Research articles for the 2020-03-31
SSRN
In this work, we study an equilibrium-based continuous asset pricing problem which seeks to form a price process endogenously by requiring it to balance the flow of sales-and-purchase orders in the exchange market, where a large number of agents are interacting through the market price. Adopting a mean field game (MFG) approach, we find a special form of forward-backward stochastic differential equations of McKean-Vlasov type with common noise whose solution provides a good approximate of the market price. We show the convergence of the net order flow to zero in the large N-limit and get the order of convergence in N under some conditions. We also extend the model to a setup with multiple populations where the agents within each population share the same cost and coefficient functions but they can be different population by population.
SSRN
Valuation ratios divide stock price by accounting metrics such as earnings, earnings growth and book value. This study adapts the general valuation framework in Ohlson and Juettner-Nauroth (2005) and Ohlson (2005) to present a unified approach for developing valuation ratios based on fundamentals, referred to as fundamental valuation ratios. One starts with a valuation model that is driven by an accounting metric a and its abnormal growth, then divides the valuation model by a to get a fundamental valuation ratio. For any valuation ratio, one can find a corresponding fundamental valuation ratio, as long as the valuation model is based on the same metric a as the valuation ratio denominator.
SSRN
The role of cash-back credit cards in personal financial strategies is highly debated. For example, Dave Ramsey (Ramsey 2019) urges consumers to avoid even the most lucrative cash-back cards, while others argue that these cards offer significant savings. Herein, we construct models to analyze the use of cash-back cards by rational consumers, demonstrating that cash-back cards increase spending (and thus, reduce savings) for some consumers. While prior research focuses on behavioral issues related to credit cards, our research is the first to show that some consumers will rationally increase spending when using a cash-back credit card in lieu of cash.
arXiv
Industrial symbiosis involves creating integrated cycles of by-products and waste between networks of industrial actors in order to maximize economic value, while at the same time minimizing environmental strain. In such a network, the global environmental strain is no longer equal to the sum of the environmental strain of the individual actors, but it is dependent on how well the network performs as a whole. The development of methods to understand, manage or optimize such networks remains an open issue. In this paper we put forward a simulation model of by-product flow between industrial actors. The goal is to introduce a method for modelling symbiotic exchanges from a macro perspective. The model takes into account the effect of two main mechanisms on a multi-objective optimization of symbiotic processes. First it allows us to study the effect of geographical properties of the economic system, said differently, where actors are divided in space. Second, it allows us to study the effect of clustering complementary actors together as a function of distance, by means of a spatial correlation between the actors' by-products. Our simulations unveil patterns that are relevant for macro-level policy. First, our results show that the geographical properties are an important factor for the macro performance of symbiotic processes. Second, spatial correlations, which can be interpreted as planned clusters such as Eco-industrial parks, can lead to a very effective macro performance, but only if these are strictly implemented. Finally, we provide a proof of concept by comparing the model to real world data from the European Pollutant Release and Transfer Register database using georeferencing of the companies in the dataset. This work opens up research opportunities in interactive data-driven models and platforms to support real-world implementation of industrial symbiosis.
SSRN
With political constraints on fiscal responses and monetary policy confronting the zero lower bound, policymakers may be tempted to turn to financial deregulation as a tool to stimulate economic growth in a recession, a strategy I label âregulatory stimulus.â This article creates a framework for answer two questions: first, whether and when regulatory stimulus is effective in promoting macroeconomic growth, particularly in a severe recession or liquidity trap; and second, if regulatory stimulus is effective, whether it is worth the potential trade-offs in terms of longer-term macroeconomic policy objectives. Ultimately, I find grounds for skepticism that financial deregulation can effectively stimulate economies suffering from a liquidity trap. Policymakers must first articulate the channel by which deregulation would have a macroeconomic effect. In a given channel, it is unclear how most contemporary versions of financial deregulation would achieve sufficient magnitude without significant time lag given the constraints of legal process. Policymakers would also have to consider expectations, particularly whether financial deregulation might be âpushing on a string.â Financial institutions may not lend more even when permitted by loosened financial rules because of significant economic uncertainty. It would be difficulty to calibrate financial deregulation as a macroeconomic tool, and the political economy of financial regulation can create a one way ratchet effect toward deregulation.The article conducts a brief case study of the 2012 JOBS Act, which illustrates these challenges.Even if regulatory stimulus effectively achieves its aims, it may create significant intertemporal trade-offs. A short-term economic punch can come at the cost of economic institutions that promote long term financial stability and sustained growth. Legal rules represent more than mere regulatory âtaxesâ that can be repealed as a tool of stimulus.Accordingly, policymakers should demand clear empirical evidence of deregulationâs efficacy before weakening a particular regulation in an effort to stimulate the economy.
SSRN
Adjusting for inflation, the annual amount paid out through dividends and share repurchases by public non-financial firms is three times larger in the 2000s than from 1971 to 1999. We find that an increase in aggregate corporate income explains 38% of the increase in the average of aggregate annual payouts from 1971-1999 to the 2000s, while an increase in the aggregate payout rate explains 62%. At the firm level, changes in firm characteristics explain 71% of the increase in average payout rate for the population and 49% of the increase in the average payout rate of firms with payouts. Though there is a negative relation between payouts and investment, most of the increase in payouts is unrelated to the decrease in investment. Models estimated over 1971-1999 underpredict the payout rate of firms with payouts in the 2000s. These models perform better when we forecast non-debt-financed payouts for a sample of larger firms, but not for the sample as a whole. Payouts are more responsive to firm characteristics in the 2000s than before, which is consistent with management having stronger payout incentives.
SSRN
Property appraisers can purposefully inflate valuation estimates by misreporting the underlying attributes of subjects as well as comparable transactions selected to base their estimate. A property-level panel of appraiser reported attributes associated with 3.5 million loan applications from 2013 to 2017 is created to test whether attributes of the same property are consistently reported. We find that 98% of properties had at least one-of-seven attributes inconsistently reported, and that reporting errors were often consistent with systematic misreporting in order to inflate the valuation of subject properties. Our strongest evidence of purposeful misreporting was when an appraiser inconsistently reported attributes of the same comparable transaction selected to value different properties. We estimate highly leveraged borrowers whose appraisals had inconsistently reported attributes were 5-to-23% more likely to become seriously delinquent in their loan payments. This evidence indicates that appraisal bias is common and has significant consequences for lenders.
RePEC
Objective - This paper aims to obtain empirical evidence about the influence of specialized auditors, audit tenure, audit committee, board independence, ownership concentration, and auditor quality on audit report lag in Indonesian manufacturing firms. Methodology/Technique - The population is all manufacturing companies listed on the Indonesia Stock Exchange between 2010 and 2016. Multiple linear regressions was used as the data analysis method. Finding - The results of this research show that specialized auditors, board independence, ownership concentration and auditor quality all have an influence on audit report lag. Meanwhile, audit tenure and audit committee do not have an influence on audit report lag. Novelty - Specialized auditors will provide better performance than non-specialized auditors. Specialized auditors will apply more appropriate planning and monitoring on the audit procedure. Specialized auditors need longer time to audit financial statements, which effects audit report lag. The presence of an independent board requires higher quality financial statements. Thus, the auditor needs to put more effort into the verification process of financial statements. The largest shareholders tend to be committed and responsible to the company's reputation. Managers will demand the audit report lag in a timely manner, in order to maintain the trust and satisfaction of the company's largest shareholders.
arXiv
This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze autocorrelation of returns in major cryptocurrency markets using the following methods: Pearson's autocorrelation coefficient of different orders, Ljung-Box test, and first-order Pearson's autocorrelation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant autocorrelation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.
arXiv
Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that 49 million workers work in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Our model suggests that when businesses are forced to reduce worker contacts by half, they need a 12 percent wage subsidy to compensate for the disruption in communication. Retail, hotels and restaurants, arts and entertainment and schools are the most affected sectors. Our results can help target fiscal assistance to businesses that are most disrupted by social distancing.
SSRN
We outline an education-career investment model structured for customizable, just-in-time delivery. We suggest how FinTech collaboration can aid human capital investment planning. Students, researchers, and professionals might all benefit from scenario analysis regarding human capital investment. Our initiation of this evolutionary model results in a pilot application at www.cashncareers.org for which we include associated technical details.
SSRN
These slides accompanied a presentation provided by the American Bankruptcy Institute's Claims Trading Committee at the organization's 2019 Winter Leadership Conference. The presentation was entitled "Sponsor Liability, Including Liability Relating to Sponsors Purchasing Debt or Equity in Their Portfolios,." The presenter was Jay D. Rao and the panel was moderated by the Hon. Elizabeth S. Stong of the United States Bankruptcy Court for the Eastern District of New York. Judge Stong serves as a Co-Chair of the American Bankruptcy Institute's Claims Trading Committee.
SSRN
The Great Depression is infamous for banking panics, which were a symptomatic of a phenomenon that scholars have labeled a contagion of fear. Using geocoded, microdata on bank distress, we develop metrics that illuminate the incidence of these events and how banks that remained in operation after panics responded. We show that between 1929-32 banking panics reduced lending by 13%, relative to its 1929 value, and the money multiplier and money supply by 36%. The banking panics, in other words, caused about 41% of the decline in bank lending and about nine-tenths of the decline in the money multiplier during the Great Depression.
SSRN
The choice between lit and dark trading venues depends on market conditions, which are affected by execution priority rules in the dark pool, adverse selection, and traders' competition in order submissions. We show that dark trading activity has a non-linear relationship with the main market variables (e.g. asset volatility and liquidity), which explains previous mixed empirical results regarding the impact of dark pools. The introduction of dark pools increases welfare only for speculators, while other traders (even large traders) are worse off. Finally, we use the model to analyze traders' behavior and welfare under different market conditions.
SSRN
We examine whether measures of audit quality are able to measure the quality of audit in banks. We make use of a unique setting where the Reserve Bank of India (RBI) provided an accurate measure of asset quality in banks. We ï¬rst attempt to establish that the RBI revealed asset quality is a better estimate than that disclosed by the banks. Using the difference between the RBI-revealed and the bank-reported ï¬gures as an external validation of audit,we are unable to ï¬nd evidence that any of the generally accepted measures of audit quality do measure the quality of audit. We then examine whether the sensitivity of the bankâs asset quality to its borrowersâ health increases when the more accurate RBI-revealed ï¬gures are used and ï¬nd that they do. Using the actual sensitivity of the bankâs true health to borrower health, we construct a novel measure of audit quality.
SSRN
We analyse the drivers of total factor productivity of Spanish banks from early 2000, including the last financial crisis and the post-crisis period. This allows us to study changes in productivity following a major restructuring process in the banking sector such as the one experienced in Spain. Overall, we find that following a period of continued growth, productivity declined after the height of the crisis, though large banks were less affected.We also find that risk, capital levels, competition and input prices were important drivers of the differences in productivity change between banks. Finally, our results suggest that, by the end of our sample period, there was still some room for potential improvements in productivity via exploiting scale economies and enhancing cost efficiency. These opportunities appear to be generally greater for the smaller banks in our sample.
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We examine whether Chinaâs growing importance to Australia as both a trade partner and engine of growth has been accompanied by financial market interdependence. We consider effects on the overall Australian market as well as the iron ore sector, which has been accounting for over half of Australiaâs exports to China in the years since the global financial crisis. Markov-switching analysis yields evidence of the Shanghai Composite being connected not only with the Australian iron ore sector but also the broad market Australian All Ordinaries index. These ties are found to be significant only during low volatility periods, however.
SSRN
The economic dislocations caused by the coronavirus pandemic will almost certainly result in economic contraction. There have been calls for bold actions to attenuate the effects of this contraction and also to provide cash for struggling Americans whose incomes may be temporarily disrupted. But these actions, including direct cash payments to American households, will add to the federal deficit, and recent large fiscal deficits have tempered enthusiasm for immediate large federal outlays.The pandemic will lead to a deep contraction that hopefully will be short. The more that national consumption can be smoothed during this time, especially for households that experience the largest temporary hits to their incomes, the quicker the recovery will be. There may be a way to allow American households to help themselves through this period and to lessen the need for more federal spendingâ"by easing some of the restrictions on mortgage lending, to allow Americans to access their single largest asset: their homes. The nation is in the midst of an emergency, desperate for liquidity and for current sources of secured spending. It is worth considering the value of tapping this source of liquidity.
SSRN
Online financial communities provide a unique opportunity to directly examine individual investorsâ attention to accounting information on a large scale and in great detail. I analyze accounting-related content in large samples of Yahoo! message board posts and StockTwits and find investors pay attention to a range of accounting information, fixating particularly on earnings, cash, and revenues. Consistent with the expectation that investors react to relevant information events, I find accounting-related discussion elevated around the filings of earnings releases and 8-K reports, but the reaction to periodic reports is confined to small firms. I also find investors expand their acquisition of accounting information and processing efforts in poor information environments. Greater attention to accounting information at earnings releases does not appear to be meaningfully associated with better information processing.
SSRN
We study whether credit rating affirmations affect stock investors. In a sample of U.S. corporate credit ratings over the period 1995-2018, we find that information uncertainty proxied by analyst forecast dispersion and stock return volatility decreases within a 30-day window following the rating affirmation announcements. The reduction in information uncertainty is mainly driven by non-investment grade bond issuers, whereas such reduction is not statistically significant for investment-grade bond issuers. We also find that the stock market reaction to rating affirmations for the non-investment issuers is significantly positive. Our findings uncover a previously unexplored benefit of rating affirmations to stock market participants.
SSRN
We introduce a simple definition of carbon-intensive firms to measure institutional investorsâ exposure to the emission intensities of portfolio companies. The definition is based on major emission industry sectors identified by the Intergovernmental Panel on Climate Change (IPCC). All firms in these industries are classified as carbon-intensive. We show that 13F institutions on average gradually reduce their carbon exposure relative to the U.S. value-weighted market portfolio, from overweighting stocks of high-emission firms by 0.5% in 2001 to underweighting by 0.2%-0.7% since 2015, consistent with the view that investors become more concerned about the financial implications of climate change. Compared with firm-level emission data obtained from vendors, our measure is free from selection issues and can be extended to early periods and to international markets, as it only depends on industry classifications and covers all stocks. We do not find the same divestment trend before 2000, when climate risks were less eminent.
arXiv
The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed event arrivals to be naturally incorporated in a tractable manner, while the hidden Markov chain captures the effect of unobservable drivers of the data. In addition to increases in accuracy and interpretability, this method supplements analysis of the latent factors. Further, this procedure naturally incorporates data features such as over-dispersion and autocorrelation. Additional insights can be generated to assist analysis, including a procedure for iterative model improvement.
Implementation difficulties are also addressed with a focus on dealing with large data sets, where latent models are especially advantageous due the large number of observations facilitating identification of hidden factors. Namely, computational issues such as numerical underflow and high processing cost arise in this context and in this paper, we produce procedures to overcome these problems.
This modelling framework is demonstrated using a large insurance data set to illustrate theoretical, practical and computational contributions and an empirical comparison to other count models highlight the advantages of the proposed approach.
SSRN
This paper examines the stochastic behaviour of the realized betas within the one-factor CAPM for the six companies with the highest market capitalization included in the Spanish IBEX stock market index. Fractional integration methods are applied to estimate their degree of persistence at the daily, weekly and monthly frequency over the period 1 January 2000 ââ¬â 15 November 2018 using 1, 3 and 5-year samples. On the whole, the results indicate that the realized betas are highly persistent and do not exhibit mean-reverting behaviour. However, the findings are rather sensitive to the choice of frequency and time span (number of observations).
SSRN
Any engineering approach to cybersecurity must recognize that many breaches are the result of human behavior, rather that sophisticated malware. Effective cybersecurity defenses require a systematic engineering approach that recognizes the organizational, cultural and psychological barriers to effectively dealing with this problem. The U.S. Securities and Exchange Commission (SEC) defines âphishingâ as, âthe use of fraudulent emails and copy-cat websites to trick you into revealing valuable personal information ̶ such as account numbers for banking, securities, mortgage, or credit accounts, your social security numbers, and the login IDs and passwords you use when accessing online financial service providers.â Once this information is fraudulently obtained, it may be used to steal your identity, money, or both.A review of the literature reveals an alarming lack of attention to the prevalent threat of low-technology, or low-complexity phishing attacks. Accordingly, here is a primer on the prominent exploit known as phishing, illustration of several cases, and the necessity for organizational and societal education of data users as to appropriate computer hygiene.
SSRN
This paper provides direct evidence on the effect of quantitative easing (QE) policies on bank lending through the liquidity channel. By looking at changes in banks' liquidity-lending sensitivity in different rounds of QE, our results show that banks with higher levels of cash & reserve holding (especially excess reserves) are more responsive to the large-scale asset purchases (LSAPs) after the 2007-08 financial crisis, compared to their liquidity-constrained counterparts. Interestingly, we find this liquidity channel to be almost equally significant for both C&I loans and real estate lending. Our results are robust to different specifications, alternative measures of banks' liquidity constraint, and inclusion of controls for demand-side factors. Further analysis indicates that this liquidity channel is distinct from the net-worth channel and capital requirement constraint documented in the literature.
SSRN
Relative performance evaluation (âRPEâ) is a useful tool for shielding risk averse agents from systematic uncertainty. However, RPE can also destroy firm value by encouraging executives to implement excessively aggressive product market strategies to improve their relative standing through costly sabotage. We posit that explicit collusion restricts a firmâs ability to engage in sabotage, thereby improving the net benefits of RPE. Consistent with this hypothesis, we document that: (1) cartel members are more likely to use RPE, especially in more concentrated markets; (2) conditional on using RPE, cartel members include more economically similar firms in their peer group; (3) firms are disproportionately likely to drop RPE from their executivesâ pay package within one year of their cartel being dissolved by a plausibly exogenous intervention; and (4) RPE is associated with greater product market aggression, but only among non-cartel firms. Collectively, our evidence suggests that the potential for costly sabotage is an important deterrent to firmsâ reliance on RPE, and that explicit collusion mitigates this effect, thereby facilitating more efficient risk-sharing.
SSRN
Policymakers need to rediscover the organizational form of business entity as a tool of financial regulation. Recent and classic scholarship has produced evidence that financial institutions organized as alternative entity forms â" including investment bank partnerships and banks and insurance companies organized as mutual or cooperatives â" tend to take less risk, exploit customers/consumer less, or commit less misconduct compared to counterparts organized as investor-owned corporations. This article builds off the work of Hill and Painter on investment banks organized as partnerships, Hansmann on the history and economics of banks and insurance companies organized as mutuals and cooperatives, and other scholars. Encouraging either financial institutions to convert to alternative entity forms or more capital and business to flow to these firms can mitigate systemic risk, dampen risk-taking, and further consumer protection. Systemic risk can also be mitigated by organizing firms across a financial industry in an entity that mutualizes risk and encourages firms to police one anotherâs conduct. Omarovaâs and Saguatoâs work in this vein recalls the bank clearinghouse of the 19th century. The entity form furthers these policy objectives without the need for policymakers to specify standards for conduct. Instead, alternative entity forms work by changing the relationships among various claimants on the firm and the firmâs management. Beyond control and liability rules, alternative organizational forms affect manager behavior by changing the identity of the residual claimant on the firm. Management behavior shifts because they are not responsive to investors whose principal interest is maximizing their return on capital. Alternative entity forms may come with agency costs, as the residual claimants face difficulty in organizing collectively to monitor and discipline management. However, evidence from mutual insurance companies suggests that agency costs are not severe. Other costs associated with partnerships, mutuals, and collectives include limitations on the ability of firms to raise capital. However, this drawback has the benefit of creating an embedded check on the size and complexity of financial firms and providing an alternative to breaking up large financial conglomerates for too-big-to-fail and other concerns. Similarly, alternative entities better serve the interests of residual claimants when those claimants have homogenous interests, creating disincentives for firms to expand across business lines.Policymakers could encourage financial firms to convert their organizational form or promote more capital or business flowing to alternative entities by providing or restoring historical tax incentives or creating regulatory preferences in the policy areas in which alternative entities outperform investor-owned corporations. In some cases, such as risk-adjusted deposit insurance premiums, regulators may be required to provide this treatment. Regulators and prosecutors should consider requiring financial institutions that have committed serious violations of laws to convert to an alternative organizational form as an alternative to imposing fines or revoking a charter or license.This article was written for a Cornell symposium in memory of Lynn Stout.
SSRN
Default by sovereign governments depends upon their willingness to default and the nationâs capacity to pay. These are major factors considered by rating analysts and both may be affected by national culture. We hypothesise that ratings are related to culture and empirically examine the relation between culture and both levels and changes in sovereign ratings. Sovereign ratings have traditionally been modelled in terms of macro-economic variables, rating outlook and rating history. Culture variables are significant when included in such models and their addition results in better models as judged by the QIC statistic and likelihood ratio tests. The significance of culture variables is robust to replication and to estimation using instrumental variables.
SSRN
A 1970 New York Times essay on corporate social responsibility by Milton Friedman is often said to have launched a shareholder-focused reorientation of managerial priorities in Americaâs public companies. The essay correspondingly is a primary target of those critical of a shareholder-centric approach to corporate governance. This paper argues that it is erroneous to blame (or credit) Milton Friedman for the rise of shareholder primacy in corporate America. In order for Friedmanâs views to be as influential as has been assumed, his essay should have constituted a fundamental break from prevailing thinking that changed minds with some alacrity. In fact, what Friedman said was largely familiar to readers in 1970 and his essay did little to change managerial priorities at that point in time. The shareholder-first mentality that would come to dominate in corporate America would only take hold in the mid-1980s. This occurred due to an unprecedented wave of hostile takeovers rather than anything Friedman said and was sustained by a dramatic shift in favor of incentive-laden executive pay. Correspondingly, the time has come to stop blaming him for Americaâs shareholder-oriented capitalism.
SSRN
Economic (Bhagwat, Dam and Harford, 2016), political (Cao, Li and Liu, 2019), and policy (Nam and Hieu, 2017; Bonaime, Gulen and Ion, 2018) uncertainty affect merger and acquisition (M&A) activity. In this paper, we use Department of Justice (DOJ) and Federal Trade Commission (FTC) interventions in the M&A market to investigate whether regulatory uncertainty also matters. Our results support this conjecture. Using the Hoberg and Phillips (2010) similarity scores to identify product market competitors, we uncover a clear and significant DOJ/FTC intervention deterrence effect on future M&A transaction attempts. This deterrence effect is driven by the length of the investigation procedure, a principal factor that exacerbates regulatory uncertainty. Our results identify an (un)intended channel through which M&A regulation hampers efficient resources allocation.
SSRN
In this tutorial we introduce three approaches to preprocess text data with Natural Language Processing (NLP) and perform text document classification using machine learning. The first approach is based on 'bag-of-' models, the second one on word embeddings, while the third one introduces the two most popular Recurrent Neural Networks (RNNs), i.e. the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. We apply all approaches on a case study where we classify movie reviews using Python and Tensorflow 2.0. The results of the case study show that extreme gradient boosting algorithms outperform adaptive boosting and random forests on bag-of-words and word embedding models, as well as LSTM and GRU RNNs, but at a steep computational cost. Finally, we provide the reader with comments on NLP applications for the insurance industry.
arXiv
We revisit the optimal capital structure model with endogenous bankruptcy first studied by Leland \cite{Leland94} and Leland and Toft \cite{Leland96}. Differently from the standard case, where shareholders observe continuously the asset value and bankruptcy is executed instantaneously without delay, we assume that the information of the asset value is updated only at intervals, modeled by the jump times of an independent Poisson process. Under the spectrally negative L\'evy model, we obtain the optimal bankruptcy strategy and the corresponding capital structure. A series of numerical studies are given to analyze the sensitivity of observation frequency on the optimal solutions, the optimal leverage and the credit spreads.
SSRN
We study loans from banking and non-banking lenders to different groups of borrowers in order to unveil significant differences on how those respond to a shock and evaluate possible alternative explanations for such differences. The objective is to gain insights useful to explain the loan puzzle: the unexpected increase of loans to firms in case of a monetary tightening. The analysis is based on a vector autoregression, estimated using Bayesian techniques, and has as object the US economy.
SSRN
We extend prior research examining the relation between aggregate recommendation changes and future returns by documenting that this relation varies over time as a function of the predictability of future earnings growth. When industry-level earnings growth is more predictable, we find that recommendation changes relate negatively to future returns. Our evidence suggests that this negative relation results from analysts revising recommendations upward for higher expected earnings growth but failing to adjust downward for a related decrease in investor risk aversion and demand for risk premia leading to lower expected returns. In contrast, when industry-level earnings growth is less predictable, we find that recommendation changes relate positively to future returns. However, this positive relation results from analysts and investors similarly underestimating earnings growth persistence. Overall, the evidence fails to support the claim that analystsâ recommendation changes incorporate aggregate information in a manner that adds value to investors by predicting future returns.
SSRN
* The draft reform of the European Stability Mechanism (henceforth ESM) was not approved as scheduled in the Euro Summit in December 2019 because the Italian Prime Minister was obliged to ask for a delay in the face of strong domestic opposition to the reform coming from populist parties. *It is not clear if the reform will ever be approved and if, in this case, the Conte government will survive.* The arguments used by the populists against the reform are deeply flawed.* The proposed changes in the text of the EMS Treaty are relatively minor and do not contain any mechanism of automatic restructuring of the debt of countries asking for financial assistance from the ESM.* However, the small changes in the text reflect the idea that Italy will soon be obliged to restructure the debt. The only possible answer by the Italian authorities is to set up a plan for a gradual reduction of the debt to GDP ratio.* Restructuring the debt may be a painful necessity, but it is not a way to solve the problem ofthe debt in a country in which most of the debt is held by residents.
SSRN
This paper investigates the common, yet previously opaque, practice of using foreign audit firms (component auditors) to conduct portions of audit work for U.S. public companies. U.S. regulators have expressed concern for the transparency and quality of audits using component auditors. Employing data disclosed in the newly-mandated PCAOB Form AP, we find that component auditor use is largely structural, determined by the size and complexity of clientsâ multinational operations. We do not find the mere use of component auditors is detrimental to audit outcomes, rather, the amount of work conducted by component auditors is associated with lower audit quality (i.e., higher likelihood of misstatement), higher likelihood of non-timely reporting, and higher audit fees, which collectively suggest that component auditor engagements are associated with adverse outcomes. Further, we find that only work performed by less competent component auditors and those facing geographic and cultural/language barriers, including significant geographic and cultural distance, weak rule of law, and low English language proficiency, are associated with adverse audit outcomes. Overall, these findings provide initial archival evidence that the use of certain component auditors on U.S. multinational audits is associated with audit coordination issues, which suggests that PCAOB Form AP disclosures provide relevant information.
SSRN
Many quantitative firms have suffered substantial losses as a result of the COVID-19 selloff. In this note we highlight three lessons that quantitative researchers could learn.
SSRN
We analyze the effect of skewness in a simple two-asset framework. Returns follow the split bivariate normal distribution, which is a combination of bivariate normal distributions with different standard deviations. We show that expected returns deviate from the CAPM in equilibrium if assets differ in skewness. In addition, if the more positively skewed asset is more volatile, the high beta asset underperforms and has a higher max return, higher idiosyncratic skewness, and higher systematic skewness â" consistent with empirical evidence. We also derive simple formulas and analyze the role of skewness for portfolio choice and recently proposed conditional risk metrics. Finally, we show that the distribution provides a good empirical fit and thereby calculate the standard error of co-skewness in closed form.
SSRN
We explore the effects of the ECBââ¬â¢s unconventional monetary policy on the banksââ¬â¢ sovereign debt portfolios. In particular, using panel vector autoregressive (VAR) models we analyze whether banks increased their domestic government bond holdings in response to non-standard monetary policy shocks, thereby possibly promoting the sovereign-bank nexus, i.e. the exposure of banks to the debt issued by the national government. Our results suggest that euro area crisis countriesââ¬â¢ banks enlarged their exposure to domestic sovereign debt after innovations related to unconventional monetary policy. Moreover, the restructuring of sovereign debt portfolios was characterized by a home bias.
SSRN
This paper provides empirical evidence that a firmâs ability to substitute labor with automated capital is an important determinant of corporate financial policy. Using a novel occupational measure for laborâs susceptibility to job automation, we show that firms with a more substitutable workforce hold less cash, use more financial leverage, and pay higher dividends. Following exogenous negative cash flow shocks, firms with a more substitutable workforce are more likely to replace labor with automated capital and increase financial leverage. Overall, the results suggest that the ability to substitute labor with automated capital reduces operating leverage for firms, allowing for less conservative financial policies.
SSRN
Artificial intelligence poses a particular challenge in its application to finance/treasury management because most treasury functions are no longer physical processes, but rather virtual processes that are increasingly highly automated. Most finance/ treasury teams are knowledge workers who make decisions and conduct analytics within often dynamic frameworks that must incorporate environmental considerations (foreign exchange rates, GDP forecasts), internal considerations (growth needs, business trends), as well as the impact of any actions on related corporate decisions which are also highly complex (e.g., hedging, investing, capital structure, liquidity levels). Artificial intelligence in finance and treasury is thus most analogous to the complexity of a human nervous system as it encompasses far more than the automation of tasks. Similar to the human nervous system, AI systems in finance/treasury must manage data quickly and accurately, including the capture and classification of data and its integration into larger datasets. At present, the AI network neural system has been gradually improved and is widely used in many fields of treasury management, such as early warning of potential financial crisis, diagnosis of financial risk, control of financial information data quality and mining of hidden financial data, information, etc.