# Research articles for the 2019-04-10

A Normative Dual-value Theory for Bitcoin and other Cryptocurrencies
Zhiyong Tu,Lan Ju
arXiv

Bitcoin as well as other cryptocurrencies are all plagued by the impact from bifurcation. Since the marginal cost of bifurcation is theoretically zero, it causes the coin holders to doubt on the existence of the coin's intrinsic value. This paper suggests a normative dual-value theory to assess the fundamental value of Bitcoin. We draw on the experience from the art market, where similar replication problems are prevalent. The idea is to decompose the total value of a cryptocurrency into two parts: one is its art value and the other is its use value. The tradeoff between these two values is also analyzed, which enlightens our proposal of an image coin for Bitcoin so as to elevate its use value without sacrificing its art value. To show the general validity of the dual-value theory, we also apply it to evaluate the prospects of four major cryptocurrencies. We find this framework is helpful for both the investors and the exchanges to examine a new coin's value when it first appears in the market.

Banking & Economic Implications of Farm Loan Waivers
Manda, Vijaya Kittu,Yamijala, Satya
SSRN
Governments of developing and agrarian-intensive economies such as India depend on banking channels to extend farming and agriculture credit. Agricultural credit is a priority sector lending in such countries and banks have to invariably have a part of their loan book pie allocated it`. Statistical trends show that some farmers who take these benefits are turning "smart" and intentionally delaying loan repayments with the expectation of a loan waiver, particularly few months ahead of the elections. They feel that their loan will get waived once the political party for which they voted comes to power. Overtime, it gets revealed that a very small number of farmers actually get the loan waiver benefit as promised. By then, the loan account balloons because of interest and penalties - too big for him to ever repay. The helpless farmer shows his inability to repay while the deficit budget-driven Governments could not reimburse. This puts banks into a difficult situation and have to mark the loans as Non-Performing Asset (NPA)s and to eventually write-offs.This paper examines the various dimensions surrounding the agriculture loan waiver. The problem is examined from a bank-specific perspective. Loan waivers can turn into epidemic and spread to other segments causing a systemic risk to the economy. We study the economic implications with a specific focus on the impact on the banking system. The findings of this study will be of importance to policy makers, the banking regulator and banks.

Bayesian prediction of jumps in large panels of time series data
Angelos Alexopoulos,Petros Dellaportas,Omiros Papaspiliopoulos
arXiv

We take a new look at the problem of disentangling the volatility and jumps processes in a panel of stock daily returns. We first provide an efficient computational framework that deals with the stochastic volatility model with Poisson-driven jumps in a univariate scenario that offers a competitive inference alternative to the existing implementation tools. This methodology is then extended to a large set of stocks in which it is assumed that the unobserved jump intensities of each stock co-evolve in time through a dynamic factor model. A carefully designed sequential Monte Carlo algorithm provides out-of-sample empirical evidence that our suggested model outperforms, with respect to predictive Bayes factors, models that do not exploit the panel structure of stocks.

Bitcoin Price Prediction: An ARIMA Approach
Amin Azari
arXiv

Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-crypto-economic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA) model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction, e.g. 1-day. On the other hand, when we try to train the ARIMA model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model, trained over a large dataset, and a limited test window of the bitcoin price, with length $w$, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of ($p,q,d$), and window size $w$.

Can Information and Communication Technology Improve Stock Market Efficiency? A Crossâ€Country Study
Lee, Mingâ€Hsuan,Tsai, Touâ€Chin,Chen, Jauâ€er,Lio, Monâ€Chi
SSRN
The rapid advance of information and communication technology (ICT) has revolutionized the dissemination of stock market information. Based on the noise trading theory, this study discusses whether the changes brought by ICT have promoted the transparency of stock market information or instead flooded the stock market with misinformation. A cross‐country panel dataset of 71 countries from 2002 to 2014 was established. The empirical methodologies include panel unit root tests, panel variance ratio tests, and panel multiple regressions. The results of panel unit root tests and panel variance ratio tests show that stock markets in countries with high ICT diffusion are efficient while stock markets in countries with low or medium ICT diffusion are not all efficient. The results of panel regressions further show that the effect of ICT diffusion in reducing market noises was more significant than its effect in magnifying the noises.

Cross-Venue Liquidity Provision: High Frequency Trading and Ghost Liquidity
Degryse, Hans,De Winne, Rudy,Gresse, Carole,Payne, Richard
SSRN
We measure the extent to which consolidated liquidity in modern fragmented equity markets overstates true liquidity due to a phenomenon that we call Ghost Liquidity (GL). GL exists when traders place duplicate limit orders on competing venues, intending for only one of the orders to execute, and when one does execute, duplicates are cancelled. By employing data from 2013 for 91 stocks trading on their primary exchanges and three alternative platforms where order submitters are identified consistently across venues, we find that simply measured consolidated liquidity exceeds true consolidated liquidity due to the existence of GL. On average, for every 100 shares passively traded by a multi-market liquidity supplier on a given venue, around 19 shares are immediately cancelled by the same liquidity supplier on a different venue. Yet the average weight of GL in total consolidated depth, i.e., slightly more than 4%, does not challenge the liquidity benefits of fragmentation. GL can however reach substantial levels for some categories of stocks, traders, and platforms, namely larger and less volatile stocks, high-frequency traders (HFTs), and non-primary exchanges. The greatest GL is observed for the HFTs who mostly behave as liquidity takers, on more heavily traded and less volatile stocks, across alternative platforms.

Deconstructing the Yield Curve
Crump, Richard K.,Gospodinov, Nikolay
SSRN
We investigate the factor structure of the term structure of interest rates and argue that characterizing the minimal dimension of the data-generating process is more challenging than currently appreciated. To circumvent these difficulties, we introduce a novel nonparametric bootstrap that is robust to general forms of time and cross-sectional dependence and conditional heteroskedasticity of unknown form. We show that our bootstrap procedure is asymptotically valid and exhibits excellent finite-sample properties in simulations. We demonstrate the applicability of our results in two empirical exercises: First, we show that measures of equity market tail risk and the state of the macroeconomy predict bond returns beyond the level or slope of the yield curve; second, we provide a bootstrap-based bias correction and confidence intervals for the probability of recession based on the shape of the yield curve. Our results apply more generally to all assets with a finite maturity structure.

Deep Learning for Energy Markets
arXiv

Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theory (EVT) to capture theses effects and in particular the tail behavior of load spikes. Deep LSTM architectures with ReLU and $\tanh$ activation functions can model trends and temporal dependencies while EVT captures highly volatile load spikes above a pre-specified threshold. To illustrate our methodology, we use hourly price and demand data from 4719 nodes of the PJM interconnection, and we construct a deep predictor. We show that DL-EVT outperforms traditional Fourier time series methods, both in-and out-of-sample, by capturing the observed nonlinearities in prices. Finally, we conclude with directions for future research.

Dynamic Runs and Optimal Termination
Zhong, Hongda,Zhou, Zhen
SSRN
Investors may fail to coordinate and run on distressed firms, which often forces those firms to terminate. How can firms design termination rules to promote coordination among investors? To address this question, we build a dynamic coordination model in which investors learn about a hidden bad shock in an asynchronous manner and then decide when to withdraw capital. The firm in the model can choose the termination threshold and clawback payments made to investors who withdraw within a certain window prior to its termination. Surprisingly, the firm can survive longer if it commits to terminate while there are still assets left for the remaining investors, because a higher termination payoff alleviates investors' ex-ante incentives to run. We analytically characterize the optimal clawback window and show that it should not be excessively long. A longer clawback window lowers the chance for an investor to exit the firm successfully, and therefore may lead investors to leave sooner.

Enhancing Time Series Momentum Strategies Using Deep Neural Networks
Bryan Lim,Stefan Zohren,Stephen Roberts
arXiv

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.

Estimating An Implied Cost of Capital for Australian Firms
Paton, Alex,Cannavan, Damien,Gray, Stephen,Hoang, Khoa T.A.
SSRN
We find that a composite implied cost of capital (ICC) estimate - based on the earnings forecasts generated by cross-sectional models is highly correlated with future realised returns in both portfolio- and regression-based tests. By contrast, we find very little evidence for an association with future realised returns for an ICC estimate based on analyst earnings forecasts. We also document the time-varying nature of expected returns and risk premia, and provide up-to-date estimates of an implied Australian market risk premium. Additionally, we provide industry-level estimates; highlighting differences in expected returns and risk perceptions across sectors.

FDI, banking crisis and growth: direct and spill over effects
Brahim Gaies,Khaled Guesmi,Stéphane Goutte
arXiv

This study suggests a new decomposition of the effect of Foreign Direct Investment (FDI) on long-term growth in developing countries. It reveals that FDI not only have a positive direct effect on growth, but also increase the latter by reducing the recessionary effect resulting from a banking crisis. Even more, they reduce its occurrence. JEL: F65, F36, G01, G15

Global Settlements: Promise and Peril
Coffee, John C.
SSRN
In 2010, Morrison v. National Australia Bank Ltd. destabilized the world of securities litigation by denying those who purchased their securities outside the U.S. the ability to sue in the U.S. (as they had previously often done). Nature, however abhors a vacuum, and practitioners and other jurisdictions began to seek ways to regain access to U.S. courts. Several techniques have emerged: (1) expanding settlement classes so that they are broader than litigation classes and treating the location of the transaction as strictly a merits issue that defendants could waive; (2) adopting U.S. law as applicable to securities issued abroad by crosslisted companies (as Israel has done); (3) use of the Netherlandâ€™s WCAM statute to effect a global resolution of a settlement class; and (4) coordination between the courts in both jurisdictions in the case of a cross-listed stock. On the horizon is still a more ambitious technique: use of supplemental jurisdiction to permit a class of foreign claimants to be combined with a class of U.S. claimants. Early decisions have divided on this technique. This article suggests guidelines for courts to follow in whether to allow foreign claimants in securities actions to re-enter the U.S.

Herding in Smart-Beta Investment Products
SSRN
We highlight herding of investors as one major risk factor that is typically ignored in statistical approaches to portfolio modelling and risk management. Our survey focuses on smart-beta investing where such methods and investor herding seem particularly relevant but its negative effects have not yet come to the fore. We point out promising and novel approaches of modelling herding risk which merit empirical analysis. This financial economists' perspective supplements the vast statistical exploration of implementing factor strategies.

ICOs and Cryptotokens: A New Fundraising Asset Classâ€"An Australian Perspective
SSRN
Most start-ups find it difficult to raise finance. In the last few years a new form of funding has emergedâ€"the initial coin offering (ICO). ICOs provide investors with cryptotokens that often have multiple roles as stored value token, pure utility token, security token, and hybrid token. ICOs attempt to create involvement in projects without giving away equity. However, as ICOs have risen in number regulators have become more interested and sought to provide rules for their proper functioning. In this paper we argue that the regulators often overlook the advantages of ICOs in order to focus on their faults. We propose ICOs ought to comprise a new asset class with their own rules.

Information, Incentives, and Effects of Risk-Sharing on the Real Economy
Liu, Mark H.,Wu, Wenfeng,Yu, Tong
SSRN
In the absence of market imperfections, the mutuality principle leads to efficient risk sharing and the Pareto optimal asset allocations. With market imperfections such as transaction costs and information asymmetry, risk-sharing becomes costly, and it can even lead to financial crises. We emphasize the impact of risk-sharing on the real economy, especially the incentives for the insured party to take on excessive risks because the downsides are borne by the insurer (i.e., the moral hazard problem). We then review selective literature and summarize papers included in this issue, grouping them into three broad categories: risk identification, risk measurement, and risk management techniques. We conclude by outlining several streams of future research, including mechanisms to monitor excessive risk-taking, how to mitigate risk interconnectedness, and the potential applications of FinTech in risk sharing.

Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey
Yoshiharu Sato
arXiv

Financial portfolio management is one of the problems that are most frequently encountered in the investment industry. Nevertheless, it is not widely recognized that both Kelly Criterion and Risk Parity collapse into Mean Variance under some conditions, which implies that a universal solution to the portfolio optimization problem could potentially exist. In fact, the process of sequential computation of optimal component weights that maximize the portfolio's expected return subject to a certain risk budget can be reformulated as a discrete-time Markov Decision Process (MDP) and hence as a stochastic optimal control, where the system being controlled is a portfolio consisting of multiple investment components, and the control is its component weights. Consequently, the problem could be solved using model-free Reinforcement Learning (RL) without knowing specific component dynamics. By examining existing methods of both value-based and policy-based model-free RL for the portfolio optimization problem, we identify some of the key unresolved questions and difficulties facing today's portfolio managers of applying model-free RL to their investment portfolios.

On the Co-movement of Crude, Gold Prices and Stock Index in Indian Market
Abhibasu Sen,Prof. Karabi Dutta Chaudhury
arXiv

This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained during some period, but as a whole no such cointegration relation can be found out. Using the \textit{Discrete Wavelet Analysis}, the data was decomposed and the presence of Granger Causal relations was tested. Unfortunately no significant relationships are being found. We then studied the \textit{Wavelet Coherence} of the two pairs viz. NSE-Nifty \& Gold and NSE-Nifty \& Crude. For different frequencies, the coherence between the pairs have been studied. At lower frequencies, some relatively good coherence have been found. In this paper, we report for the first time the co-movements between Crude Oil, Gold and Indian Stock Market Index using Wavelet Analysis (both Discrete and Continuous), a technique which is most sophisticated and recent in market analysis. Thus for long term traders they can include gold and/or crude in their portfolio along with NSE-Nifty index in order to decrease the risk(volatility) of the portfolio for Indian Market. But for short term traders, it will not be effective, not to include all the three in their portfolio.

Peer Monitoring, Syndication, and the Dynamics of Venture Capitalist Interactions
Chemmanur, Thomas J.,Tian, Xuan
SSRN
We develop a new rationale for the formation of syndicates by financial intermediaries, theoretically analyze the dynamics of interactions by syndicate members, and test the implications of our theory. The specific context in which we develop our analysis is that of venture capital (VC) syndicates. In our model, an entrepreneur needs financing from a VC investor to implement his firmâ€™s positive net present value project. In addition to financing, VCs can provide the firm with two inputs (each in a different area of activity), which can increase the probability of project success: these inputs can be provided either by a single VC, or by two different VCs, each operating in his own area of expertise. The effort exerted by a VC in providing the above inputs is unobservable to the entrepreneur but observable to other VCs who may form part of a syndicate with him. We analyze the firmâ€™s equilibrium choice between financing the project by contracting with a single VC, by contracting individually with two VCs, or by contracting with a syndicate consisting of two VCs. Our analysis generates several testable predictions for the equilibrium choice of the structure of VC financing, for the evolution of this structure across financing rounds, and for the dynamics of the composition of VC syndicates. First, firms with more complex projects are more likely to seek financing from a VC syndicate. Second, while VC specialists are more likely to join in a VC syndicate to finance an entrepreneurial firm, VC generalists are more likely to finance the firm alone. Third, firms obtaining financing from a VC syndicate throughout various financing rounds are more likely to have a successful exit compared to those which have syndicate financing in earlier rounds but switch to financing from a single VC in later rounds. Fourth, firms financed by a syndicate consisting of the same set of VC investors throughout various financing rounds are more likely to have a successful exit compared to those which are financed by VC syndicates whose membership changes across financing rounds. Finally, VCs forming part of a syndicate which financed a successful firm are more likely to form a syndicate again financing future projects. We present empirical evidence consistent with the above predictions of our model.

Preventing Information Leakage
Brogaard, Jonathan,Li, Dan,Ma, Matthew,Riordan, Ryan
SSRN

Replicating Financial Market Dynamics with a Simple Self-Organized Critical Lattice Model
Dupoyet, Brice V.,Fiebig, Rudolf H,Musgrove, David
SSRN
We explore a simple lattice field model intended to describe statistical properties of high frequency financial markets. The model is relevant in the cross-disciplinary area of econophysics. Its signature feature is the emergence of a self-organized critical state. This implies scale invariance of the model, without tuning parameters. Prominent results of our simulation are time series of gains, prices, volatility, and gains frequency distributions, which all compare favorably to features of historical market data. Applying a standard GARCH(1,1) fit to the lattice model gives results that are almost indistinguishable from historical NASDAQ data.

Representations and Warranties Insurance and Valuation Uncertainty in Mergers and Acquisitions
Even-Tov, Omri,Ryans, James
SSRN
We empirically examine representations and warranties insurance policies to better understand valuation uncertainty in mergers and acquisitions, and in particular the insights this setting gives regarding information production, disclosure, and verification costs. We show that material breaches in acquisition representations and warranties occur in approximately 25 percent of acquisitions, and most frequently relate to financial statement errors, followed by issues with material contracts, taxes, and compliance with laws and regulations. We find a significant association between the demand for representation and warranty insurance and proxies of valuation uncertainty, contracting costs, and risk aversion. Our tests of the determinants of the insurance premiums support the view that the insurance is priced to account for expected valuation uncertainty as well as to mitigate adverse selection and moral hazard in acquisition due diligence.

Robust Mathematical Formulation and Implementation of Agent-Based Computational Economic Market Models
Maximilian Beikirch,Simon Cramer,Martin Frank,Philipp Otte,Emma Pabich,Torsten Trimborn
arXiv

Monte Carlo Simulations of agent-based models in science and especially in the economic literature have become a widely used modeling approach. In many applications the number of agents is huge and the models are formulated as a large system of difference equations. In this study we discuss four numerical aspects which we present exemplified by two agent-based computational economic market models; the Levy-Levy-Solomon model and the Franke-Westerhoff model. First, we discuss finite-size effects present in the Levy-Levy-Solomon model and show that this behavior originates from the scaling within the model. Secondly, we discuss the impact of a low-quality random number generator on the simulation output. Furthermore, we discuss the continuous formulation of difference equations and the impact on the model behavior. Finally, we show that a continuous formulation makes it possible to employ correct numerical solvers in order to obtain correct simulation results. We conclude that it is of immanent importance to simulate the model with a large number of agents in order to exclude finite-size effects and to use a well tested pseudo random number generator. Furthermore, we argue that a continuous formulation of agent-based models is advantageous since it allows the application of proper numerical methods and it admits a unique continuum limit.

Shedding Light on the Exposure of Mutual Funds â€" Which Investments Drive Mutual Fund Characteristics?
Benz, Lukas,Rohleder, Martin,Syryca, Janik,Wilkens, Marco
SSRN
This paper is the first to identify and classify virtually all investment instruments held by equity funds from their portfolio holdings. This enables us to analyze the effects of long and short exposures from different complex instruments including short sales, options and futures but also previously neglected derivatives like warrants and units on fundsâ€™ risk, performance and characteristics. These analyses are of general interest, especially in the light of ongoing discussions regarding further regulation of complex instrument use by mutual funds. Our empirical analyses document that more than 40% of funds use complex instruments. However, relative to their total assets, fundsâ€™ average exposure from such positions is very small with values below 1%. Consequentially, the effects of instruments are often weaker than suggested by previous research or even show opposite directions.

Stock Option Exercise, Earnings Management, and Abnormal Stock Returns
Safdar, Irfan
SSRN
This essay uses a large sample to examine whether stock option plans provide incentives to executives to manage earnings when exercising their options. The evidence presented is consistent with a hypothesis where managers use accruals to shift earnings to increase the stock price prior to and during option exercise periods. However, the results indicate that the magnitude of earnings management related to stock options may be limited. Reported income peaks at the earnings announcement immediately preceding option exercise activity and is followed by both reversals in income and discretionary accruals as well as negative abnormal stock returns during the post-exercise period for up to one year. Current discretionary accruals range from 0.3% to 0.62% of assets, depending upon the accrual model, during the quarterly earnings announcement immediately preceding option exercise activity. Over the two quarters following option exercise, sample firms experience small but statistically significant reversals in discretionary accruals and on average experience negative abnormal returns of approximately -3%. The magnitude of the return reversals is shown to be cross-sectionally positively related to the magnitude of the pre-exercise discretionary accrual proxies, even after adjusting for the Sloan anomaly. I find similar evidence for a sample of firms that experience option expiration but weaker evidence of earnings management for stock sales unrelated to stock option exercise.

Using Abnormal Analyst Coverage to Unlock New Evidence on Stock Price Crash Risk
Chowdhury, Hasibul,Faff, Robert W.,Hoang, Khoa T.A.
SSRN
We employ a characteristic-based model to decompose total analyst coverage into abnormal and expected components and show that abnormal coverage contains valuable information about individual firm ex-ante crash risk (proxied by implied volatility smirk from options data). Specifically, one standard deviation increase in unexpected or abnormal coverage is associated with a 5.5% decrease in the ex-ante crash risk. The abnormal coverage signal is more useful in firms with a more transparent information environment, proxied by lower analyst dispersed opinions, lower financial opacity, and more comparable financial statements. Collectively, the results suggest that options market investors utilise abnormal coverage to identify and assess crash risk of mispriced firms.

Vague Talk in ECB Press Conference: News or Noise?
Hu, Nan,Sun, Zexi
SSRN
We answer the question by quantitatively measuring the amount of vague talk in the European Central Bankâ€™s press conferences and investigating its effect on the stock market performance of the Eurozone. Different than the results of current research, our study unveils the dual role vague talk plays in the market: It not only is considered as positive news that increases returns and lifts liquidity but also acts as a stabilizer that decreases realized and expected volatility. By decomposing the overall vagueness at the word- and sentence-levels, we find that the stock market only responds to the vague explanation of monetary policy strategies, but not the vague description of economic environments. An increase in the former type of vagueness helps investors develop a deeper understanding of the conduct of monetary policy, enhances the predictability of central bank's future policy actions, and thus contributes to a reduction of the volatility factor in the market. This paper presents the first piece of evidence that relates to the theoretical dispute on the social value of public information originating in Morris and Shin (2002).

What Option Prices Tell Us About the ECBâ€™s Unconventional Monetary Policies
Olijslagers, Stan,Petersen, Annelie,de Vette, Nander,van Wijnbergen, Sweder
SSRN
We use a series of different approaches to extract information about crash risk from option prices for the Euro-Dollar exchange rate, with each step sharpening the focus on extracting more specific measures of crash risk around dates of ECB measures of Unconventional Monetary Policy. Several messages emerge from the analysis. Announcing policies in general terms without precisely describing what exactly they entail does not instantly move asset markets or actually increases crash risk. Also, policies directly focused on changing relative asset supplies do seem to have an impact, while measures aiming at easing financing costs of commercial banks do not.

When Losses Turn into Loans: The Cost of Undercapitalized Banks
Blattner, Laura,Farinha, Luísa,Rebelo, Francisca
SSRN
We provide evidence that a weak banking sector has contributed to low productivity growth following the European sovereign debt crisis. An unexpected increase in capital requirements for a subset of Portuguese banks in 2011 provides a natural experiment to study the effects of reduced bank capital adequacy on productivity. Affected banks respond not only by cutting back on lending but also by reallocating credit to firms in financial distress with prior underreported loan loss provisioning. We develop a method to detect when banks delay loss reporting using detailed loan-level data. We then show that the credit reallocation leads to a reallocation of production factors across firms. A partial equilibrium exercise suggests that the resulting increase in factor misallocation accounts for 20% of the decline in productivity in Portugal in 2012.