Are you interested in the truth?

I mean the truth as it applies to financial markets.
Albert Einstein said: “The grand aim of all science is to cover the greatest number of
empirical facts by logical deduction from the smallest number of hypothesis or axioms”.
What the hell does that mean?

There is a famous quote from a famous movie by a famous actor whose name escapes me and it goes as follows: “It’s what you don’t know you don’t know that gets you in the end”. It is important to remember that markets are irregular and stochastic in nature. Like all natural phenomenon, they are rough-edged. For too long the economic modeling assuming a standardized probability distribution has provided mis-guided assumptions and often dangerous conclusions. Investors need to educate themselves on the inherent weaknesses within traditional financial theory. We begin with truth number 1. Unrecoverable and even excessive losses are an unnecessary tragedy. Let us look a little closer at how these standard theories determine the odds of an outlier event occurring. The experts put the following odds of some well known market corrections occurring:

  • 1997 Dow Industrial Average sell off
    The Dow fell by 7.7% in a single day. The assumed probability according to these sophisticated models: 1 in 50 billion event
  • 1998 Russian debt crisis: 1 in 20,000,000 event
    To place some context around these odds, if you traded daily for nearly 100,000 years you would not expect this probability to occur
  • In the summer of 2002, the Dow experienced a one in four trillion event

We could go on and on here. “Black Monday” is a name given to an infamous date [October 19th, 1987] in U.S financial markets history when the Dow suddenly and sharply dropped by over 22% in a single day. The statisticians assumed this event so rare that its probability was almost outside the scale of nature itself. However, it happened and more importantly investors suffered unrecoverable losses. The above relatively short list forces us to pose the obvious question; how do we model and capture these outlier events?

The first lesson to take away here is that the probability of financial ruin is grossly under-estimated if your portfolio is heavily exposed to risk assets. Our aim therefore should be to source a more robust model of risk. Unfortunately this is easier to say than practically apply and one of the main reasons why financial markets are such wonderfully exciting and intriguing environments.

A fairly generous dose of humility is required here to enhance self-preservation. Too often practitioners focus on whether prices are predictable and less on whether “market risk” is. While price patterns are all but impossible to predict, risk is less so. To become better investors, we first must improve our understanding of financial markets. The casual alignment of price action outcome and causality by the mainstream media masks the truth. In fact the underlying causes are often unknown factors. Fundamental & Technical analysis are the pillars of active asset allocation. The famous mathematician, Benoit Mandelbrot, liken technical analysis to financial astrology. The fundamental principal under-pinning modern financial theory infers that although prices are not predictable, their movements can be captured by the laws of probability and mathematical chance. This chance is characterized as scaled deviations or variances from the mean number or average. The industry is programmed to accept that 98% of potential probabilities fall within a three standard deviations from some random average or mean number. We may be able to apply a standard normal probability distribution to ordinary phenomenon including male heights at a seminar or the national 2019 leaving certificate results. However, its effectiveness in capturing the probability of more extreme market events that are influenced by unpredictable human behavior remains much more challenging.

The truth is that we cannot model future risk events without first acknowledging that our approach will always rely on priors [historical data] and also involve a whole lot of conjecture – no matter how clever we think our model is.

The efficient markets hypothesis leant heavily on the works of the French mathematician Louis Bachelier, most notably the random walk hypothesis. The latter infers that price movement has equal probability. Hence, equal probability as translated or developed by Fama’s EMH implies that price change today is independent from the price yesterday. The assumptions underpinning the theory are problematic owing to two very obvious flaws. Firstly, price changes/deviations are not statistically independent from each other and most definitely not normally distributed. Price has memory. Everyday normal phenomena including volatility clustering, price trends and support & resistance support this argument.


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