Uncovering dynamic stock return correlations with multilayer network analysis

Rubin, Danielle N.; Bassett, Danielle S.; Ready, Robert

doi:10.1007/s41109-019-0132-5

Cited by 4 publications

(3 citation statements)

References 35 publications

(38 reference statements)

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“…For more accurate predictions on the stock market, researchers propose researching the correlation between corporations and incorporating the information on the related corporations of a target company [12]. To underscore the potential for using multilayer network tools to study the time-varying correlations of financial assets, the authors of [13] apply recent innovations in network science to analyze how correlations of stock returns evolve over time. A complex network could be used to research stock correlation, so Yan et al proposed to use part mutual information for developing the stock network [14].…”

Section: Correlationmentioning

confidence: 99%

A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Liang

Wang

2021

Entropy

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Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

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Section: Correlationmentioning

confidence: 99%

A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Liang

Wang

2021

Entropy

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“…The network structure that we aim to extract from the news stories has also been studied extensively on financial time series only. Rubin et al (2019) look at how correlations of stock returns evolve through time and then uncover communities of stocks, which move in highly correlated matter and can be interpreted as groups from the same industry sector. Isogai (2017) goes a step further and applies network clustering algorithms to Japanese stock returns to detect several stock groups that extend the existing business sector classifications, while also reducing the dimensionality of the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Characterizing financial markets from the event driven perspective

Torkar

Mladenić

2021

Appl Netw Sci

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In this work we study how company co-occurrence in news events can be used to discover business links between them. We develop a methodology that is able to process raw textual data, embed it into a numerical form, and extract a meaningful network of connections. Each news event is considered as a node on the graph and we define the similarity between the two events as the cosine similarity between their vectors in the embedded space. Using this procedure, we contribute to the literature by successfully reconstructing business links between companies, which is usually a difficult task since the data on this topic is either outdated, incomplete or not widely available. We then demonstrate possible uses of this network in two forecasting applications. First, we show how the network can be used as an exogenous feature vector, which improves the prediction of the correlation between companies in the network. This correlation is determined from their realized variance as well as using a wide set of machine learning models for prediction. Second, we demonstrate the use of network for predicting future events with point processes. Our methodology can be applied on any series of events, where we have demonstrated and evaluated its applicability on news events and large market moves. For most of the tested algorithms the experimental results show an improvement in performance when including information from our graphs. More specifically, in certain sectors using Neural Networks shows improved performance by up to 50%.

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“…This paper studies the impact of the 2008 financial crisis on Asian markets. Rubin [13] and others use the latest innovative technologies in the field of network science to analyze the correlation of stock returns and how they evolve. Predecessors have studied how the prices of many stocks are affected by various factors, but at present, there is a lack of case studies on stocks in which a stock is affected by other stocks and related indexes.…”

Section: Introductionmentioning

confidence: 99%