Eight Years of Blogging And Tweeting — What Have I learned?
A summary of what I have learned in eight years of blogging about markets and quantitative trading and tweeting.
Time is a peculiar phenomenon and even modern science does not understand how it emerges. Is it present due to thermodynamics, due to a universal clock, or an illusion in a timeless universe? No one knows the answer. From a practical perspective, time is what your clock measures. And my clock has measured eight years of blogging and tweeting. I have learned and unlearned many things during these years. Below are some of them that actually have generated some heuristics.
1. Nassim Nicholas Taleb is the most important contemporary thinker
You may not agree with everything he says, or the way he says it, but at this point not only in finance, probability and statistics, but also in social science, including economics and politics, he is the most important thinker. Some are trying to imitate him but in their desperate effort fall victims of complexity and even become absurd.
Most important things I have learned from N. N. Taleb:
- The intolerance of minorities and how they are shaping policies in democratic societies at the expense and detriment of the silent majority. I had a feeling about this but never been able to realize it before I read Taleb.
- The importance of fat tails and the silliness of naive statistics. For example Ebola virus did not kill millions because we spent a lot of money to prevent that. It is silly to say that Ebola kills less people than cars do. Only imbeciles will say that. Same for terrorism. Governments spend billions to prevent terror attacks. Only an imbecile will claim that more people get killed falling from ladders than from terrorists especially given the fact that the latter could kill millions at a time. Terrorism has tail risk.
- Maybe the most important for finance: You will never realize the expected alpha. If the probability of ruin is not zero, investors at some point will hit uncle point. I had a vague conception of this but Taleb helped to transform it to understanding.
2. Permabear is a serious cognitive bias and maybe a mental condition
Trying to constantly find reasons for a market correction while ignoring all the reasons for the market to rise is a bias but maybe also some form of mental condition if it is a persistent and relentless pursuit. Permabears in U.S. stock markets have generated most of the noise in financial social media since 2010 to no avail. Yet, they have gained recognition and many followers. This is a paradox that needs to be addressed: why do people elevate silliness to a high level?
3. Articles with many quotes to mainstream media articles including blogs are usually noise
These are usually written by people under pressure to write an article. They select a popular article from a mainstream source, including very popular blogs, and then build a story around it, by adding noise. The problem is not that those individuals are silly and just try to maintain their position. It is also the fact that this style of referring to mainstream sources creates an image of credibility. But the reality is the opposite exactly as there is no credibility. Investors and traders may be better off totally ignoring those who cannot produce original work.
4. In most articles by mainstream media and popular blogs there are at least one strawman argument and one red herring
It is hard to come up with original content unless it involves quantitative analysis, i.e., some math. Most authors even if they are capable of quant analysis are afraid of alienating followers and readers of the “lazy” type and resort to “thinking in words”. This inevitably leads to logical fallacies at some point since there is no clear mapping between natural language and strict logic. Those who are not familiar with issues in logic may not know the paradox of material implication. This is the fact that all false propositions imply true proposition, for example, the implication: If 2 = 3, then New York is in USA, is true. In other words, the more one deals about quantitative subjects with words, the higher the probability that fallacies will be generated. These fallacies are common in most articles that resort to “thinking in words”, including market technical analysis, finance and social science in general.
5. The quants in suits have little of value to say
Usually the best ideas do not come from suits since their main objective is to find a supervisory position in sales of a large passive index fund. They will write tons of articles trying to please the moguls of passive investing in exchange for recognition. Of course, when the market hits -55% twice in five years as in 2003 and 2008, they have little to say other than “use diversification”.
6. Most people value your work if it is free
The highest value is the free in the world of social media. Only a few people understand that there is no free and free is actually subsidized from not free. I have decided to stop writing free articles with quantitative analysis because in this way the subscribers to my blog finance the free quant content for those who are not willing to pay for it. There is also another problem: many people just like to read a lot of articles and this is only possible if they are free. However, the net result of reading many articles is an increase in the level of noise and indecision. Only a few people can filter out the noise.
7. Traders, investors but also social science researchers do not understand probability and expectation
Most traders and investors do not understand trading expectation. In other words, they do not understand trading and investing. This is evident from all those chartists pointing to charts with trendlines and indicators without any reference to probability and expectation. Expectation is not the average of investment or trading results but the limit of the average under sufficient samples, which rarely exist in many cases and especially in finance and social science where there are power laws and fat tails. I try to clarify this in my book. As a result of lack of sufficient samples in most cases, the best you can do is rule out investing and trading schemes at the risk of missed discoveries (Type II error.)
Those are among some things I have learned in the last years from blogging and tweeting. There is more of less importance.
If you have any questions or comments, happy to connect on Twitter:@mikeharrisNY
This article was originally published in Price Action Lab Blog
About the author: Michael Harris is a trader and best selling author. He is also the developer of the first commercial software for identifying parameter-less patterns in price action 17 years ago. In the last seven years he has worked on the development of DLPAL, a software program that can be used to identify short-term anomalies in market data for use with fixed and machine learning models. Click here for more.