pairs trading mean reversion strategy

Highlights

This Deutsche Bank research shows how to improve pairs trading with RavenPack's news analytics. Their enhanced signalise significantly reduces divergence put on the line and also boosts the average rejoinder per pair.

The percentage of non-converged pairs dropped aside about half from 15% to 7%.
Average profit per pair increased from 2.3% to 2.8% for the European strategy, and from 1.6% to 1.9% for the US Strategy.
Return dispersion becomes Thomas More positively skewed.

Below is a pairs example for Sports Direct International plc and Dixons Carphone plc between Nov 2022 to January 2022.

mean reversion

Overall, the Sports Direct Intl price dropped by to a higher degree 40% over a period of two months. Clearly, trading the geminate would induce complete a release. Still, with access to a real-time news analytics flow, the personnel casualty could have been avoided aside ignoring pair trades with toll divergence supported away negative opinion and abnormal news loudness on either of the two companies (in this case, "Sports Direct Intl.").

mean reversion

White Paper

In a previous report, Deutsche Money box discussed plane section mean reversion strategies in equity markets. Pairs trading, which attempts to exploits a terminable mispricing between two securities with a stabilized congeneric toll relationship, is another typecast of signify reversion strategy. In that news report, Deutsche Bank show how you can improve some the selection and trading aspects of a formal pairs trading strategy.

Fundamental risk models help to identify profitable pairs

Pairs trading strategies typically seem for co-integrated relationships between stocks belonging to the same land and sector/industry group. They consider there are superior means with which to capture the stage of "fundamental law of similarity" between stocks. For example, they show that utilizing a fundamental risk model to identify stock pairs importantly reduces divergence peril, and also improves the average return per pair.

News analytics overlayer to further enhance pairs trading performance

Disagreement risk increases in the proportion of idiosyncratic risk associated with a pair's constituent stocks. A news analytics overlay which helps to differentiate between cost divergency due to news program atomic number 3 conflicting due to haphazard price movements, importantly improves the performance of the trading strategy by reducing the number of not-convergent trades.

On the far side regular pairs

In looking for potential pairs candidates, they do not have to limit ourselves to stock pairs. Deutsche Bank proposes a novel method based on clustering and can-do tree-cutting to systematically identify clusters of stocks as voltage constituents for synthetic pairs trading strategies.

mean reversion

Introduction
Pairs Trading: The Bedroc

Present Pais Trading Approaches
The Benchmark Scheme

An Increased Pairs Trading Manikin

Identifying Pairs Exploitation a Profound Risk Modeling
Applying A News Analytics Overlie to Pairs Trading

An Addendum to Stock Pairs
Conclusions
References
Cecal appendage

Summarizing the Results

mean reversion Peter Hafez - Chief Information Scientist - RavenPack

mean reversion

The rationale of pairs trading is remarkably lanceolate, but the devil is in the detail.

An investor finds assets whose prices stirred together historically, unstoppered a trade by shorting the winner and buying the loser when the banquet between them widens. The trade is closed when the banquet converges. Only that is not so simple...

Over the years, pairs trading has become combined of the most popular statistical arbitrage strategies. The strategy exploits temporary anomalies between prices of assets that have both equilibrium relationship. While methods whitethorn disagree in sophistication, each implementations rely on the use of statistical analysis of historical prices to identify span candidates with stable inter-relationships.

The main challenge in building such strategies is that, often, cointegration between two assets breaks down out-of-sample – making the trade a losing proposition.

Fundamental Similarity

In an attempt to solve the dispute of cointegration breakdown, investors can benefit from looking pairs that have some degree of "fundamental similarity". Typically, pairs trading programs are looking for cointegration relationships between stocks belonging to the Same country and sector/industry mathematical group.

Withal, In a recent study, Deutsche Bank utilized a risk model to proxy fundamental similarity. Overall, they found that taking so much approach significantly reduced divergence danger across their portfolio, and too improved the norm return per pair.

Differentiating Between "Superb" and "Corky" Discrepancy

Even though fundamentally similar stocks are more verisimilar to move in tandem in the near proximo, there are no guarantees for much behavior. Considering any single stock, a large proportion of the cost movement is nonvoluntary by idiosyncratic risk, which could permanently alter the equilibrium relationship between a company pair.

The profits and risks from trading stock pairs are precise untold related to the type of information event which creates divergence. If divergence is caused by a while of news enatic specifically to one constituent of the pair, there is a good chance that prices will diverge further. Happening the other hand, if divergence is caused by ergodic price movements or a differential response to common information, convergence is more probable to keep abreast after the initial difference.

To test the effects of news on a pairs trading scheme, Deutsche Bank used two aggregated indicators based along RavenPack's Colossal Information analytics derived from newsworthiness and social media data measuring sentiment and media attention. Specifically, exploitation the two indicators, Deutsche Bank created a filter that would ignore trades where deviation was supported past veto sentiment and antidromic news volume. Figure 16, from the report, illustrates the pairs trading process with the news overlay.

mean reversion

Below is an overview of Deutsche Bank's key findings - applying the RavenPack Big Data analytics overlay (see Figure 17):

Lour Divergence Risk: the percentage of not-converged pairs dropped by over a fractional from 15% to 7%
High Return: average profit per pair also enhanced from 2.3% to 2.8%, and the payof distribution becomes more positively skewed
Significant P-Values: the increase in average returns is confirmed by significant p-values (danlt;0.05) from the colored pair-wise t-test

mean reversion

Figure 19, from the report, shows the results of the pairs strategies applied on the MSCI U.S. universe of discourse. As can embody seen from the graph, the same conclusions can be reached, albeit the strategies have relatively let down returns in the U.S. The average return per pair low-level the benchmark strategy, the enhanced strategy using the risk model, and the final strategy with both lay on the line model and news overlay are 0.2%, 1.6% and 1.9% severally.

mean reversion

Overall, Deutsche Bank finds that applying a news analytics overlay can help differentiate between "good" toll divergence (which is likely to converge) from "bad" divergence. Sir Thomas More importantly, such ability provides significant improvements to the carrying out of a traditional pairs trading scheme, particularly by reducing divergence risk.

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