# Transaction Network Structural Shift under Crisis: Macro and Micro Perspectives

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Theoretical Background

#### 2.1. Predicting Economic Crises

#### 2.2. Network Model

#### 2.3. Network Topological Properties

#### 2.4. Network Motifs

#### 2.5. Transaction Network

## 3. Research Methodology

## 4. Result and Discussions

#### 4.1. Macro Measurement from Network Topological Measurement

#### 4.2. Micro Measurement from Network Motif Exploration

## 5. Conclusions and Future Works

- (1)
- The transaction network was in an abnormal state 5 months before the peak of the crisis based on the macro perspective;
- (2)
- Based on the micro perspective, the transaction network was abnormal 2 months before the crisis peak;
- (3)
- Motif 1 and motif 2 are the most suitable triadic motifs to indicate the early crisis warning.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Alamsyah, Andry, Budi Rahardjo, and Kuspriyanto Kuspriyanto. 2013. Social Network Analysis Taxonomy Based on Graph Representation. Paper presented at the 5th Indonesian International Conference on Innovation, Entrepreneurship, and Small Business, Bandung, Indonesia, June 24–29. [Google Scholar]
- Alamsyah, Andry, Dian Puteri Ramadhani, and Farida Titik Kristanti. 2019. Event-based Dynamic Banking Network Exploration for Economic Anomaly Detection. Journal of Theoretical and Applied Information Technology 98: 1089–100. [Google Scholar]
- Alamsyah, Andry, Made Kevin Bratawisnu, and Puput H. Sanjani. 2018. Finding Pattern in Dynamic Network Analysis. Paper presented at the 6th International Conference on Information and Communication Technology (ICoICT), Bandung, Indonesia, May 3–4. [Google Scholar]
- Aprigliano, Valentina, Guerino Ardizzi, and Libero Monteforte. 2019. Using Payment System Data to Forecast Economic Activity. Internasional Journal of Central Bank 15: 55–60. [Google Scholar]
- Bank Indonesia. 2017. Kajian Stabilitas Keuangan No. 29, September. Available online: https://www.bi.go.id/id/publikasi/kajian/Pages/KSK_0917.aspx (accessed on 1 December 2021).
- Barabasi, Albert-Laszlo. 2016. Network Science. Cambridge: Cambridge University Press. [Google Scholar]
- Becher, Christopher, Stephen Millad, and Kimmo Soramaki. 2008. The Network Topology of CHAPS Sterling. SSRN Electronic Journal. [Google Scholar] [CrossRef] [Green Version]
- Berg, Andrew, and Catherine Pattillo. 2000. The Challanges of Predicting Economic Crises. IMF Economic Issues No 22. Available online: https://www.imf.org/external/pubs/ft/issues/issues22/ (accessed on 10 February 2022).
- Bosworth, Barry, and Aaron Flaeen. 2009. America’s Financial Crisis: The End of an Era. In Asian Development Bank Institute. Available online: https://hdl.handle.net/11540/3730 (accessed on 1 December 2021).
- Claessens, Stijn, and Ayhan Kose. 2013. Financial Crises Explanations, Types, and Implications. In International Monetary Fund Working Paper No 13/28. Washington, DC: International Monetary Fund, ISBN 9781475561005. ISSN 1018-5941. [Google Scholar]
- Dehmer, Matthias, and Subhash C. Basak. 2012. Statistical and Machine Learning Approaches for Network Analysis. Hoboken: John Wiley & Sons Publication. [Google Scholar]
- Dell’Ariccia, Giovanni, Deniz Igan, Luc Laeven, and Hui Tong. 2012. Policies for Macrofinancial Stability: How to Deal with Credit Booms. Washington, DC: International Monetary Fund, vol. 2012. [Google Scholar] [CrossRef] [Green Version]
- Easley, David, and Jon Kleinberg. 2010. Network, Crowd, and Markets: Reasoning about a Highly Connected World. Cambridge: Cambridge University Press. [Google Scholar]
- Greenwood, Robin, Samuel G. Hanson, Andrei Shleifer, and Jacob Ahm Sorensen. 2021. Predictable Financial Crises. In Harvard Business School Working Paper 20-130. Boston: Harvard Business School Publishing. [Google Scholar]
- Haldane, Andrew G., and Robert M. May. 2011. Systematic Risk in Banking Ecosystem. Nature 469: 351–55. [Google Scholar] [CrossRef] [PubMed]
- Int’ Veld, Daan, Marco Van der Leij, and Cars Hommers. 2020. The Formation of a Core-Periphery Structure in Heterogeneous Financial Networks. Journal of Economic Dynamics and Control 119: 103972. [Google Scholar] [CrossRef]
- Itzchak, Royi, Yelena Mogilevski, and Yoram Louzoun. 2007. An optimal algorithm for counting network motifs. Physica A: Statistical Mechanics and Its Applications 381: 482–49. [Google Scholar]
- Jackson, Matthew O. 2008. Social and Economic Networks. Princeton: Princeton University Press. [Google Scholar]
- Longstaff, Francis. A. 2010. The Subprime Credit Crisis and Contagion in Financial Markets. Journal of Financial Economics 97: 436–50. [Google Scholar] [CrossRef]
- Marsili, Matteo, Iacopo Mastromatteo, and Elia Zarinelli. 2011. Reconstruction of Financial Networks for Robust Estimation of Systematic Risk. Available online: https://ssrn.com/abstract=1934766 (accessed on 11 February 2022).
- Milo, Ron, Shai S. Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon. 2002. Network motifs: Simple building blocks of complex networks. Science 298: 824–27. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Newman, Mark E. J. 2011. Network: An Introduction. New York: University Michigan and Santa Fe Institute. Oxford University Press. [Google Scholar]
- Reinhart, Carmen M., and Kenneth S. Rogoff. 2009. The Aftermath of Financial Crises. American Economic Review 99: 466–72. [Google Scholar] [CrossRef] [Green Version]
- Reserver Bank of Australia. 2019. The Global Financial Crisis. Melbourne: Australia. [Google Scholar]
- Roszkowska, Paulina, and Lukasz Prorokowski. 2013. Model of Financial Crisis Contagion: A Survey Based Simulation by Means of The Modified Kaplan Meier Survival Plots. Folia Oeconomica Stetinensia 13: 22–55. [Google Scholar] [CrossRef]
- Spange, Morten. 2010. Can Crises Be Predicted. Available online: https://www.nationalbanken.dk/en/publications/Pages/2010/07/Can-Crises-be-Predicted.aspx (accessed on 10 February 2022).
- Squartini, Tiziano, Iman Van Lelyverld, and Diego Garlaschelli. 2013. Early-Warning Signals of Topological Collapse in Interbank Networks. Scientific Reports 3: 3357. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Stone, Lewi, Daniel Simberloft, and Yael Artzy Randrup. 2019. Network Motifs and Their Origins. PLoS Computational Biology 15: e1006749. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Upper, Christian. 2011. Simulation Methods to Assess the Danger of Contagion in Interbank Markets. Journal of Financial Stability 7: 111–25. [Google Scholar] [CrossRef]
- Vladimir, Batagelj, and Praptronik Selena. 2016. An algebraic approach to temporal network analysis based on temporal quantities. Social Network Analysis and Mining 6: 1–22. [Google Scholar]

Network Topology | Explanation |
---|---|

Number of Nodes | Measure the number of entities or actors within the network. |

Number of Edges | Measure the number of connections among nodes within the network. This measurement distinguishes network topology since a network with the same number of nodes does not necessarily have the same characteristics. |

Average Distance | The average of the shortest path length between any nodes in the network. Another similar measurement is the Network Diameter, which measures the longest of the shortest distances between any nodes. |

Density | The ratio between the existing connections (edges) compared to the total potential relation among nodes within the network |

Time | Sender (Bank) | Receiver (Bank) | Transaction Value |
---|---|---|---|

12 December 2008 | XXXX | ZZZZ | 0.0002 |

12 December 2008 | XXXX | YYYY | 0.0005 |

13 December 2008 | YYYY | XXXX | 0.0006 |

13 December 2008 | ZZZZ | YYYY | 0.0004 |

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**MDPI and ACS Style**

Alamsyah, A.; Ramadhani, D.P.; Kristanti, F.T.; Khairunnisa, K.
Transaction Network Structural Shift under Crisis: Macro and Micro Perspectives. *Economies* **2022**, *10*, 56.
https://doi.org/10.3390/economies10030056

**AMA Style**

Alamsyah A, Ramadhani DP, Kristanti FT, Khairunnisa K.
Transaction Network Structural Shift under Crisis: Macro and Micro Perspectives. *Economies*. 2022; 10(3):56.
https://doi.org/10.3390/economies10030056

**Chicago/Turabian Style**

Alamsyah, Andry, Dian Puteri Ramadhani, Farida Titik Kristanti, and Khairunnisa Khairunnisa.
2022. "Transaction Network Structural Shift under Crisis: Macro and Micro Perspectives" *Economies* 10, no. 3: 56.
https://doi.org/10.3390/economies10030056