Showing papers on "Collateral published in 2020"

Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit

Abstract: Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires an upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e. loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which have been exploited recently (February 2020). This paper is the first to explore the implication of flash loans for the nascent decentralized finance (DeFi) ecosystem. We analyze two existing attacks vectors with significant ROIs (beyond 500k%), and then go on to formulate finding flash loan-based attack parameters as an optimization problem over the state of the underlying Ethereum blockchain as well as the state of the DeFi ecosystem. Specifically, we show how two previously executed attacks can be "boosted" to result in a profit of 829.5k USD and 1.1M USD, respectively, which is a boost of 2.37x and 1.73x, respectively.

Monetary Policy when Households have Debt: New Evidence on the Transmission Mechanism

Abstract: In response to an unanticipated change in interest rates, households with mortgage debt adjust their expenditure signicantly, especially on durable goods, renters react to a lesser extent and outright home-owners do not react at all. All housing tenure groups experience a signicant change in disposable income (over and above the direct impact on mortgage repayments). The response of house prices is sizable, driving a signicant adjustment in loanto-income ratios but little change in loan-to-value ratios. A simple collateral constraint model augmented with durable goods and a renting decision generates predictions consistent with these novel empirical ndings and suggests that heterogeneity in housing debt positions plays an important role in the transmission of monetary policy.

Bank Monitoring: Evidence from Syndicated Loans

Abstract: The rise in popularity of syndicated lending raises questions about the incentives of banks to actively monitor borrowers. We empirically investigate these incentives using a novel dataset that includes the frequency with which banks monitor borrowers' financial condition and the collateral underlying syndicated bank loans. We find that banks monitor frequently with approximately 50% of loans being monitored at least on a monthly basis. Monitoring frequency is increasing in the lead arranger's loan share for private borrowers and the lead bank's reputation for public borrowers. These results are consistent with lender reputation mitigating moral hazard only to the extent that monitoring effort is verifiable. Lead banks also monitor more when monitoring is likely to produce new information and when information is more valuable, such as when borrower financial health deteriorates. Overall, our results suggest that banks actively monitor the average syndicated loan in a manner consistent with theoretical predictions in the literature.

Bank Quality, Judicial Efficiency, and Loan Repayment Delays in Italy

Abstract: Italian firms delay payment to banks weakened by past loan losses. Exploiting Credit Register data, we fully absorb borrower fundamentals with firm‐quarter effects. Identification therefore reflects firm choices to delay payment to some banks, depending on their health. This selective delay occurs more where legal enforcement of collateral recovery is slow. Poor enforcement encourages borrowers not to pay when the value of their bank relationship comes into doubt. Selective delays occur even by firms able to pay all lenders. Credit losses in Italy have thus been worsened by the combination of weak banks and weak legal enforcement.

Insider Share-Pledging and Equity Risk

Abstract: Corporate insiders frequently borrow from lending institutions and pledge their personal equity as collateral for the loan. This borrowing, or pledging, potentially affects shareholder risk through changing managerial incentives or contingency risk. Using an exogenous shock to lending supply, we document a significant increase in risk arising from pledging. Difference-in-differences regressions indicate that insider pledging corresponds with a 16.5% relative increase in risk despite unchanged firm fundamentals. The empirical analysis supports contingency risk in linking pledging to volatility. Overall, our findings suggest that pledging allows influential insiders to extract private benefits of control at the expense of outside shareholders.

The Decentralized Financial Crisis

Abstract: The Global Financial Crisis of 2008, caused by the accumulation of excessive financial risk, inspired Satoshi Nakamoto to create Bitcoin Now, more than ten years later, Decentralized Finance (DeFi), a peer-to-peer financial paradigm which leverages blockchain-based smart contracts to ensure its integrity and security, contains over 702m USD of capital as of April 15th, 2020 As this ecosystem develops, it is at risk of the very sort of financial meltdown it is supposed to be preventing In this paper we explore how design weaknesses and price fluctuations in DeFi protocols could lead to a DeFi crisis We focus on DeFi lending protocols as they currently constitute most of the DeFi ecosystem with a 76% market share by capital as of April 15th, 2020 First, we demonstrate the feasibility of attacking Maker's governance design to take full control of the protocol, the largest DeFi protocol by market share, which would have allowed the theft of 05bn USD of collateral and the minting of an unlimited supply of DAI tokens In doing so, we present a novel strategy utilizing so-called flash loans that would have in principle allowed the execution of the governance attack in just two transactions and without the need to lock any assets Approximately two weeks after we disclosed the attack details, Maker modified the governance parameters mitigating the attack vectors Second, we turn to a central component of financial risk in DeFi lending protocols Inspired by stress-testing as performed by central banks, we develop a stress-testing framework for a stylized DeFi lending protocol, focusing our attention on the impact of a drying-up of liquidity on protocol solvency Based on our parameters, we find that with sufficiently illiquidity a lending protocol with a total debt of 400m USD could become undercollateralized within 19 days

Gender and bank lending after the global financial crisis: are women entrepreneurs safer bets?

Abstract: Using gender as a theoretical framework, we analyse the dynamics of bank lending to small- and medium-sized enterprises (SME) in the aftermath of the 2008 global financial crisis. Using six waves of the SME Finance Monitor survey, we apply a formal Oaxaca–Blinder decomposition to test whether gender impacts upon the supply and demand for debt finance by women. Reflecting established evidence, we found women had a lower demand for bank loans; contradicting accepted wisdom however, we found that women who did apply were more likely to be successful. We argue that feminised risk aversion might inform more conservative applications during a period of financial uncertainty which may be beneficial for women in terms of gaining loans. However, we also uncover more subtle evidence suggesting that bank decisions may differ for women who may be unfairly treated in terms of collateral but regarded more positively when holding large cash balances.

Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit

Abstract: Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e., loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which were exploited in February 2020.

Financing the Sustainable Development Goals

Abstract: This paper contends that carving out pathways to finance the sustainable development goal (SDG) agenda entails to reconsider tacit assumptions regarding the functioning of financial systems. We first use a history of economic thought perspective to demonstrate the flaws of the loanable fund theory, which has come to underlie SDG finance strategies. We then introduce the alternative endogenous money theory using a consistent theoretical and accounting framework. This allows us to identify and discuss a set of financing mechanisms that would permit to bridge the SDG budget gap. These mechanisms include the issuing of sovereign green bonds, the modification of the European Central Bank’s collateral framework, changes in capital adequacy ratios, a market of SDG lending certificates and the introduction of rediscounting policies. We back up the discussion with examples from economic history.

The Rise of Finance Companies and FinTech Lenders in Small Business Lending

Abstract: We document that finance companies and FinTech lenders increased lending to small businesses after the 2008 financial crisis We show that most of the increase substituted for a reduction in lending by banks In counties where banks had a larger market share before the crisis, finance companies and FinTech lenders increased their lending more By 2016, the increase in finance company and FinTech lending almost perfectly offset the decrease in bank lending We control for firms' credit demand by examining lending by different lenders to the same firm, by comparing firms within the same narrow industry, and by comparing firms pledging the same type of collateral Consistent with the substitution of bank lending with finance company and FinTech lending, we find that reduced bank lending had no effect on employment, wages, new business creation, or business expansion Our results show that finance companies and FinTech lenders are major suppliers of credit to small businesses and played an important role in the recovery from the 2008 financial crisis

Good Booms, Bad Booms

Abstract: Credit booms usually precede financial crises. However, some credit booms end in a crisis (bad booms) and others do not (good booms). We document that, while all booms start with an increase of Total Factor Productivity (TFP) and Labor Productivity (LP), such growth falls much faster subsequently for bad booms. We then develop a simple framework to explain this. Firms finance investment opportunities with short-term collateralized debt. If agents do not produce information about the collateral quality, a credit boom develops, accommodatingfirms with lower quality projects and increasing the incentives of lenders to acquire information about the collateral, eventually triggering a crisis. When the average quality of investment opportunities also grows, the credit boom may not end in a crisis because the gradual adoption of low quality projects is not strong enough to induce information about collateral. Finally, we also test the main predictions of the model.

Lack of access to external finance and SME labor productivity: does project quality matter?

Abstract: Small and medium-sized enterprises (SMEs) are the main engine of local economic development. However, SME growth remains an issue as labor productivity is low in emerging economies. Due to information asymmetries, constraints in access to external finance prevent a larger participation in the economy, hindering SMEs from expanding their business operations. In the absence of collateral requirements, small and medium-sized firms may rely on exporting activities to signal lenders project quality since this may indicate that firms have good projects to invest. The main purpose of this study was to investigate the impact of project quality on both SME labor productivity and on the relationship between lack of adequate access to external finance and labor productivity. Our results indicate a positive relationship between project quality and labor productivity. We also found that SMEs that applied for bank loans but were rejected have lower levels of labor productivity than SMEs that obtained financing. In addition, constrained SMEs that export internationally were found to have higher labor productivity than constrained firms with lower access to export markets, although the role of project quality in explaining labor productivity for constrained SMEs may be due to direct export sales in most part.

The Macroeconomics of a Pandemic: A Minimalist Model

Abstract: We build a minimalist model of the macroeconomics of a pandemic, with two essential components. The first is productivity-related: if the virus forces firms to shed labor beyond a certain threshold, productivity suffers. The second component is a credit market imperfection: because lenders cannot be sure a borrower will repay, they only lend against collateral. Expected productivity determines collateral value;in turn, collateral value can limit borrowing and productivity. As a result, adverse shocks have large magnification effects, in an unemployment and asset price deflation doom loop. There may be multiple equilibria, so that pessimistic expectations can push the economy to a bad equilibrium with limited borrowing and low employment and productivity. The model helps identify policies to fight the effects of the pandemic. Traditional expansionary fiscal policy has no beneficial effects, while cutting interest rates has a limited effect if the initial real interest rate is low. By contrast, several unconventional policies, including wage subsidies, helicopter drops of liquid assets, equity injections, and loan guarantees, can keep the economy in a full-employment, high-productivity equilibrium. Such policies can be fiscally expensive, so their implementation is feasible only with ample fiscal space or emergency financing from abroad.

Appraisal Accuracy and Automated Valuation Models in Rural Areas

Abstract: Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.

The Decentralized Financial Crisis

Abstract: The Global Financial Crisis of 2008, caused by the accumulation of excessive financial risk, inspired Satoshi Nakamoto to create Bitcoin. Now, more than ten years later, Decentralized Finance (DeFi), a peer-to-peer financial paradigm which leverages blockchain-based smart contracts to ensure its integrity and security, contains over 702m USD of capital as of April 15th, 2020. As this ecosystem develops, it is at risk of the very sort of financial meltdown it is supposed to be preventing. In this paper we explore how design weaknesses and price fluctuations in DeFi protocols could lead to a DeFi crisis. We focus on DeFi lending protocols as they currently constitute most of the DeFi ecosystem with a 76% market share by capital as of April 15th, 2020. First, we demonstrate the feasibility of attacking Maker's governance design to take full control of the protocol, the largest DeFi protocol by market share, which would have allowed the theft of 0.5bn USD of collateral and the minting of an unlimited supply of DAI tokens. In doing so, we present a novel strategy utilizing so-called flash loans that would have in principle allowed the execution of the governance attack in just two transactions and without the need to lock any assets. Approximately two weeks after we disclosed the attack details, Maker modified the governance parameters mitigating the attack vectors. Second, we turn to a central component of financial risk in DeFi lending protocols. Inspired by stress-testing as performed by central banks, we develop a stress-testing framework for a stylized DeFi lending protocol, focusing our attention on the impact of a drying-up of liquidity on protocol solvency. Based on our parameters, we find that with sufficiently illiquidity a lending protocol with a total debt of 400m USD could become undercollateralized within 19 days.

Access to Collateral and the Democratization of Credit: France's Reform of the Napoleonic Security Code

Abstract: France's Ordonnance 2006‐346 repudiated the notion of possessory ownership in the Napoleonic Code, easing the pledge of physical assets in a country where credit was highly concentrated. A differences‐test strategy shows that firms operating newly pledgeable assets significantly increased their borrowing following the reform. Small, young, and financially constrained businesses benefitted the most, observing improved credit access and real‐side outcomes. Start‐ups emerged with higher “at‐inception” leverage, located farther from large cities, with more assets‐in‐place than before. Their exit and bankruptcy rates declined. Spatial analyses show that the reform reached firms in rural areas, reducing credit access inequality across France's countryside.

When Online Lending Meets Real Estate: Examining Investment Decisions in Lending-Based Real Estate Crowdfunding

Abstract: Lending-based real estate crowdfunding, which involves the use of real estate to secure loans, has emerged as a promising alternative with lower risk than peer-to-peer lending. This study provides ...

Property Rights without Transfer Rights: A Study of Indian Land Allotment

Abstract: Governments often place restrictions on the transferability of property rights to protect property owners from making “mistakes” such as selling their property under value. However, these restrictions entail costs: they reduce the property’s value as collateral in credit markets, limit owners’ ability and incentives to invest in the land, and create various transaction costs that constrain optimal land use. We investigate these costs over the long run, using a natural experiment whereby millions of acres of reservation lands were allotted to Native American households under differing land-titles between 1887–1934. We compare non-transferable land plots to neighboring plots held with full property rights, using fine-grained satellite imagery to study differences in land development and agricultural activity from 1974–today. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Creditor Rights, Technology Adoption, and Productivity: Plant-Level Evidence

Abstract: I analyze the effect of stronger creditor rights on productivity, using U.S. Census microdata. Following the adoption of anti-recharacterization laws that give lenders greater access to the collateral of firms in financial distress, total factor productivity of treated plants increases by 2.6 percent. This effect is concentrated among plants belonging to financially constrained firms. I explore the underlying mechanism and find that treated plants change the composition of their investments and their workforce toward newer capital and skilled labor. My results suggest that stronger creditor rights relax borrowing constraints and help firms adopt production technologies that are more efficient.

Data vs collateral

Abstract: The use of massive amounts of data by large technology firms (big techs) to assess firms' creditworthiness could reduce the need for collateral in solving asymmetric information problems in credit markets. Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices and firm characteristics. We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that a greater use of big tech credit â?? granted on the basis of machine learning and big data â?? could reduce the importance of collateral in credit markets and potentially weaken the financial accelerator mechanism.

The Design and Transmission of Central Bank Liquidity Provisions

Abstract: We analyze the role of loan maturity and collateral eligibility for the transmission of central bank liquidity provisions to banks following a wholesale funding dry-up. We analyze the transmission of the three-year LTRO—which substantially extended the ECB liquidity maturity—in Italy, where banks benefited from a government guarantee program that effectively relaxed the ECB collateral requirements. Combining the national credit register with banks’ securities holdings, we find that (i) the maturity extension supported banks’ credit supply and (ii) banks used most liquidity to buy domestic government bonds and substitute missing wholesale funding, two possibly unstated goals of the intervention.

A Fully Automated Adjustment of Ensemble Methods in Machine Learning for Modeling Complex Real Estate Systems

Abstract: The close relationship between collateral value and bank stability has led to a considerable need to a rapid and economical appraisal of real estate. The greater availability of information related to housing stock has prompted to the use of so-called big data and machine learning in the estimation of property prices. Although this methodology has already been applied to the real estate market to identify which variables influence dwelling prices, its use for estimating the price of properties is not so frequent. The application of this methodology has become more sophisticated over time, from applying simple methods to using the so-called ensemble methods and, while the estimation capacity has improved, it has only been applied to specific geographical areas. The main contribution of this article lies in developing an application for the entire Spanish market that fully automatically provides the best model for each municipality. Real estate property prices in 433 municipalities are estimated from a sample of 790,631 dwellings, using different ensemble methods based on decision trees such as bagging, boosting, and random forest. The results for estimating the price of dwellings show a good performance of the techniques developed, in terms of the error measures, with the best results being achieved using the techniques of bagging and random forest.

Competition and Appraisal Inflation

Abstract: In mortgage debt contracts, real property serves as collateral and the terms of mortgage financing are largely conditional on the certification of collateral value by appraisers. However, overstatement of collateral value is common in the appraisal industry, causing troubles in the mortgage market as observed in the recent crisis. In this paper, we examine whether competition in the appraisal industry affects appraisal bias. We model appraiser behavior given a loan officer’s preference for favorable appraisals (i.e. appraisal values at least as high as the transaction prices). As appraisers cater to loan officers to increase their probability of winning future business, our model predicts more inflated appraisals in more competitive markets. We confirm this prediction using a sample of purchase mortgages originated between 2003-2006 by a large subprime mortgage lender. Our results show that a one standard deviation increase in appraiser competition, measured at the MSA/year level, is associated with a 1.6–3.7 percentage point increase in the share of at-price appraisals. Furthermore, the effect is stronger in areas experiencing high house price growth.