Category Archives: Applied Investment and Trading

15/1/18: Of Fraud and Whales: Bitcoin Price Manipulation

Recently, I wrote about the potential risks that concentration of Bitcoin in the hands of few holders ('whales') presents and the promising avenue for trading and investment fraud that this phenomena holds (see post here:

Now, some serious evidence that these risks have played out in the past to superficially inflate the price of bitcoins: a popular version here, and technical paper on which this is based here (ungated version)

Key conclusion: "The suspicious trading activity of a single actor caused the massive spike in the USD-BTC exchange rate to rise from around $150 to over $1 000 in late 2013. The fall was even more dramatic and rapid, and it has taken more than three years for Bitcoin to match the rise prompted by fraudulent transactions."

Oops... so much for 'security' of Bitcoin...

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.

16/5/17: Insiders Trading: Concentration and Liquidity Risk Alpha, Anyone?

Disclosed insiders trading has long been used by both passive and active managers as a common screen for value. With varying efficacy and time-unstable returns, the strategy is hardly a convincing factor in terms of identifying specific investment targets, but can be seen as a signal for validation or negation of a previously established and tested strategy.

Much of this corresponds to my personal experience over the years, and is hardly that controversial. However, despite sufficient evidence to the contrary, insiders’ disclosures are still being routinely used for simultaneous asset selection and strategy validation. Which, of course, sets an investor for absorbing the risks inherent in any and all biases present in the insiders’ activities.

In their March 2016 paper, titled “Trading Skill: Evidence from Trades of Corporate Insiders in Their Personal Portfolios”, Ben-David, Itzhak and Birru, Justin and Rossi, Andrea, (NBER Working Paper No. w22115: looked at “trading patterns of corporate insiders in their own personal portfolios” across a large dataset from a retail discount broker. The authors “…show that insiders overweight firms from their own industry. Furthermore, insiders earn substantial abnormal returns only on stocks from their industry, especially obscure stocks (small, low analyst coverage, high volatility).” In other words, insiders returns are not distinguishable from liquidity risk premium, which makes insiders-strategy alpha potentially as dumb as blind ‘long lowest percentile returns’ strategy (which induces extreme bias toward bankruptcy-prone names).

The authors also “… find no evidence that corporate insiders use private information and conclude that insiders have an informational advantage in trading stocks from their own industry over outsiders to the industry.”

Which means that using insiders’ disclosures requires (1) correcting for proximity of insider’s own firm to the specific sub-sector and firm the insider is trading in; (2) using a diversified base of insiders to be tracked; and (3) systemically rebalance the portfolio to avoid concentration bias in the stocks with low liquidity and smaller cap (keep in mind that this applies to both portfolio strategy, and portfolio trading risks).