PDF Summary:Flash Boys, by Michael Lewis

In Flash Boys, best-selling author Michael Lewis explores a poorly-understood threat to your investments, whether you’re investing in a retirement account or day trading: high-frequency trading, electronic trading that uses automated computer algorithms to very quickly buy and sell large quantities of stocks, leading to big financial pay-offs—often at the expense of regular investors who can’t trade in microseconds as the algorithms can. Flash Boys unpacks high-frequency trading—and Wall Street’s greedy response to it—and details one group of traders’ efforts to protect investors from its tactics.

In this guide, we’ll examine the role HFT plays in the investment world. Along the way, we’ll provide alternative perspectives on why some experts view high-frequency trading as a positive development, and we’ll provide updated information about trading research and regulations.

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Tactic 4: Using New Regulation

As the stock market changed due to technology, stock market regulations changed with it. In 2007, the Securities and Exchange Commission (SEC) enacted a new rule that would have lasting effects on the market—the Regulation National Market System (or Reg NMS). This rule stated that traders must buy stock at the lowest market price for investors. So if stock for a certain company ranged from $10.00 and $10.08, the trader is legally obligated to buy it at whichever exchange sells it for $10.00. Reg NMS was intended to ease investors’ worries that traders weren’t getting them the best deals.

However, the new regulation ended up benefitting HF traders more than ordinary investors, because it allowed them to more quickly identify price changes at any given time.

(Shortform note: Like Lewis, other experts are concerned about the SEC’s regulations and their impact on markets. Some experts urge the SEC to reevaluate and update Reg NMS, while others recommend a complete overhaul of their rules. While Reg NMS may have positive effects on fair pricing and market competition, critics note that the regulations have led to further market fragmentation, difficulty executing orders quickly, and pricing inefficiency, showing that financial experts can’t agree on the effects of the regulations. Some experts have even proposed scrapping the idea of government oversight altogether, recommending self-regulation as an alternative to SEC regulation.)

Tactic 5: Electronic Front-Running

Another consequence of Reg NMS was to allow HF traders to front run trades—to use advance knowledge of an investor’s intentions for profit. Under Reg NMS, traders must buy stock at the lowest price, but sometimes, the exchange with the lowest price won’t have all the shares the investor wants to buy. Thus, a broker might have to go to multiple exchanges to fulfill an investor’s entire order. When she does this, her order tips off HF traders of her intentions to buy or sell a certain stock. The HF traders can then race to other exchanges to buy the stock she wants, only to sell it to her at a higher price.

(Shortform note: Other experts agree with Lewis’s argument that traders have found ways to exploit well-intentioned regulation, such as Reg NMS. Some experts even recommend replacing Reg NMS with entirely new—albeit more relaxed—regulation. One suggestion is to outline several factors—not just price—that a broker must consider when executing customer trade orders, such as the price of the stock or how secure the market is at the time of purchase.)

Tactic 6: Using Order Types

Lewis describes order types as sets of instructions determining how traders place stock orders. For instance, a trader can use an order type to stipulate that her order goes through only if she’ll receive a rebate or only if the price doesn’t exceed a certain number. As electronic trading became more popular, the number of order types increased from three to over 150 and became so complex that very few people could understand them—typically, only people whose job it specifically was to understand them.

Lewis argues that most of these order types were actually unnecessary and gave HF traders more opportunities to exploit investors by tapping into their trading strategies and intentions. HF traders could examine order types to know what conditions investors were looking out for and then use that information to get to trades first, increasing their own profit at the expense of other investors. HF traders also used these order types to push their orders ahead of regular investors, so that, for example, if an ordinary investor tried to buy 100 shares of a certain stock, an HF trader's standing order could be activated to scoop up those shares first.

(Shortform note: Another way to think of HFT and order type baiting is through the lens of game theory. In The Undercover Economist, Tim Hartford explains that game theory is a discipline adjacent to economics and mathematics, where a “game” is defined as an activity in which predicting another’s actions affects your own actions. By analyzing and predicting the actions of investors through order type baiting, HF traders—algorithmic, rational decision-makers—use this data to maximize their payoffs.)

Effects of HFT

These high-frequency trading tactics capitalized on the precedents set by electronic trading. Lewis explains that anyone involved in the stock market—brokers, investors, hedge fund managers, and so on—felt the effects of HFT, which were exacerbated by two factors: Wall Street’s greed and the “flash crash.”

Wall Street’s Greed

Lewis argues that the Wall Street firms and banks, whose proper role is to help investors, adopted HFT techniques despite knowing that HFT made trading unfair for average investors. They did this because they were making so much money. One firm made over a billion dollars in one year from its HFT department alone.

(Shortform note: Lewis is no stranger to exposing Wall Street’s greed: In The Big Short, he argues that greed and short-sightedness were the prime drivers of the financial crisis. All major stakeholders in the ecosystem were fueled by the desire for profit, which made it easy to overlook systemic problems.)

Flash Crash

After years of unregulated high-frequency trading, Wall Street experienced a “flash crash” in 2010. Lewis defines a flash crash as a stock market crash that happens quickly—stock prices drop dramatically before recovering within a few minutes. But during the 2010 flash crash, 20,000 stocks traded at dramatically different stock values.

According to Lewis, no one could pinpoint a reason behind the flash crash. Katsuyama couldn’t access the data that could prove HFT caused the crash, since this information wasn’t public—it belonged to the HFT firms and exchanges—but he knew that the flash crash was a result of HFT.

The flash crash marked a turning point in the investing world. Investors were interested in what was going on in the market—and why it had dropped by 600 points so suddenly. People called Katsuyama and his team to set up informational meetings where he educated investors and executives about HFT.

Who Is Responsible for the Flash Crash?

While Lewis blames the trillion-dollar flash crash on high-frequency traders, the US courts found another reason: a self-taught stock market trader named Navinder Singh Sarao. He was convicted of “spoofing”—creating a huge number of fake buy or sell orders. High-frequency trading firms program their algorithms to get out of the market when it gets too volatile, and when Sarao’s spoofing operation created artificial volatility, all the algorithms pulled out at once, resulting in a crash. He capitalized on the changing prices, earning about $900,000 from the market manipulation.

While the Commodity Futures Trading Commission (CFTC) found Sarao responsible for the flash crash, some experts contend that blaming Sarao doesn’t get to the heart of the issue—that both human and algorithmic traders still have the ability to manipulate markets despite existing regulations.

IEX: A Fair Stock Exchange

After discovering these tactics, Katsuyama and his team decided to fight HFT rather than mimic its tactics to make money, so they quit their jobs at RBC to create their own stock exchange. They called it the Investors Exchange, or IEX. The purpose of IEX was to make investing fair by preventing the predatory behavior of high-frequency traders.

When creating IEX, Katsuyama and his team knew they couldn’t ban HFT on their exchange. But they could take preventative measures against the tactics HF traders used to exploit investors. Lewis discusses the team’s solutions addressing each of the HFT tactics:

(Shortform note: While Lewis praises IEX for fighting HFT, some critics note that in doing so, he perpetuates the idea that stock exchanges will correct themselves without regulation. Critics believe that regulatory bodies like the SEC—not exchanges or traders—should be responsible for keeping up with changes in stock trading, especially for something as influential as HFT, to better protect investors. Experts have suggested a small tax on every trade, called the Tobin Tax, that would have little effect on regular investors but would deter HF traders by further reducing the margins on their already-small profits per trade.)

Solution 1: Counteract Speed Advantages

IEX increased the amount of time it would take for any investor—including HF traders—to access the exchange’s market. To do this, they moved their "point of presence,” or a point where traders connect to an exchange, 10 miles away from their matching engine in New Jersey and coiled the cable between the two locations to lengthen the time it took to get a signal from one to the other. In doing so, IEX created a 350-microsecond delay that slowed down HF traders accessing their exchange and therefore allowed IEX to interact with other exchanges at the same time as HF traders, instead of behind them.

(Shortform note: While Lewis claims IEX’s tactics (the point of presence and 350-microsecond delay) make trading fairer for regular investors, some experts object to IEX slowing down traders’ orders. They believe that IEX is effectively using latency arbitrage against HF traders since the exchange is using old prices on other exchanges before traders on those exchanges can update their own prices, thus making trading with IEX unfair for HF traders. They believe these tactics only benefit big investors and portfolio managers. However, this argument seems to acknowledge that using “old” prices to perform latency arbitrage is unfair—it’s just a question of whether humans or technology are on the receiving end of it.)

Solution 2: Investors Can Request Banks Send Orders to IEX

Lewis explains that IEX wanted to prevent the banks from allowing customers’ orders to sit unfulfilled in the dark pool. IEX thus encouraged investors to request that their orders be sent to IEX.

(Shortform note: While encouraging investors to ask their bank to send their orders to IEX was a solution, it didn’t completely fix IEX’s problem since it put the responsibility on the investor to make sure their bank cooperated and sent an investor’s order to IEX. To address this problem, in 2016, the SEC approved IEX’s application to become an official exchange, thus requiring brokers to send their orders to IEX when it offers stocks at the best price. This made it no longer up to the investor to make sure their order went to IEX.)

Solution 3: Eliminate Rebates

As their next solution, IEX eliminated rebates. Instead, IEX charged the people involved in both sides of the trade $0.009 per share. This approach differed from the previous model, which charged one side of the trade and paid the other a rebate, thus removing HF traders’ ability to use rebates to predict where investors would sell orders and then get there first.

(Shortform note: Following IEX’s decision to remove rebates from its trading process, the SEC reexamined whether or not rebates are an effective quality of the exchange. Although the SEC still allows rebates, it has admitted that rebates may create conflicts of interest when brokers choose where to send their orders. The SEC has considered testing how markets would function without rebates. NYSE and Nasdaq sued to stop this experiment before it went into effect.)

Solution 4: Three Order Types

To reduce information baiting, Katsuyama and his team limited the number of order types to three, as opposed to the 150 offered by other exchanges at the time. Lewis argues that fewer order types made trading more straightforward, encouraging investors who actually wanted to buy and sell stocks to trade with IEX. By limiting the number of order types, IEX prevented HF traders from baiting information out of investors: HF traders had fewer ways of determining which stocks an investor wanted or how much an investor was willing to pay.

(Shortform note: The exchange has increased the number of order types it offers since the publication of the book. IEX now has three classes of order types—ones for standard and retail investing, as well as what IEX calls their Signal Series order types, which are supposed to offer “enhanced price protection.”)

Solution 5: Publish Trading Rules

To counteract the secrecy of dark pools, IEX published the trading rules they use for their matching engine. They also showed what order types they allowed on IEX, as well as whether or not any other investors were given special access to their exchange. They hoped being transparent would build investor trust and encourage other exchanges to do the same.

(Shortform note: Lewis believes IEX fostered trust with investors through being honest and transparent about what was happening in their trades, which is a strategy other experts recommend. In Algorithms to Live By, Brian Christian and Tom Griffiths note that one simple way to improve the equilibrium of a competitive game, such as stock trading, is to change the rules to make honesty and transparency the optimal strategy. The authors argue that when you design a game in which players are incentivized not to hide their intentions, it eases the burden on everyone. Players no longer have to stress about predicting what others are going to do, or what others think they’re going to do.)