Same as Usual, Only Different

Instead of embracing and celebrating change — or lying about it and pretending to embrace it — I think we ought to stop talking about change altogether. Let’s ignore it, avoid it, and sidestep it. Instead of spending time thinking about change, let’s all sign up for zooming lessons.
— Seth Godin (1999)
Doing the same thing as usual, only different.

Doing the same thing as usual, only different.

Seth Godin defines “zooming” as “doing the same thing as usual, only different”. Zooming, according to Godin, is about stretching your limits without threatening your foundation. It's about handling new ideas, new opportunities, and new challenges without triggering the change-avoidance reflex.

There are all kinds of zoomers, and all kinds of categories in which you can learn to zoom. A person who is able to zoom across a large area without getting stressed out is said to have a broad “zoomwidth”.

Take the franchised restaurants — McDonald’s, Baskin-Robbins, Pizza Hut — for example, none of them has any zoomwidth at all. The structure of these organizations, Godin explained, made any sort of adjustment seem like a major threat, rather than an opportunity to zoom. In fact, Kentucky Fried Chicken even had to change its name to KFC, just so it could start selling non-fried foods!

In contrast, Limited Inc. is a company that has great zoomwidth. At Limited stores, introducing a new clothing style is easy. It changes its merchandise at every store at least once a month — whether it needs to or not.

The big question: Why is it that the big opportunities, the really obvious chances that we get to improve our businesses and our careers, almost always pass us by? The answer: big opportunities bring change, and change is painful. Godin concluded that as long as opportunity means “change”, and as long as change means “pain”, we will continue to miss our chances, unless we learn to zoom. Or, if it’s the business world, the escape route from doom leads to growing, adapting and transforming the organization so it finally has ample room to zoom.

Alpha Above All

If a market were informationally efficient, i.e., all relevant information is reflected in market prices, then no single agent would have sufficient incentive to acquire the information on which prices are based.
— Joseph Stiglitz (2001)

If markets are efficient, they reflect all information, so there is no profit to be had from trading on information. If there is no profit to be had, traders with information won’t trade, so markets won’t reflect it, and will not be efficient. This is the Grossman-Stiglitz paradox in a nutshell. Indeed, if there is no profit to be had from trading on information, then why would anyone expend resources to acquire the information upon which process are based in the first place?

Indeed, a visit to the “hall of fame” of equity market efficiencies popular with quantitative traders reveals a potpourri of sources of alpha (i.e., active investment ideas for out-performing a passive benchmark), e.g., earnings forecast analysis, earnings surprises, insider trading disclosure, stock splits, secondary equity offerings and stock buybacks, mergers and acquisitions, sector analysis, common factor analysis, message board counts, Twitter sentiments, web traffic analysis, etc. Financial markets in general are far from perfect; many sources of inefficiencies can be found at different times if one has the right tools and knows where to look.

A spectrum of return sources: from  Bet a to  Exotic Beta  to  Alpha ; it's all about having the right tools and knowing where to look.

A spectrum of return sources: from Beta to Exotic Beta to Alpha; it's all about having the right tools and knowing where to look.

An investment strategy that is quite popular with hedge funds is what is called the “market neutral long-short portfolio”. In a typical setup starting with, say $100 million, for example, a long-short, market neutral portfolio consists of $100 million in long positions and $100 million in short positions. After receiving $100 million from the short sale and spending $100 million on the long side, there is still $100 million in cash (which is the amount that the fund starts with). There is no net capital requirement to put on a position such as this. What the hedge fund manager typical does then is to use the cash to put on an unleveraged futures position, e.g., the S&P 500 index futures, so as to capture market return. This is because an ongoing market index futures position, reinvested at the contract expiration dates, closely tracks the index return.

So when a long-short portfolio and an index futures position are put together, what results is a total return equal to the return on the index (i.e., beta) plus whatever return captured from the long-short portfolios (i.e., alpha). This is called an “equitized” portfolio, named for the market return captured through putting on an equity market index futures position. Notice that all the money in the fund is working twice: once on the long side of the portfolio and once on the short side. And this alpha return comes on top of the market return. It is no wonder that David Leinweber dubbed this the “James Brown of quant stock strategies, the hardest working portfolio in the equity business.

The general plan of quantitative strategies, such as the popular market neutral long-short portfolio aforementioned, is no mystery. After all, quantitative strategies are really just mathematical expressions of fundamental investment ideas, if one look inside the process. Quantitative methodology allows many disparate concepts to come together in a single forecast. Because the process is automated, it can be applied to many financial securities, thus spreading little bets across many active positions and limiting risk in the process. So in many ways, quantitative investing is really not that much different from traditional investing, although it may sound quite dissimilar.

To err is human, but to really screw up… you need a computer.
— Anonymous
This monkey has a future career in  statistical arbitrage ; he is starting to see it now from the vantage point of  Arrow-Debreu ...

This monkey has a future career in statistical arbitrage; he is starting to see it now from the vantage point of Arrow-Debreu...

Some quantitative strategies work by pure arbitrage, essentially finding the three-and-a-half-cent pennies in the market before anyone else does. Arbitrage opportunities are sweet if you can find them; statistical arbitrage works just as well. But in an increasingly wired world where the global financial markets are fully electronic, such arbitrage trading opportunities are rare, and available only to those with bleeding-edge infrastructure, or scale of capital, or both. For the rest of the trading masses, strategies based on prediction of financial markets (adjusted for risks) is far more common place. The objective here is now two-fold: increasing predictability increases investment return; while improving the consistency and downside error of predictive models reduces risk.

Maximizing Predictability: Just 3 places to look, but many  stochastic  combinations are possible (and don't forget about  time horizon !).

Maximizing Predictability: Just 3 places to look, but many stochastic combinations are possible (and don't forget about time horizon!).

A useful perspective on maximizing predictability in financial markets is depicted above. The perspective is attributed to Andrew Lo, but the picture is adapted from an illustration found in David Leinweber’s Nerds on Wall Street. When viewed from a high level, there are only three key decisions to make in any financial market prediction:

  1. What to predict: One can choose to predict returns to an asset class, e.g., a broad market or an industry group, an exchange rate, interest rates, or returns to individual securities of many types. One can also choose to predict spreads (i.e., return differences) between individual securities or groups of securities. Predictions of volatility are useful for options-based strategies.
  2. How to predict: One can choose from a wide variety of statistical and mathematical methods of prediction. Many use simple windowed regression methods, which are popular. Some choose more advanced regression methods, such as moving or expanding windows, kernel estimation, auto-regressive integrated moving average (ARIMA) time series models, or even neural networks.
  3. What to predict with: This is the raw materials that feed into the prediction methods. Technical traders use only past prices to predict future prices; but this is quite rare in institutional trading. A wide selection of financial and economic data, e.g., commodity prices, foreign exchange rates, GDP announcements, analysts opinions, messages on bulletin boards or even measures of Twitter sentiments could find their respective predictive powers within the right context.

In an uncertain world, a stochastic world view and associated methodologies for conducting experiments, interpreting outcomes and take-away results might be important. Last but not least, the time horizon of how everything interacts together (i.e., long or short), plays just as important a role in determining success or failure, especially for electronic markets participated by high-frequency traders.

Now, here is an interesting meta-level question: Can hedge fund returns be predicted? Can hedge fund returns, assuming they are good, be replicated?


  1. Grossman, Sanford J. and Stiglitz, Joseph E. (1980, June). On the Impossibility of Informationally Efficient Markets. The American Economic Review. Retrieved from:
  2. Lo, Andrew, and MacKinlay, Craig (1995, February). Maximizing Predictability in the Stocks and Bond Markets. NBER Working Paper No. 5027. Retrieved from:
  3. Litterman, Bob (2005). Active Alpha Investing. Goldman Sachs, Open Letters to Investors. Retrieved from:
  4. Geer, Carolyn (2011, February 7). Index Funds Get a Makeover. Wall Street Journal. Retrieved from:
  5. Hulbert, M. (2008, July 3). The Prescient Are Few. New York Times. Retrieved from:
  6. Barras, Laurent, Scaillet, Olivier, and Wermers, Russ (2010). False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas. The Journal of Finances, LXV(1), 179-216. Retrieved from:
  7. Zweig, Jason (2014, August 22). The Decline and Fall of Fund Managers. Wall Street Journal. Retrieved from:
  8. Leinweber, David (2009). Nerds on Wall Street: Math, Machines and Wired Markets. John Wiley and Sons.

Trying to Make It Real

Trying to make it real, compared to what?
— Eddie Harris & Les McCann (Montreuex Jazz Festival, 1969)

Excerpted from Seth Godin’s blog: “Real — Compared to what? The Pale Imitation” and book: “Small is the New Big”:

“Eddie Harris and Les McCann walked onto the stage and though they had hardly rehearsed at all, launched into an ad-libbed song that made history. Ironically enough, the song contained the line, “Real... compared to what?

“The vinyl souvenir of that live performance is a million-selling classic:  Les McCann & Eddie Harris - Swiss Movement: Montreux. Listening to the vinyl LP isn’t the same as attending the original concert, but it’s convenient and sounds great.

“Twenty years later, “perfect sound forever” brought us the CD version. There’s no pops and crackles, but to my ears, it’s just a reminder of the depth of the LP.

Montreuex Jazz Festival (1969): Better on YouTube?
Trying to make it real...  What!?

Trying to make it real... What!?

“Then they had us move everything to MP3. Now I’ve got the CD version ripped on my iPod. There are far fewer bits of data and it doesn’t sound as good, but it reminds me of the original.

“Now, I’ve got a Monster cable for my car that lets me broadcast the MP3 version of the CD version of the vinyl version of the live event over the FM airwaves to my car radio. It sounds like Eddie’s in the Holland Tunnel. And it’s not even close to music, but it reminds me of the way I felt when I heard the album.

“This is not just happening to music. It’s not just traditional media, either. An e-mail doesn’t communicate as much information as a meeting, and a voice mail is really hard to file. A PowerBar may have plenty of vitamins and stuff, but it’s just not as good as a real meal…

“This phenomenon creates a big opportunity. The opportunity to provide sensory richness, to deliver experiences that don’t pale in comparison to the old stuff. It’s not just … nostalgia — it’s a human desire for texture.

1936 Deluxe Edition Monopoly Game Extremely Rare.

1936 Deluxe Edition Monopoly Game Extremely Rare.

Maps and Territories

Terrain doesn’t fight wars. Machines don’t fight wars. People fight wars. It’s in the minds of men that war must be fought.
— John Boyd (1927-1997)

Developed by maverick military strategist and USAF Colonel John Boyd, the phrase “OODA Loop” refers to the decision cycle of observe, orient, decide, and act. Boyd applied the concept to the combat operations process, often at the strategic level in military operations. Boyd believed that “getting inside the decision cycle of an adversary” is crucial for winning wars. In a recent April 1 issue of the “Breaking Smart” series, Venkatesh Rao formulated the general concept of “map-territory distinction” and explained in detail how finding exploitable weaknesses in the adversary's map can be an important source of competitive advantage.

Red is operating with finger-tip feeling, and has a map of Blue's map. Blue is map-blind, and has no idea what Red is thinking.  Who do you think is going to come out ahead?  (Image Credit:  Breaking Smart ).

Red is operating with finger-tip feeling, and has a map of Blue's map. Blue is map-blind, and has no idea what Red is thinking. Who do you think is going to come out ahead? (Image Credit: Breaking Smart).

According to Rao, maps are used everywhere: geographic maps, organization charts, market evolution maps, genome maps, neural circuit maps, biome maps, sheet music, etc. In competitive situation, there are maps, maps pf maps, maps of maps of maps, etc. One can also make maps of others’ behaviors. Maps can thus be viewed as the basis of all competition. After all, a map is a simplified model of directly experienced reality, or phenomenology in the context of discourse related to the philosophy of science.

Like models, maps are efficient and useful. They reduce the cognitive load of mindful attention to phenomenology via one’s senses. Phenomenological awareness is much more expensive than listening to a model in one’s head. A good map can lower the cost of actions by orders of magnitude. But, like models, there is a hiden cost. When reality changes and catches one unaware, costly failures can occur (e.g., the spectacular failure of LTCM in 1998, or the financial crisis of 2008).

There is also a less dramatic, but more serious, cumulative cost to “map addiction”, according to Rao, i.e., an atrophy of sense-awareness. “Map blindness” turns mere known-unknowns into unknown-unknowns. Almgren and Chriss have this to say about the limitations of all model-driven strategies:

Finally, we note that any optimal execution strategy is vulnerable to unanticipated events. If such an event occurs during the course of trading and causes a material shift in the parameters of the price dynamics, then indeed a shift in the optimal trading strategy must also occur. However, if one makes the simplifying assumption that all events are either "scheduled" or "unanticipated," then one concludes that optimal execution is always a game of static trading punctuated by shifts in trading strategy that adapt to material changes in price dynamics.

The opportunity cost of not developing phenomenological awareness is quite high: one is effectively denied the use of tacit knowledge that has not been organized into maps (or models) in conscious awareness. German World War II military strategists refer to this particular sense-awareness as Fingerspitzengefühl, or “finger-tip feeling”. Unlike closed-loop feedback that signals where the model is wrong and how to adjust and compensate for the discrepancy, finger-tip feeling sensitizes one to the things the model does not even “know” about ( i.e., where the model is not even wrong!).

A pure map-based navigation strategy is what control theorists call open-loop strategy. One simply assumes the map is the territory, and navigate by it with eyes closed. This strategy is very cheap: a decision to not pay attention. Adding error feedback results in a closed-loop strategy, an incremental improvement that is quite a bit costlier. Now one must budget attention based on what the model assumes is important, and navigate by it with eyes wide shut. But a navigation strategy based on finger-tip feeling attempts to eliminate explicit maps from the loop altogether. By “instrumenting the phenomenology” directly, in a manner of speaking, one is finally navigating the territory not only with eyes open, but with an open mind.

In finger-top feeling based navigation, rather than budget attention based on assumed priorities, one deploys attention without importance judgment. This is a stage that precedes map-making and is vastly more expensive in terms of cognitive processing load. But this approach can achieve radical improvements in the long term. Incidentally, this is why recent advances in deep learning technology are widely considered to be significant. By instrumenting phenomenology rather than models, they can make sense of situations the model does not know about. But how does this work exactly?

I’d rather write programs to write programs than write programs.
— Richard Sites

A low-quality map requires a lot of expensive error feedback to just barely function. Sometimes it might even be worse than having no map. A high-quality map, on the other hand, might easily function well even with little feedback. But in competitive situations, one does not win with a better or more detailed map than the adversary. Instead, one wins by using finger-tip feeling to find exploitable weaknesses in the adversary’s map. “Fight the enemy, not the terrain”, as military strategist John Boyd once said. During a crisis, a feedback loop could be worse than an open-loop map; it is an automatic, subconscious habit that can be used against itself to cause a cascade of damage. For example, the Flash Crash of May 6, 2010 can be considered an extreme case of “feedback-amplified map-blindness” among an active subset of the market participants.

Unlike explicit map-and-model building, finger-tip feeling is not a one-time investment. Because the environment and one’s priorities can shift constantly, one has to always allocate a certain amount of attention to “finger-tip feeling” of the territory. One must also keep in mind that phenomenology is not reality; it is merely one’s experience of reality, limited by one’s senses and subconscious mental models. Therefore, it is advantageous to strive for continuous improvement in Fingerspitzengefühl through constant practice and deepening self-awareness; like it is a form of cognitive basic R&D.

Venkatesh Rao recognized the value of multiple models, an insight he gained from an earlier study of map-territory gaps in formal models. When multiple models collide, as Rao observed, they create dissonances; and phenomenology tends to win over all of them. One can thus see reality through the debris. Furthermore, by simply deciding to value phenomenology over maps, one can realize much of the benefit of Fingerspitzengefühl. This happens to be the approach that the MIT roboticist Rodney Brooks had earlier adopted for building his collection of “robotic creatures”, whose “insect-level intelligence” made possible by the underlying “subsumption architecture” was first described in a seminal paper in 1987 titled “Intelligence without Representation”. Brook’s main insight was that AI suffers from abstraction, and that a system cannot reason beyond its representation. So by reacting directly on the real world instead, representations (aka models) become unnecessary, thus greatly simplifying the construction of robots.

Mobile robots at the MIT AI Lab (from left to right): Allen, Tom and Jerry, Herbert (Image Credit:  Rodney Brooks ).

Mobile robots at the MIT AI Lab (from left to right): Allen, Tom and Jerry, Herbert (Image Credit: Rodney Brooks).

BellKor’s Pragmatic Chaos, winners of the Netflix Prize, display their million-dollar check (Image Credit:  Eliot Van Buskirk/ ).

BellKor’s Pragmatic Chaos, winners of the Netflix Prize, display their million-dollar check (Image Credit: Eliot Van Buskirk/

However, there is also value in blending multiple models together. In a surprising turn of events, the winning team of the $1 million dollar Netflix Prize, BellKor’s Pragmatic Chaos, was actually a hybrid team. BellKor (AT&T Research), which won the first Progress Prize milestone in the contest, initially combined with the Austrian team Big Chaos to improve their scores. To pass the 10 percent mark, Quebecois team Pragmatic Theory later joined up to create “BellKor’s Pragmatic Chaos.” The second-place team The Ensemble was also a composite. Arguably, the Netflix Prize’s most convincing lesson is that a disparity of approaches drawn from a diverse crowd is more effective than a smaller number of more powerful techniques. Joining forces allowed both teams to incorporate small, outlying techniques that are relatively inconsequential in the big picture, but crucial during the final stages where tweaking matters most.

“When we were approaching the first progress prize as the BellKor team, there were several other teams that joined together to make a real run at us, and that was surprising to us,” according to Chris Volinsky, originally of team BellKor. “The success of that collaboration told us that this was a real, powerful way to improve our scores. When you’re banging heads together in an office trying to come up with new ideas, you sometimes run out of ideas, and you need to bring in new people into the team, and that turned out to have a great benefit in terms of the predictive power of the models.”

Better solutions come from unorganized people who are allowed to organize organically. But something else also happened that was not entirely expected: Teams that had it basically wrong — but for a few good ideas — made the difference when combined with teams which had it basically right, but couldn’t close the deal on their own. The top two teams beat the challenge by combining teams and their algorithms into more complex algorithms incorporating everybody’s work. The more people joined, the more the resulting team’s score would increase. “One of the big lessons was developing diverse models that captured distinct effects,” commented Joe Sill of The Ensemble, “even if they’re very small effects.”

What lessons might we draw from this that would illuminate the path forward for organizing a community of traders centered on a trading platform? How do we use models? What happens when models collide? How should we blend models? What is the phenomenology of financial trading? Can intelligence emerge from phenomenology?

What form of intelligence would arise...

What form of intelligence would arise...

... when map-territory distinction disappears?

... when map-territory distinction disappears?


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