Research article Special Issues

Lending Sociodynamics and Drivers of the Financial Business Cycle

  • Received: 01 May 2017 Accepted: 26 July 2017 Published: 12 October 2017
  • We extend sociodynamic modeling of the financial business cycle to the Euro Area and Japan. Using an opinion-formation model and machine learning techniques we find stable model estimation of the financial business cycle using central bank lending surveys and a few selected macroeconomic variables. We find that banks have asymmetric response to good and bad economic information, and that banks adapt to their peers' opinions when changing lending policies.

    Citation: Raymond J. Hawkins, Hengyu Kuang. Lending Sociodynamics and Drivers of the Financial Business Cycle[J]. Quantitative Finance and Economics, 2017, 1(3): 219-252. doi: 10.3934/QFE.2017.3.219

    Related Papers:

  • We extend sociodynamic modeling of the financial business cycle to the Euro Area and Japan. Using an opinion-formation model and machine learning techniques we find stable model estimation of the financial business cycle using central bank lending surveys and a few selected macroeconomic variables. We find that banks have asymmetric response to good and bad economic information, and that banks adapt to their peers' opinions when changing lending policies.


    加载中
    [1] Altavilla C, Darracq Paries M, Nicoletti G, et al. (2015) Loan supply, credit markets and the euro area financial crisis. ECB Working Paper 1861, European Central Bank.
    [2] Bouwman CH, Malmendier U (2015) Does a bank's history affect its risk-taking? Am Econ Rev 105: 321-325. doi: 10.1257/aer.p20151093
    [3] Breiman L (2001) Random forests. Mach Learn 45: 5-32. doi: 10.1023/A:1010933404324
    [4] Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning, ACM, 161-168.
    [5] Favara G, Gilchrist S, Lewis K, Zakrajsek E (2016) Recession risk and the excess bond premium. FEDS Notes, Board of Governors of the Federal Reserve System.
    [6] Ghonghadze J, Lux T (2012) Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach. Appl Econ 44: 3065-3088. doi: 10.1080/00036846.2011.570716
    [7] Ghonghadze J, Lux T (2016) Variation of lending standards and the financial instability hypothesis. FinMaP Working Paper, Kiel University.
    [8] Gilchrist S, Zakraj (2012) Credit spreads and business cycle fluctuations. Am Econ Rev 102: 1692-1720. doi: 10.1257/aer.102.4.1692
    [9] Granovetter M (2005) The impact of social structure on economic outcomes. J Econ Perspect 19: 33-50.
    [10] Hawkins RJ (2011) Lending sociodynamics and economic instability. Phys A: Stat Mech its Appl 390: 4355-4369. doi: 10.1016/j.physa.2011.07.003
    [11] Helbing D (2010) Quantitative Sociodynamics: Stochastic Methods and Models of Social Interaction Processes, Berlin: Springer.
    [12] Holmes A (1969) Operational constraints on the stabilization of money supply growth. Controlling Monetary Aggregates, (Boston MA: Federal Reserve Bank of Boston) 65-77.
    [13] Kane MJ, Price N, Scotch M, Rabinowitz P (2014) Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinf 15: 276. doi: 10.1186/1471-2105-15-276
    [14] Kawashima T, Nakabayashi M (June, 2014) Non-performing loan reduction with regulatory transition: A Japanese experience, 1998--2013. FSA Institute Discussion Paper Series DP2014-2, Financial Research Center (FSA Institute) Financial Services Agency.
    [15] Keynes JM (1931) The consequences to the banks of the collapse in money values (Aug. 1931). In Essays in Persuasion, New York: W. W. Norton \& Company, book published in 1963, 168-180.
    [16] Keynes JM (1937) The general theory of employment. Q J Econ 51: 209-223. doi: 10.2307/1882087
    [17] Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003.
    [18] Kindleberger CP, O'Keefe R (2011) Manias, Panics, and Crashes, Springer.
    [19] Köhler-Ulbrich P, Hempell HS, Scopel S (2016) The euro area bank lending survey: Role, development and use in monetary policy preparation. Occasional Paper 179, European Central Bank.
    [20] Kursa MB, Rudnicki WR (2010) Feature selection with the Boruta package. J Stat Software 36: 1-13.
    [21] Lempérière Y, Deremble C, Seager P, Potters M, Bouchaud JP (2014) Two centuries of trend following. arXiv preprint arXiv:1404.3274.
    [22] Lux T (1997) Time variation of second moments from a noise trader/infection model. J Econ Dyn Control 22: 1-38. doi: 10.1016/S0165-1889(97)00061-4
    [23] Lux T (2009a) Applications of statistical physics in finance and economics. In Jr., J. B. R. and Jr., K. L. C., editors, Handbook of Research on Complexity, chapter 9. Edward Elgar, Cheltenham, UK.
    [24] Lux T (2009b) Rational forecast or social opinion dynamics? Identification of interaction effects in a business climate survey. J Econ Behav Organ 72: 638-655.
    [25] McLeay M, Radia A, Thomas R (2014) Money in the modern economy: an introduction. Bank of England Quarterly Bulletin Q1.
    [26] Menkhoff L, Sarno L, Schmeling M, Schrimpf A (2012) Currency momentum strategies. J Financ Econ 106: 660-684. doi: 10.1016/j.jfineco.2012.06.009
    [27] Minsky, H. P. (1977) The financial instability hypothesis: An interpretation of Keynes and an alternative to "standard" theory. Nebraska J Econ Bus 16: 5-16.
    [28] Minsky HP (2008) Stabilizing an Unstable Economy. McGraw-Hill, New York, NY. A reissue of 1986 publication.
    [29] Moore MJ (2009) Morgan Stanley's Mack welcomes regulation by Fed (update 2). Bloomberg.
    [30] Osborn DR (1995) Moving average detrending and the analysis of business cycles. Oxford Bulletin of Economics and Statistics 57: 547-558. doi: 10.1111/j.1468-0084.1995.tb00039.x
    [31] Towers G (1939) Minutes of proceedings and evidence respecting the Bank of Canada. Committee on Banking and Commerce, Ottawa: Government Printing Bureau.
    [32] Weidlich W (1972) The use of statistical models in sociology. Collective Phenomena 1: 51-59.
    [33] Weidlich W (2000) Sociodynamics: A Systematic Approach to Mathematical Modeling in the Social Sciences. Dover, Mineola, NY.
    [34] Weidlich W, Haag G (1983) Concepts and Models of a Quantitative Sociology: The Dynamics of Interacting Populations, volume 14 of Springer Series in Synergetics. Springer-Verlag, Berlin.
    [35] Werner RA (2014) Can banks individually create money out of nothing? -- The theories and the empirical evidence. Int Rev Financ Anal 36: 1-19.
    [36] Werner RA (2016) A lost century in economics: Three theories of banking and the conclusive evidence. Int Rev Financ Anal 46: 361-379. doi: 10.1016/j.irfa.2015.08.014
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2788) PDF downloads(1017) Cited by(2)

Article outline

Figures and Tables

Figures(17)  /  Tables(13)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog