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 Physics Applied is 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. 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 but could include security-specific fundamentals or factors that influence the market as a whole. I am currently coding primarily with Python and NumPy.
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? A recent working paper of mine, rich in tests of significance, accessible here, reports on my research on momentum— including a version that does have step-out-of-harms-way capability. It seems that we have stragegies that do indeed work.
The Insider's Alpha
In 1968 Michael Cole Jensen invented “alpha”, the famous measure of portfolio performance. The “insider's alpha” is an extension of Jensen's concept that is applicable not only or particularly to portfolios whose exposure to systematic market risk is rather constant but also to portfolios that are on occasion heavily in cash. The disclosure of its invention by me is in this working paper.