Curbing Shocks to Corporate Liquidity: The Role of Trade Credit

Amberg, Niklas; Jacobson, Tor; Schedvin, Erik von; Townsend, Robert M.

doi:10.3386/w22286

Cited by 11 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 Some notable exceptions that have used contract-level data are Giannetti et al (2011), Murfin and Njoroge (2014), Antràs and Foley (2015), Barrot (2016), and Amberg et al (2020). 4 Figure A.1 in appendix E shows that the same relationship holds for aggregate U.S. data when plotting total trade of the U.S. non-financial sector over U.S. GDP against the log of aggregate U.S. markups, as estimated by De Loecker and Warzynski (2012).…”

Section: Introductionmentioning

confidence: 87%

Trade Credit, Markups, and Relationships

2019

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 87%

Trade Credit, Markups, and Relationships

2019

View full text Add to dashboard Cite

“…More recent papers, in contrast, attempt to identify one side or the other. Amberg et al (2016), for example, use the failure of Panaxia-a Swedish cashin-transit firm-as a plausibly exogenous shock to liquidity demand for firms exposed to the failure. They find that this shock leads to an increase in the use of trade credit and a decline in firms' cash balances.…”

mentioning

confidence: 99%

Funding Liquidity without Banks: Evidence from a Shock to the Cost of Very Short‐Term Debt

Restrepo¹,

Cardona-Sosa²,

Strahan

2019

The Journal of Finance

View full text Add to dashboard Cite

In 2011, Colombia instituted a tax on repayment of bank loans, which increased the cost of short‐term bank credit more than long‐term credit. Firms responded by cutting short‐term loans for liquidity management purposes and increasing the use of cash and trade credit. In industries in which trade credit is more accessible (based on U.S. Compustat firms), we find substitution into accounts payable and little effect on cash and investment. Where trade credit is less available, firms increase cash and cut investment. Thus, trade credit provides an alternative source of liquidity that can insulate some firms from bank liquidity shocks.

show abstract

“…They show that suppliers of credit who are exposed to credit losses due to failing customers are in turn subject to an elevated risk of failure. It is also worth mentioning a recent development of their work, jointly with Amberg et al [ 36 ]. The paper focuses on how firms manage liquidity shortfalls by using their cash reserves and increasing their trade credit margins.…”

Section: Introductionmentioning

confidence: 99%

Predicting corporate credit risk: Network contagion via trade credit

et al. 2021

View full text Add to dashboard Cite

Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.

show abstract

Curbing Shocks to Corporate Liquidity: The Role of Trade Credit

Cited by 11 publications

References 25 publications

Trade Credit, Markups, and Relationships

Trade Credit, Markups, and Relationships

Funding Liquidity without Banks: Evidence from a Shock to the Cost of Very Short‐Term Debt

Predicting corporate credit risk: Network contagion via trade credit

Contact Info

Product

Resources

About