Global stock market investment strategies based on financial network indicators using machine learning techniques

Lee, Tae Kyun; Cho, Joon Hyung; Kwon, Deuk Sin; Sohn, So Young

doi:10.1016/j.eswa.2018.09.005

Cited by 138 publications

(58 citation statements)

References 43 publications

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“…Supervised learning algorithms can be implemented for portfolio optimization problems. Support vector machines can appropriately identify the non-linear relationships among the market variables [20]. Performance of such supervised learning models were further improved by using novel regularization or cross-validation techniques [115].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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“…The production efficiency and competitiveness of a country can be enhanced by constantly improving the country's innovation ability. With the increasingly frequent exchanges between regions, the relations among regions, countries, and even the whole world are improved [12][13][14][15][16]. Reportedly, the spatial dependence of innovation makes the spatial knowledge spillover the main factor to affect the innovation distribution [17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

Regional innovation distribution and its dynamic evolution: Policy impact and spillover effect—Based on the perspective of innovation motivation

Yan

2020

PLoS ONE

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This study aims to explore the direct effect and spillover effect of innovation policy in the distribution and dynamic evolution of the regional innovation from the perspective of innovation motivation using the spatial econometric model. Substantive innovation and strategic innovation in one region could affect innovation in another region. In addition, the direct effect and spatial spillover effect of innovation policy could significantly affect innovation; however, they exert a higher impact on substantive innovation. Considering different policy tools, we found that government subsidies exert a significant positive impact on substantive innovation and strategic innovation, whereas financial institution loans exert a significant negative impact on substantive innovation and strategic innovation. And for the impact range, the government subsidies are higher than that of financial institution loans. Furthermore, this study reveals the leading environmental factors affecting regional innovation and provide a policy basis to promote the construction of an innovation-oriented country.

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“…See for example, Kenett et al (2010;2012a;2012b; [10][11][12][13], [14] and Wang et al (2018) [15]. Other method adopts other correlation to measure the network, such as mutual information-based network [1,16,17], connectedness-based network [18,19], cointegration-based network [20][21][22], and entropy-based network [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

The impact of financial contagion on real economy-An empirical research based on combination of complex network technology and spatial econometrics model

Chen

Hao

2020

PLoS ONE

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This study presents financial network indicators that can be applied to inspect the financial contagion on real economy, as well as the spatial spillover and industry aggregation effects. We propose to design both a directed and undirected networks of financial sectors of top 20 countries in GDP based on symbolized transfer entropy and Pearson correlation coefficients. We examine the effect and usefulness of the network indicators by newly using them instead of the original Dow Jones financial sector as explanatory variables to construct the higher-order information spatial econometric models. The results demonstrate that the estimated accuracies obtained from both the two networks are improved significantly compared with the spatial econometric model using the original data. It indicates that the network indictors are more effective to capture the dynamic information of financial systems. And meanwhile, the accuracy based on the directed network is a little higher than the undirected network, which indicates the symbolized transfer entropy, i.e. the directed and weighted network, is more suitable and effective to reflect relationships in the financial field. In addition, the results also show that under the global financial crisis, the co-movement between financial sectors of a country/region and the global financial sector as well as between financial sectors and real economy sectors is increased. However, some sectors in particular Utilities and Healthcare are impacted slightly. This study tries to use the financial network indicators in modeling to study contagion channels on the real economy and the industry aggregation effects and suggest how network indicators can be practically used in financial fields.

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Global stock market investment strategies based on financial network indicators using machine learning techniques

Cited by 138 publications

References 43 publications

Machine Learning for Financial Risk Management: A Survey

Machine Learning for Financial Risk Management: A Survey

Regional innovation distribution and its dynamic evolution: Policy impact and spillover effect—Based on the perspective of innovation motivation

The impact of financial contagion on real economy-An empirical research based on combination of complex network technology and spatial econometrics model

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