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ORMI Indices and Analytics

INDICES-AND-ANALYTICS(2)

Orpheus Risk Management Indices (ORMI) © and Analytics

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 ORMI Indices are based on the 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).

Orpheus Risk Management Indices (ORMI) is running four Index styles now viz. Active, Worst 20, Extreme Reversion and Relative Performance (upcoming)

ACTIVE STYLES are with periodical entry and exit signals like the ORMI US 30, ORMI Toronto 15, ORMI UK 20, ORMI India 30 and ORMI India 10. The difference between them is the underlying universe. For example ORMI India 30 and India 10 selects from BSE 500 and CNX 100 respectively. Active styles are cash conserving, absolute return Indexed models. They actively enter and exit a position and go cash if needed.

THE WORST 20 STYLE is about selecting the worst components from top 100 Universe (India, UK, USA, Canada, Japan etc.). This is a quarterly rebalanced portfolio and is more about relative performance vs. the underlying top 100. This style is not a cash conserving absolute return model, but about beating its respective peer universe. Because of the idea of negative outliers outperforming, the worst 20 style outperforms the universe. So it’s an easier basket to create and hold.

THE EXTREME REVERSION STYLE is about recreating the top benchmarks and sector indices. It’s an all invested strategy. For example the Dow 30, TSX 60, Sensex 30 components, or the Nifty 50, or various regional sector indices like Banking, Auto, CNXIT, Pharma etc. Why do we need to recreate the top benchmarks? There is a section of market that is not active and wants to outperform or assume exposure to top blue chip components and sector indices like Auto.

For example in India the current available ETF indices are just NIFTY BEES, which allow for benchmarking Nifty but don’t offer superior returns or relative outperformance vs. Nifty. NIFTY BEES just benchmarks NIFTY, while ORMI IFTY 50 recreates NIFTY and offers 5 to 10% more every year compared to NIFTY. A passive market audience considers this an investible performance if the holding period is 6 months or 12 months. It’s a convenient allocation.

THE RELATIVE PERFORMANCE STYLE is about recreating the top benchmarks and sector indices using relative performance. It’s an all invested strategy. For example the Dow 30, TSX 60, Sensex 30 components, or the Nifty 50, or various regional sector indices like Banking, Auto, CNXIT, Pharma etc.

THE TACTICAL STYLE works around a macro portfolio that needs to allocate across various asset classes like Fixed Income, Commodity, Forex or Equity. Tactical styles can work around a group risk level.

Risk Management Styles

Risk Management Models (Rest)

Risk Management Models (ETF)

Real Money

The Extreme Reversion (Brief)

Jiseki Query

5 Year Rolling Return Cases - Simulating Fundamental Index

Multiple_Jiseki Cycles USA and Toronto Top 30

Multiple_Jiseki_Cycles Index_and_Commodity

Divergence Cyclicality

Presentations, Product Profile, Summary

The recorded link for the Orpheus Global Webcast - Performance Cyles - Filters, Signals and Indices
Prezi presentation for the webcast
ORMI - Brief Presentation
ORMI - Product Profile
ORMI - Product Summary Apr 2013
ORMI - Active Performance
The Orpheus Risk Management Framework
Orpheus - Econohistory.0413
Budapest Conference 19 Sep 2013

Orpheus Risk Management Indices ORMI © (India)

ORMI INDIA Active 30
ORMI INDIA Extreme Reversion TECH
ORMI INDIA Worst 20
ORMI INDIA Extreme Reversion PHARMA
ORMI INDIA Extreme Reversion AUTO
ORMI INDIA Extreme reversion Ifty 50
ORMI INDIA Relative Performance Ifty 50
ORMI INDIA Relative Performance Senzex 30
ORMI INDIA Extreme Reversion Senzex 30
ORMI India Active IFTY 5 13.09.13

Orpheus Risk Management Indices ORMI 
© (Global)

ORMI US 30
ORMI DAO JONES 30
ORMI Toronto 15
ORMI UK Worst 20
ORMI Australia
ORMI US Extreme Reversion SNP
ORMI North America Active 30
ORMI North American Fixed Income 10
ORMI Toronto Extreme Reversion TSAXE 60
ORMI Global Active ETF
ORMI Austria Extreme Reversion ATX Prime
ORMI Japan Nikkei Active 20
ORMI Japan Extreme Reversion 23.08.13
ORMI UK Active 20 28.08.13
ORMI UK FTSE 100 Extreme Reversion 28.08.13

Orpheus sub-advisor services for Portfolio Management

CM RMI Toronto 15 Performance Profile
CM RMI US 30 Performance Profile

Case studies

Godrej up @ 572%
The Apple Top
Yahoo, Rona, Marissa and Jiseki
Madras Cement up @ 53%
Dow @ 14000
Dow fall and Brokers rise
Prestige Estate exits @ 54%
Hathway exits @ 15% gains
Where is Indian Banking headed
Remembering NHPC
Remembering HLL
Too late for United Spirits

FAQ

FAQ - The ORMI Indices

Orpheus Webinars

Orpheus WEBINAR Schedule

Research Papers

SSRN - Social Science Research Papers

 


Chronology of Crisis

1637 Tulip mania damages the futures market and Dutch trade, in general.
1720 French and British stocks of firms cashing in on New World resources hit bottom.
1772 The financial crisis that occurred in the UK in 1771 leads to credit crisis, which spreads to North America.
1792 The Panic of 1792 was a financial credit crisis that occurred due to the speculation of William Duer and Alexander Macomb against stocks held by the Bank of New York.
1797 Reserves in the UK fall low, creating a monetary crisis. Bank of England put a hold on cash payments, creating widespread public panic.
1810 English credit crunch.
1813 Danish state bankruptcy.
1819 The Panic of 1819 was the first major financial crisis in the US.
1825 London stock market panic from over-speculation in Latin American investments.
1836 US real estate speculation causes stock markets to crash in the UK, Europe, and then the US.
1847 Credit crisis and bank panic ensue when railroad stock prices crash in France and the UK.
1857 During the Civil War in the US, credit crisis crashed equity prices. All nations that trade with the US were affected.
1866 ‘Black Friday’ happens from railroad speculation. A bank panic starts, which leads to lack of credit.
1873 Vienna Stock Exchange collapses, causing the ‘great stagnation’ on a global scale, which lasts until 1896.
1882 In France, Union Generale goes bankrupt, causing banking crisis and market crash.
1890 The UK’s oldest bank, Barings, nearly collapses from its exposure to Argentine debt.
1893 The Panic of 1893 in the US was marked by the collapse of railroad overbuilding and shaky railroad financing, which set off a series of bank failures.
1893 Australian banking crisis.
1896 The Panic of 1896 was an acute economic depression in the US, precipitated by a drop in silver reserves and market concerns on the effects it would have on the Gold Standard.
1907 The US bank panic spreads to France and Italy after the stock market collapse.
1910 Shanghai rubber stock market crisis.
1910-11 The Panic of 1910-11 was a slight economic depression that followed the enforcement of the Sherman Anti-Trust Act.
1921 Commodity prices crash.
1923 Hyperinflation in Germany starts monetary crisis.
1929 ‘The Great Depression’ begins after equity crash.
1931 The UK, Japan, Germany, and Austria experience financial crises.
1933 Gold Standard given up by the US, starting panic in the banking system.
1966 US credit crisis creates deflation and huge economic slump.
1973 OPEC quadruples the price of oil, which leads to global financial crisis.
1982 Global credit crunch prevents many developing countries from paying their debt.
1987 Bond and equity markets crash.
1987 ‘Black Monday’ – the largest one-day percentage decline in stock market history.
1989 Japanese bubble.
1989 Junk bond crisis.
1989-91 Savings and loan crises in the US.
1992 French Maastricht Treaty sparks crisis in European Monetary System.
1994 Major bond market correction.
995 Mexican financial crisis caused by the peso’s peg to the dollar during excessive inflation.
997 Asian financial crisis creates exchange rate and banking crises, created from stock market
and real estate speculation along with many Asian currencies pegged to the US dollar.
1998 Russia defaults on payment obligations during major financial crisis.
2000 Dot-com bubble pops, creating a massive fall in equity markets from over-speculation in
tech stocks.
2001 Another junk bond crisis.
2001 September 11 attacks hinder various critical communication hubs necessary for payment in
the financial markets.
2001 Economic crisis in Argentina, resulting in the government defaulting on payment obligations.
2002 Bond market crisis in Brazil.
2007 US real estate crisis causes the collapse of massive international banks and financial
institutions. Equity markets take a dive.
2008 Credit crunch and a frozen interbank market create financial crisis.

Source: Financial Technologies Knowledge Management Company


THE TECH SPECIAL

Will CNXIT underperform?
For how long?
What TECH Sector components are at key levels?
Why did CNXIT outperform till now?
What are the drivers for performance?

To read about the TECHNOLOGY SPECIAL download the latest ALPHA report from the Orpheus e-store.


Avinash Barnwal is Master of Science in Statistics and Informatics from IIT Kharagpur. He has worked on human response time at Department of Psychology, University of Amsterdam.  Avinash is a Quantitative Analyst at Orpheus developing money management solutions and building statistical models to address temporal challenges.


Filtering the Signal

 

We at Orpheus reach a trade signal by filtering from a large universe of assets. These are the two kind of filters we have used to generate short ideas.

Filter One

1) We took the best 24 month performers above 80% percentile ranking. (LIST1)
2) We filtered them for falling Jiseki (LIST 2)
3) Then we ran a price filter. How many of LIST 2 assets were also below 20 day average.

This filter returned just one stock among CNX 100 stocks

Filter two

1)Take the best 24 month performers above 80% percentile ranking. (LIST1)
2)Take the LIST 1 and filter it for quarterly 80% percentile ranking. (LIST2)
3)Filter this for negative Jiseki (LIST 3)
4)Rank (LIST3) from nearest to farthest from Historical Highs. (LIST4)
5)Filter LIST 4 for top ranking sectors.

This filter also returned just one stock among CNX 100 stocks.

To read about the two trade signal ideas download the latest ALPHA India reports from the Orpheus e-store.


Avinash Barnwal is Master of Science in Statistics and Informatics from IIT Kharagpur. He has worked on human response time at Department of Psychology, University of Amsterdam.  Avinash is a Quantitative Analyst at Orpheus developing money management solutions and building statistical models to address temporal challenges.


Ranking Indian Sectors

Recently I have met more advisors and money managers in one place than I have ever met (call it clustering). And strangely a part of this cluster voiced lack of reverence for Warren Buffet. This was in Toronto. I was there for a conference and even a mention of Buffet evoked “I am not a fan” response. When I hear money managers saying that he did poorly on the Bank of America deal, on one side I see some criticism of what the sage is doing and on the other side I see an era of sideways market and increasing market knowledge that challenges the old thought leaders.

It’s all psychology at one level because Buffet never admitted being God. It’s the market participants who bestowed him with that status. It was also not his fault that he was 11 year old growing up in his father’s brokerage company at the end of great depression, which was incidentally also the best time in a century to buy and hold. Irrespective of Warren’s timing, there is one thing we should give him credit for. He practices what he preaches. He learnt his lessons well and understood that contrarianism works. And this is why we at Orpheus stand by him for his contrarian ability. At a certain stage contrarianism should become intuitive, especially if you have an ability to hold on to your investments for decades. Few can do this today.

At a certain stage if you can identify an extreme underperformer and have the courage to accumulate it, you don’t need anything else. Sooner or later…

Let’s take the current market for example….

To read the 8 page sector special download the latest Alpha India.

This article was written for  Business Standard.

Our Jiseki Time cycles are seasonal patterns of strength or weakness in assets. They are derived from percentile rankings of 0 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.

Alpha is a daily strategy signal product that gives trading and investment signals. Alpha is a numeric Ranking product based on TIME fractals. The signals are illustrated through tracker and running portfolios. Alpha can be used by fund managers for relative allocations, traders for leverage bets and high net worth clients for selective trades. Alpha is a part of the time triads analytics developed by Orpheus Research.

Coverage India: CNX100 traded stocks and Indian Indices.


Time Analytics - Is Correlation and Jiseki connected?

 

Jiseki is a performance ranking idea for an asset in a group for different holding periods. Conventional tools don’t look at sectors as a proxy of a group but not components perse. Our idea of extreme reversion is designed to understand performance cyclicality.

This week we look at the Indian Energy sector correlation from Oct 2009 to 2011 and juxtapose it with change in quarterly performance rankings during that period. The correlation of the energy sector components was made with Reliance.

What we could observe? The higher the correlation of the stocks with the Reliance the worst the stocks did in performance and the lower the correlation of the stocks with Reliance the better they did in performance.

Petronet for example moved up 50% in Jiseki performance rankings during the period along with Castrol. While the high correlation and positive correlation stocks with reliance like Seamec, Suzlon saw not only a drop in rankings by 50% but also absolute price loss.

The connection of correlation with Jiseki change in rankings might seem strange. But it’s not because if Reliance is at the top of the group, it will underperform and because it’s a sector leader, anything correlated positively correlated with it will also underperform.

This is simple logic, which the data expresses visually. Does the data tell us something else? The histogram also tells us that the only stock which should have fallen and did not fall as much was Reliance. This is what we may call a sector leader’s premium, which keeps us away from Reliance as a buy opportunity confirming what we wrote recently in the article, “should I buy Reliance?”


Avinash Barnwal 
is a final year master student of Statistics and Informatics at IIT Kharagpur. He has worked on human response time at Department of Psychology, University of Amsterdam.  He has worked on marketing analytics for the Customer Intelligence Unit, HDFC Bank . Avinash is passionate about developing statistical models and believes that statistics could address temporal challenges.


Late Economic’s worst

Today’s Alpha India report carries technical cases on the two worst performer Late Economic stocks, Suzlon and Reliance Power. Suzlon is ranked near 7% rankings, while Reliance Power is near 10% rankings (1% means the worst, while 100% is the best).

The market bias is neutral for the day, as we expect NIFTY to complete the current b circle wave correction up near immediate confluence resistances at 5,200 levels.

Our Jiseki Time cycles are seasonal patterns of strength or weakness in assets. They are derived from percentile rankings of 0 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.

Alpha is a daily strategy signal product that gives trading and investment signals. Alpha is a numeric Ranking product based on TIME fractals. The signals are illustrated through tracker and running portfolios. Alpha can be used by fund managers for relative allocations, traders for leverage bets and high net worth clients for selective trades. Alpha is a part of the time triads analytics developed by Orpheus Research.

Coverage India: CNX100 traded stocks and Indian Indices.

Michesan Anna-Maria, Head of India Research. Anna discovered her interest of markets immediately after completing her graduate studies in Economics. She followed it up with post graduate studies in corporate finance. A host of research work in behavioral finance, option strategies and quantifying market sentiment followed. Anna covers Indian equity and combines Elliott, Time Fractals and Time Analytics to deliver accuracy across time frames. To review some of her work, check out the annual India accuracy report 2009.


The Ising Model

The Ising model is a mathematical model of ferromagnetism in statistical mechanics. The model consists of discrete variables called spins that can be in one of two states. The spins are arranged in a lattice or graph, and each spin interacts at most with its nearest neighbors. The goal is to find phase changes in the Ising model, as a simplified model of phase changes in real substances.

In 2000 while working on the Murphy’s Price - Volume - Open Interest I started scribbling arrows in a 3 by 3 grid writing about how Price - Volume - Open Interest (PVO) should define trends. The PVO model looked like an Ising model.

 

 

Today I will try to explain the 10 year old analogy. In an antiferromagnet there is a tendency for the intrinsic magnetic moments of neighboring valence electrons to point in opposite directions. When all atoms are arranged in a substance so that each neighbor is ‘anti-aligned’, the substance is antiferromagnetic. Antiferromagnets have a zero net magnetic moment, meaning no field is produced by them. Antiferromagnetism can be considered like a neutral market as anti aligned spins (Fig. 1) are similar to non confirmations. Many non confirmations also mean undecided market.

From a PVO perspective, it could be a stock with a positive spin and another with a negative spin causing the aggregate market to be neutral.With the passage of time the neutral situation leads to a topping or bottoming situation, in other words a market bias, spin, direction, Ferromagenetism. A topping, where a market reverses direction sees the price pointing lower, volume leading higher and drop in open interest position (as longs square off – Fig 3). On the other hand a bottoming market ready for reversal is when the prices point up, volumes are still lackluster and negative, but open interest starts to build up new long positions (accumulation – Fig 2). This confirmation among stocks finally gives a negative and positive bias to the market. This is how stock markets could have a physics parallel in the Ising model spins. The Ising model could also validate the weight of evidence approach in technical analysis.

This article was written for ATMA.

 


Orpheus Time Analytics - India Rankings

These are the multi week percentile rankings for a group of Indian stocks. 100 percentile means best performers and near zero ranking percentile are the worst rankers. Rankings change with changing prices as they depict changing performance. Performance is cyclical for us at all degrees of time. And worst rankers suggest value while top rankers are expensive. Jiseki performance cycles depict these changes in rankings.


VIX reverses again. This time for real?

The American fear gauge index VIX reverses again. This time could be for real. VIX has been a low ranking rather worst ranking performer for quite some time now. And as the rule of performance cycle goes, the worst performer will surprise. This means that VIX should rise and when it rises the fear should increase. The left hand scale carries 3 Jiseki cycles of (1month, 2month and 3 months) while the Right Hand Side scale are the actual VIX prices. This is some signal we won’t like to miss, considering our preferred view on global indices still look at a topping global market. The latest CEE, Global Alpha Indices carries the Russell 2000 broad index Jiseki cycle. Broad market cues are important.

Alpha is a daily strategy signal product that gives trading and investment signals. Alpha is a numeric Ranking product based on TIME fractals. The signals are illustrated through tracker and running portfolios. Alpha can be used by fund managers for relative allocations, traders for leverage bets and high net worth clients for selective trades. Alpha is a part of the time triads analytics developed by Orpheus Research.


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