Statistical Arbitrage with Pairs Trading

2015 ◽  
Author(s):  
Ahmet Goncu ◽  
Erdinc Akyildirim
Keyword(s):  
Pairs Trading ◽  
Statistical Arbitrage

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

scholarly journals Some Notes on the Formation of a Pair in Pairs Trading

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 348 ◽  
Author(s):  
José Pedro Ramos-Requena ◽  
Juan Evangelista Trinidad-Segovia ◽  
Miguel Ángel Sánchez-Granero
Keyword(s):  
Hurst Exponent ◽  
Optimal Allocation ◽  
Mean Reversion ◽  
Equal Weight ◽  
Allocation Strategy ◽  
Pairs Trading ◽  
Distance Correlation ◽  
Statistical Arbitrage ◽  
New Methodologies ◽  
Set Up

The main goal of the paper is to introduce different models to calculate the amount of money that must be allocated to each stock in a statistical arbitrage technique known as pairs trading. The traditional allocation strategy is based on an equal weight methodology. However, we will show how, with an optimal allocation, the performance of pairs trading increases significantly. Four methodologies are proposed to set up the optimal allocation. These methodologies are based on distance, correlation, cointegration and Hurst exponent (mean reversion). It is showed that the new methodologies provide an improvement in the obtained results with respect to an equal weighted strategy.

Unsupervised Learning for Pairs Trading in the National Stock Exchange of India

2021 ◽  
Vol 23 (06) ◽  
pp. 1068-1082
Author(s):  
Chetan Tayal ◽  
◽  
Lalitha V.P ◽  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Unsupervised Learning ◽  
Stock Exchange ◽  
Statistical Significance ◽  
Search Space ◽  
Trading Strategy ◽  
Pairs Trading ◽  
Statistical Arbitrage ◽  
Main Challenge

Pairs Trading is a widely known and used market-neutral trading strategy that utilizes the concept of statistical arbitrage. It is based on the idea of mean-reverting time series and relies on the spread between two assets to demonstrate that property to buy an asset at a relatively undervalued price and an asset at a relatively overvalued price. This allows investors to manage risk if the market moves strongly in only one direction by making money on one side of the bet. The main challenge of pairs trading is selecting pairs that have an actual underlying relationship and their spread has real statistical significance. In this paper, we present the use of machine learning, specifically unsupervised clustering to construct our search space for pair selection and compare it against a traditional way of selecting pairs. We see that not only are we able to pick out more profitable pairs, these pairs are also less volatile and have less exposure to the market.

Statistical Arbitrage with Pairs Trading

2016 ◽  
Vol 16 (2) ◽  
pp. 307-319 ◽  
Author(s):  
Ahmet Göncü ◽  
Erdinç Akyıldırım
Keyword(s):  
Pairs Trading ◽  
Statistical Arbitrage

Statistical Arbitrage: Profits through Pairs Trading

2015 ◽  
Vol 2 (1) ◽  
pp. 140-148 ◽  
Author(s):  
Saloni Gupta
Keyword(s):  
Statistical Methods ◽  
High Probability ◽  
Efficient Market Hypothesis ◽  
Trading Strategy ◽  
Pairs Trading ◽  
Efficient Market ◽  
Basic Premise ◽  
Statistical Arbitrage ◽  
Starting Point ◽  
Persistent Anomalies

Statistical arbitrage is a popular device among hedge fund managers and assets management professionals. It refers to simultaneous buying and selling two different capital assets to earn super-normal profit. By identifying persistent anomalies that violate the efficient market hypothesis, statistical methods can be used to create a trading strategy to generate profit with high probability. A pair trading is one such trading strategy which is based on statistical arbitrage process. Pairs trading can be simple in concept, but can be one of the most complex types of trading in practice. The starting point of this strategy is that stocks that have historically had the same trading patters will have so in future as well. If there is a deviation from the historical mean this creates a trading opportunity, which can be exploited. Gains are earned when the price relationship is resorted. The basic premise of this strategy is that stock prices follow a mean reverting process. The objective of this paper is to identify arbitrage opportunities and calculating profits earned through these opportunities by using statistical tools. Many questions need to be answered before one can implement such strategy viz. which pair of stocks should be traded, how much do we buy/sell of each stock, how to catch the signal of an opportunity (i.e opening a position) and when to close the position so that profit could be earned. In this paper we have taken daily closing prices from 1/1/2010 to 1/1/2011 of thirty scrips of BSE-Sensex to form pairs. Pairs are formed on the basis of minimum distances between two stocks. We have decided not to invest anything. That is, purchase the same rupee amount of the long stock as we sell of the short stock so that strategy is self-financing. We open a position when the absolute value of the difference gets larger than two of its historical standardization.  To unwind the position, we wait until the first time it crosses zero. To calculate the profit/loss of this strategy, we have used “R-Software”. It is observed that profit could be earned through pairs trading if it is applied without losing patience. By identifying persistent anomalies that violate the efficient market hypothesis, statistical methods can be used to create a trading strategy to generate profit with high probability.

scholarly journals Which Pairs of Stocks should we Trade? Selection of Pairs for Statistical Arbitrage and Pairs Trading in Karachi Stock Exchange

2015 ◽  
Vol 54 (3) ◽  
pp. 215-244
Author(s):  
Laila Taskeen Qazi ◽  
Atta Ur Rahman . ◽  
Saleem Gul
Keyword(s):  
Financial Services ◽  
Commercial Banks ◽  
Stock Exchange ◽  
Pairs Trading ◽  
Services Sector ◽  
Statistical Arbitrage ◽  
Financial Services Sector ◽  
Price Series ◽  
Arbitrage Opportunity ◽  
Selection Of

Pairs Trading refers to a statistical arbitrage approach devised to take advantage from short term fluctuations simultaneously depicted by two stocks from long run equilibrium position. In this study a technique has been designed for the selection of pairs for pairs trading strategy. Engle-Granger 2-step Cointegration approach has been applied for identifying the trading pairs. The data employed in this study comprised of daily stock prices of Commercial Banks and Financial Services Sector. Restricted pairs have been formed out of highly liquid log share price series of 22 Commercial Banks and 19 Financial Services companies listed on Karachi Stock Exchange. Sample time period extended from November 2, 2009 to June 28, 2013 having total 911 observations for each share prices series incorporated in the study. Out of 231 pairs of commercial banks 25 were found cointegrated whereas 40 cointegrated pairs were identified among 156 pairs formed in Financial Services Sector. Furthermore a Cointegration relationship was estimated by regressing one stock price series on another, whereas the order of regression is accessed through Granger Causality Test. The mean reverting residual of Cointegration regression is modeled through the Vector Error Correction Model in order to assess the speed of adjustment coefficient for the statistical arbitrage opportunity. The findings of the study depict that the cointegrated stocks can be combined linearly in a long/short portfolio having stationary dynamics. Although for the given strategy profitability has not been assessed in this study yet the VECM results for residual series show significant deviations around the mean which identify the statistical arbitrage opportunity and ensure profitability of the pairs trading strategy. JEL classifications: C32, C53, G17 Keywords: Pairs Trading, Statistical Arbitrage, Engle-Granger 2-step Cointegration Approach, VECM.

Cointegration-based trading: evidence on index tracking & market-neutral strategies

2016 ◽  
Vol 42 (5) ◽  
pp. 449-471 ◽  
Author(s):  
Ioannis Papantonis
Keyword(s):  
Market Efficiency ◽  
Trading Strategies ◽  
Portfolio Performance ◽  
Pairs Trading ◽  
Content Type ◽  
Statistical Arbitrage ◽  
Long Run ◽  
Wide Range ◽  
Short Run ◽  
Depth Sensitivity

Purpose – The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations. Design/methodology/approach – First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency. Findings – The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected. Originality/value – The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

Pairs Trading in Commodity Futures: Evidence from the Indian Market

2020 ◽  
pp. 363-371
Keyword(s):  
Trading Strategy ◽  
Indian Market ◽  
Pairs Trading ◽  
Distance Method ◽  
Statistical Arbitrage ◽  
Cointegration Tests ◽  
Trading Period ◽  
Expected Values ◽  
Two Stages ◽  
Selection Of

The pairs trading, one of the techniques of the statistical arbitrage, is a market-neutral trading strategy that employs time series methods to identify relative mispricing between securities based on the expected values of these assets. The main objective of this study was to investigate the profitability and risks of pairs trading based on the selection of pairs through minimising the sum of squared deviation (distance method) and the selection based on cointegration tests (cointegration method) using the future daily prices of commodities traded and listed on The Multi Commodity Exchange of India (MCX) over 2011-2017 on a rolling basis. The pairs trading strategy was performed in two stages: the formation period and the trading period. The strategy involved long position in one commodity and short position in other commodity of the pair identified. The study revealed that pairs trading in commodities were significantly profitable, with average annualised profitability of up to 59 percent, including transaction costs.

Algorithmic pairs trading with expert inputs, a fuzzy statistical arbitrage framework

2020 ◽  
Vol 38 (1) ◽  
pp. 697-707
Author(s):  
Mehmet Bayram ◽  
Muzaffer Akat ◽  
Serol Bulkan
Keyword(s):  
Pairs Trading ◽  
Statistical Arbitrage
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