in gray. Green nodes are observations from before the crisis.

Market preferences reduced from 63 to 2 dimensions in USA

Something has happened to the market pricing of stocks. It is incredibly systematic. It looks like the value people left the auction room. Remember that value characteristics dominated the first dimension (first eigenvector). The first dimension is plotted on the horizontal axis.

Since the debt crisis, the average of the exposure to this dimension is close to zero. Many commentators will say that the value payoff has been negative, but we don’t observe that. Not in these plots, and not in the actual factor returns. There is just nothing left of the payoff to value. Markets don’t care anymore. That’s very different from being negative.

Now, for you who already think that the second dimension, the growth style, has taken over. Note that the average exposure to that dimension after the debt crisis is close to zero as well. Risk has really never paid off. If you measure the payoff to risk, all other things being equal, the payoff to risk is clearly negative.

The change above is systematic. You cannot find one single observation after 2008 that goes out of a very small area where the grey nodes reside.

The world is new. I’m not saying it will never go back or that we will never see value performance again, but this is 12 years of monthly data. There are no signs of these preferences ‘snapping back’ or even moving in a value-favoring environment. We haven’t seen that in 12 years.

Death of value

Does this happen only in the US? No, this is going on everywhere. In all major equity markets, the observations after the debt crisis cluster up like this. The disappearance of value payoff is not as strong in all of them, but the performance in value stocks was particularly strong in the US in the first place, so the ‘death of value’ has not been as dramatic in Canada, Germany, Japan, France or UK.

Other markets form other first and second-order eigenvectors, so the orientations of the plots below are not important. The clustering of post debt crisis observations is what we are looking for if the world is new.

Make up your own mind. The world is new. I said it!

Canada

Market preferences reduced from 63 to 2 dimensions in Canada

Japan

Market preferences reduced from 63 to 2 dimensions in Japan

United Kingdom

Market preferences reduced from 63 to 2 dimensions in UK

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