Pairs Of Stocks Research Articles

Representation of Female Sexuality and Freedom: Kate Chopins Selected Short Stories

The writer Kate Chopin has written about female sexuality and the restrictions put by marriage as an institution upon the lives of women. Her writings delineate the lives of female characters who are burdened by the relationships they have in society. They are self-assertive and courageous women of the nineteenth century. They are thought to be ahead of their time. They have refused to accept the traditional roles and conventions of the society and have shown the deepest drives of their hearts. Chopin has explored these female souls as a woman writer. Her first attempt is to highlight the sense of female freedom and a true understanding of their nature in her writings. The purpose of this article is to analyze the representation of female sexuality and freedom in Kate Chopin’s selected short stories entitled “The Story of an Hour,” “The Storm,”and “A Pair of Silk Stockings”.

Analytic Value Function for Pairs Trading Strategy With a Levy-Driven Ornstein-Uhlenbeck Process

This paper studies the performance of pairs trading strategy under a specific spread model. Based on the empirical evidence of mean reversion and jumps in the spread between pairs of stocks, we assume that the spread follows a Levy-driven Ornstein-Uhlenbeck process with twosided jumps. To evaluate the performance of a pairs trading strategy, we propose the expected return per unit time as the value function of the strategy. Significantly different from the current related works, we incorporate an excess jump component into the calculation of return and time cost. Further, we obtain the analytic expression of strategy value function, where we solve out the probabilities of crossing thresholds via the Laplace transform of first passage time of the Levy driven Ornstein-Uhlenbeck process in one-sided and two-sided exit problems. Through numerical illustrations, we calculate the value function and optimal thresholds for a spread model with symmetric jumps, reveal the non-negligible contribution of incorporating the excess jumps into the value function, and analyze the impact of model parameters on the strategy performance.

Pairs-trading under geometric Brownian motions: An optimal strategy with cutting losses

Pairs trading is about simultaneously trading a pair of stocks. A pairs trade is triggered when their prices diverge and consists of a short position of the strong stock and a long position of the weak one. Pairs trading bets on the reversal of their price strengths. In this paper, we study the optimal pairs-trading problem under general geometric Brownian motions and focus on trading with cutting losses. The objective is to trade the pairs over time to maximize an overall return with a fixed transaction cost. Our optimal policy is characterized by threshold curves obtained by solving the associated HJB equations. We provide sufficient conditions that guarantee the optimality of our trading rules. We also present numerical examples to illustrate.

Selecting stock pairs for pairs trading while incorporating lead–lag relationship

Pairs Trading is carried out in the financial market to earn huge profits from any known equilibrium relation between a pair of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead–lag relationship has been empirically studied in various financial markets. Earlier research works have suggested some measures for identifying the best pairs for pairs trading, but they do not consider this lead–lag effect. The present study proposes a new distance measure which incorporates the lead–lag relationship between the stocks while selecting the best pairs for pairs trading. Further, the lead–lag value between the stocks is allowed to vary continuously over time. The importance of the measure has been show-cased through experimentation on three different datasets, consisting of Indian, American and Japanese companies. When the proposed measure is clubbed with SSD measure, i.e., when pairs are identified through optimising both these measures, then the selected pairs consistently generate the best profit, as compared to all other measures. Finally, possible generalisation and extension of the proposed distance measure have been discussed.

Improving Pairs Trading Using Neural Network Techniques and Fundamental Ratios

Pairs trading is a quantitative trading strategy consisting on identifying two stocks that historically move together and, using the assumption that their prices difference has mean- reverting properties, exploit the deviation from the mean by taking long – short position in the chosen pair to profit. Throughout the years, different approaches have been developed in order to exploit this strategy. However, there is little literature who looks whether the divergences in the prices are generated by poor company results, i.e. whether the deviation from the mean are product of bad (or good) fundamentals and are justified, or if they generate a new equilibrium point for the pair. In addition, since machine learning techniques are becoming more popular in finance, this work aims to analyze the performance of pairs trading strategy using neural network techniques applied to S&P 500 index components, selecting pairs of stocks from same industry and picking up the effects of the fundamental ratios in the pairs before taking a trade decision.

Orthant-based variance decomposition in investment portfolios

A traditional and useful approach in portfolio theory is to consider the total risk of one security partitioned into two components: market risk and specific risk. In this paper, we propose a new variance decomposition based on a four-orthant partitioning of a bivariate normal distribution representing the returns on two stock portfolios. Four Euclidian quadrants around the central mean point are considered with their correspondent truncated distributions. We can consider stock pairs as events in which both stocks rise together, both decline or one rises and the other declines. The question that arises is what the contribution is of each quadrant to the overall mean return. And, what is the contribution of each quadrant to the total variance? We consider the mixture of four truncated bivariate normal distributions, where the weighting coefficients coincide with the quadrant probabilities. Through the law of total variance and the first and second moments of each truncated distribution, the requested decomposition formulas are deduced. These results are validated with straightforward simulations. The equations obtained here show higher variance concentration when considering diagonal quadrants, more than could be expected when compared to the subset probability mass. These results show that pair trading and low variance strategies could be better interpreted with this variance decomposition. Finally, a comparison with principal component theory is carried out showing that greater variance concentration can be found within this orthant scheme.

Testing the efficient market hypothesis in Latin American stock markets

We propose a novel approach to study if Latin America Stock Markets are Efficient. This test is based on a statistical arbitrage technique known as Pairs Trading, which is a relative value trading strategy consisting in taking a position in a pair of stocks that are chosen to have similar characteristics and taking a long position in one stock and a short position in the other stock. We use an approach introduced in Ramos et al. (2007) based on the evolution of the Hurst Exponent of a pair. We will show how in emerging markets this trading strategy is profitable though it is not in developed markets, which is according with the weak form of efficiency.

Cointegration Analysis of Major African Stock Markets

Globalization, technological advancements and financial liberalization have made it possible for stock markets in different countries to interact and affect and/or influence each other both in the short-run and in the long-run. This study uses the Dickey-Fuller, Engle-Granger method and the Johansen method to test for cointegration using a pair wise analysis between the stock markets in five African countries. These countries are Nigeria, Egypt, South Africa, Morocco, and Mauritius. The Granger causality test is equally used to understand the short-run linkages between the stock markets. All statistical tests are carried out using the R statistical software. Weekly stock indices from January 2010 to December 2015 are employed, with each stock price expressed in local currency. Correlation between the stock markets is very low, with the strongest correlation coefficient being just 31% between the stock Exchange of Mauritius and the Johannesburg Stock Exchange. Cointegration analyses reveal long-run associationship between twelve, out of the twenty-one pairs of stock markets. Granger causality tests reveal bidirectional causality between the South African and Mauritian stock markets and the South African and Nigerian stock markets. Unidirectional causality was also found from the Nigerian stock market to the Moroccan stock market and from the Nigerian stock market to the Mauritian stock market. This research will help investors to invest wisely in the stock markets examined. It will equally aid economic experts and policy makers in these countries to understand the impact of a shock on economic variables on the economy.

Robust optimal insurance and investment strategies for the government and the insurance company under mispricing phenomenon

This paper considers the robust optimal insurance-investment problem for the policyholder who is specified as a government and the insurance company with considering mispricing phenomenon. The government is allowed to purchase proportional insurance from the insurance company whose surplus process is assumed to follow the classical Cramér-Lundberg (C-L) model. Suppose the insurance company can invest in different financial markets and the government is limited to invest in one financial market. Thus, the insurance company is allowed to invest in a pair of mispriced stocks which offer statistical arbitrage opportunities, a risk-free asset, and a market index while the government is assumed to invest in a risk-free asset and a risky asset whose price process satisfies the geometric Brownian motion. Furthermore, we take the joint interest of the government and the insurance company into account. The government and the insurance company are both ambiguity-averse, aiming to maximize their expected joint product exponential utility of terminal wealth. By applying stochastic optimal control approach, we derive the robust equilibrium insurance-investment strategies and the corresponding equilibrium value function explicitly. In addition, we present some special cases of the model as extended results. Finally, numerical simulations are presented to illustrate the effects of model parameters on the optimal strategies.

Display clutter and its effects on visual attention distribution and financial risk judgment

Display clutter is a widely studied phenomenon in ergonomics, where information density and other properties of task-relevant visualizations are related to effective user performance and visual attention. This paper examines the impact of clutter in the context of financial stock visualizations. Depending on their expertise, traders can use a variety of different cues to judge the current and future value of a stock and to assess its riskiness. In our study, two groups of participants (novices and experts) judge the riskiness of 28 pairs of stocks under two clutter conditions (low and high). Consistency of judgments and group concordance serve as measures for judgment performance, while mean fixation duration, fixation frequency, and transition matrix density are employed to capture visual attention. Our results reveal significant effects of display clutter and expertise on both the performance measures as well as the visual attention measures.

Diversification and optimal hedges for socially responsible investment in Brazil

This study contributes to the literature on socially responsible investing by examining the diversification potential of commodities, specifically oil, gold and clean energy together with the Brazilian Corporate Sustainability Index (ISE). Multivariate GARCH models are used to model volatility spillovers and conditional correlation in pairs of stocks containing ISE. Specifically, A-BEKK and A-DCC models with spillovers are estimated. The models’ results are used to compute and analyze the optimal weights and hedge ratios for stock portfolio holdings. The greatest benefit from diversification is obtained through the acquisition of gold and then OVX.

Explaining future market return and evaluating market condition with common preferred spread index

We build CPS-index (Common Preferred Spread Index) using the spread return between common and preferred stock pairs, and show that CPS-index has explanatory power for long term market return. Common stocks are more sensitive to the market condition than preferred stocks so that CPS-index tends to oscillate according to market condition. We observe that the future realized market return becomes high when CPS-index is low and vice versa. There is an inverse relationship between CPS-index and the future market return of S&P500 index. The statistical analysis of the regression between CPS-index and future market return shows that CPS-index has a significant power to explain the future realized market return in 21 months or up to 48 months ahead of time. Multivariate regression analysis confirms that the inclusion of CPS-index as an explanatory variable enhances the market predictability. We apply neural network to predict the future market return and observe that CPS-index provides better prediction results in any time horizon longer than twenty seven months.

Optimal Pairs Trading with Dynamic Mean-Variance

Pairs trading is one of convergence trades, which simultaneously buy relatively underpriced assets and sell relatively overpriced assets to exploit temporary mispricing. This paper studies time consistency optimal pairs trading strategies under both symmetric'' and non-symmetric'' trading constraints. We provide fully analytical trading strategies under Mean-Variance (MV) framework by assuming the price spread of a pair of related securities follows an Ornstein-Uhlenbeck (OU) process. Model calibration is discussed and real-data experiments based on pairs of stocks and futures traded on China security markets are presented to illustrate the practical implementation of the proposed approach.

Empirical analysis of the dynamic dependence between WTI oil and Chinese energy stocks

In this paper, we employ the dynamic copula approach to explore the dependence structure between WTI oil and Chinese energy stock index (300ENI). We first use the AIC (Akaike Information Criteria) together with the binary segmentation procedure to detect the change points of copula families and copula parameters, which extend the existing time-varying copula method. Then we can get the optimal copula between WTI oil and 300ENI for each time period, and analyze the dependence between them combining with the national and international economic environment. We find that the recession of both the international oil market and Chinese energy stock market causes the significant concordance and symmetric tail dependence. The asymmetric dependence turns out to be in Chinese economic recession and recovery time. Furthermore, we apply our results to analyze the effectiveness of hedging WTI oil using 300ENI. Results show that the hedging is more effective in China's sluggish time than in economic boom. Finally, our economic cycle analysis shows that different economic situation pairs of WTI oil and Chinese energy stocks exhibit different tail dependency, which could be used to predict the market future and provide market participants and policy makers with some suggestions on investment, hedging and decision making. • The dynamic copula with change in parameters and types is modeled. • A new method of applying Akaike Information Criterion into binary segmentation procedure is proposed. • The relationship between WTI oil prices and 300ENI returns have been studied. • Different economic state pairs of two markets exhibit different concordance and tail dependency. • The proposed model is applied to calculate the hedge ratio and hedging effectiveness of 300ENI against the WTI oil risk.

Simulation Modelling of an Algorithm for Protection from Skidding and Wheel Locking of Wheel Pairs of Electric Rolling Stock

Modern electric rolling stock is equipped with a microprocessor control system of the traction electric drive and smooth contactless voltage regulation on traction motors. It allows implementing a flexible skidding (wheel locking) algorithm of protection. Untimely detection and location of skidding (wheel locking) processes leads to a decrease of the traction (braking) forces, damage to the wheel motor unit, and increased wear of wheels and rails. The simulation model of the electric rolling stock with the rotational-motor electric drive and smooth zone-phase voltage regulation on the traction electric motor was developed. Analysis of the algorithms for protection from skidding and wheel locking in the mode of traction and regenerative braking was made with the help of the above model. The model contains blocks of the MATLAB system and Simulink extension package: traction transformer blocks, traction motor blocks, digital automatic control system of the traction electric drive, current and voltage sensors, and an output module of the modelling results. Skidding (wheel locking) algorithms for protection from skidding (wheel locking) were considered. Their location is a result of the reduction of the armature current of the traction motors with the skidding (wheel–locking) wheel set. In the traction mode, the reduction of the armature current takes place due to the impact on the armature current regulator of the traction electrical motors, and in the mode of the regenerative braking on the exiting current regulator of the traction motors group with the wheel locking wheel set. The considered protective algorithms enable effective localization of the skidding (wheel locking) processes with a minimum loss of the traction (braking) forces.

Research on profit abilities of order placement strategies in pairs trading

This paper discusses profit abilities of three pairs-trading strategies. When spreads of one pair reach an entry threshold, traders submit limit orders for one lowly liquid stock and market orders for the other highly liquid stock. In order to research on profit abilities, this paper models spreads of that pair of stocks as an Ornstein-Uhlenbeck (OU) process and supposes execution time of limit orders as a random variable independent of spreads of the pair. Strategy 1 is a traditional pairs-trading strategy with market orders. Inversely, strategies 2 and 3 are pairs-trading strategies related to limit orders. Finally, this research finds out that pairs-trading strategies with limit orders can beat pairs-trading strategies with market orders through an empirical experiment with real-world data. The contribution of this paper is to analyse three strategies and verify that strategy 3 has the best performance when the investment threshold is low. The results of this paper can help investors to make rational investment.

Research on profit abilities of order placement strategies in pairs trading

This paper discusses profit abilities of three pairs-trading strategies. When spreads of one pair reach an entry threshold, traders submit limit orders for one lowly liquid stock and market orders for the other highly liquid stock. In order to research on profit abilities, this paper models spreads of that pair of stocks as an Ornstein-Uhlenbeck (OU) process and supposes execution time of limit orders as a random variable independent of spreads of the pair. Strategy 1 is a traditional pairs-trading strategy with market orders. Inversely, strategies 2 and 3 are pairs-trading strategies related to limit orders. Finally, this research finds out that pairs-trading strategies with limit orders can beat pairs-trading strategies with market orders through an empirical experiment with real-world data. The contribution of this paper is to analyse three strategies and verify that strategy 3 has the best performance when the investment threshold is low. The results of this paper can help investors to make rational investment.

On some nonlinear dependence structure in portfolio design

Constructing portfolios with high returns and low risks is always in great demand. Markowitz (1952) utilized correlation coefficients between pairs of stocks to build portfolios satisfying different levels of risk tolerance. The correlation coefficient describes the linear dependence structure between two stocks, but cannot capture a lot of nonlinear independence structures. Therefore, sometimes, portfolio performances are not up to investors' expectations. In this paper, based on the theory of copula by Sklar (see [19]), we investigate several new methods to detect nonlinear dependence structures. These new methods allow us to estimate the density of the portfolio which leads to calculations of some popular risk measurements like the value at risk (VaR) of investment portfolios. As for applications, making use of the listed stocks on the Ho Chi Minh city Stock Exchange (HoSE), some Markowitz optimal portfolios are constructed together with their risk measurements. Apparently, with nonlinear dependence structures, the risk evaluations of some pairs of stocks have noticeable twists. This, in turn, may lead to changes of decisions from investors.

Complex Similarity and Fluctuation Dynamics of Financial Markets on Voter Interacting Dynamic System

A financial price dynamics is developed based on the voter interacting system, in an attempt to investigate and reproduce the complex similarity and the fluctuation dynamics of financial markets. The complexity-invariance distance (CID) is applied to study the similarity of each stock pairs. A simple classification of seven real indexes and the simulated data is obtained according to the CID values for each stock pairs. The corresponding multiscale dynamical behaviors of CID values are also studied by combining CID with the multiscale method. Further, the similarity of the newest data and the historical data of the returns is investigated by a novel auto-CID analysis, and a corresponding exponent relationship is exhibited. Moveover, the cross correlation function (CCF) is applied to study the correlation of each stock pairs and the causalities of these stock pairs are investigated by the Granger causality method. Besides, the complexity and the randomness of fluctuations of returns, surrogate returns, shuffled returns and intrinsic mode functions (derived from empirical mode decomposition) are also explored at different thresholds with Lempel–Ziv complexity. The empirical study shows complex similarity and similar random property between the proposed price model and the real stock markets, which exhibits that the proposed model is feasible to some extent.

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500

This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.