The timing of the crash
Temporal dynamics of medieval and modern banking systems
Johannes Preiser-Kapeller, IMAFO/ABF, Austrian Academy of Sciences
Social Complexity and Financial Markets
In the last decades, the concept of complex systems has revolutionised our understanding of natural as well as social phenomena. Complex systems are understood as large networks of individual components, whose interactions at the microscopic level produce “complex” changing patterns of behaviour of the whole system on the macroscopic level; in the field of social systems, these patterns stem from the actions and interactions of individuals, ranging from families and small communities up to the globalized society of today (MILLER/PAGE, 2007; WHITE, 2008).
Most impressive examples of social complexity are financial markets, as has been explained for instance by the Swiss scientist Didier Sornette: “Financial markets constitute one among many other systems exhibiting a complex organization and dynamics with similar behavior. Systems with a large number of mutually interacting parts, often open to their environment, self-organize their internal structure and their dynamics with novel and sometimes surprising macroscopic (“emergent”) properties. (…) This view tends to replace the previous “analytical” approach, consisting of decomposing a system in components, such that the detailed understanding of each component was believed to bring understanding of the functioning of the whole. A central property of a complex system is the possible occurrence of coherent large-scale collective behaviors with a very rich structure, resulting from the repeated nonlinear interactions among its constituents: the whole turns out to be much more than the sum of its parts.” (SORNETTE, 2003, 15-16)
One of the most relevant questions is of course the resilience respectively the failure of social systems, since the impact of such phenomena on societies can be dramatic, as also most recent events (the financial crisis of 2008 and its aftermath) have demonstrated. While exogenous impacts (such as natural catastrophes) can stress or even over-stress the resilience of social systems, in the field of finances, most times “a crash has fundamentally an endogenous, or internal, origin (…) exogenous, or external, shocks only serve as triggering factors” (SORNETTE, 2003, 4). Therefore, while the interdependencies between a social system and its (complex) environment have to be analysed as well, the endogenous dynamics within the financial system shall be of primary concern: “Market crashes exemplify in a dramatic way the spontaneous emergence of extreme events in selforganizing systems. (…) Sudden transitions from a quiescent state to a crisis or catastrophic event provide the most dramatic fingerprints of the system dynamics. (…) Stasis and equilibrium are illusions, whereas dynamics and out-of-equilibrium are the rule. The quest for balance and constancy will always be unsuccessful. (…) The outstanding scientific question is thus how such large-scale patterns of catastrophic nature might evolve from a series of interactions on the smallest and increasingly larger scales. In complex systems, it has been found that the organization of spatial and temporal correlations do not stem, in general, from a nucleation phase diffusing across the system. It results rather from a progressive and more global cooperative process occurring over the whole system by repetitive interactions.” (SORNETTE, 2003, xv and 19; cf. also ALBEVERIO/JENTSCH/KANTZ, 2006, and TALEB, 2008, on extreme events; REINHART/ROGOFF, 2009).
Didier Sornette also states: “Crises are extreme events that occur rarely, albeit with extraordinary impact, and are thus completely undersampled and poorly constrained” (SORNETTE, 2003, 19-20). In this respect, historical analysis can contribute to an enlargement of our data basis on such events and their better understanding (cf. for a comparative approach also REINHART/ROGOFF, 2009).
A Complex Historical Analysis of Medieval Financial Markets
The origins of modern-day financial markets lie in the commercial centres of late medieval Italy (FERGUSON, 2008); in Florence, Genoa and Venice not only banks and banking techniques were developed, but also public borrowing via the emerging financial markets. In Florence, the first large mercantile and banking companies (“super-companies”) with far reaching networks of branches abroad were established at the end of the 13th century in order to answer to the requirements of large scale trade with grain and textiles and of the financing of the papacy and monarchs such as the King of England (RENOUARD, 1941; HUNT, 1994; NAJEMY, 2006; GOLDTHWAITE, 2009). Venice, otherwise a more conservative financial community, was on “the leading edge” for the development of giro and fractional reserve banking (meaning that funds deposited into a bank are mostly lent out, and a bank keeps only a fraction of the quantity of deposits as reserves, cf. GISCHER/HERZ/MENKHOFF, 2005) (LANE, 1973; MUELLER, 1997; STAHL, 2000). And Genoa saw the foundation of the first public bank in Italy in 1407/1408 (the “Banco di San Giorgio”), which offered “deposit taking, clearance and lending services” for privates as well as for the commune of Genoa (cf. esp. the studies of FELLONI; EPSTEIN, 1996). In all three cities, the growing pressure on public finance led to the establishment of a new form of public debt, “known as the monte because it was regarded literally as mountain-sized, consisting of shares issued by the commune, redeemable at its option, and paying a fixed low rate of interest.” (HUNT/MURRARY, 1999, 207; cf. also EPSTEIN, 2000; PEZZOLO, 2007; FERGUSON, 2008; STASAVAGE, 2011). These shares could also be sold and used as a form of private investment, thus increasing the complexity of financial markets.
Fig.: One page from the Libro delle Colonne of the Banco di San Giorgio in Genoa (1485; Archivio di Stato di Genova, San Giorgio, Colonne, Nr. 359)
Yet, these early financial markets soon experienced the systemic vulnerability to failures and crises up to extreme scales in a way similar to modern-day phenomena: between 1343 and 1346, the three largest Florentine “Super-Companies” of the Bardi, Peruzzi and Acciaiuoli all collapsed within 30 months, leading to major social upheavals in the city (HUNT, 1994; NAJEMY, 2006). The only survivor of similar scale, the Alberti Company, continued existence one more century, but in the form of a group of essentially independent branches in Florence and abroad, connected through family relationships. Companies of the scale of the Bardi or Peruzzi never again emerged in Renaissance Florence; also the famous Bank of the Medici (1397-1494) never achieved a similar predominance on the financial market as did its forerunners (DE ROOVER, 1966; ORIGO, 1985; HUNT/MURRARY, 1999; NAJEMY, 2006; GOLDTHWAITE, 2009). A different development we can observe in Venice; there, after a series of bank failures in the mid-14th century, banking houses became larger, also on demand of the legislator, who considered bigger companies less vulnerable to “unpredictabilities” such as bank runs and panics as well as better qualified to function as borrower to the state. But by these measures, in the medium term, bank failures become not only not less frequent, but also bigger (MUELLER, 1997; HUNT/MURRARY, 1999; PEZZOLO, 2006). Also in Genoa, the Banco di San Giorgio failed in 1444, because of “a lack of own capital necessary to face occasional liquidity shortages and insistent demands for money by the state which the bank could not turn down”; only in 1531, the bank would open its doors again (FELLONI; EPSTEIN, 1996; PEZZOLO, 2007; VALÍČKOVÁ, 2010). Therefore, an analysis of these three first emerging financial markets could substantially contribute to the complex dynamics of such social systems.
Fig.: A 14th century manuscript depicting bankers in an Italian counting house (British Library, Cocharelli, Cuttings from a Latin prose treatise on the Seven Vices: avarice)
Medieval Venice and the United States in Comparison: Methods and Results
The statistical analysis is based on binary time series (1 = presence of a major bank crash in that year, 0 = absence). Mean waiting times between years with events were calculated on the basis of an expectation test for a poisson process for simple columns of event times for the five phenomena. Probabilities of transition between years with events and years without were calculated with the help of Markov chain analysis on the basis of the above-mentioned time series. All calculations and graphs were done by the author with the help of the software programmes Microsoft-Excel* and PAST* (Version 2.17).
Figures: Frequencies (above) and histogramme of years with banking crises in Venice (blue) and the USA (red) during the 200 years periods
Interesting is of course the similar number of years with major bank failures and the similar mean waiting time between years with bank crashes in the two 200 years time series for Venice (1300-1500) and the USA (1800-2000). Both time series also show a certain cyclicity (which is more pronounced for the USA time series), indicating comparable system dynamics up to a certain degree.
This is also indicated by the scalograms of wavelet transformations of the two binary time series; these scalograms help to identify cycles at various time scales (y-axis; a cycle at a scale of 4, for instance, indicates a length of 24 = 16 years, in our case) of different power (indicated with colours from blue [absent] to red [strong]) along the time line (x-axis) of 200 years (the black lines indicate the p = 0.05 significance level). We observe a significant increase in power for cycles at a scale of 4.2 = 18.38 years and upwards for the USA-timeline beginning from ca. 1900, for instance (which would be valid also for the interval between the bank crashes around 1990 and the most resent crisis starting in 2008). The 16 years cyclicity (at a scale of around 4 at the y-axis in the scalogram) for the Venetian time series is on the contrast less pronounced.
Fig.: Scalogram of the wavelet transformation of the binary time series of years with bank crashes
for Venice (1300-1500)
Fig.: Scalogram of the wavelet transformation of the binary time series of years with bank crashes for the USA (1800-2000)
Figure: Transition probabilities: years with major failures of banks in Venice (1300-1500)
Figure: Transition probabilities: years with major failures of banks in the USA (1800-2000)
Note the much higher transition probability between subsequent years of major bank crashes in comparison with the medieval Venetian time series, indicating a higher persistency of crisis periods once broken out in this modern day banking system of the United States.
Figure: Run on the Seamens Savings Bank during the Panic of 1857
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