Quantopian will be remembered as an amazing effort by a small group of motivated and highly capable platform developers to crowd-source alpha in the quantitative trading space. A lot of hard work went into developing a platform where thousands of quant traders could code and test strategies hoping that they will receive an allocation and eventually profit. It did not work and in this article I present the reasons that caused this unexpected failure in my opinion.
The failure of Quantopian left me saddened not because I have used the free service and the software I have developed to search for anomalous behavior in market price action generates code also for that platform. The failure was bad for quantitative trading in general because people do not understand the crucial details but see it as a general problem. …
I am not a mathematician but I find it interesting that while some people are preparing to go to Mars, mathematics, the most basic science and the language used to model the world hasn’t made up its mind yet about the choice of mathematical functions for base 10 and natural logarithms.
“Well, it’s a matter of convention!” some will reply. Obviously, but the problem comes when someone changes the convention rules in the middle of the game.
Specifically, in Compendium of Mathematics, the function for base 10 algorithm is given as log x and for natural as ln x. …
Financial social media growth accelerated with the advent of microblogging and social networking. The biggest boost came in late 2000s with Twitter. By 2012, Twitter had more than 100 million users posting more than 340 million tweets a day. A fraction of those users were professionals in the finance sector, traders and investors.
Before the explosive growth in social media, anyone interested in market-related information had to use search engines to find a relevant website, or follow links in financial magazines and other publications. The flocking to social media initiated competitions for dissemination of information.
I joined Twitter in August 2010 and I immediately noticed the efforts to compete for financial information dissemination. These efforts were based on different methods but the two predominant were link aggregation and network boosting, or combination of the two. Success for these methods depended among other things on how early in the game one started to implement them, their credentials and network size. …
There is an excellent paper by Spyros Makridakis, Evangelos Spiliotis and Vassilios Assimakopoulos you can download here that includes a detailed account of the philosophy and objectives of the M competitions since 1982 and the organization, implementation and results of the latest M5 competition that ended June 30, 2020. The paper also includes many useful references those interested in forecasting will find invaluable.
According to the organizers, there were more than 7,000 participants in M5 competition from 101 countries and more than 88,700 submissions. …
Some have argued mainly in social media that multiplicative processes have higher risks than additive. This is a misunderstanding: risk does not depend on the model of a phenomenon but on its impact on an observable quantity. In fact, some additive processes may have higher impact than multiplicative. After all, the relative impact depends on how things are multiplied versus how they are added. It can be shown that every additive process has an implicit multiplicative nature.
In another article I wrote why most deaths are multiplicative. The reason for that is as follows: the death of a person in reproductive age reduces future population growth in a multiplicative fashion because descendant generation is terminated. Since the average family in USA for example has about 1.9 children, then the effect on population reduction due to a death of a person in reproductive age is shadow-multiplicative according to the model in the referenced article above. …
Steven Pinker insisted in Twitter yesterday that
The two world wars were spikes, not harbingers of a trend. Since end of WWII, death rates have roller-coastered down.
Here is the tweet:
Nassim Nicholas Taleb offered a swift reply:
N. Taleb writes in Statistical Consequences of Fat Tails:
For fat-tailed variables, the mean is almost entirely determined by extremes. If you are uncertain about the tails, then you are uncertain about the mean.
Many traders and investors know the above but apparently a large population of social scientists who are otherwise good and enjoyable people fail to understand it.
One fundamental reason for the lack of understanding is that fat tails are not covered in undergraduate curriculum and even in the first part of graduate courses. …
This article was originally published in Price Action Lab Blog.
I am not saying this, the BEA states that on their website but they went ahead and extrapolated a large fall in GDP for second quarter four months into the future. Below are the details.
The way second quarter GDP was reported has left the impression in the minds of many people not familiar with the math that the economy shrank 32.9% in the second quarter. I saw numerous tweets claiming that by individuals but also by mainstream financial media.
Although the BEA release was technically correct, many focused on the -32.9% …
Disclaimer: I am not an epidemiologist and I consider epidemiology a very complex and specialized field. This brief article is not about the advantages or disadvantages of any policies to combat covid-19 spread and reduce fatalities and their impact on economic growth. Any such analysis is naive and premature at this point and results will probably become clearer after several years.
The covid-19 pandemic has ignited debates between those who propose strict policies to reduce fatalities and those who claim that measures to deal with other more common causes of deaths have not been considered with comparable urgency while the impact on economy is disproportionally severe. The latter group brings up examples of deaths from common diseases, accidents and substance abuse, to name a few causes. …
Dalpha stands for dead alpha. This is the alpha that shows up in some longer-term performance records of some funds or backtests of momentum strategies with data from time periods when there were no computers, telephones, even cars and electricity.
More importantly, dalpha is the hypothetical (backtested) alpha from time periods when strategies based on mathematical models were not even used. No one can know the actual performance of those strategies in the distant past had they actually been used to invest real money.
In recent years there has been a significant change in market dynamics. After 2008. and especially after 2013, central banks play a dominant role in equity markets with the goal of sustainable uptrends. …