My trading tools and portfolio calculator have carried all of us through the good times and the bad times, and now, based on the performance characteristics since 2007, it’s time to incorporate what we’ve observed and learned over the past seven years and update the code.
There is no change in the fundamental insight that:
- The need for owning different asset classes (stocks, bonds, real estate, commodities, gold, etc.) is key, and short of clairvoyance, having many irons in the fire is only appropriate investment response to an uncertain future. The question is which ones, and how much of each one?
- The path of least volatility is, at least until human nature changes, still the best approach for position sizing any given group of ticker symbols; and,
- The current definition of observed volatility devised by me, based on the nature of price action I witnessed as a trader, is superior to all other quantitative methods I have studied.
Yet, questions will arise, as they should, on the eve of this change. This excellent question came from a member this morning:
Given our discussion last week regarding adjustment, I held off on putting the new numbers into play on Friday. I am wondering if you plan to issue a comprehensive discussion of the philosophical change to the portfolio weightings or do we just make it? As you are aware the percentage revisions are beyond significant. Without revealing any top secret techniques, it would be comforting to know the analysis that has prompted the radical (my word) shift in portfolio construction.
As always, I am ready to pull the trigger on your go ahead.
If you need the answer right now, skip to the bottom of this page, but I suggest reading this entire article so that you know how I go about doing things, much like Fischer Black. One of the most important passages for investors and traders to meditate upon is this:
“One of the things I like about doing science,” Fischer once said, “the thing that is the most fun, is coming up with something that seems ridiculous when you first hear it, but finally seems obvious when you’re finished.” Fischer not only didn’t mind being a minority of one, he actually found it fun, and along multiple dimensions.
Most important, he simply enjoyed the process of using his mind to solve problems. One telltale symptom: At Goldman Sachs he had to leave his Game Boy at home lest he be tempted to play his favorite video game, Super Mario Brothers, when he should be working. Problems in finance were much like video games for him. He would try one way, and then another, and then another, playing around with the problem until he understood it. And then he would come up with an approach that no one had ever before considered, a way of thinking about the problem that was new. His goal was not merely to find his way through all the levels of the Mushroom Kingdom in order to free Princess Toadstool from the evil Koopa turtle king, but even more to find a way to best the top score that had yet been achieved. His competition was not so much against other players, as it was against the game itself, and the game was play, not work.
Satisfying as it was to achieve new solutions, most of the fun actually came from all the other things one inevitably learned along the way. Fischer was never one to learn passively what others told him he ought to know. He preferred to learn whatever he needed in order to solve whatever problem he was working on at the time. “Research should have a goal,” he would say. Luckily there were lots of problems that interested him, enough so that moving from one to the next kept him learning new things for his entire life. Fischer was like a toddler in a room full of toys, picking up each one in turn and exhausting its fascination before moving on to the next.
The best problems, like the best toys, are hard to exhaust. You can approach them from a variety of different angles, each new angle making the problem fresh again, and bringing the opportunity to discover something new. Any idea, no matter how crazy seeming, might work and can be worth exploring. Indeed, the harder the problem, the more degrees of freedom one can allow in tackling it. Fischer relished hard problems because he relished that freedom, but in practice he did not try just anything. In his view, if a problem does not yield to known methods, that doesn’t mean we need more sophisticated methods, indeed probably just the opposite. Usually problems are hard not because our technique is deficient but because our understanding is deficient.
Fischer’s instinct was therefore to limit himself to elementary and direct approaches in an effort to find his way in to the very heart of the problem. Emanuel Derman, Fischer’s colleague at Goldman Sachs, remembers: “His approach seemed to me to consist of unafraid hard thinking, intuition, and no great reliance on advanced mathematics. He attacked problems directly, with whatever skills he had at his command, and often they worked.” Hard problems offer the possibility of fundamental discovery—that’s what makes them so exciting to work on—but only if we approach them in a fundamental way. Mehrling, Perry (2011-11-30). Fischer Black and the Revolutionary Idea of Finance. Wiley. Kindle Edition.
First, our understanding must be sufficient
On Day 17 of the search for Malaysian Airlines Flight 370, we are told that it “ended” in the Indian Ocean. The conclusion was based on analysis of data from the Inmarsat Satellite that tracked the “pings” from the aircraft.
The task was described by Inmarsat officials as “moderately difficult,” but it was a MacGyver-like NOVEL APPLICATION of “traditional, math-based” data analysis based on scientific first-principles.
In other words, someone at Inmarsat realized that, IN PRINCIPLE, the Doppler Effect on the pings could be measured and with trigonometry, it was at least theoretically possible to triangulate a position based on these signals, even though that was never the purpose of the ACARS system.
The team then obtained additional data from other similar flights in order to build and verify a model that could i.) eliminate the so-called Northern Corridor, and ii.) estimate the final position given other variables such as fuel, air speed, etc.:
How ‘groundbreaking’ number crunching found path of Flight 370
The mathematics-based process used by Inmarsat and the UK’s Air Accidents Investigation Branch (AAIB) to reveal the definitive path was described by McLaughlin as “groundbreaking.”
“We’ve done something new,” he said.
Here’s how the process works in a nutshell: Inmarsat officials and engineers were able to determine whether the plane was flying away or toward the satellite’s location by expansion or compression of the satellite’s signal.
What does expansion or compression mean? You may have heard about something called the Doppler effect.
“If you sit at a train station and you listen to the train whistle — the pitch of the whistle changes as it moves past. That’s exactly what we have,” explained CNN Meteorologist Chad Myers, who has studied Doppler technology. “It’s the Doppler effect that they’re using on this ping or handshake back from the airplane. They know by nanoseconds whether that signal was compressed a little — or expanded — by whether the plane was moving closer or away from 64.5 degrees — which is the latitude of the orbiting satellite.”
Each ping was analyzed for its direction of travel, Myers said. The new calculations, McLaughlin said, underwent a peer review process with space agency experts and contributions by Boeing.
It’s possible to use this analysis to determine more specifically the area where the plane went down, Myers said. “Using trigonometry, engineers are capable of finding angles of flight.”
(Reuters) – Britain’s Inmarsat used a wave phenomenon discovered in the nineteenth century to analyze the seven pings its satellite picked up from Malaysia Airlines Flight MH370 to determine its final destination.
The new findings led Malaysian Prime Minister Najib Razak to conclude on Monday that the Boeing 777, which disappeared more than two weeks ago, crashed thousands of miles away in the southern Indian Ocean, killing all 239 people on board.
The pings, automatically transmitted every hour from the aircraft after the rest of its communications systems had stopped, indicated it continued flying for hours after it disappeared from its flight path from Kuala Lumpur to Beijing.
From the time the signals took to reach the satellite and the angle of elevation, Inmarsat was able to provide two arcs, one north and one south that the aircraft could have taken.
Inmarsat’s scientists then interrogated the faint pings using a technique based on the Doppler effect, which describes how a wave changes frequency relative to the movement of an observer, in this case the satellite, a spokesman said.
Britain’s Air Accidents Investigation Branch was also involved in the analysis.
The Doppler effect is why the sound of a police car siren changes as it approaches and then overtakes an observer.
“We then took the data we had from the aircraft and plotted it against the two tracks, and it came out as following the southern track,” Jonathan Sinnatt, head of corporate communications at Inmarsat, said.
The company then compared its theoretical flight path with data received from Boeing 777s it knew had flown the same route, he said, and it matched exactly.
The findings were passed to another satellite company to check, he said, before being released to investigators on Monday.
Second, learn whatever is needed to solve the problem
After sufficient understanding of the problem and fundamental insight of the phenomenon is in hand, the last part is to apply techniques to maximize what we’ve got.
My original intuition about position sizing — as a TRADER — was gradual adjustment was the most efficient since all-in, all-out leaves too much money on the table. And it turns out, this is absolutely the case when trading only a SINGLE ticker symbol, and the alternative is cash.
Within a portfolio which is almost always ZERO cash, fast RE-allocation away from excess volatility results in more profits AND less drawn-downs, because we can immediately overweight to the asset classes that are moving in the right direction, or at least moving in the wrong direction slower. What seems initially counterintuitive — Fischer Black’s “something that seems ridiculous when you first hear it, but finally seems obvious when you’re finished” — makes us more money and gives us less stress.
So the answer to the member’s question is this:
The change to the weighting system is statistical rather than philosophical and will be documented. We are measuring the same thing, but in a way that responds faster to market conditions compared to the past.
It turns out that making larger, faster changes (as opposed to gradual) to the asset allocation percentages results in slightly better overall performance of the entire portfolio. This seems counterintuitive until you factor in the fact that the asset classes (stocks, bonds, real estate, etc.) generally do not move together under normal market conditions.
If you are really curious, here it is. The old, “smoothed” line and the new, jaggled line have the same profile, because we are measuring the same phenomenon with different methods.
MORE: Flight MH370: how Inmarsat homed in on missing Malaysia Airlines’ plane
MORE: How British satellite company Inmarsat tracked down MH370
MORE: Could the search for the missing Malaysia Airlines plane have gone much faster?