How Statistical Arbitrage Can Lead to Big Profits

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How Statistical Arbitrage Can Lead to Big Profits

According to the efficient market hypothesis (EMH), financial markets are “informationally efficient,” meaning that the prices of traded assets represent all available information at any one moment. But, if this is true, why do prices fluctuate from day to day despite the absence of fresh basic information? The explanation includes a factor that many individual traders overlook: liquidity.

Throughout the day, many huge institutional transactions have little to do with information and everything to do with liquidity. Investors that feel overexposed may hedge or sell holdings aggressively, impacting the price. These liquidity demanders are often prepared to pay a price to leave their holdings, resulting in a profit for liquidity providers. This capacity to benefit from knowledge seems to violate the efficient market theory, yet it is the basis of statistical arbitrage.

The goal of statistical arbitrage is to benefit from the perceived mispricing of one or more assets based on the anticipated value of the assets given by a statistical model.

Key Takeaways

  • Statistical arbitrage is an investing technique that tries to benefit from the closing of a price difference between two or more assets.
  • Stat arb employs a variety of methods, many of which depend on statistical or correlational regularities between diverse assets in a market that tends toward efficiency.
  • Despite the term “arbitrage” in its name, stat arb may be very dangerous and result in massive and systemic losses, as shown in the catastrophic collapse of the hedge fund Long Term Capital Management (LTCM).

What Is Statistical Arbitrage?

Statistical arbitrage, sometimes known as “stat arb,” arose in the 1980s as a result of hedging needs generated by Morgan Stanley’s equities block trading desk activities. Morgan Stanley avoided the price penalty associated with massive block acquisitions by acquiring shares as a hedge against its huge holdings rather than closely-correlated equities.

For example, if the trading desk acquired a significant block of Coca-Cola stock, it might short a closely-correlated company such as PepsiCo to hedge against any severe market downturns in the near term. This successfully reduced some market risk while the business attempted to sell the shares in a block transaction.

Traders quickly started to view of these “pairs” as two sides of the same trading strategy, where gains might be generated rather than merely as a hedging tool. These pair trades ultimately developed into a number of more complex methods aimed at exploiting statistical disparities in asset prices caused by liquidity, volatility, risk, or other fundamental or technical characteristics. We now refer to these tactics as statistical arbitrage.

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Types of Statistical Arbitrage

There are several forms of statistical arbitrage developed to capitalize on various sorts of chances. While certain kinds have been phased out by a more efficient economy, other new chances have emerged to take their place. Here are a handful of the most common stat arb techniques.

Risk Arbitrage

Risk arbitrage is a kind of statistical arbitrage in which profits are sought from merger circumstances. Investors buy shares in the target while simultaneously shorting the acquirer’s stock (assuming the transaction is a stock transaction). As a consequence, the difference between the buyout price and the market price yields a profit.

Risk arbitrage, as opposed to classic statistical arbitrage, entails some risk. The most significant risk is that the merger will fail and the target’s shares will revert to pre-merger levels. Another danger is the temporal value of money invested. Mergers that take a long time to complete might reduce investors’ yearly profits.

The key to risk arbitrage success is estimating the probability and timeliness of the merger and comparing it to the price differential between the target stock and the buyout offer. Some risk arbitrageurs have started to bet on takeover targets as well, which may result in much higher rewards with significantly more risk.

Volatility Arbitrage

Volatility arbitrage is a prominent form of statistical arbitrage that focuses on exploiting discrepancies between an option’s implied volatility and a projection of future actual volatility in a delta-neutral portfolio. Volatility arbitrageurs, on the other hand, speculate on the volatility of the underlying asset rather than placing a directional wager on the security’s price.

The key to this technique is precisely projecting future volatility, which might deviate for a number of causes, such as:

  • Patent disputes
  • Clinical trial results
  • Uncertain earnings
  • M&A speculation

Once a volatility arbitrageur has calculated the future realized volatility, they may start looking for options with implied volatility that is much lower or higher than the underlying security’s anticipated realized volatility. If the implied volatility is low enough, the trader may purchase the option and hedge it with the underlying investment to create a delta-neutral portfolio. In the same way, if the implied volatility is greater, the trader may sell the option and hedge with the underlying securities to create a delta-neutral portfolio.

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The trader will earn when the realized volatility of the underlying asset swings closer to their projection than it does to the market’s estimate (or implied volatility).The benefit from the deal is earned by the constant re-hedging necessary to maintain the portfolio delta neutral.

Neural Networks

Because of their capacity to discover complicated mathematical linkages that seem unseen to the human eye, neural networks are becoming more attractive in the statistical arbitrage sector. Based on biological brain networks, these networks are mathematical or computer models. They are made up of a network of linked artificial neurons that process information using a connectionist approach to computing, which implies that their structure changes depending on the external or internal information that comes through the network during the learning phase.

In essence, neural networks are non-linear statistical data models that are used to detect patterns in data by modeling complicated interactions between inputs and outputs. Obviously, any pattern in the price movements of assets may be profitably utilized.

High-Frequency Trading

High-frequency trading (HFT) is a relatively recent technique that seeks to leverage on computers’ capacity to perform deals fast. Spending in the trading industry has increased dramatically over the years, and as a consequence, many systems are now capable of executing thousands of deals every second. Because most statistical arbitrage possibilities are now constrained by competition, the ability to execute trades fast is the only method to grow earnings.

Arbitrageurs’ future profitability will be determined by more complicated neural networks and statistical models linked with computers that can crunch statistics and execute deals quicker.

How Statistical Arbitrage Affects Markets

Statistical arbitrage has evolved to play an important role in supplying much of the market’s day-to-day liquidity. Initially, it enabled huge block traders to conduct transactions without substantially impacting market prices, while also lowering volatility in issues such as American depositary receipts (ADRs) by more closely linking them with their parent equities.

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Indeed, when stat arb methods become more commonly adopted and automated, the market tends to become more efficient. When arbitrage possibilities between assets occur, these techniques swiftly remove them. As a consequence, stat arb may result in a more liquid and stable market.

However, statistical arbitrage gone wrong has resulted in some severe issues. The failure of Long Term Capital Management (LTCM) in 1998 almost destroyed the market. It is vital to use enormous leverage to benefit from such little price fluctuations.

Furthermore, since these deals are automated, security precautions are built in. In the case of LTCM, this meant that it would liquidate upon a negative move; the issue was that LTCM’s liquidation orders just generated additional sell orders, creating a vicious cycle that would only be broken by government action.

Remember that most stock market disasters are caused by liquidity and leverage issues—exactly what statistical arbitrageurs do. Stat arb algorithms have also been blamed in part for the “flash crashes” that have begun to occur in the market over the last decade. A flash crash is an occurrence that occurs in electronic securities markets when a quick sell-off of assets creates a negative feedback loop that may result in substantial price decreases in a matter of minutes.

The Bottom Line

Statistical arbitrage, despite its minor decline in popularity during the 1990s, remains one of the most significant trading methods ever invented. The majority of statistical arbitrage is now done via high-frequency trading, which employs a mix of neural networks and statistical models. These methods not only offer liquidity, but they have also been substantially responsible for some of the greatest collapses witnessed in organizations like as LTCM in the past. As long as liquidity and leverage difficulties are coupled, the technique is likely to be recognized even by the average investor.

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