A Network-Based Dynamic Analysis in an Equity Stock Market

Eberhard, Juan; Lavín, Jaime F.; Montecinos‐Pearce, Alejandro

doi:10.1155/2017/3979836

Cited by 12 publications

(20 citation statements)

References 34 publications

(58 reference statements)

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“…From this complete dataset, we used 80% of the samples in training data and the remaining 20% for testing data. This selection method is the same as the previous literature [5,24].…”

Section: Methodsmentioning

confidence: 99%

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

et al. 2019

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An increasing number of the renowned company's investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.Hindawi Complexity Volume 2019, Article ID 9202457, 15 pages https://doi.

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“…From this complete dataset, we used 80% of the samples in training data and the remaining 20% for testing data. This selection method is the same as the previous literature [5,24].…”

Section: Methodsmentioning

confidence: 99%

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

et al. 2019

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“…Another important element to consider is the transactions considered to build the networks in study. As in Eberhard and others [22], we study the correlation for networks constructed for different thresholds of the total amounts traded between brokers (volume deciles are used as thresholds). Decile 10 corresponds to 100% of the transactions, while decile 1 only contains the top 10% of the transaction volume.…”

Section: Methodsmentioning

confidence: 99%

“…This need stems from the fact that underdeveloped markets present a level of market efficiency that is lower than in developed countries [21]. Eberhard, Lavín, and Montecinos-Pearce [22] find evidence that structural changes in the brokers' transaction network are associated with positive increases of the stock market return in an emerging economy.…”

Section: Related Literaturementioning

confidence: 99%

“…The dataset provided in anonymized form, with no association between the broker's name and their transactions, includes the following information: day of transaction, instrument traded, type of operation, units exchanged, purchase price, and the identification of brokers involved in buying and selling. We use the latter data to construct the daily directed transaction network based on sales for the Chilean equity market following Eberhard and others [22]. 12 We consider this brokers' transaction network, henceforth transaction network, as the space in which we apply a spatial econometrics technique to detect spatial dependence.…”

Section: Main Data Sourcesmentioning

confidence: 99%

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Analyzing Stock Brokers’ Trading Patterns: A Network Decomposition and Spatial Econometrics Approach

et al. 2019

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Using a unique data set with all the daily transactions from the Santiago Stock Exchange, we develop a novel methodology that combines a network decomposition with a spatial econometrics technique to study how brokers’ characteristics and trading decisions may affect the stock market return. We present suggestive evidence of a mechanism by which structural changes of the transaction network between brokers affect the aggregate returns of the stock market. We find that brokers tend to trade with counterparties with dissimilar intraday selling volume when market return significantly increases. Moreover, brokers with a research department tend to sell to brokers without a research department when the market experiences a considerable increase of its return. From the financial perspective, these results highlight new ways in which intermediaries may affect market equilibrium and the efficiency of the market.

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“…In modern conditions, the problems of studying the dynamics of reproductive processes at the macroeconomic level are particularly acute, since the systemic crisis of the Russian economy, accompanied by violations of the main reproductive proportions, although it was softened, but not completely overcome, and then worsened under the influence of the global financial crisis [1,2]. Within this direction the researches on creation of complexes of economic and mathematical models and program tools for the solution of various problems of the macroeconomic analysis [3][4][5][6][7][8][9][10][11][12][13][14][15] are conducted.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Model of Controlling the Behavior of an Economic Agent Using the Mechanism of Self-Regulation of Resource Flows

Ilyasov¹,

Макарова²,

Zakieva³

et al. 2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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The problem of development of the analytical system of simulation modeling (ASSM) of functioning processes of the economic system investigated at the macro level is considered. The structure of the complex of dynamic models of economic agents, which are considered at the macro level and presented in the form of firms, households (population), banks and the state, is presented. The concept of dynamic nonequilibrium mode of functioning of the macroeconomic system (MES) is introduced. The system principles for constructing dynamic models of behavior of macroeconomic agents (MEA) are stated. The dynamic model of the controlled behavior of the MEA which is analyzed from the standpoint of various approaches that demands performance of a number of transformations of the scheme of dynamic model is presented. The three variants of interpretation of the scheme of the dynamic model of the MEA behavior (from the standpoint of economic theory, control theory and systems theory) allowed to reveal the regularities of the MEA behavior in non-equilibrium modes. It is shown that non-equilibrium modes arising in the behavior of one agent extend to other agents. The proposed algorithm provides the restoration of the dynamic equilibrium mode of the whole MES. The application of controlled dynamic models of the MEA and the developed ASSM is advisable as a research system for the purpose of analyzing different management scenarios of the functioning process of the MES as a whole.

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A Network-Based Dynamic Analysis in an Equity Stock Market

Cited by 12 publications

References 34 publications

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

Analyzing Stock Brokers’ Trading Patterns: A Network Decomposition and Spatial Econometrics Approach

Dynamic Model of Controlling the Behavior of an Economic Agent Using the Mechanism of Self-Regulation of Resource Flows

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