US 15 ORMI ©

Orpheus CAPITALS ©
US 15 ORMI ©
The latest US 15 ORMI © Index update carries the equity curve from Aug 2001 till date. The Orpheus Risk Management Index (ORMI) is up 160% since 2002. The S&P 500 is up 25% since 2001. The ORMI outperformed the S&P 500 by 135% (2002 till date).
In the latest enhancements we have an improved cash allocation, reduced drawdowns and better exits.
Mean Reversion
Regression to the mean was discovered and named late in the nineteenth century by Sir Francis Galton, a half cousin of Charles Darwin and a renowned polymath. What is truly noteworthy is that he was surprised by a statistical regularity that is as common as the air we breathe. Regression effects can be found wherever we look, but we do not recognize them for what they are.
Behavioral Finance
Mean Reversion concept has been extensively used by behavioral finance experts to challenge conventional economics, which considers markets totally random. Behavioral finance has now proved that extreme groups regress to the mean over time.
Findings of reversion in stock prices towards some fundamental values remain in literature for a decade. DeBondt and Thaler -1985 using overreaction showcased that a stock experiencing a poor performance over a 3-5 year of period subsequently tend to outperform that had previously performed relatively well. This implies that, on average, stocks which are ‘losers’ in terms of returns subsequently become ‘winners’ and vice versa.
Researchers in finance have long been interested in the long-run time-series properties of equity prices, with particular attention to whether stock prices can be characterized as random walk (unit root) or mean reverting (trend stationary) processes. If stock price follows a mean reverting process, then there exists a tendency for the price level to return to its trend path over time. If stocks which are losers becom winners that means they are showing the property of mean reversion. Fama and French (1988) also report mean reversion in U.S. equity market using long-horizon regressions, and Poterba and Summers (1988) document evidence of mean reversion using the variance ratio test.
Stationarity
Test for random walk hypothesis can be done by Dickey and Fuller [1979, 1981] and the Philips and Perron [1988, PP] method. ADF and PP tests are not so strong to test stationarity (mean reversion) because the test fails to detect slow-speed mean reversion in small samples. Hence the failure to reject the null hypothesis may not be interpreted as decisive evidence against mean reversion. Because of this inherent problem, researchers have advocated pooling data (testing various time series simultaneously) and testing the hypothesis in a panel framework to gain test power. Choudhuri and Wu [2002] showed the presence of mean reversion in emerging market using panel based test. We have applied the panel based tests on outliers from our performance ranking data.
In our academic submission to SSRN (Social Science Research Network) we tested a database of composite group of assets from 2005 to 2011. We created various groups of assets and ranked them on a scale of 0.1% to 100% based on their performances over various holding peridsin last 1.5 years. The ranking data was a weekly time series of 1000 assets.
The worst performers, negative outliers are chosen based on the 81 week performance (1.5 years) i.e. rankings < 20 %. We tested this list of assets for change in ranking percentile. Positive change in ranking percentile suggests an outperformance and vice versa. Then the respective assets are tracked till 2011. All the assets which have reached the 50% rankings limit are tabulated. The assets that changed in rankings from below 20% to above 50% witnessed mean reversion (earlier losers to current winners). A test was made on 20 asset outliers (ranking < 20%), which moved from sub 20% ranking to above 50 % ranking limit during 2008 till 2011 period. Outlier Performance in last 5-6 years suggested that 44% to 25% of these negative outliers witnessed mean reversion tendency. The percentage number of reverting stocks increased to 60% as we reduced the reversion limit up till 50% ranking for different groups.
US 15 ORMI © VS. S&P 500 (INDEXED)

Indexing
The indices values that are disseminated today are broadly based on market capitalization methodology. Market capitalization methodology has been challenged globally for a few broad reasons. 1) As an asset strengthens it is given more weight 2) As an asset weakens it is given lesser weight. This on one side captures momentum but on the other side suggests investors to focus more on growth compared to value. This increases portfolio risk when market growth slows down or reverses, as has been the case since 2007. When markets contract, the erstwhile top performers push into red for extended period of time causing large drawdowns and emotional pain.
The US 15 Index is based on the above extreme reversion idea i.e. outliers tend to reverse, which suggests that investing is about value picking and extremes are prone to reversion. Our Index extends and fine tunes the idea first mooted by De Bondt and Thaler in their 1981 paper suggesting that 3 year worst losers portfolio tends to outperform the 3 year best winners portfolio.
However instead of just choosing the worst 3 year losers, we have tested worst losers on different time frames. The aim was to see if the mean reversion results can be simulated to different holding period durations. This makes the extreme reversion idea more investible (reduced holding period).
Methodology
1) The Index invests on filtered negative outliers from the S&P 500 group components.
2) It filters the components for rising performance cycles.
3) It filters them for relative outperformance.
4) We allocate equally among the filtered assets.
5) Performance cycles give clear entry and exit signals.
Risk Metrics
The US 15 ORMI © uses various risk metrics for better understanding of the Index components. The risk metrics classify components using performance ranking and statistical parameters. The metrics illustrate primary multi month performance perspectives for the US 15 components. After filtering the stocks through statistical filters we have generated the running signals for the respective, Jiseki Cycles and other risk metrics.
PORTFOLIO RISK METRICS
The portfolio risk metrics compares the relative outperformance of the US 15 ORMI vs. S&P 500. Our Index not only outperforms the universe (S&P 500) but also with an inherent lower risk. The drawdown from peak analysis illustrates the resilience in ORMI construction. Our index shows not only a quicker recovery but also reduced drawdowns from peak. For example the S&P 500 fell 59.6% from Oct 2008 while ORMI fell 24%. And above this S&P 500 has not recovered back above 2008 highs, while ORMI has moved above 2008 highs.



JISEKI CYCLES
The risk metrics are driven by our Jiseki Time cycles, which are seasonal patterns of strength or weakness in assets. They are derived from percentile rankings from 1 to 100. The higher the percentile more the chance for an asset to weaken and worst the ranking, better the chance for the respective asset to outperform. 100 is top relative performance and 1 is worst performance. The idea is that performance is cyclical. A top performer will underperform in future and vice versa. A top relative performer is also the worst value pick and the top relative underperformer is the best value pick. Jiseki is another name for Performance cycles, time triads and time fractals. The signals are illustrated as a running portfolio and as Jiseki Indices. These signals can be used by fund managers for relative allocations, traders for leverage bets and high net worth clients for selective trades.
Jiseki Interpretation. Signals are interpreted as crossovers between various Jiseki Cycles. All three Jiseki cycles (Jiseki 1,2 and 3) depict different time frames. Example: An asset is ranked above 80 percentile and all the three Jiseki cycles are pointing lower, this suggests a running SHORT SIGNAL. Our Jiseki Indices use different kind of exits based on price and Jiseki Cycles. We have color coded the (Jiseki 1>Jiseki 2) SHORT zones with brown sandy (burlywood) and grey (Jiseki 1>Jiseki2) for LONG SIGNALS.

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CastleMoore Inc. (North American Sales Office)
Mailing Address: 12-2441 Lakeshore Rd West, Oakville ON L6L 1H6
Phone: 001.905.847.1400
Toll Free: 001.877.289.5673
Fax: 416.352.0190

Megh is the algorithmic trading developer at Orpheus Capitals.  He completed Bachelors of Engineering in Information Technology from Mumbai University and has worked extensively in the area of stochastic calculus while pursuing his Masters in Statistical Science from La Trobe University in Melbourne, Australia. He has keen interest in pricing, hedging and replicating structured products along with developing numerical algorithms. He interned with Orpheus Capitals in early 2011, developing proprietary algorithms for trading and continued working with Orpheus Capitals while completing his Masters program.