``Imitation in markets often gets a bad name, but it's important to recognize how vital imitation is in our day-to-day lives. We imitate each other all the time, primarily because other people often have better information than we do. It becomes a problem in markets when everyone starts to imitate one another, and they forget about what they're doing in the first place. It leads to excesses.”- Michael Mauboussin
Rather focusing on tangential issues, I’d like to impart to you some pointers on how to improve your portfolio returns from Michael Mauboussin, chief investment strategist at Legg Mason, an $850 billion highly acclaimed global asset management company in the US and author of the new book More Than You Know. In an interview with David Meier of Fool.com, Mr. Mauboussin cites three critical factors to concentrate on (emphasis mine)...
``The first thing, I would say, is to focus on process versus outcome. So process means understanding the research process, trying to understand specifically if expectations in the prevailing stock are too high or too low, and recognizing that even if you have a good process, periodically things will not turn out as you had hoped. [However,] a good, quality process will lead to good long-term results.
``The second thing I would say is recognize the role of time. In the very short term, it is almost impossible to discern between luck and skill, because there is just simply too much noise in the system. But over the long haul, certainly a good process will prevail. So [recognize] that it is important to be patient, and often, as you said before, to do absolutely nothing. This is not a business where more activity leads to better results. I think, in general, people perceive that. And certainly in America, there is an idea that doing more work is better. That simply does not hold for the stock market.
``The third thing is to recognize that markets are fundamentally probabilistic, and because they are probabilistic, it requires a certain psychological approach and psychological mindset. By the way, in Robert Rubin's wonderful book, In an Uncertain World, he talks a lot about the probabilistic mindset. He says many people believe they are [taking such an approach,] but very few people have the disposition or time or training to do it properly. So when you have a probabilistic mindset, you are constantly thinking about different alternatives. You are constantly taking information and updating your probabilities and outcome assessments and recognizing, again, that there are many psychological biases that can come into your doing that properly.
Of course you don’t normally obtain such judicious advice from the investing business, such as conventional brokers or some domestic financial institutions alike, as they are hardwired to think and impress upon their clients that returns from the markets entail frequent churning activities. As I’ve said before, it’s mainly because of their revenue models; in contrast to the investing profession, like Mr. Mauboussin of Legg Mason, which focuses on generating “Alphas” or above benchmark returns.
And neither would you hear from investing business community the probabilistic approach towards investing. Probability of outcome is the primer for risk management and essentially differentiates gambling from calculated risk taking ventures. By determining the odds, or the ratio of favorable outcomes to unfavorable outcomes, one could act to reduce or limit risk exposure and optimize on returns.
In my case, the investing-research process, as you probably all figured out, has been largely an amalgam of the Big Picture (particularly capital flows dynamics and monetary and cross market analysis), behavioral finance/economics (understanding the thought process to limit heuristics and mental shortcuts or biases) and a combo of sentiment and the technical picture with occasional dabbles on valuations. In short, I try to be as methodical and systematic as what Mr. Mauboussin suggests and recommend that you do as well.
2 comments:
Dear Mr. Te,
I always wondered, especially in creating Decision Trees, where the values of the probability of success or falure of such an option, were taken from? Is it from heuristics, simulation, or deductive reasoning, or past performance? Probability may be a God, but then, if they come from human guesses to the probabilistic amounts, are open to bias. Simulation is as only good as the model, and, as we are a long way off from modelling behavioral economics on herd mentality. Past performance, is it accurate enough to predict future performance? Maybe, but don't the markets change with the changing dynamics of the situation? As more money goes to the top 1%, might not they just collude and cause market imperfections? With the success of Warren Buffett, wont that make us all Value investors and reduce the penchant of the investors for Growth stocks ?
Behavioral finance or economics mainly deal with the irrationality of investors, particularly the social cognitive and emotional biases, in contrast to the Efficient Market Theory (EMT) which assumes that the current market reflects all information. While the herd mentality or the comfort of the crowds is a subset of one such bias under investor irrationality.
Decision trees on the other hand are predictive models that are used for calculating or measuring conditional probabilities. In other words, decision trees can be utilized as an instrument or tool to construct multi-dimensional scenarios arising from different thought processes (logical, deductive, inferential, or emotional/cognitive biases). And by assigning the odds to its variegated scenarios one can expect to act accordingly. Of course, simulation models are only as good as those who construct them.
Think the Long Term Capital Management (LTCM) fiasco in 1998. Despite the complex mathematical models developed and utilized by Nobel Laureates Myron Scholes and Robert Merton, they failed to anticipate the Russian government default on its debts which almost rippled to a systemic collapse following their $4.6 billion loss. According to Wikepidia.org, ``Thus the primary lesson of 1998 and the collapse of LTCM for Value at Risk (VaR) users is not a liquidity one, but more fundamentally that the underlying covariance matrix used in VaR analysis is not static but changes over time.” In short, a rigid model.
But according to Nicolas Taleb, ``Somehow they thought they could SCIENTIFICALLY “measure” their risks. They made absolutely no allowance in the LTCM episode for the possibility of their NOT(underscore-mine) understanding markets and their methods being wrong.” Hence, to Mr. Taleb, a case of overconfidence.
One must not forget that market dynamics are fluid enough to change structurally (financial innovation-derivatives, structured products, deepened capital markets, etc...), but investor psychology backed by human instincts (fear, greed and hope) are fundamentally what drives or underpins the cycles, be it economic, business or the financial markets.
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