Some Notes on the Formation of a Pair in Pairs Trading

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.

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Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

Mathematics ◽

10.3390/math9020179 ◽

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.

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

Studies of Applied Economics ◽

10.25115/eea.v38i3.3145 ◽

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.

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

Journal of Business Theory and Practice ◽

10.22158/jbtp.v1n2p329 ◽

2013 ◽

Vol 1 (2) ◽

pp. 329 ◽

Cited By ~ 2

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

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.

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Research on Manufacturing Resources Optimal Allocation Strategy of 3D Printing Cloud Service Platform

2018 24th International Conference on Automation and Computing (ICAC) ◽

10.23919/iconac.2018.8749123 ◽

2018 ◽

Author(s):

Fangmin Cheng ◽

Suihuai Yu ◽

Jianjie Chu ◽

Jiashuang Fan

Keyword(s):

3D Printing ◽

Optimal Allocation ◽

Cloud Service ◽

Allocation Strategy ◽

Service Platform ◽

Manufacturing Resources ◽

Cloud Service Platform

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Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China

Frontiers in Energy ◽

10.1007/s11708-020-0660-9 ◽

2020 ◽

Vol 14 (2) ◽

pp. 241-253

Author(s):

Pei Li ◽

Guotian Cai ◽

Yuntao Zhang ◽

Shangjun Ke ◽

Peng Wang ◽

...

Keyword(s):

Optimal Allocation ◽

Allocation Strategy ◽

Energy Internet ◽

Multi Objective

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Storage Space Allocation Strategy for Digital Data with Message Importance

Entropy ◽

10.3390/e22050591 ◽

2020 ◽

Vol 22 (5) ◽

pp. 591

Author(s):

Shanyun Liu ◽

Rui She ◽

Zheqi Zhu ◽

Pingyi Fan

Keyword(s):

Data Compression ◽

Optimal Allocation ◽

Reconstruction Error ◽

Digital Data ◽

Storage Space ◽

Allocation Strategy ◽

Storage Size ◽

Space Allocation ◽

Storage Space Allocation ◽

Data Value

This paper mainly focuses on the problem of lossy compression storage based on the data value that represents the subjective assessment of users when the storage size is still not enough after the conventional lossless data compression. To this end, we transform this problem to an optimization, which pursues the least importance-weighted reconstruction error in data reconstruction within limited total storage size, where the importance is adopted to characterize the data value from the viewpoint of users. Based on it, this paper puts forward an optimal allocation strategy in the storage of digital data by the exponential distortion measurement, which can make rational use of all the storage space. In fact, the theoretical results show that it is a kind of restrictive water-filling. It also characterizes the trade-off between the relative weighted reconstruction error and the available storage size. Consequently, if a relatively small part of total data value is allowed to lose, this strategy will improve the performance of data compression. Furthermore, this paper also presents that both the users’ preferences and the special characteristics of data distribution can trigger the small-probability event scenarios where only a fraction of data can cover the vast majority of users’ interests. Whether it is for one of the reasons above, the data with highly clustered message importance is beneficial to compression storage. In contrast, from the perspective of optimal storage space allocation based on data value, the data with a uniform information distribution is incompressible, which is consistent with that in the information theory.

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

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/05396 ◽

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.

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Rapid Assay for Detecting Enhanced Atrazine Degradation in Soil

Weed Science ◽

10.1614/ws-07-015.1 ◽

2007 ◽

Vol 55 (5) ◽

pp. 528-535 ◽

Cited By ~ 25

Author(s):

Dale L. Shaner ◽

W. Brien Henry ◽

L. Jason Krutz ◽

Brad Hanson

Keyword(s):

Half Life ◽

Water Extract ◽

Equal Weight ◽

History Of ◽

Set Up ◽

History Of Use ◽

Rapid Laboratory ◽

Degradation In Soil ◽

Atrazine Degradation ◽

Laboratory Assay

Atrazine is widely used to control broadleaf weeds and grasses in corn, sorghum, and sugarcane. Field persistence data published before 1995 showed that the average half-life of atrazine in soil was 66 d, and farmers expect to achieve weed control with a single application for the full season. However, reports of enhanced atrazine degradation in soil from fields that have a history of atrazine applications are increasing. A rapid laboratory assay was developed to screen soils for enhanced atrazine degradation. Soil (50 g) was placed in a 250 ml glass jar and treated with 7.5 ml of water containing atrazine (5 µg ai ml−1) and capped with a Teflon-lined lid. The assay was conducted at room temperature (25 C). Soil subsamples (1.5 to 3 g) were removed at 0, 1, 2, 4, 8, and 16 d after treatment and extracted with an equal weight of water (wt/vol). The atrazine in the water extract was assayed with high-pressure liquid chromatography (HPLC). The half-life of atrazine in soils with a history of use was ≤ 1.5 d, whereas the half-life in soils with no history of atrazine use was > 8 d. The advantages of this assay are (1) the ease of set up; (2) the rapidity of extraction, and (3) the simplicity of the quantification of the atrazine.

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