Archive for the ‘Time Triads’ category

The Data Universality

A worst performers’ portfolio delivers more than a best winners’ portfolio over longer terms of investing.

UNIVERSALITY

 

Building my case from last time (the fortune index), where I suggested databases should talk to themselves and if natural systems express universal laws like patterns, divergences, seasonality and constants then the data generated or derived from these natural systems should also express this universality. And if the data also express this universality then the question to be asked here is whether the universality character lies in something common to data rather than to a natural system.

This is what we refer to as data universality. Universality can be defined as “The aspects of a system’s behaviour which are independent of the behaviour of its components. And even systems whose elements differ widely may nevertheless have common emergent features”, then Data universality can be defined as the “common universal behaviour of any data set irrespective of its organic source of generation or derivation.” Assuming data universality is a science. What does it bring down? The elephant blows away the blind folds and finally psychologists, technicians, fundamentalists, statisticians, mathematicians, scientists, etc. talk the same language. What is the problem we solve? Apart from pushing stock market into the scientific domain, we could address the problem linked with all complex systems and, we could understand complexity at a unit level. Why could we not do it till now? Interdisciplinary science is relatively new. An economist or physicist never thought their paths could meet, till one physicist jumped the ship and created Econophysics.

There is a key fundamental idea of value and growth cycles and that the premium markets give to growth over value. Robert Arnott has researched extensively on the subject and has built a novel fundamental indexing approach around it. We recreated the similar value and growth divergence using derived ranking rather than using pure price or fundamental data.

We took the top composite indices of India, Japan, Austria, UK, Australia and US, ranked their components using our data innovation approach and created two portfolios, a worst losers’ portfolio and a best winners’ portfolio. Barring Austria and US where the worst losers matched in performance with the best winners’ portfolio, the worst losers’ portfolio beat their respective benchmark performance over a five-year rolling return for all the markets.

What does this suggest? This suggests a few things. First; a worst performers’ portfolio delivers more than a best winners’ portfolio over longer terms of investing. Second; Worst performers’ portfolio generally does better than the market i.e. value is superior to growth over the longer term. Third; fundamental behaviour between value and growth does not need just fundamental data, the same value and growth divergence can be illustrated in ranking data from any group of market assets. The question arises yet again, is the value and growth divergence owing to fundamentals or owing to data universality?

Another paper written in 2004 by “Universality in multi agent systems”, Parunak, Brueckner, Savit on Universality talks about universal behaviour of natural systems. The three stages consist of randomness, order and herding. Call it coincidence but stock markets have a similar three system behaviour (previous article). The extreme reversion universal system explained in chapter IV ranks performance over three broad segments; worst and best (below 20 and above 80 percentile ranking), near 50 percentile, and rest of the region. The universal behaviour expresses itself in rankings. Again the data representation prevails despite different group characteristics.

The universality paper also suggests that the reason agents don’t optimise our decision making is owing to time constraints. The gap here is that the authors don’t connect seasonality with universal behaviour, or in other words they don’t connect and study the confluence of universalities.

Extreme reversion comes from a confluence of universalities. Because seasonality is an essential component of extreme reversion (best becomes worst and vice versa) there is a focus on understanding timing constraints (The question of when? is addressed). Which stage is more likely after randomness, order or herding? The performance cyclicality (extreme reversion systems) is also connected to multiple holding periods. Performance is growing or decaying differently for different periods. This is why any system that assumes timing aspect to be inherent to the decision making process could be a better model for understanding universality and hence comprehending risk better.


Nobel’s Interdisciplinary Connections

NOBEL PRIZE

Somehow my Interdisciplinary mind registered Eugene faster than Fama. After all Eugene Stanley the father of Econophysics could also get a Nobel. If Psychologists could get the biggest award for Economics, a physicist could have been there too. But then the surprise became bigger, not because it was Fama not Stanley but because Robert Shiller shared the award.

When Behavioural Finance got the Nobel for Economics in 2005, the economist magazine carried an article pointing out how a new theory had junked 200 year old classical economics. It was not just media, but even the psychologists who were bent on burying the classical economist. Efficient market hypothesis was presumed dead. It was considered deficient. I indulged in it too (early 2007). But over years, defending the underdog changed to understanding the new theories and then finally even questioning them (Shiller’s exuberance, End of behavioral finance). It’s a fight between perception and reality at a certain point of time, which ofcourse is dynamic, leading to new perceptions and new realities at new points in time.

Now that Fama has been acknowledged yet again, his tough stand against Behavioural finance as stories of anomalies can be seen in a milder light. After all standing there with Shiller, would definitely make him believe, “even together we don’t know all the truth”. The blind men and the elephant metaphor remains a strong theme. My elephant is efficient, while yours is inefficient has been overruled by the Nobel committee which believes that the elephant is both efficient and inefficient sometime. The failure of Behavioural finance to take it from the fundamentalists can be viewed as a victory of sorts, but it’s still an illusion. The Nobel Prize bashers like Taleb also won’t enjoy this as there randomness theories get weaker by the day.

The committee needed Lars Peter Hansen to balance. What did Hansen do? Hansen says that both efficient and inefficient schools could be understood better with more testing as the economic system is not static, it’s a dynamic system with multiple moments. Is this not a step ahead toward assuming markets as natural systems? Are the laureates not struggling to understand divergence, why are markets not predictable on short term and why are they predictable on long term? Where does the predictive element vanish and reappear? Is the question for the Nobel Prize winners not about understanding error in context of time? Which is what Hansen is saying, we need to look at the fluctuations, errors and divergences over multiple moments before we start to understand what works and why it works.

We may call it conflict resolution or Hansen’s way to explain why interconnected variables fail and succeed but at the end of the day the objective is to understand complexity and risk. So the 2013 Nobel concluded a few things. First markets are natural dynamic systems with coexisting efficiency and inefficiency. And we may use psychology case to explain the effects, but we are still far away from determining the underlying laws driving risk, growth, and decay in all natural systems.

Do the answers still lie in understanding the framework for universal laws, their confluences and interactions and the macrocosm in which they operate and influence the multitude of groups creating the deterministic disordered chaos. The 2013 Nobel has forced us further in the interdisciplinary path where fundamentalist, statisticians, psychologists, physicists must work hand in hand to find new universal laws which can select, invest and predict trends.


The Fortune Index

googly

Data models should work across regions, across nature, across data sets. This means “Data Universality”. If natural phenomenon exhibit universal patterns like geometry, outliers, 80-20 principle, mean reversion, fat tails etc. then the data these natural phenomena generate should also express a similar behavior; actually they do. But we still consider data sets as religious, the stock market data is for financial analyst, while the sub atomic data is for physicists, the social network data is for marketers.

We see similar trends in all data sets but we don’t mix and match sub atomic, social and stock market data. Why? Because it would be kind of blasphemy when Higgs particle would illustrate a high correlation with a high frequency dollar signal. The objective thought would be that stock market data can’t be reconciled with sub atomic data as the respective elements vibrate at a different frequency. Yes, that’s true, but social data has a workable frequency with stock markets. This is the reason twitter forecasting is in vogue, and companies are relying on the sentiment today to under consumers and market trends. It would not shock me if there are market systems out there using twitter data to trade.

What’s the problem? The problem is like Charles Handy mentioned paraphernalia. Calling a seamstress a designer does not change her real role. In the process of finding and worshiping big data tools, we claim to have moved to the next stage without acknowledging the elephant in the room. It’s the same elephant but we call it something else. We chose to ignore that the answer to tomorrow’s problems is not in a discipline, but between disciplines.

It was about 36 months back we started compiling small non capital market exercise. We took Google search data for Fortune 500 companies, various emotions (fear, greed, happiness etc.) and ran our data algorithms on the same. Just like gold, oil, dollar we could create cycles of growth and decay for simple web data. We could predict which Fortune 500 companies would be searched more and which will be see decay in search. Last week we went a step ahead. We actually benchmarked it to the google search data. Assuming Fortune 500 search date to be a set of time series like that of stocks, we applied the ORMI (Orpheus Risk Management Index) Active methodology to select which of the top Fortune 500 companies will be searched more, assuming we made money if our portfolio of 10 selected companies were searched more. This is what the companies actually need to know, are they going to be searched more or searched less (assuming positive search is positive bandwidth).

In a short period of 24 months (search data has limited history) our ORMI Fortune Index moved up from 100 points to 120 while an equal weighted google search data of Fortune 500 fell by a negative 10%. ORMI Fortune Index outperformed its respective universe by 30% over 24 months. This outperformance was a proof of predictiveness, which is where data mining should head rather than subjective extrapolation, which can’t be quantified. How much is your data-mining adding to your bottom-line is quantifiable. The top 10 potential search growth companies on google search lead to six selections viz. AMGEN, DOW CHEMICAL, HALLIBURTON, MCKENSSON, DANAHER, CHEVRON, four components in the model were cash. How we integrate this model to stock market forecasting is another step. But the current work proves how lacking the current data tools are. We illustrated more such non capital market or social mood cases over the last few years. When Spain came from the negative outlier to end of winning the world cup; Long Football-Short Baseball illustrated how time series for sports can be dissected like any other stock market time series.

We explained the problem with current web search in ‘Jazz and Trading’.  Despite all the current search tools, I can’t find the American jazz singer I heard a few years back. The search is not cognitive or semantic yet. Google could not help me reach the singer because I needed related search parameters. What was her age? Was she an African-American? What was her net worth? Suddenly, something so relevant for me got lost in the deluge of information. The problem with search is lack of smart catalogued databases, which can understand each other. Only when databases are able to understand each other can data come alive and make search smarter. In the article ‘Researching Google Search’, we explained how everything from the number of searches to what we search is connected with time, and any predictive cycle tool that can measure and anticipate emotion can help researchers understand where a society is headed and where it was when hope was rising and where will it go when hope shall fall.

DOW.CHEMICAL

The age of big data accompanies numerous data types like web date, social data, and consumer data. Hence it has become essential to lay down a framework for data universality. This means commonality of behavior, commonality of patterns and data character. Such guidelines could make data visualization, transformation and interpretation easier. The natural universalities leading to data universality can harmonize big data classification and improve predictive model.

What does real data seasonality tell us? Variable growth and decay for multiple periods (e.g. intraday, weekly, quarterly, or for the decade ahead). Why is it important? Because nature is not just about growth, it’s also about decay. Extrapolation of a trend is incomplete science, understanding when that extrapolation will peak is as important. What is missing today and where is the need for value addition? The society is missing the connection of growth with decay (seasonality), the temporal element of growth and decay, the lack of data universality, the lack of acceptance of interconnectedness of everything with everything, the lack of anticipation whether it’s really the butterfly creating the tornado or did the role switch to the tortoise from the butterfly last week as the butterfly decided not to flutter. What does this mean in business terms? Everything, if you can’t anticipate, you can’t recreate past or construct future, you don’t understand the data set. What is the secret sauce? The secret sauce is universality. If all data sets exhibit a universal behavior, data manipulation should be based on these universalities and seeks, identify and enhance the respective natural patterns. Tomorrow’s data should know that google and googly are two different things.

ORMI.GOOGLE

Related Links

Jazz and Trading
Researching Google Search
How did Spain reach that far?
Long Football, Short Baseball
Forecasting stock markets with Twitter
Economist
Predicting the Future With Social Media
The Pulse of News in Social Media
Forecasting with Twitter Data
Twitter mood predicts the stock market
Fortune Index 09.09.2013


Stock Market Science

photo

Why stock market is not a science? Today many elements of our life have a degree of predictability, consumption patterns, social behaviour, earthquakes, etc. However the predictive measure is lacking when it comes to stock markets. Behavioural finance highlighted this lacking measure and accountability. Even statisticians limit themselves to prediction of stock market volatility rather than stock market direction.

Can scientists include stock market as a part of the scientific framework? If you think scientists have more important things to do than stock markets. Believe me this is not all true, you will be surprised how interested physicists can be in stock markets. It’s just that the stock market science is indeed in its early stages.

What will happen if a framework for stock markets is established? Nothing will dramatically change, first the argument between behavioural finance, economists, mathematicians and physicists will get more colourful and then it may take years for some new accepted norms to win over and emerge from the debate. Stock market science just like societal learning is a gradual process.

How will stock market science help? Apart from the fact that it could help understand risk (other name for creating and conserving wealth), could stock market science somehow complete science itself? Could understanding stock markets help us understand nature? Well nothing is impossible when you search for universal rules. If stock markets are natural; stock market science could help us understand nature.

Was it random? I met Professor János Kertész in Budapest. Though I was connected to the professor through an acquaintance at MSCI Budapest, we found that we were also connected through Boston University top global physicist Eugene Stanley (I have a paper with Prof. Stanley written in the early 2000). Janos had worked with Laszlo Barabasi (another leading physicist) while Laszlo did his doctorate under Eugene Stanley. In crux Bombay Stock Exchange was the reason I got randomly connected to three giants of Physics.

Network and knowledge. Laszlo has been featured as the man who could change the world. His work can predict network behaviour, map, comprehend and control a complex system. In his top selling “Bursts” Laszlo explains numerous natural systems. But just like Mlodinow (The Drunkard’s Walk), he does not refer to stock market systems. Networks today are a big data domain issue. Stock market is a database, a network and hence a part of the same big data domain. If there is order within networks starting from Hollywood to proteins to consumer behaviour and today we can treat callers as particles and predict their location with 93 per cent accuracy, then stock market predictably cannot be anathema.

In Bursts, Laszlo explains how natural events portray a statistical Poisson distribution but when we assign weights and priorities to events (tasks) the distribution changes to levy flight, which expresses outliers better. When we create a stock market portfolio consisting of a composite index, a sector, an asset, we are talking about preferences (priorities) leading to weightages (equal or unequal). Even stock market performance, regional performance is owing to the weight global money assigns to the various regions. This is why stock markets universally exhibit the levy flight or the power law behaviour. If physicists would frame the stock market problem differently, we could understand performance (underperformance), comprehend groups (portfolios), improve our selection systems and end up laying a framework for stock market science.

What if? If Pareto would have met Benner, or if Einstein would have met Pareto, interdisciplinary science would be a generation old and the stock market science may already have been in place and space time geometry could look different. Even if we assume to be born in this great time when stock markets could redefine science and vice versa, the network would have to attain critical mass, before we wonder how we did not see it.

Mukul Pal


Banking Special

BANKING

In this special Banking issue we have reviewed the NSE BANK sector index. The monthly prices are retesting 2007 highs near 9,000 levels. The RSI momentum is also heading to historical key supports at 40. This is a double oversold situation accompanied by price and momentum support. To expect prices to just slide below 9,000 and RSI to break a decade low support seems unlikely. We think Banking Sector Index will sustain at the respective lows and eventually lead to a reversal which will take Banking Sector back above previous historical highs at 13,000. Globally OIL has no follow through buying as momentum has got overbought. This suggests that OIL could ease from current levels and face some resistance ahead. Gold also has an over reactive momentum. The current up move seems a counter-trend that should resolve lower. The current update carries some key banking stock perspective also.

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Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.


The Smart Beta

 

beta

 

Passive products like exchange traded funds are a relatively new sector to India. It can increase market size and enhance financial infrastructure.

In case you missed it, the Bloomberg Businessweek cover leveraged the male anatomy to make a statement about hedge-fund returns. Though catchy and good for sales, the cover evoked a mixed response and a few considered it was in bad taste. The social mood is so much about jailing bankers that hedge funds should be happy being just derided on covers. The society, like nature, is about extremes, it goes all the way from praising to trashing. We can now draw a four-stage product cycle for hedge-fund success and failure, but active money management also goes through seasonality.

According to The Economist, Smart Beta is a new passive investing approach with more than a 100 billion tracking it compared to about two trillion tracking other passive styles. The Smart Beta’s idea is to enhance returns tracking an asset. Skill and judgment come under test as quants take over. This is the new age of indexing, the commoditisation of alpha and beating the index.

This is some innovation, but why did this take 150 years for passive indexing to wake up. The key reason is the competing active versus passive broad styles. Markets have gone through a succession of crises, in 2000, 2002 and 2008. Investor and consequently regulatory perception has gone through a sea change. Investor psychology judges the worth of active money management differently. Active could not outperform passive as the markets whipsawed, delivered negative returns for a decade. There was a lot of anger against active money managers as they failed to match expectations.

To read the complete article visit Business Standard or subscribe to the Time Triads Newsletter

Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.


The New Active 2

the new age

We can connect cause to an effect retrospectively now. What happened to India? Are we going to NIFTY 4,500 or is Nifty at 8,000 still running? Apart from the ongoing payment crisis that dampens confidence, the rupee scare, the falling Nifty; there are actually job losses and downsizing. So the ongoing seasonality could be serious.

Can you be insulated? If you thought you were out, currency does it for you. If you thought that also did not matter, it was a job loss. If you still overcome it than it could be onions. You are basically trapped with nowhere to go. We are in difficult times and there are no two opinions about that. Ultimately it’s a concerted effort (polycentricism) that can take us from point A to point B. But this is still cause and effect. Excesses happen because humans extrapolate a trend (run after winners and shun losers), build in capacities and this prices the whole industry out, forcing a balancing decay. However, just like growth all decay and difficult times end, so this will end too. The idea is what different should we do now, which makes us better prepared next time.

From an investor perspective, there is a jargon you should understand. If you are not diversified (multi assets), if you are not looking at more than a few years of holding, if you are worried now about NIFTY (down 12% for the year), and you will be more worried if NIFTY reaches 4,500 you are an active investor who has low tolerance for a loss. There is nothing wrong in being an active investor, majority of us don’t look beyond a year. A few who looked beyond a year got trapped in the 2000-2010 decade and refuse to go passive again. So the passive-active make up changes depending on how the markets surprise us. I have met a few passive investors, who don’t feel trapped, but understand the game and risk that comes with it.

To read the complete article visit Business Standard or subscribe to the Time Triads Newsletter

Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.


Killing the Insider

st-george-killing-the-dragon-1507

Now with SAC Capital Advisors, a multi-billion dollar hedge fund under attack and potentially ready to shut shop, one wonders what is getting destroyed or created in this process? Who should be excited? Who should feel distraught?

Is it destruction? Rajat Gupta’s wiki page labels him as a corporate criminal before talking about his philanthropist activities. Bad is stronger than good (Nov 1, 2012). Corporate criminal evokes a stronger context than philanthropy. Markets are ruthless. Steven A Cohen’s wiki page does not indicate him as a criminal of insider trading yet. George Zimmerman on the other hand has no wiki page and has been acquitted him from second degree murder. On the face of it, it seems that financial crime is worse than a probable homicide. But it’s the society which has decided to punish the industry and its poster boys. And since negative mood does not know balance, the forces tend to exaggerate and will continue to have a “jail the bankers” approach.

What are we creating? Well because markets are alive, the inefficient is pushed aside and efficient take its place till the cycle repeats. Who survives? The underdog, the underperformer, the inactive, the small size, the big size is cut down either by the society (antitrust) or proactively by the entity itself (downsizing). A girl complained at school about a mischief done by the boys. There were many boys. The girl could remember the few who were leading the mischief. Not everyone from the group came to attention and hence escaped the punishment; availability bias. Society always creates opportunities for the underdog even if he is a part of the same industry under attack, a level playing field for David against Goliath.

To read the complete article visit Business Standard or subscribe to the Time Triads Newsletter

Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.


The Drunkard’s Walk

BROWNIAN-MOTION

Proof of Randomness

In 1827 Robert Brown saw a random movement of pollen particles in water. Pollen particles were jumping around randomly. This was called Brownian motion aka the drunkard’s walk. In 1902 Einstein proved this phenomenon and confirmed the presence of atoms and molecules. This was 100 years after atomic theory was proposed by Dalton and was the first attempt to reconcile wave and particle theory in science. The debate has taken another form now as classical physics can’t be reconciled with quantum entanglement (superdeterminism), which meant that free will does not work, a particle can’t make his fate by hard work and awareness, his fate was predetermined.

Is it all random?

A few Physicists have extended the same debate to portfolio performance suggesting investment management is a vain business, where money is made not because of skill but because of luck, stock picking cannot be done and outperformance cannot be delivered and profit is nothing but hot hands. In his book, The Drunkard’s walk, Leonard Mlodinow, unequivocally suggests that “success or outperformance is a myth”. Suddenly the physicist’s societal framework for randomness resembles Malcolm Gladwell’s sequel.

Is societal success just because of chance? There is nothing wrong in accepting chance. But what is new here? Despite all uncertainty, generations of thinkers have spent lifetimes estimating that chance be it Gerolamo Cardano, Abraham de Moivre or Joseph Jaggar (The man who broke the Monte Carlo bank). Though Mlodinow goes about detailing the chronology of randomness driven by the evolution of probability and human understanding of universal patterns, he fails to address the subject (randomness) in totality and simply gives in to a subjective conclusion, of fighting it out and persistence as possible answers to success. “Social behavior has other dynamics than physics.”

To read the complete article visit Business Standard or subscribe to the Time Triads Newsletter

Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.


The Rupee Scare

rupee scare

There is only one problem here, psychology is prone to reversal, after greed comes fear, and then the cycle repeats.

Scare is an emotional need

Like we need soap operas, we need things to be scared of. Emotions are why we live, so if we don’t have greed and fear, we might not feel alive. This is true with markets. How we interpret indicators (a currency) could also be linked with how psychological we want them to be. Robert Prechter would agree since he believes that social mood drives markets. This should mean even macroeconomic indicators or currency value could be driven by social mood. And, if a 60 rupee-dollar value does not scare us enough markets weakness could continue and vice versa. There is only one problem here: psychology is prone to reversal, after greed comes fear, and then the cycle repeats. So, if you think the rupee has scared us enough, the reversal is round the corner. It might all sound too simple. Actually, markets are very simple. “Simplicity is the most undermined investment technique,” said Garfield Drew.

Indicator as a system

Coming back to the currency. A currency is supposed to be a macroeconomic indicator, which is a true reflection of the economy. How good is this true measurement? We have been taught as technicians that a currency has an inter-market relationship with the local equity benchmark. The currency strengthens the equity benchmark moves higher and vice versa. Ok! It works up to a certain extent. You can see that visually. But are visuals enough? We have doubts about visuals being enough for risk management.

Refining the indicator…

To read the complete article visit Business Standard or subscribe to the Time Triads Newsletter

Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.