Portfolios— Optimal in Real Time
Studying existing portfolio management schemes and researching and developing new ones, using robust and fully-disclosed methods of quantitative analysis— on behalf of professional money managers and investment advisors who are disposed toward asset allocations that are optimal yet responsive to shifting market conditions… who either want their own systems tuned and qualified and made more reliable or want new ones created for them.
MO'C Portfolio Analytics is a service of a Washington, USA sole proprietorship.
Michael C. O'Connor, Proprietor
Fortran >> Basic >> C > > Smalltalk >> early Internet > > Visual Basic >> Java > > Python >> web app frameworks, etc.
college >> U.S. Navy civilian > > grad school >> decades as a consultant > > reinvention of career
Financial analyses are all about history mattering. We can even take it for granted that the nihilistic “efficient market” hypothesis arose due to its authors being unable to find answers… in what? In the course of their studies of historical data, that's what. Of course we can learn from what the marketplace has done in the past. And the data under study need not be limited to past performance of returns but could include security-specific fundamentals or factors that influence the market as a whole. I am currently coding primarily with Python and NumPy (and sometimes in R).
During the last two steep stock market declines of 2000–03 and 2007–09 diversification didn't help; getting into cash did. Can that be done in real time? Two recent working papers of mine— one accessible here now and the other soon available— report on my early research on momentum, which met with limited success but did include a way of effectively avoiding debacles, and on a quite recent development that is utterly definitive with respect to quelling market risk without loss of return.
A Recent Development
One of the main things that we would like to do with financial analysis is test hypotheses. “Hypothesis testing” has a well defined meaning in statistics. But to test to see if an optimized portfolio is likely to continue to outperform or not is tricker than many suppose. I have recently been working on exposing certain limitations to effective hypothesis testing that we encounter when using the “Sharpe ratio”, which is a risk-adjusted return performance measure. In this article of mine I show that unless the returns of the two portfolios are highly correlated the test gives the wrong answer all too frequently.
Details of the New Method
It is principally based on the use of a repurposed “shrinkage estimator”, where the repurposing has to do with how the procedure is caused not only to shrink the uncertainty in the outcome but also to improve its mean value. Provided only that the risk-on securities to be held are ETFs or mutual funds the returns are improved with and without risk adjustment. Visit the Traded Portfolio for interactive charts and from there you will soon be able to download the working paper— it's all that you need to know.