Research articles for the 2020-08-18
SSRN
We present a simple continuous-time model of clearing in financial networks. Financial firms are represented as ``tanks'' filled with fluid (money), flowing in and out. Once ``pipes'' connecting ``tanks'' are open, the system reaches the clearing payment vector in finite time. This approach provides a simple recursive solution to a classical static model of financial clearing in bankruptcy, and suggests a practical payment mechanism. With sufficient resources, a system of mutual obligations can be restructured into an equivalent system that has a cascade structure: there is a group of banks that paid off their debts, another group that owes money only to banks in the first group, and so on. Technically, we use the machinery of Markov chains to analyze evolution of a deterministic dynamical system.
SSRN
We introduce a nonparametric measure to quantify the degree of heteroskedasticity at a fixed quantile of the conditional distribution of a random variable. Our measure of heteroskedasticity is based on nonparametric quantile regressions and is expressed in terms of unrestricted and restricted expectations of quantile loss functions. It can be consistently estimated by replacing the unknown expectations by their nonparametric estimates. We derive a Bahadur-type representation for the nonparametric estimator of the measure. We provide the asymptotic distribution of this estimator, which one can use to build tests for the statistical significance of the measure. Thereafter, we establish the validity of a fixed regressor bootstrap that one can use in finite-sample settings to perform tests. A Monte Carlo simulation study reveals that the bootstrap-based test has a good finite sample size and power for a variety of data generating processes and different sample sizes. Finally, two empirical applications are provided to illustrate the importance of the proposed measure.
arXiv
In this article, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.
SSRN
A judicial determination of fair value in a private company can be a difficult and imprecise process. This difficulty coupled with variations in way mergers are negotiated and structured and the potential for conflicts of interest lend uncertainty to appraisal proceedings. As a result, corporate participants have powerful reasons to seek to limit the uncertainty associated with an appraisal proceeding ex ante. The response has been the growing use of shareholder agreements that limit appraisal rights.Appraisal waivers also offer a potentially attractive solution to a somewhat different concern, the growth of appraisal litigation in publicly traded companies. As with private companies, public companies face the problem that appraisal proceedings involve substantial cost and uncertainty. Although courts and commentators have grappled with how best to calculate fair value and the impact of that methodology on the incentives of participants in the merger process, they have failed to reach consensus. Appraisal waivers provide an alternative approach - a market-based mechanism to determine the efficient level of merger litigation.Public companies have not followed the lead of private companies, however, in using appraisal waivers. As this Article explains, the likely reason is the impracticality of using shareholder agreements in public companies and a concern that appraisal waivers in a charter or bylaw would be invalid.This Article considers both the normative and legal case for appraisal waivers. It argues that, with appropriate procedural protections â€" specifically the requirement that such waivers take the form of charter provisions -- appraisal waivers are normatively desirable. It then questions whether distinguishing between the use of appraisal waivers in private and public companies is appropriate and argues that it is not. The source of this distinction is a potential difference in the scope of private ordering available through shareholder agreements as opposed to the charter or bylaws, a difference that this Article critiques.The Article concludes that, under current law, the legal status of appraisal waivers is unclear. Given the potential value that such waivers provide, and the particular value that market discipline would bring to the scope and structure of such waivers, the Article argues for legislation validating a corporation’s authority to limit or eliminate appraisal rights in its charter.
SSRN
We study a segmented-markets setting in which self-fulfilling volatility can arise. The only requirements are (i) asset price movements redistribute wealth across markets (e.g., equities rise as bonds fall) and (ii) some stabilizing force keeps valuation ratios stationary (e.g., cash flow growth rises when valuations rise). We prove that when self-fulfilling volatility exists, arbitrage opportunities must also exist. Conversely, at times when arbitrage profits exist, asset markets are susceptible to self-fulfilling fluctuations. The tight theoretical connection between price volatility and arbitrage is detectable in currency markets by studying deviations from covered interest parity.
SSRN
This study shows that auditors are more likely to charge higher audit fees, issue false-positive going concern opinions (i.e., Type I error), and resign from high asset redeployability (AR) firms. In supplemental tests, we use path analysis to show that the significant associations between AR and auditor responses can be explained by higher inherent risk (earnings management and abnormal asset sales) and audit business risk (misstatements and litigation risk). Collectively, our results suggest that auditors tend to react conservatively when firms are associated with high AR.
SSRN
COVID-19 has severely disrupted the conduct of business around the globe. In jurisdictions that impose one or more ‘lockdowns’, multiple sectors of the real economy must endure prolonged periods of reduced trading or even total shutdowns. The associated revenue losses will push many businesses into bankruptcy. No public policy response can recover these losses. States can, however, act to reduce the amplification of the shock by the way in which they treat the cohort of newly bankrupt businesses. In jurisdictions where a well-functioning reorganisation procedure is capable of producing value-maximising outcomes in normal conditions, the temptation may be to subject this cohort to treatment by such procedures. This temptation should be resisted, not only because of the (significant) costs of these procedures, or because of concerns about institutional capacity to treat a high volume of cases, but also because such procedures are likely to be a poor ‘fit’ for the treatment of COVID-19 distress. In our view, the more attractive routes to relief are bail-ins (one-time orders to creditors or counterparties, or some class thereof, to forgive), bail-outs (offers to assume the debtor’s liabilities, or a class thereof), or some combination of the two. In this paper, we explain why a public policy response is necessary to mitigate the amplification of the shock caused by trading shut-downs, and compare treatment by the prevailing bankruptcy law with treatment by bail-ins or bail-outs along a range of dimensions. We conclude by tentatively suggesting some principles to help guide the choice between bail-ins and bail-outs, and the design of either form of intervention.
SSRN
One of the fastest-growing areas of finance research is the study of managerial biases and their implications for firm outcomes. Since the mid 2000s, this strand of Behavioral Corporate Finance has provided theoretical and empirical evidence on the influence of biases in the corporate realm, such as overconfidence, experience effects, and the sunk-cost fallacy. The field has been a leading force in dismantling the argument that traditional economic mechanisms- selection, learning, and market discipline-would suffice to uphold the rational manager paradigm. Instead, the evidence reveals behavioral forces to exert a significant influence at every stage of a CEO's career. First, at the appointment stage, selection does not impede the promotion of behavioral managers. Instead, competitive environments oftentimes promote their advancement, even under value-maximizing selection mechanisms. Second, while at the helm of the company, learning opportunities are limited since many managerial decisions occur at low frequency, and their causal effect is clouded by self-attribution bias and difficult to disentangle from that of concurrent events. Third, at the dismissal stage, market discipline does not ensure the firing of biased decision-makers as board members themselves are subject to biases in their evaluation of CEOs.
arXiv
For incomplete preference relations that are represented by multiple priors and/or multiple -- possibly multivariate -- utility functions, we define a certainty equivalent as well as the utility buy and sell prices and indifference price bounds as set-valued functions of the claim. Furthermore, we motivate and introduce the notion of a weak and a strong certainty equivalent. We will show that our definitions contain as special cases some definitions found in the literature so far on complete or special incomplete preferences. We prove monotonicity and convexity properties of utility buy and sell prices that hold in total analogy to the properties of the scalar indifference prices for complete preferences. We show how the (weak and strong) set-valued certainty equivalent as well as the indifference price bounds can be computed or approximated by solving convex vector optimization problems. Numerical examples and their economic interpretations are given for the univariate as well as for the multivariate case.
SSRN
This study examines implications for mergers and acquisitions (M&As) by focusing on deals that are subsequently divested. A comprehensive data set on the corporate divorce phenomenon during the past quarter of a century is constructed with financial news as well as SEC filings. Using this sample, the long-term success of M&As can be evaluated based on the companies' pre-M&A status and post-deal development as divorced targets are qualitatively traceable. In total, 46% of M&As are divested subsequently, however, the trend is decreasing as informational efficiency increased from the 1970s and early 1980s. Besides ex-ante information asymmetries, ex-post industry shocks are associated with the incidence of a divorce. Given that up to 77% of divorces can be attributed to M&A failure, and hence creative destruction, we find that cultural dissimilarities are especially responsible for divorces and their lack of success. The analysis has important implications for our understanding of the economic forces behind the significant takeover dynamics of Corporate America.
SSRN
This paper shows that individual beliefs on the effectiveness of formal and informal sources of risk sharing determine financial precautionary behavior. We present empirical evidence demonstrating that higher trust in public insurance systems reduces net liquid wealth while higher trust in communal insurance increases it. This dichotomy is consistent with theories on access to private risk sharing networks. Moreover, we find that both types of trust associate positively with the probability to take on financial risk for the purpose of becoming a homeowner and the related loan-to-value ratio. Our findings are robust across a wide range of econometric controls and specifications.
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We obtain detailed data on bank lending agreements and derivative positions of U.S. oil and gas producers during the 1999-2019 period to study interactions between financing and hedging decisions. Using the emergence of fracking technology as an exogenous shock to firms' financing needs, we document sharp increases in debt financing accompanied by increased hedge ratios and longer hedge maturities. We document that bank lending contracts often include covenants that require hedging, with the majority specifying an explicit minimum. Our results support the interpretation that these hedge covenants are binding, and are structured to mitigate potential agency conflicts originating from the priority of derivatives in default and incentives for firms to speculate with derivatives. We also show that firms with bank-imposed hedging requirements perform better during the COVID-19 pandemic. Our results support the interpretation that firms' hedging decisions should be viewed as outcomes from constrained optimization shaped in part by prior financing choices.
SSRN
We compare the effects of corporate social responsibility (CSR) on perceived firm risk under two different (non-)financial reporting regimes: the risk-based U.S. and the content-based European system. We find that the risk-reducing effect of CSR is driven by volatile market phases in the U.S. system, while regulatory developments dominate the effect in the EU. Risk reductions in the former are moreover most strongly associated with environmental activity, while social activity is most effective in the latter. Surprisingly, we observe that return-to-risk ratios decrease with CSR activity in both the U.S. and Europe over the period 2003 - 2017.
arXiv
We develop a dynamic version of the SSVI parameterisation for the total implied variance, ensuring that European vanilla option prices are martingales, hence preventing the occurrence of arbitrage, both static and dynamic. Insisting on the constraint that the total implied variance needs to be null at the maturity of the option, we show that no model--in our setting--allows for such behaviour. This naturally gives rise to the concept of implied volatility bubbles, whereby trading in an arbitrage-free way is only possible during part of the life of the contract, but not all the way until expiry.
SSRN
Environmental, social, and governance (“ESGâ€) scores have been widely touted as indicators of share price resilience during the COVID-19 humanitarian crisis. We undertake extensive analyses to investigate this claim and present robust evidence that, once the firm’s industry affiliation and accounting- and market-based measures of risk have been properly controlled for, ESG scores offer no such positive explanatory power for returns during COVID-19. Specifically, ESG is insignificant in fully specified returns regressions for the first quarter of 2020 COVID crisis period, and it is weakly negatively associated with returns during the market’s “recovery†period in the second quarter of 2020. Industry affiliation, market-based measures of risk, and accounting-based variables that capture the firm’s financial flexibility (liquidity and leverage) and their investments in internally-developed intangible assets together dominate the explanatory power of the COVID returns models. Relying on data from the global financial crisis (“GFCâ€) of 2008-2009, we develop parsimonious logit-based models to explain GFC period “winners†and “losers†(i.e., top and bottom deciles of returns performance), and we use these fitted models to predict winners and losers in the subsequent COVID crisis. Employing receiver operating characteristic (“ROCâ€) curves, we demonstrate that various accounting- and market-based models perform well both within-sample for the GFC period, as well as out-of-sample for the COVID crisis, but that ESG does not meaningfully add to the combined accounting and market models’ performance. We develop hedge strategies that go long (short) in firms during the COVID crisis that the GFC-based models predict will be winners (losers) and document that these predictions yield highly significant abnormal returns. Once again, ESG offers no enhancement to the out-of-sample returns performance. We conclude that celebrations of ESG as an important resilience factor in times of crisis are, at best, premature.
SSRN
Decision makers called to evaluate and approve a reform, proposed by an interest group, a politician, or a bureaucracy, suffer from a double asymmetric information problem: about the competence of the proposer and the consequences of the proposal. Moreover, the ability of decision makers to evaluate proposals depends on the complexity of the legislative environment, itself a product of past reforms. We model the strategic interaction between reformers and decision makers as a function of legislative complexity, and study the dynamics of endogenous complexity and stability of reforms. Complexi cation-simpli cation cycles can occur on the equilibrium path, and expected long-run complexity may be higher when competence of reform proposers is lower. The results apply to regulatory reforms, legislative politics, and institutional design.
arXiv
We introduce a new software toolbox, called Multi-Agent eXchange Environment (MAXE), for agent-based simulation of limit order books. Offering both efficient C++ implementations and Python APIs, it allows the user to simulate large-scale agent-based market models while providing user-friendliness for rapid prototyping. Furthermore, it benefits from a versatile message-driven architecture that offers the flexibility to simulate a range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics.
Showcasing its utility for research, we employ our simulator to investigate the influence the choice of the matching algorithm has on the behaviour of artificial trader agents in a zero-intelligence model. In addition, we investigate the role of the order processing delay in normal trading on an exchange and in the scenario of a significant price change. Our results include the findings that (i) the variance of the bid-ask spread exhibits a behavior similar to resonance of a damped harmonic oscillator with respect to the processing delay and that (ii) the delay markedly affects the impact a large trade has on the limit order book.
SSRN
We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an endogenous regime-switching process for output, the risk-free rate, excess returns, debt, and leverage. The regime-switching generates i) multimodal distributions of the variables above; ii) time-varying levels of volatility and skewness for the same variables; and iii) supercycles of borrowing and deleveraging. All of these are important properties of the data. In comparison, the representative household version of the model cannot generate any of these features. Methodologically, we discuss how nonlinear DSGE models with heterogeneous agents can be efficiently computed using machine learning and how they can be estimated with a likelihood function, using inference with diffusions.
arXiv
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by performing a literature survey of state-of-the-art models, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.
arXiv
An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk.
SSRN
Using a unique dataset of individual professional forecasts we document disagreement
arXiv
Generalisation of Fractional-Cox-Ingersoll-Ross Process
arXiv
The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learning and image processing methods to define and predict market states with intraday financial data. A wavelet transform is applied to the log-return of stock prices for both image extraction and denoising. A convolutional neural network then extracts patterns from denoised wavelet images to classify daily time series, i.e. a market state is associated with the binary prediction of the daily close price movement based on the wavelet image constructed from the price changes in the first hours of the day. This method overcomes the low signal-to-noise ratio problem in financial time series and gets a competitive prediction accuracy of the market states 'Up' and 'Down' of financial data as tested on the S&P 500.
SSRN
Pairs trading is a quantitative trading strategy consisting on identifying two stocks that historically move together and, using the assumption that their prices difference has mean- reverting properties, exploit the deviation from the mean by taking long â€" short position in the chosen pair to profit. Throughout the years, different approaches have been developed in order to exploit this strategy. However, there is little literature who looks whether the divergences in the prices are generated by poor company results, i.e. whether the deviation from the mean are product of bad (or good) fundamentals and are justified, or if they generate a new equilibrium point for the pair. In addition, since machine learning techniques are becoming more popular in finance, this work aims to analyze the performance of pairs trading strategy using neural network techniques applied to S&P 500 index components, selecting pairs of stocks from same industry and picking up the effects of the fundamental ratios in the pairs before taking a trade decision.
SSRN
We examine the impact of information processing costs on firm-level investment efficiency by exploiting the SEC’s eXtensible Business Reporting Language (XBRL) mandate as a natural experiment. To the extent that XBRL adoption reduces information processing costs and enhances the external information environment, it should allow for strengthened monitoring which affects firms’ real economic decisions (i.e., investment decisions). We document a significant negative association between XBRL adoption and investment inefficiency. In addition, we find that this reduction in investment inefficiency is more pronounced for firms with greater ex ante information asymmetry and that the magnitude of reduction is conditioned on the use of standardized official elements versus customized extension elements. Our results are robust to a difference-in-differences design and a battery of sensitivity tests. Additional analyses reveal that firms’ return on assets and Tobin’s q increase following XBRL adoption, consistent with greater ex post efficiency and effectiveness of investments. Overall, the results suggest that the reduction in information processing costs followed by XBRL adoption strengthens external monitoring from market participants, inducing improved investment efficiency within adopting firms.
SSRN
We consider demand function competition with a finite number of agents and private information. We show that any degree of market power can arise in the unique equilibrium under an information structure that is arbitrarily close to complete information. Regardless of the number of agents and the correlation of payo¤ shocks, market power may be arbitrarily close to zero (the competitive outcome) or arbitrarily large (so there is no trade). By contrast, price volatility is always lower than the variance of the aggregate shock across all information structures. Alternative trading mechanisms lead to very distinct bounds as a comparison with Cournot competition establishes.
SSRN
Researchers have well documented the positive effects of innovation on growth and productivity. However, innovation might be associated with unexpected consequences, especially in term of earnings quality. In this paper, we employ the Bayesian linear regression to shed light on the relationship between innovation and earnings quality in the Vietnamese financial market. While other researches often use the Research and Development (R&D) expenditures, the number of patents and citations as measures of innovation, in this study, we use a new metric of innovation which is obtained from the Cobb-Douglas production function. Our research results indicate that firms with more innovations can lead to a decrease in earnings quality. Based on the findings, we recommend practitioners, especially investors and analysts should pay more attention to innovative firms when making investment decisions because these firms are more likely to participate in earnings management.
SSRN
This study draws on the investor protection literature to examine differences in a country’s information environment that are likely to explain cross country variation in the extent to which macroeconomic forecasters take account of current earnings when forecasting future growth in GDP. Using a unique sample of analysts’ GDP forecasts from thirty countries, we find that economies with stronger levels of disclosure and enforcement regulation have a lower association between changes in aggregate earnings and errors in forecasts of GDP growth. The results are consistent with macroeconomic forecasters in countries with stronger investor protections taking greater account of changes in aggregate earnings when forecasting growth in GDP than analysts in countries with lower levels of investor protection.
SSRN
We develop a dynamic general-equilibrium framework with multiple households and multiple risky assets to explain how less- and more-sophisticated households differ in their portfolio and wealth dynamics. Differences in sophistication are modeled via heterogeneous confidence about asset returns, coupled with Bayesian learning. Consistent with recent empirical evidence, less-sophisticated households overinvest in safe assets, hold underdiversified portfolios concentrated in familiar assets, are trend chasers, and earn lower absolute and risk-adjusted investment returns. Notably, this behavior is a consequence of optimal choices rather than investment mistakes. The model explains why this behavior, despite learning, persists for long periods, thereby exacerbating wealth inequality.
SSRN
The New Deal created a separate and unequal credit marketâ€"high-interest, non-bank, installment lenders in black ghettos and low-cost, securitized, and revolving credit card market in the white suburbs. Organized protest against this racialized inequality was an essential but forgotten part of the civil rights movement. After protests and riots drew attention to the reality that the poor were paying more for essential consumer products than the wealthy, the nation’s policymakers began to pay attention. Congress held hearings and agencies, and academics issued reports examining the economic situation. These hearings led to new federal agencies and programs, executive actions, as well as several acts of legislation. These Congressional investigations and the theories and explanations emanating from policymakers and academics were the genesis of decades of legislation aimed at supporting minority banks and other institutions. The resulting policy framework is still in effect and includes: the Community Reinvestment Act (CRA), the Community Development Financial Institution Act (CDFIA), as well as several key provisions and mandates regarding minority banks in banking legislation. In this Article, I will argue that the foundational theoretical premise of these laws and policies is flawed. Though policymakers and scholars accurately diagnosed the root causes of the disparate credit market, the solutions did not correspond with the problem and have therefore been ineffective. These laws and policies were not aimed to address the systemic causes of the disparity but only served to treat its symptoms. The misguided focus on small community banking, minority-owned banks, and mission-oriented institutions as a response to structural inequality has been the dominant framework in banking reform.In analyzing the varied, but theoretically consistent response to lending inequality, this Article also challenges a long-standing banking myth that “small community banking†or “microfinance†is the answer to poverty, specifically for marginalized communities. This idea was the foundational theory of the minority banking industry, the CRA, the CDFIA, and almost every legislative response to credit inequality for the past fifty years. The premise of these laws is that that marginalized communities, having been left out of the dominant banking industry, will pool their resources and collectively lift themselves out of poverty. As such, these laws are rooted in neoliberal and libertarian concepts of banking market even as they have been championed by progressive reformers and community activists. For most policymakers, activists, and scholars, the buzzword is “community empowerment†and they have legislated accordingly. In doing so, they have avoided addressing the root causes of the problem and have shifted the responsibility of a solution to the disenfranchised communities themselves instead of devising comprehensive federal policy solutions. This Article will trace the genealogy of this legislation and offer solutions that will address the root causes of this inequality.
SSRN
This paper provides a theory of strategic innovation project choice by incumbents and start-ups. We show that prohibiting killer acquisitions strictly reduces the variety of innovation projects. By contrast, we find that prohibiting other acquisitions only has a weakly negative innovation effect, and we provide conditions under which the effect is zero. Furthermore, for both killer and other acquisitions, we identify market conditions under which the innovation effect is small, so that prohibiting acquisitions to enhance competition would be justified.
SSRN
Can central banks defuse rising stability risks in financial booms by leaning against the wind with higher interest rates? This paper studies the state-dependent effects of monetary policy on financial stability. Based on the near-universe of advanced economy financial cycles since the 19th century, we show that discretionary leaning against the wind policies during credit and asset price booms are more likely to trigger crises than prevent them.
SSRN
We study the impacts of local gender imbalance on corporate risk-taking. We find that firms in areas with higher local male-female ratios have higher stock volatilities and leverage, less corporate hedging, and more capital expenditures. Consequently, such firms face higher loan spreads and more covenant restrictions. We address endogeneity concerns with two sets of instrumental variables: local male-female ratio at birth in 1960, and local mortality rates of prostate cancer and breast cancer. We show that both employee and local investor channels transit local residents’ risk preferences embedded in gender imbalance.
arXiv
Using a large dataset on major stock indexes and FX rates, we test the robustness of the rough fractional volatility model over different time scales. We include the estimation error as well as the microstructure noise into the analysis. Our findings lead to new stylized facts regarding the volatility that are not described by models introduced so far: in the fractal analysis using the absolute moment approach, log-log plots are nonlinear and reveal very low perceived Hurst exponents at small scales, consistent with the rough framework, and higher perceived Hurst exponents for larger scales, along with stationarity of the volatility. These results, obtained for time series of realized volatilities, are confirmed by another measure of volatility, namely Parkinson's volatility, taking into account its specificities regarding measurement errors.
SSRN
We analyze optimal monetary policy when asset prices influence aggregate demand with a lag (as is well documented). In this context, as long as the central bank's main objective is to minimize the output gap, the central bank optimally induces asset price overshooting in response to the emergence of a negative output gap. In fact, even if there is no output gap in the present but the central bank anticipates a weak recovery dragged down by insufficient demand, the optimal policy is to preemptively support asset prices today. This support is stronger if the acute phase of the recession is expected to be short lived. These dynamic aspects of optimal policy give rise to potentially large temporary gaps between the performance of financial markets and the real economy. One vivid example of this situation is the wide disconnect between the main stock market indices and the state of the real economy in the U.S. following the Fed's powerful response to the Covid-19 shock.
arXiv
Recent advancements in the fields of artificial intelligence and machine learning methods resulted in a significant increase of their popularity in the literature, including electricity price forecasting. Said methods cover a very broad spectrum, from decision trees, through random forests to various artificial neural network models and hybrid approaches. In electricity price forecasting, neural networks are the most popular machine learning method as they provide a non-linear counterpart for well-tested linear regression models. Their application, however, is not straightforward, with multiple implementation factors to consider. One of such factors is the network's structure. This paper provides a comprehensive comparison of two most common structures when using the deep neural networks -- one that focuses on each hour of the day separately, and one that reflects the daily auction structure and models vectors of the prices. The results show a significant accuracy advantage of using the latter, confirmed on data from five distinct power exchanges.
SSRN
Results of Lappalainen (2020a, 2020b) suggest that total value of equity regresses toward total value of debt over time in the USA. Using methodology similar to Lappalainen (2020a, 2020b), I study if this regression is observable in 26 European countries and in Eurozone. I find that the regression is observable in Eurozone, Czechia, Finland, France, Germany, Ireland, Italy, Netherlands, Sweden and United Kingdom but unobservable in Bulgaria, Cyprus, Estonia, Lithuania, Portugal, Slovenia and Spain. For the remaining countries Austria, Belgium, Greece, Hungary, Latvia, Luxembourg, Malta, Poland, Romania and Slovakia, the results are mixed.
arXiv
This paper studies an optimal asset allocation problem for a surplus-driven financial institution facing a Value-at-Risk (VaR) or an Expected Shortfall (ES) constraint corresponding to a non-concave optimization problem under constraints. We obtain the closed-form optimal wealth with the ES constraint as well as with the VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions. We find that both VaR and ES-based regulation can effectively reduce the probability of default for a surplus-driven financial institution. However, the liability holders' benefits cannot be fully protected under either VaR- or ES-based regulation. In addition, we show that the VaR and ES-based regulation can induce the same optimal portfolio choice for a surplus-driven financial institution. This differs from the conclusion drawn in Basak and Shapiro 2001 where the financial institution aims at maximizing the expected utility of the total assets, and ES provides better loss protection.
SSRN
Ownership is fundamental to firm strategy, organization, and governance. Standard ownership conceptsâ€"mainly derived from agency and incomplete contracting theoriesâ€"focus on its incentive effects. However, these concepts and theories neglect ownership’s role as an instrument to match judgment about resource use and governance with the firm’s evolving environment under uncertainty. We develop the concept of ownership competenceâ€"the skill with which ownership is used as an instrument to create valueâ€"and decompose it into matching competence (what to own), governance competence (how to own), and timing competence (when to own). We describe how property rights of use, appropriation, and transfer relate to the three ownership competences and show how our theory offers a fresh perspective into the role of ownership for value generation.
SSRN
Instead of data-mining methods, the author proposes a portfolio committee approach to portfolio selection. Because each optimal portfolio is a combination of three basic elements: strategy, covariance matrix, and risk type; therefore, the author first augments the combination to 250 optimal portfolios at each estimation period, and then the author defines a score to select the best portfolio to hold in the next period. Survival of the fittest, the superior performance of the combination portfolio justifies the committee approach to portfolio selection is not only effective, but also easy to implement.
SSRN
Does more political power always lead to more favoritism? The usual affirmative answer overlooks scrutiny's role in shaping the pattern of favoritism over the ladder of power. When attaining higher-powered positions under even stricter scrutiny, politicians may reduce quid-pro-quo favors towards connected firms to preserve their career prospect. Around close Congress elections, we find RDD-based evidence of this adverse effect that a politician's win reduces his former classmates' firms stock value by 2.8%. As predicted, this effect varies by cross-state scrutiny, politicians' power, firms' size and governance, and connection strength. It diminishes as a politician's career concern fades over time.
SSRN
Corporate law has embraced private ordering -- tailoring a firm’s corporate governance to meet its individual needs. Firms, particularly venture-capital backed start-ups, are increasingly adopting firm-specific governance provisions such as dual-class voting structures, arrangements to create stable shared control rights among a coalition of minority shareholders, and provisions that limit the permissible fora for shareholder litigation. Courts have broadly upheld these provisions as consistent with the contractual theory of the firm. Commentators too, while finding some governance provisions objectionable, nonetheless support a private ordering approach as facilitating innovation and enhancing efficiency.Although most analyses of private ordering focus on provisions in a corporation’s charter and bylaws, private corporations are increasingly turning to an alternative governance mechanism â€" shareholder agreements. Shareholder agreements have largely escaped both judicial and academic scrutiny, but language in a handful of judicial opinions suggests that corporate participants have greater latitude to engage in private ordering through a shareholder agreement and even that shareholder agreements can be used to avoid otherwise-mandatory provisions of corporate law.This Article offers the first broad-based analysis of shareholder agreements, detailing the scope of issues to which they are addressed and identifying the challenges that they pose for corporate governance. Although shareholder agreements are a natural component of the small closely-held corporations that essentially operate as incorporated partnerships, they rely on principles of contract that are in tension with the fundamental structure of corporate law. This tension is particularly problematic for the increasing number of large privately-held corporations whose governance structures are shielded from the transparency and price discipline of the public capital markets.The Article challenges the growing use of shareholder agreements and maintains instead that corporations should engage in private ordering exclusively through their charter and bylaws. It further critiques efforts to use shareholder agreements to evade statutory or common law limits on private ordering and argues that, to the extent such limits are undesirable, they should be the subject of legislative reform.
SSRN
Institutional funds have concentrated ownership by a few institutional investors,
SSRN
We examine whether the potential for costly sabotage is a deterrent to firms' use of
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We study the effects of ability and liquidity constraints on entrepreneurship. We develop a three sector Roy model that differentiates between entrepreneurs and other self-employed to address puzzling gaps that have emerged between theory and evidence on entry into entrepreneurship. The model predicts-and the data confirm-that entrepreneurs are positively selected on highly-remunerated cognitive and non-cognitive human capital skills, but other self-employed are negatively selected on those same abilities; entrepreneurs are positively selected on collateral, but other self-employed are not; and entrepreneurship is procyclical, but self-employment is countercyclical.
SSRN
We study the dynamic properties of sovereign bonds in emerging markets and their associated risk premiums. We focus on the properties of credit spreads, exchange rates, and their interaction. Relying on the term structure of local currency bonds issued by Asia-Pacific sovereigns, we find that local variables are significant in the dynamics of currency and credit risk, and the components of bond risk premiums reflecting these risks. Local currency bonds dramatically improve the investment frontier.
arXiv
Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium sufficiency to cover future claims and the adequacy of the current reserves to pay outstanding claims. Both risks are calibrated using stochastic models due to their nature. This paper introduces a reserving model based on a set of machine learning techniques such as Gradient Boosting, Random Forest and Artificial Neural Networks. These algorithms and other widely used reserving models are stacked to predict the shape of the runoff. To compute the deviation around a former prediction, a log-normal approach is combined with the suggested model. The empirical results demonstrate that the proposed methodology can be used to improve the performance of the traditional reserving techniques based on Bayesian statistics and a Chain Ladder, leading to a more accurate assessment of the reserving risk.
SSRN
This article aims to study the link between Twitter announces and the stock prices of sports companies concerned with class actions. In many instances, news, announces, social media content affects the evolution of stock prices. The study focuses on companies from the sports sector due to their popularity and the consistent number of followers on social networks, which provide a sound basis of analysis, thereby making manipulation through hoaxes more difficult. The study encompasses the causality of tweets' sentiment on stock prices and the event study related to the period of class actions. The results analyze a sample of 7 listed companies in the sports industry.
SSRN
It is hard to overstate the importance that the concept of symmetry has had in every field of physics, a fact alluded to by the Nobel Prize winner P.W. Anderson, who once wrote that “physics is the study of symmetryâ€. Whereas the idea of symmetry is widely used in science in general, very few (if not almost no) applications has found its way into the field of finance. Still, the phenomenon appears relevant in terms of for example the symmetry of strategies that can happen in the decision making to buy or sell financial shares. Game theory is therefore one obvious avenue where to look for symmetry, but as will be shown, also technical analysis and long term economic growth could be phenomena which show the hallmark of a symmetry.
SSRN
Scholars, practitioners and policymakers continue to debate what constitutes “good†corporate governance. Academic efforts to evaluate the effect of governance provisions such as dual class voting structures, staggered boards of directors and separating the positions of CEO and Chairman of the Board, have produced inconsistent or inconclusive results. The consequence is that the debate over corporate governance is increasingly political and discordant.We offer a way to address this debate. The rise of index-based investing provides a market-based alternative to governance regulation. Through the creation of bespoke governance index funds, asset managers can offer investors the opportunity to choose an index that corresponds to their governance preferences. We term this approach synthetic governance. At the same time, synthetic governance offers a new tool to collect evidence on the economic impact of corporate governance by providing a market-based tool for evaluating the relationship between corporate governance and stock returns. We illustrate the potential of synthetic governance with the creation of a new governance-based index, the Dual Index, which selects portfolio companies on the basis of a dual class voting structure. We compare the performance of the Dual Index to various benchmarks and demonstrate the potential, through governance-based indexing, for investors to realize superior returns. We further modify the Dual Index by implementing synthetic sunsets to highlight the value creation of dual-class companies in their early years and provide evidence on the appropriate length of a time-based sunset provision. Finally, we expand our analysis of synthetic governance with a second index â€" the Split Index â€" which tests the effect of separating the positions of CEO and Chairman of the Board. We conclude that synthetic governance offers a meaningful way for investors and issuers to more economically adopt and invest in governance provisions. We thus provide a way out of the corporate current war over what exactly constitutes “good†governance.
SSRN
The financial industry has been struggling with widespread misconduct and public mistrust. Here we argue that the lack of trust into the financial industry may stem from the selection of subjects with little, if any, trustworthiness into the financial industry. We identify the social preferences of business and economics students, and follow up on their first job placements. We find that during college, students who want to start their career in the financial industry are substantially less trustworthy. Most importantly, actual job placements several years later confirm this association. The job market in the financial industry does not screen out less trustworthy subjects. If anything the opposite seems to be the case: Even among students who are highly motivated to work in finance after graduation, those who actually start their career in finance are significantly less trustworthy than those who work elsewhere.
arXiv
Financial data, such as financial statements, stores valuable and critical information to potentially assist stakeholders and investors optimize their capital so that it maximizes overall economic growth. Since there are many variables in financial statements, it is important to determine the causal relationships, that is, the directional influence between them in a structural way, as well as to understand the related accounting mechanisms. However, the analysis of variable-to-variable relationships in financial information by using the standard correlation functions is not sufficient to unveil directionality. Here, we use the volatility constrained correlation (VC correlation) method that enables us to predict the directional relationship between the two variables. To be precise, we apply the VC correlation method to five major financial information variables (revenue, net income, operating income, own capital and market capitalization) of 2321 firms in 28 years from 1990 to 2018 listed on Tokyo Stock Exchange in order to identify which variables are influential and which are susceptible variables. Our findings show that operating income is the most influential variable and market capital and revenue are the most susceptible variables among the five major accounting variables. Surprisingly, the results are different from the existing intuitive understanding suggested by widely used investment strategy indicators known as PER and PBR, which report that net income and own capital are the most influential variable on market capital. Taken together, the presented analysis may assist managers, stakeholders and investors to improve performance of financial management as well as optimize financial strategies for firms in future operations.
SSRN
Many forms of the ARIMA (auto-regressive integrated moving average) modeling method are used across risk management and specifically within PPNR (Pre-Provision Net Revenue) for CCAR (Comprehensive Capital Analysis and Review) and DFAST (Dodd-Frank Act Stress Testing). The ARIMA method allows for flexible modeling of PPNR and the inclusion of exogenous variables however model stability can be a concern. I argue that model instability is occurring because of improper ARIMA model development and the practice of forcing all data into the ARIMA framework. I apply a basic method of testing model stability over time and have chosen to model both Citigroup and the S&P 500 using Federal Reserve domestic data which is used in the actual CCAR and DFAST exercises. This paper aims to show common mistakes that occur throughout risk management from the perspective of model development, validation, implementation, and internal audit at major financial institutions.
arXiv
This paper presents probability distributions for price and returns random processes for averaging time interval {\Delta}. These probabilities determine properties of price and returns volatility. We define statistical moments for price and returns random processes as functions of the costs and the volumes of market trades aggregated during interval {\Delta}. These sets of statistical moments determine characteristic functionals for price and returns probability distributions. Volatilities are described by first two statistical moments. Second statistical moments are described by functions of second degree of the cost and the volumes of market trades aggregated during interval {\Delta}. We present price and returns volatilities as functions of number of trades and second degree costs and volumes of market trades aggregated during interval {\Delta}. These expressions support numerous results on correlations between returns volatility, number of trades and the volume of market transactions. Forecasting the price and returns volatilities depend on modeling the second degree of the costs and the volumes of market trades aggregated during interval {\Delta}. Second degree market trades impact second degree of macro variables and expectations. Description of the second degree market trades, macro variables and expectations doubles the complexity of the current macroeconomic and financial theory.
SSRN
Using data on the portfolio holdings and income of millions of U.S. retirement investors, I find that positive and persistent shocks to income lead to a significant increase in the equity share of investor portfolios, while increases in financial wealth due to realized returns lead to a small decline in the equity share. In a standard homothetic life-cycle model with human capital and constant risk aversion, the portfolio responses to these two wealth shocks should be of equal magnitude and opposite sign. The positive net effect in the data is evidence for risk aversion that decreases in total wealth. I estimate a structural life-cycle consumption and portfolio choice model that accounts for inertia in portfolio rebalancing and matches the reduced-form estimates with a significant degree of non-homotheticity in risk preferences, such that a 10% permanent income growth leads to an average decrease in risk aversion by 1.7%. Decreasing relative risk aversion preferences concentrate equity in the hands of the wealthy and double the share of wealth at the top of the wealth distribution.
SSRN
We ask when and how a diverse board can benefit shareholders. Board diversity may be value-increasing even if some directors have agendas that are not perfectly aligned with shareholders' interests. Diversity commits the board to a high information standard because directors with opposing agendas are deadlocked unless they have persuasive information in support of the optimal course of action. Since deadlock is costly, diversity strengthens directors' incentives to gather information ex ante, which raises expected firm value. Diversity is more likely desirable if the firm's information environment is poor and if directors' opposing agendas are accompanied by sufficiently strong incentives for value maximization. However, if directors cannot credibly communicate their information, a homogeneous board dominates a diverse board.
SSRN
This paper analyses of how risk is allocated in China's markets for debt issued by non-financial enterprises. Compared to other major corporate bond markets China's is unusual in that unlisted, state-owned enterprises account for a large fraction of the debt issued and that the foundations of the corporate and bankruptcy law are young and still evolving. The implications of these features are described and quantified. The results show that the major changes in relative pricing across different market segments cannot be explained well by standard measures of solvency and liquidity. Rather, the most successful explanation is that major policy actions have had the effect of withdrawing implicit guarantees from private issuers and making more explicit the limits of guarantees afforded to state issuers.
