| 1. |
- Xie, Kefan, et al.
(författare)
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Research on the group decision-making about emergency event based on network technology
- 2011
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Ingår i: Information Technology and Management. - : Springer. - 1385-951X .- 1573-7667. ; 12:2, s. 137-147
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Tidskriftsartikel (refereegranskat)abstract
- In order to improve decision-making efficiency about emergency event, this paper proposes a novel concept, i.e., Agile-Delphi Method, which is an integration of agile decision and Delphi Method implicating that the decision-makers instantly deliver, respond, treat, and utilize information via Delphi process while conducting group decision-making about emergency event. The paper details the mechanism of group decision-making about emergency event based on network technology and Agile-Delphi Method. Finally, the paper conducts an empiric analysis taking the “111 event”, i.e., the liquid ammonia spill event happened on November 1, 2006 in a phosphorus chemical company in China, as an example.
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| 2. |
- Chen, Yan, et al.
(författare)
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Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation
- 2024
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Ingår i: European Journal of Finance. - : ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD. - 1351-847X .- 1466-4364.
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Tidskriftsartikel (refereegranskat)abstract
- We study systemic risk drivers of FinTech and traditional financial institutions under normal and extreme market conditions. We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm characteristics, and network topologies as systemic risk drivers and perform the ML-based interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; namely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downside and upside) market conditions, while under normal market conditions, institutions with high price-earnings ratio, large MC, and low IVOL play an essential role in stabilizing markets; (ii) macroeconomic variables are the most important extreme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions. The interactions between macroeconomic variables are the most prominent in systemic risk under different market conditions.
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| 3. |
- Chen, Yan, et al.
(författare)
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Quantile connectedness and the determinants between FinTech and traditional financial institutions: Evidence from China
- 2023
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Ingår i: Global Finance Journal. - : ELSEVIER. - 1044-0283 .- 1873-5665. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- This study examines the connectedness and risk spillovers between Chinese FinTech and traditional financial institutions by using quantile-based vector autoregression (QVAR) networks. Specifically, by using daily data from January 2014 to June 2022, we focus on system-, sector-, and institution-level quantile connectedness characteristics, with the following findings. At the system level, the QVAR networks linking FinTech and traditional financial institutions are more connected at the extreme quantiles than at the median quantile. At the sector level, banks, real estate firms, and FinTech sectors act as net risk receivers, whereas securities and insurers act as net risk emitters. At the institutional level, risk transmission and reception of institutions significantly increase when market conditions rapidly change. We also investigate the determinants of quantile connectedness by using an exponential random graph model and find that (i) across different quantiles, the book-to-market and return on equity of institutions have a positive impact on their risk spillovers; (ii) at the extreme quantiles, the book-to-market is more pronounced than the return on equity; and (iii) at the median quantile, banks and FinTech institutions are more connected than insurers, real estate firms, securities, and other financials.
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| 4. |
- Wang, Gang-Jin, et al.
(författare)
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Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets
- 2023
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Ingår i: International Review of Financial Analysis. - : ELSEVIER SCIENCE INC. - 1057-5219 .- 1873-8079. ; 86
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.
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| 5. |
- Wang, Gang-Jin, et al.
(författare)
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Portfolio optimization based on network centralities: Which centrality is better for asset selection during global crises?
- 2024
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Ingår i: JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING. - : KEAI PUBLISHING LTD. - 2096-2320. ; 9:3, s. 348-375
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Tidskriftsartikel (refereegranskat)abstract
- We construct correlation-based networks linking 86 assets (stock indices, bond indices, foreign exchange rates, commodity futures, and cryptocurrencies) and analyze the impact of asset selection on portfolio optimization using different centrality measures (including degree, eigenvector, eccentricity, betweenness, PageRank, and hybrid centralities). In times of a global crisis, peripheral assets located in cross-market networks are more suitable for investment. By comparing portfolio performance based on different centrality measures, we find that (i) hybrid, eigenvector, and PageRank centralities can best improve portfolio performance; (ii) degree centrality is suitable for larger portfolios; and (iii) eccentricity and betweenness centralities are unsuitable for network optimization portfolios. In response, we explain them based on the construction principle of centrality measures. Additionally, our optimal portfolios suggest that investors pay more attention to the role of emerging countries, which are less exposed to external shocks and whose financial markets are more likely to remain stable. (c) 2024 China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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| 6. |
- Zhou, Yang, et al.
(författare)
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Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning
- 2023
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Ingår i: Research In International Business and Finance. - : ELSEVIER. - 0275-5319 .- 1878-3384. ; 64
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Tidskriftsartikel (refereegranskat)abstract
- We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity; (ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak; and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants.
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