Not sure I can agree with that... I don't know whether there is an easy way to measure that kind of thing. But I would hazard a guess that the majority of papers that are published in the top tier of peer-reviewed journals are not written by graduate students, but rather by established researchers or professionals who hold a terminal degree and are no longer students. It is also very common for a paper to have multiple authors. Often one of the authors is a professor and the others are students of that professor.
I don't sell anything and I'm not in the information business at all. But I will not refuse a mutually beneficial partnership. You can create a new private thread here: https://www.elitetrader.com/et/conversations/add I was not planning to post trading signals (at least not here). At the same time my analytics could help you to make thoughtful decisions (in order to earn or at least not to loose). In return, I would like to receive donations to continue this project/research.
Trying to find cause and effect for every human decision that moves the market is a fruitless exercise and surely a path to madness. I like the simple pink noise model of price, k/pow(f,alpha), where you set k and alpha equal to 1 for simplicity sake. This gives a power spectral density of 6dB increase in power per octave decrease in frequency, or 2:1 growth rate versus 1.618:1 for a fractal data series. So price is pink noise, now what to do about it? Whiten the data to get a flat PSD, then do your favorite technical analysis on that data series.
If you model doesn't ties in to basic fundamentals it's likely to go off the rails. Looks at climate "science", a bunch of crazy models of the past several decades that have consistently been wrong. If you're going to try to predict the price of crude oil, your model should include data on inventories around the word. If it does not, or is insensitive to that variable, then I would argue that it is obviously wrong. Even if back testings shows 100% agreement with previous price, if would say that it's doomed to be wrong, it just had enough degrees of freedom in the fit of the models parameters that you could force it to match a data set.
Yes, but in financial markets you get paid to take risks. The difference between reality and the results from your model are where the risks lie. Markets will never be modeled perfectly because the complexity is too great, just as in weather. What happens when central bank models of the economy are wrong? Flucuation in the economy of course as market feedback loops try to correct the error. Unfortunately, some people win and some lose, that is the nature of life. Always has been and always will be. Marxism isn't going to solve this dilema, nor is Reaganomics.
Very we’ll said. I find this the hardest part of fractal analysis @Collapse this is a topic I’m interested in discussing. What criteria are you using to draw your fractals?