Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff
Authors: Douglas Rushkoff
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growth appears to have reached its limits. That’s a lot easier for a new digital company to do from scratch than it is for a major corporation to pivot toward after a hundred years of pursuing growth.
It’s time for the Fortune 500 company to act like a river reed instead of a mighty oak. But how?
Chapter Three
Chapter Four
INVESTING WITHOUT EXITING
“Our favorite holding period is forever.”
—Warren Buffett
DO ALGORITHMS DREAM OF DIGITAL DERIVATIVES?
Professional traders face similar challenges in the digitized environment. Like those in other disrupted industries, many finance workers have been replaced by networks and computers.
Human stockbrokers, in addition to providing access to markets, used to be responsible for giving clients the best information on a stock or sector, as well as advice and numbers on allocations and future earnings. Trades then went through the specialists—designated market makers—who owned a pool of a particular stock, which they used to fill orders whenthere was no ready counterparty. They were required to serve as buyers of last resort, preventing a stock from crashing unnecessarily due to a temporary lack of liquidity. Yes, they made money doing this, exploiting their privileged position on the trading floor to buy low and sell high. But their activity reduced volatility and kept markets more consistently liquid and orderly.
Thanks to the net, customers can now access markets—or at least virtual trading desks—directly. The digitized marketplace doesn’t require brokers or specialists to function. Trades are executed via “straight through” processes that connect buyers and sellers from around the world. Real-time quotes from electronic trading desks make pricing more transparent; the decentralized nature of the networked exchanges anonymizes deal making and reduces favoritism; and digital record keeping increases accountability and archives any nefarious practices for future audit and prosecution. With multiple and competing exchanges instead of just one specialist, traders also get tighter spreads between the ask and bid, which means less money is leaked out of each trade.
But the elimination of human specialists also eliminates the buyers of last resort and the dampening of volatility they provided. Into the power vacuum and hungry for this increased volatility come the high-frequency traders (HFTs) and algorithms—computer programs that look to profit exclusively through the exploitation of temporary, even microsecond-long, imbalances in trading.
Sure, in many cases, having an algorithm that scans for a lack of volume of a particular security on an exchange somewhere is a good thing. The high-frequency trading algorithm may charge a few pennies extra for having found the stock someone wanted, but the specialist charged a few pennies for his services, too. The problem is that, unlike the specialists who were obligated to reduce volatility in the shares they serviced, HFTs
like
volatility. For instance, one typical HFT strategy is to provide liquidity in a particular stock until the market shows signs of instability. The algorithm then suddenly withdraws all its bids and offers, leading to an immediate dearth of demand and a precipitous price drop.The smart algorithm, knowing it can make this happen, has already bet against the stock with derivative options. When the other algorithms realize what’s happening, they freeze up, too, leading to a “flash crash.” The stock goes down, but for no real-world reason. It’s just collateral damage from the game itself. 31
Another common algorithm strategy is to flood the quote and order systems with fake trades—orders of intent but not full executions—to convince human traders (or other algorithms) that the market is moving in a particular direction. More than 90 percent of all quotes are fake gestures of this sort, generated by computers. 32
Algorithms run on ultrafast computers connected as physically close to
It’s time for the Fortune 500 company to act like a river reed instead of a mighty oak. But how?
Chapter Three
Chapter Four
INVESTING WITHOUT EXITING
“Our favorite holding period is forever.”
—Warren Buffett
DO ALGORITHMS DREAM OF DIGITAL DERIVATIVES?
Professional traders face similar challenges in the digitized environment. Like those in other disrupted industries, many finance workers have been replaced by networks and computers.
Human stockbrokers, in addition to providing access to markets, used to be responsible for giving clients the best information on a stock or sector, as well as advice and numbers on allocations and future earnings. Trades then went through the specialists—designated market makers—who owned a pool of a particular stock, which they used to fill orders whenthere was no ready counterparty. They were required to serve as buyers of last resort, preventing a stock from crashing unnecessarily due to a temporary lack of liquidity. Yes, they made money doing this, exploiting their privileged position on the trading floor to buy low and sell high. But their activity reduced volatility and kept markets more consistently liquid and orderly.
Thanks to the net, customers can now access markets—or at least virtual trading desks—directly. The digitized marketplace doesn’t require brokers or specialists to function. Trades are executed via “straight through” processes that connect buyers and sellers from around the world. Real-time quotes from electronic trading desks make pricing more transparent; the decentralized nature of the networked exchanges anonymizes deal making and reduces favoritism; and digital record keeping increases accountability and archives any nefarious practices for future audit and prosecution. With multiple and competing exchanges instead of just one specialist, traders also get tighter spreads between the ask and bid, which means less money is leaked out of each trade.
But the elimination of human specialists also eliminates the buyers of last resort and the dampening of volatility they provided. Into the power vacuum and hungry for this increased volatility come the high-frequency traders (HFTs) and algorithms—computer programs that look to profit exclusively through the exploitation of temporary, even microsecond-long, imbalances in trading.
Sure, in many cases, having an algorithm that scans for a lack of volume of a particular security on an exchange somewhere is a good thing. The high-frequency trading algorithm may charge a few pennies extra for having found the stock someone wanted, but the specialist charged a few pennies for his services, too. The problem is that, unlike the specialists who were obligated to reduce volatility in the shares they serviced, HFTs
like
volatility. For instance, one typical HFT strategy is to provide liquidity in a particular stock until the market shows signs of instability. The algorithm then suddenly withdraws all its bids and offers, leading to an immediate dearth of demand and a precipitous price drop.The smart algorithm, knowing it can make this happen, has already bet against the stock with derivative options. When the other algorithms realize what’s happening, they freeze up, too, leading to a “flash crash.” The stock goes down, but for no real-world reason. It’s just collateral damage from the game itself. 31
Another common algorithm strategy is to flood the quote and order systems with fake trades—orders of intent but not full executions—to convince human traders (or other algorithms) that the market is moving in a particular direction. More than 90 percent of all quotes are fake gestures of this sort, generated by computers. 32
Algorithms run on ultrafast computers connected as physically close to
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