A Machine Learning based Pairs Trading Investment Strategy

2021 ◽  
Author(s):  
Simão Moraes Sarmento ◽  
Nuno Horta
Keyword(s):  
Machine Learning ◽  
Investment Strategy ◽  
Pairs Trading

scholarly journals Machine Learning and Algorithmic Pairs Trading in Futures Markets

2020 ◽  
Vol 12 (17) ◽  
pp. 6791
Author(s):  
Seungho Baek ◽  
Mina Glambosky ◽  
Seok Hee Oh ◽  
Jeong Lee
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Investment Strategy ◽  
Trading Strategy ◽  
Support Vector ◽  
Pairs Trading ◽  
Correction Model ◽  
Machine Learning Methods ◽  
Trading Market ◽  
Price Trends

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.

scholarly journals PAIRS TRADING UNDER DRIFT UNCERTAINTY AND RISK PENALIZATION

2018 ◽  
Vol 21 (07) ◽  
pp. 1850046 ◽  
Author(s):  
SÜHAN ALTAY ◽  
KATIA COLANERI ◽  
ZEHRA EKSI
Keyword(s):  
Markov Chain ◽  
Partial Information ◽  
Investment Strategy ◽  
Value Functions ◽  
Pairs Trading ◽  
Stochastic Filtering ◽  
Finite State ◽  
Dynamic Portfolio ◽  
Portfolio Optimization Problem ◽  
Dynamic Portfolio Optimization

In this work, we study a dynamic portfolio optimization problem related to pairs trading, which is an investment strategy that matches a long position in one security with a short position in another security with similar characteristics. The relationship between pairs, called a spread, is modeled by a Gaussian mean-reverting process whose drift rate is modulated by an unobservable continuous-time, finite-state Markov chain. Using the classical stochastic filtering theory, we reduce this problem with partial information to an equivalent one with full information and solve it for the logarithmic utility function, where the terminal wealth is penalized by the riskiness of the portfolio according to the realized volatility of the wealth process. We characterize optimal dollar-neutral strategies as well as optimal value functions under full and partial information and show that the certainty equivalence principle holds for the optimal portfolio strategy. Finally, we provide a numerical analysis for a toy example with a two-state Markov chain.

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.

scholarly journals Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
A. Wiliński ◽  
A. Bera ◽  
W. Nowicki ◽  
P. Błaszyński
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Parameter Space ◽  
Cross Validation ◽  
Investment Decision ◽  
Investment Strategy ◽  
Time Varying ◽  
Time Varying Parameters ◽  
Rule Set ◽  
The Relationship

This paper examines two transactional strategies based on the classifier which opens positions using some rules and closes them using different rules. A rule set contains time-varying parameters that when matched allow making an investment decision. Researches contain the study of variability of these parameters and the relationship between learning period and testing (using the learned parameters). The strategies are evaluated based on the time series of cumulative profit achieved in the test periods. The study was conducted on the most popular currency pair EURUSD (Euro-Dollar) sampled with interval of 1 hour. An important contribution to the theory of algotrading resulting from presented research is specification of the parameter space (quite large, consisting of 11 parameters) that achieves very good results using cross validation.

scholarly journals Portfolio optimization for cointelated pairs: SDEs vs Machine learning

2021 ◽  
Vol 8 (3-4) ◽  
pp. 101-125
Author(s):  
Babak Mahdavi-Damghani ◽  
Konul Mustafayeva ◽  
Cristin Buescu ◽  
Stephen Roberts
Keyword(s):  
Machine Learning ◽  
Portfolio Optimization ◽  
Maximization Problem ◽  
Financial Mathematics ◽  
Pairs Trading ◽  
Power Utility ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Optimization ◽  
Mean Variance

With the recent rise of Machine Learning (ML) as a candidate to partially replace classic Financial Mathematics (FM) methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined. In the Financial Mathematics approach we model the asset prices not via the common approaches used in pairs trading such as a high correlation or cointegration, but with the cointelation model in Mahdavi-Damghani (2013) that aims to reconcile both short-term risk and long-term equilibrium. We maximize the overall P&L with Financial Mathematics approach that dynamically switches between a mean-variance optimal strategy and a power utility maximizing strategy. We use a stochastic control formulation of the problem of power utility maximization and solve numerically the resulting HJB equation with the Deep Galerkin method introduced in Sirignano and Spiliopoulos (2018). We turn to Machine Learning for the same P&L maximization problem and use clustering analysis to devise bands, combined with in-band optimization. Although this approach is model agnostic, results obtained with data simulated from the same cointelation model gives a slight competitive advantage to the ML over the FM methodology1.

scholarly journals A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

2019 ◽  
Vol 11 (23) ◽  
pp. 6803
Author(s):  
Jiwoo Kim ◽  
Sanghun Shin ◽  
Hee Soo Lee ◽  
Kyong Joo Oh
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Initial Public Offering ◽  
Rough Set ◽  
Rough Set Theory ◽  
Investment Strategy ◽  
Portfolio Allocation ◽  
Individual Investors ◽  
Rule Sets ◽  
Public Offering

An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, studies on portfolio allocation strategies for IPO stocks are relatively scarce. This paper develops a machine learning investment strategy for IPO stocks based on rough set theory and a genetic algorithm (GA-rough set theory). To reduce issues of information asymmetry, we use nonfinancial data that are publicly available to individual and institutional investors in the IPO process. Based on the rule sets generated from the training sets, we conduct 120 tests with various conditions involving the target days and the partition of the training and testing sets, and we find excess returns of the constructed portfolios compared to the benchmark portfolios. Investors in IPO stocks can formulate more efficient investment strategies using our system. In this sense, the system developed in this paper contributes to the efficiency of financial markets and helps achieve sustained economic growth.

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