What are some applications of bioinformatics or genetics to generating alpha in U.S. equities?

Question

There are many disciplines that have contributed to how one model's risk and return. Physics introduced Brownian motion and RMT. Machine learning has helped to solve complex portfolio construction problems, operations research has contributed to market making & risk management, and aerospace engineering has contributed the Kalman filter.

I am looking for papers that use methods traditionally applied to bioinformatics and genetics to model the behavior of equities. For example, epidemiology makes extensive use of canonical regression -- Blackburn et al. apply canonical regression to tease out the co-movement of equities here.

I am not looking for links to neural network, SVM, or other machine learning papers. I am looking for creative and novel extensions of methods used traditionally in the bioinformatics and genetics space that are applied to generate alpha or risk models in U.S. equities?

For example, bioinformatics has developed a variety of greedy search alorithms that identify which types of traits give rise to certain phenotypes. Is there a paper that applies some of the algorithms from the bioinformatics domain to U.S. equities where traits might be financial statement variables and phenotypes might be return outcomes?

I know Carlos Carvalho (U. Chicago) has done both innovative work in the field of equities and also genetics and recall he had some research along these lines.

Answer 1

You question is quite strange: so you do not want to use methods inspired by bioinfo and genetics (neural networks, GA, geometry of folding, etc) but methods that are used in these fields?

In terms of modeling, the problematics in bioinfo and genetics are mainly:

• tree or graph matching (to build metrics in the space of molecules), like in SIGMA: a Set-cover-based Inexact Graph Matching Algorithm, by: Misael Mongiovi, Raffaele Di Natale, Rosalba Giugno, Alfredo Pulvirenti, Alfredo Ferro, Roded Sharan, Journal of Bioinformatics and Computational Biology, Vol. 8, No. 2. (2010), pp. 199-218
• fitting of dynamical systems (to identify the dynamics of chemical transformations), like in Modeling in biological chemistry. From biochemical kinetics to systems biology, by: Peter Schuster, Monatshefte für Chemie / Chemical Monthly, Vol. 139, No. 4. (1 April 2008), pp. 427-446
• prediction in discrete state space (to associate the formula of a molecule, to one of its properties), like in Molecular Modeling Of Proteins And Mathematical Prediction Of Protein Structure, by: Arnold Neumaier, In SIAM Review, Vol. 39 (1997), pp. 407-460

It is close to usual data mining, statistical or applied math problematics, so the main differences are heuristics, to take into account some specificities of the problems to avoid some local minima or increase the convergence rate.

I think that you not find anything new as you think to expect. It would be more efficient for you to identify your problems precisely, for instance trying to put each of them in one of these jars:

• continuous optimization
• discrete optimization (the state space has no obvious metric)
• estimation in presence of uncertainty
• continuous prediction (including backward and forward PDE problems, ARMA, GARCH, etc)
• identification of outliers

Then review the classical methods (remind that even in Der Mann Ohne Eigenschaften, by: Robert Musil (1930), you have clear descriptions by Ulrich of his day-to-day job as a mathematician, you have descriptions of such modelings, so nothing is really new in all this), and try to develop heuristics adapted to the specificities of your real situations. My opinion that it is better to know very well the theoretical pitfalls and essential points of the methods to develop adapted heuristics rather than to read about heuristics developed for other fields.