scholarly journals Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 179
Author(s):  
Karen Balladares ◽  
José Pedro Ramos-Requena ◽  
Juan Evangelista Trinidad-Segovia ◽  
Miguel Angel Sánchez-Granero
Keyword(s):  
Emerging Markets ◽  
Market Efficiency ◽  
Hurst Exponent ◽  
Mean Reversion ◽  
Trading Strategy ◽  
Emerging Countries ◽  
Pairs Trading ◽  
Global Test ◽  
Statistical Arbitrage

In this paper, we use a statistical arbitrage method in different developed and emerging countries to show that the profitability of the strategy is based on the degree of market efficiency. We will show that our strategy is more profitable in emerging ones and in periods with greater uncertainty. Our method consists of a Pairs Trading strategy based on the concept of mean reversion by selecting pair series that have the lower Hurst exponent. We also show that the pair selection with the lowest Hurst exponent has sense, and the lower the Hurst exponent of the pair series, the better the profitability that is obtained. The sample is composed by the 50 largest capitalized companies of 39 countries, and the performance of the strategy is analyzed during the period from 1 January 2000 to 10 April 2020. For a deeper analysis, this period is divided into three different subperiods and different portfolios are also considered.

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.

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 European Equity Pairs Trading: The Effect of Data Frequency on Risk and Return

2013 ◽  
Vol 1 (2) ◽  
pp. 329 ◽  
Author(s):  
Michael Lucey ◽  
Don Walshe
Keyword(s):  
Minimum Distance ◽  
Mean Reversion ◽  
Trading Strategy ◽  
Share Price ◽  
Trading Rule ◽  
Pairs Trading ◽  
Risk And Return ◽  
Rule Based ◽  
Data Frequency ◽  
European Equity

<p><em>This article examines an equity pairs trading strategy using daily, weekly and monthly European share price data over the period 1998 – 2007. The authors show that when stocks are matched into pairs with minimum distance between normalised historical prices, a simple trading rule based on volatility between these prices yields annualised raw returns of up to 15% for the weekly data frequency. Bootstrap results suggest returns from the strategy are attributable to skill rather than luck, while insignificant beta coefficients provide evidence that this is a market neutral strategy. Resistance of the strategy’s returns to reversal factors suggest pairs trading is fundamentally different to previously documented reversal strategies based on concepts such as mean reversion.</em><em></em></p>

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: 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.

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.

scholarly journals Loss protection in pairs trading through minimum profit bounds: A cointegration approach

2006 ◽  
Vol 2006 ◽  
pp. 1-14 ◽  
Author(s):  
Yan-Xia Lin ◽  
Michael McCrae ◽  
Chandra Gulati
Keyword(s):  
Statistical Modeling ◽  
Necessary Conditions ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Pairs Trading ◽  
Simulation Data ◽  
Equilibrium Pricing ◽  
Statistical Arbitrage ◽  
Statistical Validity ◽  
Step Procedure

Pairs trading is a comparative-value form of statistical arbitrage designed to exploit temporary random departures from equilibrium pricing between two shares. However, the strategy is not riskless. Market events as well as poor statistical modeling and parameter estimation may all erode potential profits. Since conventional loss limiting trading strategies are costly, a preferable situation is to integrate loss limitation within the statistical modeling itself. This paper uses cointegration principles to develop a procedure that embeds a minimum profit condition within a pairs trading strategy. We derive the necessary conditions for such a procedure and then use them to define and implement a five-step procedure for identifying eligible trades. The statistical validity of the procedure is verified through simulation data. Practicality is tested through actual data. The results show that, at reasonable minimum profit levels, the protocol does not greatly reduce trade numbers or absolute profits relative to an unprotected trading strategy.

Execution of Pairs Trading Strategy: Some Propositions

2015 ◽  
Author(s):  
Tamal Datta Chaudhuri ◽  
Priyam Singh
Keyword(s):  
Trading Strategy ◽  
Pairs Trading
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