Many of the metrics used in previous decades have little meaning in today's complex marketplace, writes Brian R. Brown in Chasing the Same Signals: How black-box trading influences stock markets from Wall Street to Shanghai ( www.wiley.com). Examples of outdated metrics that Brown mentions are market share, and the percentage of total market turnover by a dealer-broker.
“Market share used to be a proxy for the strength of a broker's customer business, where a larger market share was closely aligned with the broker's commission revenues. But, today that relationship is weak.”
A case in point the author cites is Lehman Brothers, which climbed the league tables of market share in the US largely due to their internal growth of proprietary trading, in the years leading to the firm's failure. “Today, all of the global investment banks generate significant turnover from proprietary trading, derivatives activities and cross-asset strategies. The public can infer little about the health of their business from market share figures alone.”
To those who wonder what the black-box is all about, here is a helpful intro from Wikipedia: “In electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on aspects of the order such as the timing, price, or quantity of the order, or in many cases initiating the order without human intervention.”
Firms that employ a black-box model (a quantitative investment strategy in which the decisions are defined by mathematical formulas) are often referred to as ‘quants' because they hire mathematicians, physicists, and computer scientists, rather than the traditional MBAs and fundamental research analysts, the author informs. “They typically engineer their models to target small price movements, rather than search for long-term investment opportunities. Their holding periods might range from weeks to hours to minutes, rather than 12-18 months like a mutual fund.”
The most successful black-box firms all have one thing in common: state-of-the-art execution platforms, Brown notes. Their technology allows them to participate in market rallies, to hedge risk in real time, and to capitalise on short-term price discrepancies, he adds. These firms apply many sciences to understand the stock markets; ‘fuzzy logic, expert systems, neural networks, and pattern recognition to name only a few.'
A chapter titled ‘the game of high frequency' cautions investors about ‘predatory algorithms,' a type of controversial tactic used by black-box firms for influencing how other investors trade.
Predatory algos try to ‘game' other investors into chasing the market, explains Brown.
“The basic tactic is for a high-frequency algorithm to improve the prevailing best bid (or offer) repeatedly in hopes other investors will follow. These algorithms place an order at the best bid price, and if more shares join the queue, they then place an additional order at the next best bid price, hoping more will join. Effectively, they are moving the bid price to a higher price range, without making a single transaction.”
Lesson from basketball game
In an environment where the market's daily fluctuations influence our views, and the market's closing price remains the staple of financial commentators' interpretations of the health of the economy, the author's guidance drawn from history can be helpful. That the long-term direction of the financial markets will ultimately go to where the economy is headed.
The day-to-day market movements are largely influenced by interactions between investment strategies which are competing for the same margins, and so a real-time snapshot of a stock's value is not a good proxy for how the fundamental investment community views the health of the stock, he observes.
“Just as a basketball game cannot be predicted by the score of the first quarter, the long-term success of a corporation is not a function of its day-to-day gyrations in the market.”
Reminding that the holders and traders of stock markets are completely different food groups, Brown's recommendation to regulators is that they discuss a framework to audit the most active traders in addition to the most significant holders.D. Murali