This article presents an interesting intraday trading strategy based on mean-reversion principles. Conceptually, its fairly simple: Trade a portfolio of ETFs and at the open of each day short the two with the largest volatility-normalized up gap, then exit at the close (MOC).
Its important to note that since the system ranks all stocks by their opening price, it cannot use market-on-open (MOO) entry orders (which are required by the exchanges to be entered >2min before the open), and therefore slippage might have a larger impact than expected. And the reported results do not include slippage or commission, so its quite possible that the system’s edge may be entirely artificial.
Slippage issues aside, the system’s performance unfortunately has been pretty flat since 2009. Nevertheless, it offers some interesting food for thought…
Very readable paper on MC resampling techniques. Classic MC techniques tend to generate smoother equity curves with shallower drawdowns than actual live results, as they “chop up” returns too finely, thereby reducing the impact of correlation during Black Swan events. The paper presents a simple method which attempts to preserve these correlations (click on title to view paper).
Forbes piece on the rise of software developers as an economic commodity, the new “precious metals” of our society:
“The one absolutely solid place to store your capital today — if you know how to do it – is in software developers’ wallets. If the world survives looming financial apocalypse dangers at all, this is the one investment that will weather the storms. It doesn’t matter whether you are an individual or a corporation, or what corner of the world you inhabit. You need to find a way to invest in software developers”
As a software developer, I fully support this view :-)
Well written e-book on various optimization strategies using bio-inspired/evolutionary techniques (click title).
A nice overview of Bio-Inspired algorithms, including Genetic Algorithms, Genetic Programming, Simulated Annealing, Neural Networks, Particle Swarms, Ant Colonies, Artificial Immune Systems, etc (click on title for article).
A quick study on how to avoid every algo trader’s worst enemy (or at least one of them): the dreaded Data Mining Bias (click title for article).
Interesting paper (commissioned by the UK government) on HFT’s contribution to Black Swans (click on title to open):
“This report thus suggests a largely positive answer to the question: “Can high frequency trading lead to crashes?” We believe it has in the past, and it can be expected to do so more and more in the future. Flash crashes are not fundamentally a new phenomenon, in that they do exhibit strong similarities with previous crashes, albeit with different specifics and of course time scales. As a consequence of the increasing inter-dependences between various financial instruments and asset classes, one can expect in the future more flash crashes involving additional markets and instruments. The technological race is not expected to provide a stabilization effect, overall. This is mainly due to the crowding of adaptive strategies that are pro-cyclical, and no level of technology can change this basic fact, which is widely documented for instance in numerical simulations of agent-based models of financial markets.”
An interesting paper on the classic Momentum strategy but with a few new twists to factor in tail risk (click on title):
“We apply risk-return ratios at the individual security level in order to drive the stock ranking process (construction of momentum portfolio) and at the portfolio level in order to evaluate and optimize the risk-return profile of the winner and loser portfolio. We investigate whether the application of risk-adjusted criteria with balanced risk-return performance can generate more profitable strategies than those based on a simple cumulative return criterion which serves as a benchmark. Moreover, by introducing risk return ratios as portfolio selection criteria, we are able to postulate a portfolio optimization problem with a ratio as an objective function. Therefore we devise an optimized-weighted strategy that creates optimal risky winner and loser portfolios”
Interesting article on WRC (click title):
“Prior to WRC, bootstrapping could be used to generate the sampling distribution to test the significance of a single rule. White’s innovation, for which he was granted a patent, allows the bootstrap to be applied to the best rule found by data mining. Specifically, WRC permits the data miner to develop the sampling distribution for the best of N-rules, where N is the number of rules tested, under the assumption that all of the rules have expected returns of zero. In other words, WRC generates the sampling distribution to test the null hypothesis that all the rules examined during data mining have expected returns of zero.”