Pairs Of Stocks Research Articles

Common investor coverage and excess return comovement: Evidence from Seeking Alpha

Investors often cover a limited number of stocks, leading to a certain level of correlation in their information sets. Despite its importance, the role of this correlation in determining the return comovement between stock pairs has been less explored. In this study, we introduce a novel measure of common investor coverage, calculated using the number of co-commentators normalized by the geometric average number of commentators for each stock pair on Seeking Alpha. This measure aims to capture the underlying correlation in investors’ information sets. Our findings reveal a positive relationship between common investor coverage and excess comovement, which cannot be explained by traditional asset pricing models. Moreover, we observe a stronger relationship between common investor coverage and excess return comovement for stocks favored by retail investors, particularly after the implementation of the reward policy in 2011. This suggests that retail investors may play a crucial role in driving this relationship.

PENGUKURAN RISIKO PORTOFOLIO SAHAM JAKARTA ISLAMIC INDEX DENGAN STOCHASTIC DOMINANCE

Stocks is one of the assets which has a high level of liquidity. Optimizing stock investment can be done by diversifying the portfolio. Stochastic Dominance is a portfolio formation method that looks at the amount of dominance of each stock pair. This study’s purpose is to apply the Stochastic Dominance method to create an optimal portfolio on sharia index called JII from April 2018 to March 2022. This study indicate that out of the 5 selected stocks, there are 4 stocks that dominate each stock pair, including ADRO, ICBP, PTBA, UNTR. ADRO proportion are 57,14% while ADRO, PTBA, and UNTR’s proportion are each 14,28%. The level of risk on this research is measured by a VaR with historical simulation method. Using this method, the maximum potential loss of the formed portfolio for the next month is 8.5% at a 95% confidence level.

Designing Efficient Pair-Trading Strategies for the Technology Stock Market

Pairs trading is a quantitative trading approach that exploits instances of financial markets exhibiting disequilibrium. Through the identification of a pair of stocks with a his-torical pattern of correlated movement, and under the assumption that their price differentials will return to a central tendency, an investor can seek to gain from this mean-reversion by establishing a long position in the designated pair. Throughout the years, numerous trading frameworks and methodologies have been devised to enhance the efficacy of this strategy. This paper proposes an approach based on cointegration and correlation, but ultimately utilizes cointegration to identify the pair trading investment strategy that can be applied to technology sector stocks and metal futures in China over the last five years. The top five ranked portfo-lios are then evaluated. The results indicate that the pairs with Silver and Copper exhibited positive returns, while the pair with Palladium yielded negative returns.

Machine Learning-Enhanced Pairs Trading

Forecasting returns in financial markets is notoriously challenging due to the resemblance of price changes to white noise. In this paper, we propose novel methods to address this challenge. Employing high-frequency Brazilian stock market data at one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including ARIMA, Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict changes in the price ratio of closely related stock pairs. Our findings indicate that a combination of reversion and machine learning-based forecasting methods yields the highest profit-per-trade. Additionally, by allowing the model to abstain from trading when the predicted magnitude of change is small, profits per trade can be further increased. Our proposed forecasting approach, utilizing a blend of methods, demonstrates superior accuracy compared to individual methods for high-frequency data.

The uncertainty of fluctuation correlations in global stock markets

Revealing the associations among stocks is fundamental for unveiling the underlying rules of global stock markets. In this work, we propose a combined entropy–complex network framework that detects fluctuation connections among stock prices. The results demonstrate that global stock markets show high uncertainty (high entropy). Based on our analysis of the system's symmetry and dominant correlation fluctuation patterns, we identified the least uncertain stock pair in the market: DEU and FRA. We also revealed the most reliable stocks as references for every stock. Quantifying the uncertainty found in global stock markets and identifying the driving fluctuation patterns can benefit investors by decreasing risk.

Inventory Management System

For organizations that handle transactions involving consumer goods, an inventory management system is critical for quality control. A huge retail store could run out of supply of a critical item if inventory is not properly managed. When it’s time to reorder, an effective inventory management system will notify the retailer. This inventory management system is also useful for tracking huge shipments automatically. Counting each pair of stocks by hand will almost certainly result in a mistake. The risk of human error can be reduced by using an automated inventory management system. An Inventory Management System also aids in the tracking of retail product theft, providing useful information regarding store revenues and the need for theft-prevention devices. Scanning a barcode on the item or a barcode scanner is how Automated Inventory Management Systems function. The central computer system then keeps track of this data. The purchase order can also include a list of items that need to be pulled for packaging and shipping. In this situation, the Inventory Management System can perform a range of tasks. It can assist a warehouse worker in locating things on an order list, encoding shipment information such as tracking numbers and delivery addresses and removing purchased items from the inventory tally to maintain a correct count of in-stock items. All of this information works together to give firms real-time inventory tracking data. The simple search in a database makes it easy to find and look at inventory information in real-time.The inventory management system project abstract tells the overall function of the software. It consists of the reason why the project was proposed and the solutions made to answer the needs of the targeted users. To enlighten you with this, here’s the Inventory Management System Project Abstract

Interpreting the Self-Awakening in A Pair of Silk Stockings from a Beauvoir Feminist Perspective

Interpreting the Self-Awakening in A Pair of Silk Stockings from a Beauvoir Feminist Perspective

Do green investments improve portfolio diversification? Evidence from mean conditional value-at-risk optimization

This study investigates how green investment assets improve optimal portfolio diversification in terms of tail downside risk. We use the wavelet conditional value-at-risk ratio to explore the benefits of adding green assets to conventional portfolios. We quantify risk based on the contagion between conventional stock market indices and green environmental assets, including a sustainability index, clean energy, and green bonds. Our findings emphasize the high variance between conventional stock pairs, providing evidence of contagion effects before and during the COVID-19 pandemic. We show that including clean energy and green bond indices in conventional portfolios reduces the extreme risk of portfolios. In addition, we find that the diversification benefits of clean energy, green bonds, and safe-haven investments apply especially in the short term during the pandemic. Finally, we show that the considered portfolios could not decrease long-term risk during the COVID-19 crisis because of the systematic risk spread. Our portfolio optimization design supports the superiority of clean energy and green bonds in portfolio diversification over the sustainability index. These insights can be used by portfolio managers to inform diversification in different investment horizons.

Improving Cointegration-Based Pairs Trading Strategy with Asymptotic Analyses and Convergence Rate Filters

AbstractA pairs trading strategy (PTS) constructs a mean-reverting portfolio whose logarithmic value moves back and forth around a mean price level. It makes profits by longing (or shorting) the portfolio when it is underpriced (overpriced) and closing the portfolio when its value converges to the mean price level. The cointegration-based PTS literature uses the historical sample mean and variance to establish their open/close thresholds, which results in bias thresholds and less converged trades. We derive the asymptotic mean around which the portfolio value oscillates. Revised open/close thresholds determined by our asymptotic mean and standard derivations significantly improve PTS performance. The derivations of asymptotic means can be extended to construct a convergence rate filter mechanism to remove stock pairs that are unlikely to be profitable from trading to further reduce trading risks. Moreover, the PTS literature oversimplifies the joint problem of examining a stock pair’s cointegration property and selecting the fittest vector error correction model (VECM). We propose a two-step model selection procedure to determine the cointegration rank and the fittest VECM via the trace and likelihood ratio tests. We also derive an approximate simple integral trading volume ratio to meet no-odd-lot trading constraints. Experiments from Yuanta/P-shares Taiwan Top 50 Exchange Traded Fund and Yuanta/P-shares Taiwan Mid-Cap 100 Exchange Traded Fund constituent stock tick-by-tick backtesting during 2015–2018 show remarkable improvements by adopting our approaches.

From cryptos to consciousness: Dynamics of return and volatility spillover between green cryptocurrencies and G7 markets

Based on TVP-VAR technique, this study examines the return and volatility spillover between green cryptocurrencies and G7 equity markets. The static connectedness measure posits that green cryptocurrencies function as receivers of return and volatility spillovers from G7 markets. The analysis of dynamic connectedness reflects a spike in total return and volatility spillovers during market stress and uncertainty. Furthermore, we estimate the optimal portfolio weights and hedge ratios for all pairs of green cryptocurrency and stock markets, implying critical insights for policymakers, hedge fund managers, and portfolio management professionals.

Time-consistent reinsurance-investment games for multiple mean-variance insurers with mispricing and default risks

This paper studies a non-zero-sum stochastic differential game for multiple mean-variance insurers. Insurers can purchase proportional reinsurance and invest in a risk-free asset, a market index, a defaultable bond and multiple pairs of mispriced stocks. The dynamics of the mispriced stocks satisfy a “cointegrated system” where the expected returns follow the mean reverting processes, and the bond is defaultable with a recovering proportional value at default. In particular, we assume that the investment opportunities in mispriced stocks are only available for a few insurers, which is more realistic and in line with the superiority of information in the competitive market. Each insurer's objective is maximizing a function of her terminal wealth and competitors' relative wealth under the mean-variance criterion. Using techniques in stochastic control theory, we establish the extended Hamilton-Jacobi-Bellman equations and obtain the equilibrium strategies. Note that the derived solutions are analytical and time-consistent, and we verify the competitive advantages gained from investment opportunities in mispriced stocks. We represent our results in terms of the M-matrices, which help us prove the existence and uniqueness of the solutions and further explicitly analyze how the crucial arguments in the model affect the equilibrium strategies. Numerical examples with detailed sensitivity analyses are presented to support our conclusions.

МЕТОДИКА ПРОГНОЗИРОВАНИЯ РЕСУРСА УЗЛОВ И ДЕТАЛЕЙ ЖЕЛЕЗНОДОРОЖНОГО ПОДВИЖНОГО СОСТАВА ПРИ ПОМОЩИ МАШИННОГО ОБУЧЕНИЯ

The study objective is to develop a system for forecasting the service life of components and parts of railway rolling stock.
 The task to which the paper is devoted is to determine the service life of wheel pairs of railway rolling stock before their next turning.
 Research methods include a technique for forecasting the resource of components and parts of railway wheel pairs using a system for estimating expected parameters, including wheel run. The study includes the use of three different machine learning algorithms: linear regression, random forest and gradient boosting. The trained models of each algorithm are shown, as well as the convergence of MSE, MAPE an d R-squared metrics at each iteration of learning. 
 The novelty of the work: the study results make it possible to predict the period of wheel pairs operation with high accuracy and include a feedback mechanism for automating and updating the model, which increases the accuracy of forecasting.
 Research results: on the basis of the proposed method, a system is developed that allows determining the time interval of operation of rolling stock wheel pairs before their next turning.
 Conclusions: the proposed method will make it possible to predict the period of operation of certain components and parts under the conditions of a given proving ground until their working condition is restored, based on a system for evaluating expected parameters using machine learning.

Can textual sentiment partially explain differences in the prices of dual-listed stocks?

This study offers a robust examination of the impact of textual sentiment on price deviations for dual-listed stocks. We analysed four (three) pairs of twin stocks to establish the association between news (Twitter) sentiment differentials and theoretical price ratios. The results reveal that: Anglo-American and Anglo-Australian twin stocks exhibit a negative relationship between news (Twitter) sentiment and deviations from theoretical price parity, while the Anglo-South African twin stocks display a positive relationship using news sentiment differentials. We shed light on the role of online sentiment in understanding price deviations and offer valuable insights for investors and analysts.

Graph embedded dynamic mode decomposition for stock price prediction

We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.

Network connectedness and portfolio hedging of green bonds, stock markets and commodities

PurposeThis study aims to investigate the impact of the COVID-19 pandemic on the time-frequency connectedness between green bonds, stock markets and commodities (Brent and Gold), with a particular focus on China and its implication for portfolio diversification across different frequencies.Design/methodology/approachTo this end, the authors implement the frequency connectedness approach of Barunik and Krehlik (2018), followed by the network connectedness before and during the COVID-19 outbreak. In particular, the authors implement more involvement in portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness for green bonds and other financial assets.FindingsThe time-frequency domain spillover results show that gold is the net transmitter of shocks to green bonds in the long run, whereas green Bonds are the net recipients of shocks, irrespective of time horizons. The subsample analysis for the pandemic crisis period shows that green bonds dominate the network connectedness dynamic, mainly because it is strongly connected with the SP500 index and China (SSE). Thus, green bonds may serve as a potential diversifier asset at different time horizons. Likewise, the authors empirically confirm that green bonds have sizeable diversification benefits and hedges for investors towards stock markets and commodity stock pairs before and during the COVID-19 outbreak for both the short and long term. Gold only offers diversification gains in the long run, while Brent does not provide the desired diversification gains. Thus, the study highlights that green bonds are only an effective diversified.Originality/valueThis study contributes to the existing literature by improving the understanding of the interconnectedness and hedging opportunities in short- and long-term horizons between green bonds, commodities and equity markets during the COVID-19 pandemic shock, with a particular focus on China. This study's findings provide more implications regarding portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness.

Robust optimal asset-liability management with mispricing and stochastic factor market dynamics

This paper investigates a robust optimal asset-liability management problem under an expected utility maximization criterion. More specifically, the manager is concerned about the potential model uncertainty and aims to seek the robust optimal investment strategies. We incorporate an uncontrollable random liability described by a generalized drifted Brownian motion. Also, the manager has access to an incomplete financial market consisting of a risk-free asset, a market index with potentially path-dependent, time-varying risk premium and volatility, and a pair of mispriced stocks. The market dynamics are assumed to rely on an affine-form, square-root factor process and the price error is modeled by a co-integrated system. We adopt a backward stochastic differential equation approach hinging on the martingale optimality principle to solve this non-Markovian robust control problem. Closed-form expressions for the robust optimal investment strategies, the probability perturbation process under the well-defined worst-case scenario and the corresponding value function are derived. The admissibility of the robust optimal controls is verified under some technical conditions. Finally, we perform some numerical examples to illustrate the effects of model parameters on the robust investment strategies and draw some economic interpretations from these results.

Расчет движения подвижного состава по железнодорожному пути сеточно-характеристическим методом

The application of a grid-characteristic method is proposed for numerical simulation of the movement of rolling stock along a railway track laid on an earth and a bridge. In the study, the railway track is presented using a dynamic system of equations of the theory of elasticity of hyperbolic type. The grid-characteristic method relies on the characteristic properties of the system under consideration and uses finite-difference schemes of high order of accuracy to obtain a space-time solution. The features of railway structures are considered by changing the boundary conditions and conditions at the contact borders. To simulate the areas of contact of wheel pairs of rolling stock with the rail – the "wheel-rail" system – a previously developed pressure boundary condition is applied, modified so as to consider multi-element rolling stock. As a result of computer simulation of the movement of rolling stock along the railway track of various types, full-wave stress distribution in the track structure is obtained, which makes it possible to predict dangerous sections of rolling stock movement.

Pre-selection in cointegration-based pairs trading

The paper compares the final profitability of a cointegration-based pairs trading strategy when pairs of stocks are pre-selected by means of seven different measures. Some of the measures considered have been extensively used in the pairs trading literature, while others represent a novelty in this type of application. We find that pre-selection matters, since the excess returns remarkably vary, in terms of both average and variability, depending on the metrics used. Differences in profitability by pre-selection metrics are retrieved even after considering commissions and cut rules, market impact, a stricter definition of the Spread reversion to the equilibrium and alternative cointegration tests. Besides, the pairs trading profitability is found to be heterogeneous across the different pre-selection metrics also in terms of exposure to the systematic stock-market risk factors.

Non-linear correlation analysis in financial markets using hierarchical clustering

Distance correlation coefficient (DCC) can be used to identify new associations and correlations between multiple variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets of variables that provide equivalent information, is zero only when variables are independent, and is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient (PCC). Hence, DCC provides more information than the PCC. We analyze numerous pairs of stocks in S&P500 database with the distance correlation coefficient and provide an overview of stochastic evolution of financial market states based on these correlation measures obtained using agglomerative clustering.

The Analysis of Ellipsis in A Pair of Silk Stockings A Short Story by Kate Chopin

The title of the research is The Analysis of Ellipsis in “A Pair of Silk Stocking” A Short Story by Kate Chopin. The writer wants to find out what ellipsis features are used and how they are expressed in the short story. The research methodology is descriptive, which means the author analyzes this research by reading the entire short story, analyzing sentence by sentence, classifying ellipsis, and classifying sentences containing ellipsis. The data found in the short story include nominal ellipsis, clausal ellipsis, and verbal ellipsis as the following examples. The writer discovered 27 ellipsis in the short story. The total number of times nominal ellipsis used is 16, with a percentage of 59.26%. Clausal ellipsis was discovered 4 times, with a percentage of 14.81%. Verbal ellipsis with a percentage of 25.93%, the use of verbal ellipsis was discovered 7 times. The impact of this research is that in a fictional writing the sentence structure is more communicative when there is ellipsis in the structure. A literary writer can implement communicativeness in conveying the storyline so that it is not rigid, through the process of ellipsis in a speech