Inferring Energy Stock Returns Based on Financial Indicators from the Network Perspective

References

• An, F., X. Y. Gao, J. H. Guan, H. J. Li, and Q. Liu. 2016. An evolution analysis of executive-based listed company relationships using complex networks. Physica A 447:276–85. doi:10.1016/j.physa.2015.12.050.
   Web of Science ®Google Scholar
• An, H. Z., X. Y. Gao, W. Fang, Y. H. Ding, and W. Q. Zhong. 2014. Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach. Applied Energy 136:1067–75. doi:10.1016/j.apenergy.2014.07.081.
   Web of Science ®Google Scholar
• Barunik, J., and B. Malinska. 2016. Forecasting the term structure of crude oil futures prices with neural networks. Applied Energy 164:366–79. doi:10.1016/j.apenergy.2015.11.051.
   Web of Science ®Google Scholar
• Billio, M., M. Getmansky, A. W. Lo, and L. Pelizzon. 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104:535–59. doi:10.1016/j.jfineco.2011.12.010.
   Web of Science ®Google Scholar
• Blondel, V. D., J. L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment P10008. doi:10.1088/1742-5468/2008/10/P10008.
   Web of Science ®Google Scholar
• Boginski, V., S. Butenko, and P. M. Pardalos. 2005. Statistical analysis of financial networks. Computational Statistics & Data Analysis 48:431–43. doi:10.1016/j.csda.2004.02.004.
   Web of Science ®Google Scholar
• Buffett, M., and D. Clark. 2011. Warren buffett and the interpretation of financial statements. American Journal of Philology 17:399–437.
   Google Scholar
• Chan, K., and Y. C. Chan. 2014. Price informativeness and stock return synchronicity: Evidence from the pricing of seasoned equity offerings. Journal of Financial Economics 114:36–53. doi:10.1016/j.jfineco.2014.07.002.
   Web of Science ®Google Scholar
• Chen, Y. A. Y., and M. N. Young. 2010. Cross-border mergers and acquisitions by Chinese listed companies: A principal-principal perspective. Asia-Pacific Journal of Management 27:523–39. doi:10.1007/s10490-009-9150-7.
   Web of Science ®Google Scholar
• Chu, E. 1997. Impact of earnings, dividends and cash flows on stock returns: Case of Taiwan‘s stock market. Review of Quantitative Finance & Accounting 9:181–202. doi:10.1023/A:1008264510023.
   Google Scholar
• Chu, P. L., C. Vanderghem, H. L. MacLean, and B. A. Saville. 2017. Financial analysis and risk assessment of hydroprocessed renewable jet fuel production from camelina, carinata and used cooking oil. Applied Energy 198:401–09. doi:10.1016/j.apenergy.2016.12.001.
   Web of Science ®Google Scholar
• Chung, H. Y., and J. B. Kim. 2001. A structured financial statement analysis and the direct prediction of stock prices in Korea. Asia-Pacific Financial Markets 8:87–117. doi:10.1023/A:1011951102564.
   Google Scholar
• Diebold, F. X., and K. Yilmaz. 2014. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics 182:119–34. doi:10.1016/j.jeconom.2014.04.012.
   Web of Science ®Google Scholar
• Edirisinghe, N. C. P., and X. Zhang. 2008. Portfolio selection under DEA-based relative financial strength indicators: Case of US industries. Journal of the Operational Research Society 59:842–56. doi:10.1057/palgrave.jors.2602442.
   Web of Science ®Google Scholar
• Freeman, L. C. 1979. Centrality in social networks conceptual clarification. Social Networks 1:215–39. doi:10.1016/0378-8733(78)90021-7.
   Web of Science ®Google Scholar
• Gao, X. Y., W. Fang, F. An, and Y. Wang. 2017. Detecting method for crude oil price fluctuation mechanism under different periodic time series. Applied Energy 192:201–12. doi:10.1016/j.apenergy.2017.02.014.
   Web of Science ®Google Scholar
• Gao, X. Y., S. P. Huang, X. Q. Sun, X. Q. Hao, and F. An. 2018. Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system. Royal Society Open Science 5:172092. doi:10.1098/rsos.172483.
   PubMed Web of Science ®Google Scholar
• Grira, J., M. K. Hassan, C. Labidi, and I. Soumaré. 2018. Equity pricing in Islamic banks: International evidence. Emerging Markets Finance & Trade 55(03):613–633, 1540496X.2018.1451323.
   Google Scholar
• Guo, W. P., X. Li, and X. F. Wang. 2007. Epidemics and immunization on Euclidean distance preferred small-world networks. Physica A Statistical Mechanics & Its Applications 380:684–90. doi:10.1016/j.physa.2007.03.007.
   Web of Science ®Google Scholar
• Hardie, W. K., W. N. Wang, and L. N. Yu. 2016. TENET: Tail-Event driven Network risk. Journal of Econometrics 192:499–513. doi:10.1016/j.jeconom.2016.02.013.
   Web of Science ®Google Scholar
• Hautsch, N., J. Schaumburg, and M. Schienle. 2015. Financial network systemic risk contributions. Review of Finance 19:685–738. doi:10.1093/rof/rfu010.
   Web of Science ®Google Scholar
• Huang, S. P., H. Z. An, X. Y. Gao, and X. Q. Sun. 2017. Do oil price asymmetric effects on the stock market persist in multiple time horizons? Applied Energy 185:1799–808. doi:10.1016/j.apenergy.2015.11.094.
   Web of Science ®Google Scholar
• Jabbari, H., Z. Sadeghi, and S. A. Askari. 2013. Cash flow, earning opacity and its impact on stock price crash risk in Tehran stock exchange. International Journal of Academic Research in Accounting Finance & Management Sciences 3:138–45.
   Google Scholar
• Ji, Q., E. Bouri, and D. Roubaud. 2018a. Dynamic network of implied volatility transmission among US equities, strategic commodities, and BRICS equities. International Review of Financial Analysis 57:1–12. doi:10.1016/j.irfa.2018.02.001.
   Web of Science ®Google Scholar
• Ji, Q., E. Bouri, D. Roubaud, and S. J. H. Shahzad. 2018b. Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Economics 75:14–27. doi:10.1016/j.eneco.2018.08.015.
   Web of Science ®Google Scholar
• Ji, Q., B. Y. Liu, and Y. Fan. 2018c. Risk dependence of CoVaR and structural change between oil prices and exchange rates: A time-varying copula model. Energy Econ 77:80–92. doi:10.1016/j.eneco.2018.07.012.
   Google Scholar
• Ji, Q., B. Y. Liu, H. Nehler, and G. S. Uddin. 2018d. Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach. Energy Economics 76:115–26. doi:10.1016/j.eneco.2018.10.010.
   Web of Science ®Google Scholar
• Ji, Q., B. Y. Liu, W. L. Zhao, and Y. Fan. 2018e. Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS. International Review of Financial Analysis. doi:10.1016/j.irfa.2018.08.002.
   Web of Science ®Google Scholar
• Ji, Q., and D. Y. Zhang. 2018. China’s crude oil futures: Introduction and some stylized facts. Finance Research Letters 28:376–380. doi:10.1016/j.frl.2018.06.005.
   Google Scholar
• Ji, Q., D. Y. Zhang, and J. B. Geng. 2018f. Information linkage, dynamic spillovers in prices and volatility between the carbon and energy markets. Journal of Cleaner Production 198:972–78. doi:10.1016/j.jclepro.2018.07.126.
   Web of Science ®Google Scholar
• Ji, Q., H. Y. Zhang, and Y. Fan. 2014. Identification of global oil trade patterns: An empirical research based on complex network theory. Energy Conversion and Management 85:856–65. doi:10.1016/j.enconman.2013.12.072.
   Web of Science ®Google Scholar
• Jiang, G. J., and T. Yao. 2013. Stock price jumps and cross-sectional return predictability. Journal of Financial and Quantitative Analysis 48:1519–44. doi:10.1017/S0022109013000513.
   Web of Science ®Google Scholar
• Kamimura, R. 2003. Information-theoretic competitive learning with inverse Euclidean distance output units. Neural Processing Letters 18:163–204. doi:10.1023/B:NEPL.0000011136.78760.22.
   Web of Science ®Google Scholar
• Kara, Y., M. A. Boyacioglu, and O. K. Baykan. 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange. Expert Systems with Applications 38:5311–19. doi:10.1016/j.eswa.2010.10.027.
   Web of Science ®Google Scholar
• Konchitchki, Y., and P. N. Patatoukas. 2014. Taking the pulse of the real economy using financial statement analysis: Implications for macro forecasting and stock valuation. Accounting Review 89:669–94. doi:10.2308/accr-50632.
   Web of Science ®Google Scholar
• Li, H. J., H. Z. An, W. Fang, Y. Wang, W. Q. Zhong, and L. L. Yan. 2017. Global energy investment structure from the energy stock market perspective based on a heterogeneous complex network model. Applied Energy 194:648–57. doi:10.1016/j.apenergy.2016.05.062.
   Web of Science ®Google Scholar
• Li, H. J., H. Z. An, J. C. Huang, X. Huang, S. T. Mou, and Y. L. Shi. 2016. The evolutionary stability of shareholders‘ co-holding behavior for China‘s listed energy companies based on associated maximal connected sub-graphs of derivative holding-based networks. Applied Energy 162:1601–07. doi:10.1016/j.apenergy.2015.04.049.
   Web of Science ®Google Scholar
• Li, Q., and A. M. Li, 2013. Empirical study on stock valuation model based on multiple linear regression analysis. The 19th International Conference on Industrial Engineering and Engineering Management. Changsha, China: Springer Berlin Heidelberg, 479–486. doi:10.1530/REP-12-0498.
   Google Scholar
• Lin, Z. H., and T. T. Guo. 2013. Financial quality evaluation of listed companies based on factor analysis method. Frontiers of manufacturing science and measuring technology Iii. Pts 1-3 (401):2247–51.
   Google Scholar
• Liu, B. Y., Q. Ji, and Y. Fan. 2017. A new time-varying optimal copula model identifying the dependence across markets. Quantitative Finance 17:437–53. doi:10.1080/14697688.2016.1205208.
   Web of Science ®Google Scholar
• Liu, Q., H. J. Li, X. Y. Liu, and M. H. Jiang. 2018. Information networks in the stock market based on the distance of the multi-attribute dimensions between listed companies. Physica A 496:505–13. doi:10.1016/j.physa.2017.12.134.
   Web of Science ®Google Scholar
• Luo, J. W., and Q. Ji. 2018. High-frequency volatility connectedness between the US crude oil market and China’s agricultural commodity markets. Energy Economics 76:424–38. doi:10.1016/j.eneco.2018.10.031.
   Web of Science ®Google Scholar
• Luo, S., F. Morone, C. Sarraute, M. Travizano, and H. A. Makse. 2017. Inferring personal economic status from social network location. Nature Communications 8:7. doi:10.1038/ncomms15227.
   PubMed Web of Science ®Google Scholar
• Ma, Y. Y., X. T. Zhuang, and L. X. Li. 2011. Research on the relationships of the domestic mutual investment of China based on the cross-shareholding networks of the listed companies. Physica A 390:749–59. doi:10.1016/j.physa.2010.10.042.
   Web of Science ®Google Scholar
• Mantegna, R. N. 1999. Hierarchical structure in financial markets. European Physical Journal B 11:193–97. doi:10.1007/s100510050929.
   Web of Science ®Google Scholar
• Newman, M. E. J., and D. J. Watts. 1999. Renormalization group analysis of the small-world network model. Physics Letters A 263:341–46. doi:10.1016/S0375-9601(99)00757-4.
   Web of Science ®Google Scholar
• Ou, J. A., and S. H. Penman. 1989. Financial statement analysis and the prediction of stock returns. Journal of Accounting & Economics 11:295–329. doi:10.1016/0165-4101(89)90017-7.
   Web of Science ®Google Scholar
• Phan, D. H. B., S. S. Sharma, and P. K. Narayan. 2015. Stock return forecasting: Some new evidence. International Review of Financial Analysis 40:38–51. doi:10.1016/j.irfa.2015.05.002.
   Web of Science ®Google Scholar
• Sabidussi, G. 1966. The centrality index of a graph. Psychometrika 31:581–603.
   PubMed Web of Science ®Google Scholar
• Shakouri, M., H. W. Lee, and Y. W. Kim. 2017. A probabilistic portfolio-based model for financial valuation of community solar. Applied Energy 191:709–26. doi:10.1016/j.apenergy.2017.01.077.
   Web of Science ®Google Scholar
• Soboh, R., A. O. Lansink, and G. van Dijk. 2011. Distinguishing dairy cooperatives from investor-owned firms in Europe using financial indicators. Agribusiness 27:34–46. doi:10.1002/agr.v27.1.
   Web of Science ®Google Scholar
• Subramanyam, K. R., and M. Venkatachalam. 2007. Earnings, cash flows, and ex post intrinsic value of equity. Accounting Review 82:457–81. doi:10.2308/accr.2007.82.2.457.
   Web of Science ®Google Scholar
• Szóka, K. 2015. Financial and payback net for SME‘s. Procedia Economics & Finance 30:808–15. doi:10.1016/S2212-5671(15)01330-1.
   Google Scholar
• Tahmoorespour, R., M. Ali-Abbar, and E. Randjbaran. 2015. The impact of capital structure on stock returns: International evidence. Hyperion Economic Journal 3:56–78.
   Google Scholar
• Tumminello, M., T. Aste, T. Di Matteo, and R. N. Mantegna. 2005. A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences of the United States of America 102:10421–26. doi:10.1073/pnas.0500298102.
   PubMed Web of Science ®Google Scholar
• Wang, B. F. 2014. The study of the correlation between financial holding company‘s financial indicators and stock price——In the case of Taiwan. Technoeconomics & Management Research 1:75–79.
   Google Scholar
• Wang, G. J., Z. Q. Jiang, M. Lin, C. Xie, and H. E. Stanley. 2018a. Interconnectedness and systemic risk of China‘s financial institutions. Emerging Markets Review 35:1–18. doi:10.1016/j.ememar.2017.12.001.
   Web of Science ®Google Scholar
• Wang, G. J., C. Xie, and S. Chen. 2017a. Multiscale correlation networks analysis of the US stock market: A wavelet analysis. Journal of Economic Interaction and Coordination 12:561–94. doi:10.1007/s11403-016-0176-x.
   Web of Science ®Google Scholar
• Wang, G. J., C. Xie, K. J. He, and H. E. Stanley. 2017b. Extreme risk spillover network: Application to financial institutions. Quantitative Finance 17:1417–33. doi:10.1080/14697688.2016.1272762.
   Web of Science ®Google Scholar
• Wang, G. J., C. Xie, and H. E. Stanley. 2018b. Correlation structure and evolution of world stock markets: Evidence from Pearson and partial correlation-based networks. Computational Economics 51:607–35. doi:10.1007/s10614-016-9627-7.
   Web of Science ®Google Scholar
• Wang, G. J., C. Xie, L. F. Zhao, and Z. Q. Jiang. 2018c. Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more? Journal of International Financial Markets, Institutions and Money 57:205–30. doi:10.1016/j.intfin.2018.07.008.
   Web of Science ®Google Scholar
• Xi, X., and H. Z. An. 2018. Research on energy stock market associated network structure based on financial indicators. Physica A 490:1309–23. doi:10.1016/j.physa.2017.08.114.
   Web of Science ®Google Scholar
• Xia, S. Y., Z. Y. Xiong, Y. G. Luo, W. Xu, and G. H. Zhang. 2015. Effectiveness of the Euclidean distance in high dimensional spaces. Optik 126:5614–19. doi:10.1016/j.ijleo.2015.09.093.
   Web of Science ®Google Scholar
• Yan, X. G., C. Xie, and G. J. Wang. 2015. Stock market network‘s topological stability: Evidence from planar maximally filtered graph and minimal spanning tree. International Journal of Modern Physics B 29:19. doi:10.1142/S0217979215501611.
   Web of Science ®Google Scholar
• Zhang, D. Y. 2017. Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics 62:323–33. doi:10.1016/j.eneco.2017.01.009.
   Web of Science ®Google Scholar
• Zhang, D. Y., L. Lei, Q. Ji, and A. M. Kutan. 2018. Economic policy uncertainty in the US and China and their impact on the global markets. Economic Modelling. doi:10.1016/j.econmod.2018.09.028.
   Web of Science ®Google Scholar
• Zhang, H. Y., Q. Ji, and Y. Fan. 2014. Competition, transmission and pattern evolution: A network analysis of global oil trade. Energy Policy 73:312–22. doi:10.1016/j.enpol.2014.06.020.
   Web of Science ®Google Scholar