Showing posts with label Normality. Show all posts
Showing posts with label Normality. Show all posts

Sunday, October 15, 2017

15/10/17: Concentration Risk & Beyond: Markets & Winners


An excellent summary of several key concepts in investment worth reading: "So Few Market Winners, So Much Dead Weight" by Barry Ritholtz of Bloomberg View.  Based on an earlier NY Times article that itself profiles new research by Hendrik Bessembinder from Arizona State University, Ritholtz notes that:

  • "Only 4 percent of all publicly traded stocks account for all of the net wealth earned by investors in the stock market since 1926, he has found. A mere 30 stocks account for 30 percent of the net wealth generated by stocks in that long period, and 50 stocks account for 40 percent of the net wealth. Let that sink in a moment: Only one in 25 companies are responsible for all stock market gains. The other 24 of 25 stocks -- that’s 96 percent -- are essentially worthless ballast."
Which brings us to the key concepts related to this observation:
  1. Concentration risk: This an obvious one. In today's markets, returns are exceptionally concentrated within just a handful of stocks. Which puts the argument in favour of diversification through a test. Traditionally, we think of diversification as a long-term protection against risks of markets decline. But it can also be seen as coming at a cost of foregone returns. Think of holding 96 stocks that have zero returns against four stocks that yield high returns, and at the same time weighing these holdings in return-neutral fashion, e.g. by their market capitalization.  
  2. Strategic approaches to capturing growth drivers in your portfolio: There are, as Ritholtz notes, two: exclusivity (active winners picking) and exclusivity (passive market indexing). Which also rounds off to diversification. 
  3. Behavioral drivers matter: Behavioral biases can wreck havoc with both selecting and holding 'winners-geared' portfolios (as noted by Rithholtz's discussion of exclusivity approach). But inclusivity  or indexing is also biases -prone, although Ritholtz does not dig deeper into that. In reality, the two approaches are almost symmetric in behavioral biases impacts. Worse, as proliferation of index-based ETFs marches on, the two approaches to investment are becoming practically indistinguishable. In pursuit of alpha, investors are increasingly being caught in chasing more specialist ETFs (index-based funds), just as they were before caught in a pursuit of more concentrated holdings of individual 'winners' shares.
  4. Statistically, markets are neither homoscedastic nor Gaussian: In most cases, there are deeper layers of statistical meaning to returns than simple "Book Profit" or "Stop-loss" heuristics can support. Which is not just a behavioral constraint, but a more fundamental point about visibility of investment returns. As Ritholtz correctly notes, long-term absolute winners do change. But that change is not gradual, even if time horizons for it can be glacial. 
All of these points is something we cover in our Investment Theory class and Applied Investment and Trading course, and some parts we also touch upon in the Risk and Resilience course. Point 4 relates to what we do, briefly, discuss in Business Statistics class. So it is quite nice to have all of these important issues touched upon in a single article.




Sunday, January 10, 2016

10/1/16: Tsallis Entropy: Do the Market Size and Liquidity Matter?


Updated version of our paper:
Gurdgiev, Constantin and Harte, Gerard, Tsallis Entropy: Do the Market Size and Liquidity Matter? (January 10, 2016), is now available at SSRN: http://ssrn.com/abstract=2507977.


Abstract:      
One of the key assumptions in financial markets analysis is that of normally distributed returns and market efficiency. Both of these assumptions have been extensively challenged in the literature. In the present paper, we examine returns for a number of FTSE 100 and AIM stocks and indices based on maximising the Tsallis entropy. This framework allows us to show how the distributions evolve and scale over time. Classical theory dictates that if markets are efficient then the time variant parameter of the Tsallis distribution should scale with a power equal to 1, or normal diffusion. We find that for the majority of securities and indices examined, the Tsallis time variant parameter is scaled with super diffusion of greater than 1. We further evaluated the fractal dimensions and Hurst exponents and found that a fractal relationship exists between main equity indices and their components.