However, the complex and dynamic landscape of financial markets suggests that no definitive theoretical or operational definition of SA is likely to be agreed. Because of this we propose to use the definition in conjunction with a classification scheme. We first introduce the classical definition of arbitrage, defined as a zero-cost trading strategy with positive expected payoff and no possibility of a loss.

• However, two stocks that operate in the same industry can remain uncorrelated for a significant amount of time due to both micro and macro factors.
• In China, quantitative investment including statistical arbitrage is not the mainstream approach to investment.
• Statistical arbitrage faces different regulatory situations in different countries or markets.
• Over a finite period of time, a low probability market movement may impose heavy short-term losses.
• Burgess also describes statistical arbitrage as a generalization of a traditional arbitrage where mispricing is statistically determined through replicating strategies.
• For pairs trading, we will explore one of the characteristics being stationarity.
• Other strategies instead focus on the spread between CDS and corporate bonds or different types of credit default swaps .

The analysis of strategies’ key features indicates that no existing definition fully describes them. To bridge this gap, we identify a general definition and propose a classification system that encompasses the current forms of SA strategies while facilitating the inclusion of new types as they emerge. The term statistical arbitrage refers to the practice of using sophisticated mathematical models to identify potential profit opportunities from a pricing inefficiency that exists between two or more securities. Statistical arbitrage requires the use of high speed computers, computational models, as well as complex trading systems.

Trading The Odds With Arbitrage

One often finds that a pair that perform poorly using the ratio method produces decent returns when a regression or Kalman Filter model is applied. From experience, there is no pattern that allows you to discern which technique, if any, is gong to work. Another application might be to construct robust portfolios of lower-correlated assets. Here for example we use the graph to identify independent vertices that have very few correlated relationships .

While trading two stocks is the most conceptually simple statistical arbitrage strategy, we’re not limited to only two stocks. Investors can use any number of financial instruments cointegrating; however, there’s only one other with a unique name – We’re trading “triplets” when we arbitrage three assets together. Statistical arbitrage still works as new instruments, exchanges, and financial markets create trading opportunities. Additionally, stat arb strategies that stopped working can come back into favor. Statistical Arbitrage strategies can be applied to different financial instruments and markets. The Executive Programme in Algorithmic Trading includes a session on “Statistical Arbitrage and Pairs Trading” as part of the “Strategies” module.

Types Of Statistical Arbitrage

They calculate the sum of Euclidean squared distance for n stocks and choose the smallest SSD to construct a portfolio. They then choose to enter the transaction when the price of the paired asset is greater than two standard deviations, and when the price matched the average price, they sell the paired asset. They find average annualized excess returns of about 11% for the top pairs portfolios and that the profits do not appear to be caused by simple mean reversion. However, Do and Faff apply the Gatev et al. methodology with more recent data and find the profit show a declining trend when the naive trading rule is used.

“But what really separates the good from the mediocre players in this arena is the quality of execution. We are high-frequency traders, so any slippage or implementation cost is magnified.” Of course, no rational person believes that there is a causal connection between cheese consumption and death by bedsheet entanglement – it is a spurious correlation that has arisen due to the random fluctuations in the two time series. And because the correlation is spurious, the apparent relationship is likely to break down in future.

Measuring The Statistical Arbitrage Risk Premium Of A Stock

In our alorithm, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. Our result is an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years. Our performance indicates using PCA combined with linear regression to measure the deviation level is reasonable. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations as a whole.

The key to success in risk arbitrage is determining the likelihood and timeliness of the merger and comparing that with the difference in price between the target stock and the buyout offer. Some risk arbitrageurs have begun to speculate on takeover targets as well, which can lead to substantially statistical arbitrage greater profits with equally greater risk. We will select our universe of stocks by dropping securities with prices lower than $5 and pick the ones with the highest dollar traded volume. I’m not into macroeconomics, but I get the impression that the benchmark for FX models is a random walk.

Statistical Arbitrage: Defined & Strategies

This thesis aims to add value by evaluating the generalized pairs trading strategy and suggest enhancements to improve out-of-sample performance. The enhanced strategy generated the daily Sharpe ratio of 6.07% in the out-of-sample period from January 2013 through October 2016 with the correlation of -.03 versus S&P 500. This thesis is differentiated from the previous relevant studies in the following three ways. First, the factor selection process in previous statistical arbitrage studies has been often unclear or rather subjective. Second, most literature focus on in-sample results, rather than out-of-sample results of the strategies, which is what the practitioners are mainly interested in. Third, by implementing hidden Markov model, it aims to detect regime change to improve the timing the trade.

A common approach to find statistical arbitrage opportunities is to conduct a correlation calculation. For starters, correlation refers to the mathematical study of relationships between assets. In the financial market, you can do this by downloading data of two assets and running a correlation study in Microsoft excel. For starters, an ETF is a financial asset that tracks a basket of financial assets like stocks and bonds. To define it in simple terms, Statistical arbitrage comprises a set of quantitatively driven algorithmic trading strategies.

Conclusion:  Why Cointegration Breaks Down

They apply Bertram’s method to the spread trading for crude oil and refined products markets. Their result showed evidence of aggregate upward and downward mean reversion, and profitable strategies with Sharpe ratios of greater than two. Their research shows that Bertram’s method has profitability potential for non-Gaussian processes.

historical exchange rates is one of the most influential trading strategies ever devised, despite having decreased slightly in popularity since the 1990s. Today, most statistical arbitrage is conducted through high-frequency trading using a combination of neural networks and statistical models. Not only do these strategies provide liquidity, but they have also been largely responsible for some of the largest crashes we’ve seen in firms like LTCM in the past.

Triplets Trading Strategy Example

Specifically the excess return of Berkshire A is the as the dependent variable and the factors on the right-hand-side of Eq and Eq are the independent variables. The estimated coefficients are then used as the portfolio weights for the construction of the replicating asset. The returns of the replicating portfolio will, in the long run, match the statistical arbitrage returns of the Berkshire A stock, since the replicating portfolio is constructed from theoretically correct asset pricing model specifications. In the remainder of our paper, we will denote the replicating portfolios as simply Buffett- or five-factor model. Second, we use the replicating portfolios as input to the pair trading simulation tests.

BY Chauncey Alcorn