Stock markets continue to lose share to private exchanges

Institutional investors with large blocks of shares to sell don’t just open up an account at E*Trade and dump them into the market.  Doing so tips their hands and astute short sellers can hop a ride on stocks being disposed, making money along the way and reducing profits for the institutional seller.

Conversely, if an institution wants to accumulate shares in a relatively thinly traded stock, they can’t go out to a retail stock broker and say, “Hey buddy, get me 10 million shares of that hot new small cap tech stock.”  Doing so would cause the price to rise just by announcing such intentions.

How Institutional Investors Trade

To handle insitutional volumes of stock trading, traders do the following

  1. VWAP: Some traders will program trading software to purchase a maximum % of volume on given days (called VWAP or Volume Weighted Average Price).
  2. Smaller trades at various brokers: Sometimes traders will parcel out trades to multiple brokers to mask the fact that a large number of shares are being traded by one institution.
  3. Dark pools: And sometimes, when there is really an impetus to sell/buy a large chunk of stock, traders will go to their brokers and ask them to cross a block of shares on the low — by not going too public with the info.  Execution speed is paramount here and the action is as much in the data centers in New Jersey as it is on Wall Street.  These dark pools now account for 1 in 3 shares of stocks traded according to the Wall Street Journal.

In ‘Dark Pools’ Pick up Stock Trading Share, the WSJ takes aim at the rise in these dark pools.

The rise of so-called dark pools and other off-exchange strategies aimed at large banks and institutional traders comes as regulators on both sides of the Atlantic grapple with balancing the market efficiencies the alternative venues say they generate with the impact on individual investors.

Private venues are seen as a more efficient way for transacting large chunks of shares, but critics worry that if so much trading is done privately, publicly available prices set by exchanges will become less accurate. Dark pools are electronic platforms designed for institutions to carry out major stock trades anonymously.

Varying forces

Having 30% of trading beyond the veil of regulators and common investors creates a tiered trading system, something inherently seen as unfair and anti-competitive.  The emergence of internal stock trading platforms like powerhouse BlackRock recently announced are not new, they’re just taking on more volume and therefore, importance.  In general, we’re witnessing the rise of the machines and algorithmic trading which is the purest combination of technology and investing.  The stock exchanges like NASDAQ OMX ($NDAQ) and NYSE Euronext ($NYX) are pleading and crying to regulators to help right this wrong.

Beyond the histrionics, the stock exchanges are also developing technology to help lure institutions back to their platforms.  The NASDAQ OMX CEO was on Forbes recently touting the work they’ve done on PSX, an exchange that doesn’t give preference only to speed but also to size of trades.  This platform has already demonstrated its ability to bring many of the institutional trades happening offline, back online.

As Felix Salmon said in Wired, “In the wake of the flash crash, Mary Schapiro, chair of the Securities and Exchange Commission, publicly mused that humans may need to wrest some control back from the machines.”

‘Automated trading systems will follow their coded logic regardless of outcome while human involvement likely would have prevented these orders from executing at absurd prices.’

Giving up control to the computers is not really what’s at stake here.  Computer trading just reflects the rules-based logic entered by the humans who program the algos.  Rather, it’s the essential bifurcation of the markets: one for pros and one for the rest of us.  It’s the unleveling of the playing field at stake here that should have everyone concerned.


Dark Pools Pick up Stock Trading Share (WSJ)

Algorithms take control of Wall Street (Wired)

BlackRock to launch trading platform (

photo courtesy of tenaciousme

How investors make investment decisions

I had a really interesting conversation today with a really smart entrepreneur (more on him in a later post) and it just got me thinking how retail investors make investment decisions. I guess if you had to break down the process of decision making in pulling the buy/sell trigger, we make our investment decisions in the following ways:

  • Simple screening: Like Yahoo Finance’s stock screener, these tools allow us to search for both basic and more advanced parameters/criteria to filter out candidates completely from the investment universe (in fact, they’re banished, never to be heard from again, until we conduct another screen). Some quantitative investors rely solely upon the output of these filters (see Joel Greenblatt’s Magic Formula). Charting falls under this umbrella, too.
    • Pros: Allows investors to sort through mounds of data to extract value
    • Cons: Once screened out, stocks that don’t fit the criteria are forgotten about and stocks that make the grade are never compared to these by-products.
  • Lateral recommendations: Like Amazon’s “people who bought X also bought Y”, here investors are using research tools like Morningstar to pivot around a particular investment of choice to broaden their research to find something similar, but maybe better performing/less risky. I see this a lot in the interpersonal interaction on message boards where one know-it-all tells everyone “If you like this stock, why don’t you check out this stock.” The point here is that there is a frame of reference and new research takes off from there.
    • Pros: Unlike buying a new phone, where a customer can describe accurately what he wants (“I want the iPhone”), many investors lack the language to describe what it is they are actually seeking in an investment and what tradeoffs they may incur in making such a decision (risk/return). Lateral recs allow investors to make decisions by saying something to the effect: I want something like that.
    • Cons: Lateral recommendations are frequently compiled by using website activity and purchasing data and then working backwards to create a personalized rec. The truth is, though, there is nothing personal about this suggestion. It’s just data. In fact, it can stray pretty far away from what a user really wants.
  • Piggyback investing: Investors buy things based on weighted opinions from others. These recommendations can come from an article in Forbes to actually hard-core piggybacking hedge fund picks like the guys at AlphaClone are helping investors do.
    • Pros: Taken as individual suggestions, many of these picks do fine in terms of future performance. In fact, building an entire portfolio of ideas like this and creating a portfolio of them grounded in historical performance can act as an investment strategy that’s as good as any out there. This takes much of the decision making out of the hands of the investor — he merely needs to decide which guru to follow.
    • Cons: Piggyback output lacks any personalized context. Because John Paulson is buying gold doesn’t mean that Ida and Murry should be buying it for their retirement portfolios.

I’m sure there are many other ways I’ve missed but these seem to be the way most investors I’ve been in contact make decisions to invest or pass on opportunities. In fact, decisions aren’t made via only one route like I’ve listed above — it’s probably a hodgepodge of ways. Each one of these directions has problems associated with them and when technology is used to help solve these problems, new issues arise that have to do with the structure of the software thrown at the problem.

Please join me in asking WHY YAHOO FINANCE HASN”T REALLY CHANGED IN 10 YEARS?! C’mon — with all the smart people we’ve got in the industry, investors deserve more.

Testosterone, EMH, and Sharpe Ratios

Reuters is out with an article entitled Hormones, incentive, experience make best traders.  The article reviewed a recent study entitled A Note on Trader Sharpe Ratio by John Coates, a Cambridge research fellow in neuroscience who previously worked on Wall Street.

The study analyzed the effects of hormones and experience on the trading performance of 53 English traders averaging 29 years old.  These prop traders are incentivized not with monetary bonuses, but with year-end stock gifts based on their performance.  Importantly, the study uses the Sharpe Ratio as a measure of risk-adjusted performance over time.

Couple of takeaways from the article:

  • Another stab at EMH: By looking at the effects of work experience Sharpe and Experienceand performance, as measured by the Sharpe Ratio, the study marks another reference point at the weakness of the Efficient Market Hypothesis to account for consistent, market-beating gains.  The researchers compared traders’ Sharpe ratios with the Sharpe ratio of the DAX German stock market index and found that more experienced traders scored significantly higher — an average of 1.02 compared with the Dax’s average 0.53.
  • Practice makes profits: The study found that Sharpe Ratios actually went up over time which signifies that traders were getting better at managing risk and squeezing out returns.  According to the study: “Our data thus suggest that Sharpe Ratios increase over time because traders learn to make more money per unit of risk they take.”
  • Hormones and trading: Whether self-selected or improved via biology,   testosterone plays an important part in the work of financial traders, with evidence that male traders will make much more aggressive trades on days when their testosterone is high.  According to the study, successful traders “are more profitable and survive longer in the markets, as was previously reported, but we now find the effect is largely mediated through a higher tolerance for risk”.

In the words of its co-author, this study demonstrates that “in trading, as in sports, biology needs the guiding hand of experience.”  It’s an interesting look at the interaction of experience, biology, and investment outperformance.