Research articles for the 2021-05-18
SSRN
The independence of Independent directors is an important factor to be considered when it comes to the corporate governance and especially when we have a concentrated shareholding pattern. The rights of the minority shareholders, maintaining transparency in the management of the company and policy decisions which are very much central part of running of any company. But in order to fulfil all these duties it is important that the independent director is independent free from any constraints of the Board of Directors or promoters.
arXiv
This paper develops a new way of analyzing the performance of asset pricing models. I show that many classical asset pricing bounds, such as the Hansen and Jagannathan (1991) (HJ) bound, can be improved upon by looking at derivative contracts. The resulting bound is found to be much tighter than the HJ bound in empirical data. A direct implication is that the SDF process is more volatile than previously assumed and poses new challenges to consumption based asset pricing models. A central ingredient of this new bound is the risk-neutral quantile function. Two additional applications consider the use of this function: (i) As a predictor of Value-at-Risk (ii) As a forward looking measure of crash risk. Both applications underscore the importance of analyzing quantiles of the data, instead of the more prevalent variance and equity premia.
arXiv
We propose an analytically tractable class of models for the dynamics of a limit order book, described through a stochastic partial differential equation (SPDE) with multiplicative noise for the order book centered at the mid-price, along with stochastic dynamics for the mid-price which is consistent with the order flow dynamics. We provide conditions under which the model admits a finite dimensional realization driven by a (low-dimensional) Markov process, leading to efficient estimation and computation methods. We study two examples of parsimonious models in this class: a two-factor model and a model with mean-reverting order book depth. For each model we analyze in detail the role of different parameters, the dynamics of the price, order book depth, volume and order imbalance, provide an intuitive financial interpretation of the variables involved and show how the model reproduces statistical properties of price changes, market depth and order flow in limit order markets.
arXiv
Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity.
arXiv
We study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.
arXiv
I describe the rationale for, and design of, an agent-based simulation model of a contemporary online sports-betting exchange: such exchanges, closely related to the exchange mechanisms at the heart of major financial markets, have revolutionized the gambling industry in the past 20 years, but gathering sufficiently large quantities of rich and temporally high-resolution data from real exchanges - i.e., the sort of data that is needed in large quantities for Deep Learning - is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator, which is what this simulation now provides. The simulator, named the "Bristol Betting Exchange" (BBE), is intended as a common platform, a data-source and experimental test-bed, for researchers studying the application of AI and machine learning (ML) techniques to issues arising in betting exchanges; and, as far as I have been able to determine, BBE is the first of its kind: a free open-source agent-based simulation model consisting not only of a sports-betting exchange, but also a minimal simulation model of racetrack sporting events (e.g., horse-races or car-races) about which bets may be made, and a population of simulated bettors who each form their own private evaluation of odds and place bets on the exchange before and - crucially - during the race itself (i.e., so-called "in-play" betting) and whose betting opinions change second-by-second as each race event unfolds. BBE is offered as a proof-of-concept system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques. This paper offers an extensive survey of relevant literature and explains the motivation and design of BBE, and presents brief illustrative results.
SSRN
This paper develops a model to study how capital flows in the financial system affect banks’ coordination problem in the credit supply process. The economy is susceptible to self-fulfilling credit freezes: banks abstain from lending when they fear that other banks will withhold lending, and the resultant credit contraction impedes economic growth. Capital flows across banks can alleviate the problem by enabling optimistic banks to borrow from pessimistic banks and extend more credit to the real economy. However, the equilibrium interest rate reveals public information about economic fundamentals and banks’ aggregate willingness to lend, increasing the fragility of the credit market. As a result, the economy can get stuck in an equilibrium where both interbank capital flows and the real credit supply freeze and they reinforce each other through a vicious feedback loop. Regulations addressing counterparty risks can help to maintain active capital flows in the financial system and stabilize the real credit market.
SSRN
We investigate whether climate transition risk is reflected in the financial performance and cross-section pricing of publicly-traded European and US firms. Using a firm-level carbon risk score (CRS) that assesses the vulnerability of a firm’s value to transition to a low-carbon economy, we find that firms with the lowest transition risk exposures perform better financially, and that European firms are more sensitive to transition risks than US firms. We also find that stocks with low exposure to transition risk offer greater returns to investors, consistent with the fact that stock prices of firms do not adequately reflect underlying climate transition risk. Relative financial performance of less vulnerable firms and underreaction effects to transition risk decreased after COP21.
SSRN
This article aims to develop an annual stress index for the Tunisian banking sector. There is as yet no such aggregate index for Tunisia; it thus makes it possible to complete the set of indicators put in place by the Bank of Tunisia and the IMF as part of the financial sector assessment program to understand the strength of this sector. The article first explains the method used. After having presented various measurement techniques, we choose a stress index constructed from five variables of the balance sheet over 22 years from 1998 to 2020. The banking stress index increases over the period 2011-2020, reflecting both the consequences of the revolution on the Tunisian economy as well as the endless aftermath of the current COVID-19 epidemic. The econometric results confirm the index's sensitivity to variations in the main macroeconomic indicators
SSRN
We find a channel through which debt holders can affect corporate transactions. We show that dual holders (equity holders who also have the firm's debt in their portfolio) are sensitive to a decline in debt value in a leveraged buyout and decrease the likelihood of LBOs. We show that the stock premium in LBO events is higher for a firm with dual holders due to their loss on their debt.
arXiv
Portfolio management aims at maximizing the return on investment while minimizing risk by continuously reallocating the assets forming the portfolio. These assets are not independent but correlated during a short time period. A graph convolutional reinforcement learning framework called DeepPocket is proposed whose objective is to exploit the time-varying interrelations between financial instruments. These interrelations are represented by a graph whose nodes correspond to the financial instruments while the edges correspond to a pair-wise correlation function in between assets. DeepPocket consists of a restricted, stacked autoencoder for feature extraction, a convolutional network to collect underlying local information shared among financial instruments, and an actor-critic reinforcement learning agent. The actor-critic structure contains two convolutional networks in which the actor learns and enforces an investment policy which is, in turn, evaluated by the critic in order to determine the best course of action by constantly reallocating the various portfolio assets to optimize the expected return on investment. The agent is initially trained offline with online stochastic batching on historical data. As new data become available, it is trained online with a passive concept drift approach to handle unexpected changes in their distributions. DeepPocket is evaluated against five real-life datasets over three distinct investment periods, including during the Covid-19 crisis, and clearly outperformed market indexes.
SSRN
Building on previous work of Kolm and Ritter (2019) and Cao et al. (2019), this paper explores the novel application of Deep Reinforcement Learning for Delta Hedging of options in an utility based framework where an agent is faced with a trade-off between hedging error and transaction costs while aiming at maximizing the expected profit and loss and minimizing its variance. In the presence of transaction costs we compare the performance of two state-of-the-art Reinforcement Learning algorithms with two simple benchmark strategies widely used in practice. We perform the analysis on synthetic data for different market characteristics, transaction costs, option maturities and hedging frequencies, and find that the agents deliver a strong performance in markets characterized by stochastic volatility and jumps in asset prices, as well as for high transaction costs, high hedging frequency and for options with long maturities. Furthermore, we apply trained algorithms to similar (but not seen before) options and present a way of improving the robustness of the algorithms to different levels of volatility. Finally, we transfer the hedging strategies learned on simulated data to empirical option data on the S&P500 index, and demonstrate that transfer learning is successful: hedge costs encountered by reinforced learning decrease by as much as 30% compared to the Black- Scholes hedging strategy. Our results indicate that the hedging strategies based on Reinforcement Learning outperform the benchmark strategies and are suitable for traders taking real-life hedging decisions, even when the networks are trained on synthetic (but versatile) data.
SSRN
We examine how private placements of equity (PPEs) affect debtholder wealth. We find that banks charge higher loan spreads, require more collateral, and impose stricter covenants for firms conducting PPEs. The results are more pronounced for firms without a value-enhancing PPE feature, particularly those with poorer governance and higher information asymmetry. These firms also invest less efficiently and underperform in the post-placement period and realize more negative bond and stock returns around PPE (post-placement M&A) announcement dates. Thus, issuers’ managerial entrenchment problems are the main source of debtholders’ loss in PPEs, and lenders use such information in adjusting lending terms.
SSRN
We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the pseudo-true value of the structural parameters in the generalized method of moments. The pseudo-true value is defined as the minimizer of the population continuous updating objective function and equals the true value of the structural parameter in the absence of misspecification. The (bounding) chi-squared limiting distribution of the DRLM statistic is robust to both misspecification and weak identification of the structural parameters, hence its name. To emphasize its importance for applied work, we use the DRLM test to analyze the return on education, which is often perceived to be weakly identified, using data from Card (1995) where misspecification occurs in case of treatment heterogeneity; and to analyze the risk premia associated with risk factors proposed in Adrian et al. (2014) and He et al. (2017), where both misspecification and weak identification need to be addressed.
SSRN
While environmental, social, and governance (ESG) trading activity has been a distinctive feature of financial markets, the debate if ESG scores can also convey information regarding a company's riskiness remains open. Regulatory authorities, such as the European Banking Authority (EBA), have acknowledged that ESG factors can contribute to risk. Therefore, it is important to model such risks and quantify what part of a company's riskiness can be attributed to the ESG ratings. This paper aims to question whether ESG scores can be used to provide information on (tail) riskiness. By analyzing the (tail) dependence structure of companies with a range of ESG scores, using high-dimensional vine copula modelling, we are able to show that risk can also depend on and be directly associated with a specific ESG rating class. Empirical findings on real-world data show positive not negligible dependencies between clusters determined by ESG scores, especially during the 2008 crisis.
SSRN
The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling frequency. We examine returns of swaps, equity and bond indices as well as the rescaling by their volatilities over different horizons (since inception to Q2-2020). Contrarily to some literature, we find that the realized distributions of logarithmic returns, scaled or not by the standard deviations, are skewed and that they may be better fitted by t-skew distributions. Our finding holds true across asset classes, maturity and developed and developing markets. This may explain why models based on dynamic conditional score (DCS) have superior performance when the underlying distribution belongs to the t-skew family. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example, extrapolating yearly scenarios from daily performances may prove not to be correct.
SSRN
The CJEU has recently ruled in the case of USDAW and B. Wilson v VW Realization 1 Ltd (in liquidation), Ethel Austin Ltd, and the Secretary of state for Business, Innovation and Skills on how the meaning of “establishment,†as used in the Collective Redundancies Directive, should be interpreted in the EU. The Directive is aimed to approximate Member State laws on procedures for making large scale redundancies to afford greater protection to workers through consultation obligations when at risk of redundancy due to an employer’s financial problems. However, the Directive is also designed to take into account the need for balanced economic and social development within the EU.
SSRN
This paper examines, from a regulatory portfolio management standpoint, the application of liquidity adjusted risk modeling in obtaining optimal and investable economic-capital structures. The newly obtained empirical results, optimization parameters and optimal and investable economic-capital structures were not evident in Al Janabi (2013) paper.
SSRN
The European Banking Union (EBU) has had a complex strategic, political, economic and legal formation, and throughout the current turmoil there has been a special emphasis on preserving its stability and further development. The EBU formally consists of three interconnected pillars applicable to the euro area: (1) the Single Supervisory Mechanism (SSM) that encompasses European Central Bank’s (ECB) direct and indirect prudential supervision; (2) the Single Resolution Mechanism (SRM) that provides for a harmonized resolution framework; and (3) an envisaged safety net in the form of the European Deposit Insurance Scheme (EDIS). Additionally, the EBU is based on the common EU-wide Single Rulebook. A strong incentive for the EBU’s creation originated both from the repercussions of the global financial crisis and the European sovereign debt crisis. The EBU has experienced constant challenges from its very beginning, including the opposition to any indication of a transfer union, and criticism related to its design. Although progress is recommended on all elements, the most compelling is timely completion of the EDIS. From its inception, the EBU’s main goal has been to break the “vicious circle†between sovereigns and their banks â€" and that is in the focus of this article. Furthermore, this article explores the structure, achievements and inadequacies of the EBU pillars, and analyses potential threats and opportunities related to this segment of European integration.
SSRN
The recent growth in financial assets trading in emerging markets indicates that more attention is required for the measurement, management and control of risks. In this research study, I discuss the methodological aspects for the assessment of liquidity risk and provide an intuitive approach for its applications to modern risk management processes in financial institutions.
SSRN
This paper proposes a reengineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (LVaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both long-only positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios with the aid of an appropriate scaling of the multiple assets’ LVaR matrix along with GARCH-M technique to forecast conditional volatility and expected return.
SSRN
Puzzling deviations from the predictions of rational finance theory have been extensively documented empirically. In this paper, we offer an explanation for one of these anomalies, the “excess volatility puzzleâ€, i.e. the observation that prices fluctuate more than fundamentally justified. Based on Expectation Maximization (EM) calibrations of a generalized Hawkes point process model to price changes of major currency pairs and equity futures, we construct a decomposition of the variance of high frequency price changes into an exogenous (and thus efficient) component and an endogenous (and thus inefficient) excess component. The endogenously induced excess volatility is found to be substantial, largely stable at longer time scales and thus provides a plausible ex-planation for the excess volatility puzzle. Furthermore, strong endogenous variations at shorter scales are found to lead to major temporary inefficiencies. For example, during the “flash crash†in the GBP/USD exchange rate on October 7, 2016, we document a significant breakdown of market efficiency and an excessive burst in volatility, almost entirely explained by endogenous feedback. Conversely, the shock to EUR/ USD volatility in response to the 2016 Brexit referendum was not accompanied by such a deterioration in market efficiency. These results underline that a more solid understanding of the microstructural origins of financial fluctuations also bears important lessons for neo-classical concepts, like market efficiency, which are fundamental to financial and economic theory.
SSRN
This paper proposes a latent-variables approach to recover biases in beliefs directly from asset prices. We focus on return extrapolation, a bias in expectations formation that has received considerable attention in recent asset pricing research. We estimate a present-value model of the price-dividend ratio of the market that embeds extrapolative beliefs alongside rational discount rates and expected dividend growth. This approach allows us to measure extrapolation bias without having to rely on survey data, and inherently guarantees that the researcher focuses on a set of beliefs that matter for price formation. We use our 70-year long time-series of extrapolative beliefs to test the key prediction of behavioral models of the stock market with return extrapolators, namely, a rise in extrapolation bias today leads to predictably lower market returns in the future. Through both in-sample and out-of-sample analysis, our results provide support for behavioral theories of asset prices in the presence of extrapolators.
SSRN
Neither a gradually rising carbon tax nor emission trading schemes can ensure that the costs of emitting greenhouse gases, in particular CO2, will steadily rise faster than the general price level. If, e.g., global fossil energy prices decline faster than a carbon tax or the emission permit price rises, then the final good and its use become cheaper. Since the prices of fossil energy as well as CO2 emission permit prices belong to the most unstable prices in the global economy, carbon taxes and trading schemes cannot anchor the long-term expectation that the effective emission costs for firms and households will rise continuously. Such an expectation, however, is a prerequisite for steadily growing investment in energy efficiency and/or renewable energy because the profits from such investments consist of the saved fossil energy costs (“opportunity profitsâ€).This paper presents an alternative approach: The EU sets a path of steadily rising prices of crude oil, coal and natural gas by skimming off the difference between the EU target price and the respective world market price through a monthly adjusted quantity tax. Instead of the prices of fossil raw materials, the (implicit) quantity tax should fluctuate. In this way, the uncertainty about future price developments of crude oil, coal and natural gas and, hence, of the effective emission costs would be eliminated. Firms and households could calculate the profitability of investments in avoiding carbon emissions. At the same time, such a tax would ensure a uniform European carbon price in all sectors, provided the initial level of the price paths of crude oil, coal and natural gas account for the different CO2 intensities of these types of fossil energy. Given the size of the EU import bill for fossil energy, the amount of potential receipts of such an implicit and flexible CO2 tax would be (very) huge.
SSRN
In an elegant and influential paper, Garleanu and Pedersen derive the optimal dynamic trading policy assuming quadratic transaction costs and assets with predictable, mean-reverting returns. We show that their approach can be extended to incorporate linear neutrality constraints, such as dollar and beta neutrality, which are of practical interest. The solutions remain analytically tractable and are closely related to those of the unconstrained policy.
SSRN
While fiscal plans are expected to provide timely information about planned fiscal budgets, little is known about their value to investors. This paper examines how governments’ fiscal plans can enrich equity investors’ information set and induce consensus about the future fiscal outlook. We exploit the mandatory disclosure introduced in the Stability and Growth Pact (SGP) that requires European Union (EU) governments to publish multi-annual fiscal plans. We find that while fiscal plans are informative, investors interpret their content differently. Also, we draw on the literature on fiscal multipliers to explore the mechanisms that drive these effects. We document that procyclical fiscal plans that consist of spending cuts during economic downturns generate substantial interest in stock markets, but they also cause strong opinion divergence among investors. These results are consistent with recent evidence on the contractionary effects of procyclical spending cuts and the uncertainty surrounding fiscal multipliers. Collectively, the findings suggest how fiscal plans can be informative for financial markets. However, their value depends on specific features of planned fiscal policy actions (e.g., sign, composition, and timing).
RePEC
This paper analyzes the determinants of interest rate spreads during the period 2014â€"2020 in the euro area, with a focus on the Baltic countries. Against the background of accommodative monetary policy, interest rates on loans in the euro area have declined markedly, except in a few countries. In Latvia, Lithuania and Estonia, interest rates on new loans to non-financial corporations in 2020 were about the same as in 2014, and at the same time they were among the highest in the euro area. In this study, we apply the Ho and Saunders (1981) theoretical framework to identify explanatory factors of spreads and use the obtained econometric estimates to calculate the so-called pure spread by subtracting the influence of the bank funding structure and other bank-specific factors from the interest rate spread. Our study shows that even after accounting for the conventional determinants of interest rate spread, differences in the pure spread between euro area countries, especially between the Baltic countries and the rest of the euro area, persist. In part, these differences can be explained by varying degrees of financial sector market concentration. However, the bulk of the gap in spreads remains unexplained. The findings of this paper suggest that properly designed policy measures are needed to reduce spreads in the Baltic countries and lessen the fragmentation in the euro area. This would allow for more effective monetary policy transmission and stimulate lending and post-Covid economic recovery in the Baltics.
SSRN
In this work we present our findings of the soâ€called CIR#, which is a modified version of the Cox, Ingersoll & Ross (CIR) model, turned into a forecasting tool for any term structure. The main feature of the CIR# model is its ability to cope with negative interest rates, cluster volatility and jumps. By considering a dataset composed of money market interest rates during turmoil and calmer periods, we show how the CIR# performs in terms of directionality of rates and forecasting error. Comparison is carried out with a revamped version of the CIR model (denoted CIRadj), the Hull and White model and the EWMA which is often adopted whenever no structure in data is assumed. Testing and validation is performed on both historical and had hoc data with different metrics and clustering criteria to confirm the analysis.
SSRN
Ensuring that firms devote sufficient resources to the reporting process is important for quality reporting. To explore the effects of resources invested in the reporting function, we use a regulatory intervention in South Korea that led to an increase in human capital invested in the reporting process. The new regulation required firms to file internally prepared, pre-audited financial statements before the start of the year-end field audit process. Using the staggered adoption of this regulation, we confirm that the regulation leads to an increase in internal accounting employees at firms, and that the increase in rank-and-file accounting employees is more pronounced than the increase in top executives. Using difference-in-differences tests, we find improvement in external reporting quality (i.e., fewer restatements) for treatment firms relative to similar-size control firms that were not subject to the regulation. The effects are greater when an increase in accounting employees is accompanied by an increase in external audit effort, measured using audit hours. Treatment firms also show a drop in their hiring of non-accounting employees suggesting reallocation of labor inside the firm. The findings suggest that regulatory efforts to increase investment in internal accounting employees lead to improved reporting quality but can reduce labor forces in other non-accounting functions.
arXiv
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers.
In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.
SSRN
Trade signing algorithms that rely on quote data, tick data, or both have been used extensively to assign a trade as either a buy or a sell. We use transaction data that includes a unique panel of signed trades on the TRACE dataset and machine learning to uncover a new trade signing model that improves upon standard trade classification methods. Insights obtained from the bond market data carry over to the stock market. We show that trade classification accuracy depends on the trading and information environment in the market at the time of the trade.
arXiv
COVID-19 pandemic has shaken the roots of healthcare facilities worldwide, with the US being one of the most affected countries irrespective of being a superpower. Along with the current pandemic, COVID-19 can cause a secondary crisis of mental health pandemic if left unignored. Various studies from past epidemics, financial turmoil and pandemic, especially SARS and MERS, have shown a steep increase in mental and psychological issues like depression, low quality of life, self-harm and suicidal tendencies among general populations. The most venerable being the individuals infected and cured due to social discrimination. The government is taking steps to contain and prevent further infections of COVID-19. However, the mental and psychological wellbeing of people is still left ignored in developing countries like India. There is a significant gap in India concerning mental and psychological health still being stigmatized and considered 'non-existent'. This study's effort is to highlight the importance of mental and psychological health and to suggest interventions based on positive psychology literature. These interventions can support the wellbeing of people acting as a psychological first aid. Keywords: COVID-19, Coronavirus, Pandemic, Mental wellbeing, Psychological Wellbeing, Positive Psychology Interventions.
KEYWORDS - COVID-19, Coronavirus, Pandemic, Wellbeing, Positive Psychology, Interventions, PPI.
SSRN
This study presents a new measure of uncertainty related to climate policy, based on news from major US newspapers. The Climate Policy Uncertainty (CPU) index spikes near important events related to climate policy, such as new emissions legislation, global strikes about climate change and President’s statements about climate policy, among other developments. Our findings suggest that climate policy uncertainty has a strong and negative effect on CO2 emissions.
SSRN
This paper reviews and examines the method development aspects of Al Janabi (2012) theoretical foundations and optimization algorithms for the assessment of Liquidity-Adjusted Value at Risk (LVaR) technique under adverse market conditions. This paper focuses on the development of robust theoretical foundation and modeling framework that attempt to tackle the issue of market/liquidity risk and economic-capital estimation at a portfolio level and within a multivariate context.
SSRN
Connections among institutions in the global financial network create the potential for risk to propagate and for failures to cascade as successive institutions fail. As conditions, such as capital requirements change, institutions may modify their behavior in ways that can fundamentally change the relationships among institutions and lead to substantially different failure dynamics. Increasing capital requirements can, for example, paradoxically increase the potential for failures to propagate by altering the intensity of relationships and risk exposures. Predicting such outcomes and directing policies to reduce overall systemic risk requires modeling of institutional responses to environmental conditions. This paper discusses an approach based on inverse optimization of relationship decisions subject to capital constraints. A model of cascading failures and data from national debt cross-holdings illustrate the approach and demonstrate how changing capital requirements may lead to distinct differences in the sequences and extent of failures.
SSRN
This study investigates the association between money laundering control systems(MLCS) and tax haven use based on a sample of U.S. multinational financial corpora-tions (MFCs). We also examine the impact of external auditor specialization on theassociation between MLCS and tax haven use. We find that MLCS is significantlynegatively associated with tax haven use. Our result is also economically significant.Based on our regression estimates, a one-standard deviation increase in MLCS isassociated with a decrease in tax haven use by around 2.75%. Additional analysisshows that external auditor specialization magnifies the negative associationbetween MLCS and tax haven use. Overall, our study indicates that MLCS and theexternal audit function have important consequences for tax haven use by MFCs
SSRN
We argue that asset liquidity risk associated with the uncertainty of liquidating multiple-assets over a given holding period, particularly for thinly traded or emerging markets securities under adverse market conditions, is a key factor in formalizing and measuring overall trading risk and is therefore an important component to model. This paper proposes a practical framework for the quantification of asset liquidity risk, and its impact on economic capital allocations, for multiple assets’ portfolios.
SSRN
Since, the early 1990s, emerging markets have started to play an important role in the trading of derivatives products. This paper aims to discuss some of the main obstacles to the inception of successful derivative products in emerging economies and to provide a number of viable solutions
SSRN
In this paper, we launch a practical modus-operandi for the assessment of potential market risk exposures for financial trading portfolios by providing an investment management perspective from the 2007-2009 global financial crisis. This proposed tactic is based on the renowned concept of Liquidity-Adjusted Value-at-Risk (LVaR) along with the innovation of a risk-engine algorithm that can estimate LVaR for both long and short-sales positions.
arXiv
In this research paper we modify a classical SIR model to better adapt to the dynamics of COVID-19, that is we propose the heterogeneous SQAIRD model where COVID-19 spreads over a population of economic agents, namely: the elderly, adults and young people. We then compute and simulate an optimal control problem faced by a Government, where its objective is to minimize the costs generated by the pandemics using as control a compulsory quarantine measure (that is, a lockdown). We first analyze the problem from a theoretical perspective, claiming that different lockdown policies (total lockdown, no lockdown or partial lockdown) may justified by different cost (concave or convex) structures of the economies. We then focus on a particular cost structure (convex costs) and we simulate a targeted optimal policy vs. a uniform optimal policy, by dividing the whole population in three demographic groups (young, adults and old). We also simulate the dynamic of the pandemic with no policy implemented. Simulations highlighted the fact that: a) a policy of lockdown is always better than the \emph{laissez faire} policy, because it limits the costs that the pandemic generates in an uncontrolled situation; b) a targeted policy based on age of the individuals outperforms a uniform policy in terms of costs that it generates, being a targeted policy less costly and equally effective in the control of the pandemic.
arXiv
Portfolio managers often evaluate performance relative to benchmark, usually taken to be the Standard & Poor 500 stock index fund. This relative portfolio wealth is defined as the absolute portfolio wealth divided by wealth from investing in the benchmark (including reinvested dividends). The classic Merton problem for portfolio optimization considers absolute portfolio wealth. We combine absolute and relative wealth in our new utility function. We also consider the case of multiple benchmarks. To both absolute and relative wealth, we apply power utility functions, possibly with different exponents. We obtain an explicit solution and compare it to the classic Merton solution. We apply our results to the Capital Asset Pricing Model setting.
SSRN
We document that firms increase their investment in ESG activities, a proxy for CSR performance, in anticipation of lawsuits from external stakeholders to mitigate the adverse consequences of such legal externalities. We find that higher CSR performance is also positively related to the litigation count and propensity for class-action lawsuits. We show that this strategy to enhance CSR performance prior to anticipated litigation pays off in multiple ways, including a smaller negative wealth effect, shorter litigation process, and lower likelihood of class-action grants or monetary penalties against the defendant firm. The findings are robust to alternative measures of CSR performance, industry factors, and endogeneity concerns. Moreover, firms seem to boost their CSR performance even further after a rise in lawsuit activity. The evidence suggests that CSR performance is an additional risk mitigation tool â€" or an on-demand insurance policy â€" against anticipated lawsuits.
SSRN
This paper aims to investigate how political determinants, namely political instability and the persistence of corruption, impact on corporate cash holding decisions in Africa. We used a sample of 541 listed firms from seven African countries over the period 2014â€"2019. We used a dynamic panel data regression to identify the relationship between the two political determinants and cash holdings. Our results confirm that political instability is negatively related to cash holdings in accordance with the precautionary motive of cash holdings. Moreover, corruption is positively related to cash holdings, which is consistent with the trade-off theory. Our results suggest that political determinants also have an impact on the cash decisions of African listed firms as they are forced to adjust their cash holdings according to how politically stable the country is. This study contributes to the literature on corporate cash holdings by establishing a conclusive relationship between political instability and corruption, and corporate cash holdings for African countries.
SSRN
The aim of this paper is to fill a gap in the trading risk management literature and particularly from the perspective of emerging and illiquid markets, such as in the context of the Mexican financial markets. In this paper, we demonstrate a comprehensive and proactive approach for the measurement, management and control of equity trading risk exposure, which considers proper adjustments for the illiquidity within a multi-security setting.
SSRN
We develop measures of certain kinds of liquidity trading risk that is useful for completing the definition of market risk and for predicting liquidity-adjusted VaR (L-VaR) under illiquid market conditions. We argue that asset liquidity risk associated with the uncertainty of liquidating multiple-asset portfolios over a given holding period is a key factor in the computation of a credible risk-capital allocations in financial trading units.
SSRN
Do women invest differently than men? Using the Deutsche Bundesbank Panel on Household Finances (PHF), we replicate earlier findings that participation in stocks and the conditional share held in equity are generally lower among women than among men, even when we account for risk aversion and other relevant factors. Above and beyond most previous literature, we also analyse financial wealth held in fund shares, fixed-income securities and certificates. Adjusted for controls, women hold less wealth in certificates but more in fund shares than men. We also report novel results when we decompose the raw gender gap in capital market participation rates. This gap is mainly explained by different risk attitudes and monetary endowments, but women would participate even less in the capital market if they reacted to risk aversion in the same way as their male counterparts, implying that risk-averse men may shy away from risky assets much more than similar women.
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The attempt of this paper is to fill a gap in contemporary risk management literature and especially from the perspective of emerging markets in light of the aftermaths of the most recent sub-prime global financial crisis. This paper develops a rigorous approach for the assessment of risk exposure under extreme conditions.
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We identify the existence of a racial gap in housing returns that is an order of magnitude larger than disparities arising from housing costs alone. The returns gap is driven almost entirely by differences in distressed home sales (i.e. foreclosures and short sales). Black and Hispanic homeowners are both more likely to experience a distressed sale and to live in neighborhoods where distressed sales carry larger foreclosure discounts. Higher rates of distressed sales among minorities are driven by pre-existing differences in economic stability and neighborhood sorting. Black and Hispanic homeowners are more likely to default in response to increases in monthly payments, consistent with racial differences in liquid wealth holdings playing a key role in creating the observed disparities. We use quasi-experimental variation in the receipt of mortgage modifications to show that policies that encourage lenders to modify loans when homeowners can no longer afford their mortgages can mitigate gaps in housing returns, particularly if they are targeted towards minority homeowners or neighborhoods.
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We analyze whether exposure to an SEC insider trading enforcement action affects how insiders trade. We find that following an insider trading enforcement action at one firm, exposed insiders earn significantly lower abnormal profits from their trades at other firms compared to non-exposed insiders. The deterrent effect is stronger when a fellow insider is convicted and is similarly significant both pre- and post-SOX. Following the enforcement event, exposed insiders do not trade less frequently, but do trade significantly fewer shares per trade. Insiders who have witnessed an enforcement action have a lower probability for future conviction than their unexposed peers.
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The global financial crisis exposed the need for coherence and transparency in bank liquidity management. The problem of asymmetry of information associated with liquidity was particularly acute during the crisis, leading to both adverse selection and moral hazard problems. The inability to refinance in the market eventually required central banks and states to intervene to purchase assets or provide guarantees at the market level and supply public funds, or nationalize individual banks, to correct the market. The European Central Bank Guide to Internal Liquidity Adequacy places significant focus on management’s oversight and responsibilities in addressing these issues before they materialize, or the existence of contingency plans to manage materialized risks. This guide is simply one component of a complex web of supervisory instruments and tools for banks’ liquidity management. This chapter argues that the crisis prevention narrative is delegated to banks to discharge their obligations in bank supervision so as to minimize the asymmetry of information problems created by their business and improve the way they manage adverse scenarios and the risks to depositors, markets, and supervisory authorities. The first part explains the rationale for a principles-based approach; the second part explores the premise of the principles for liquidity management, namely the responsibility of the bank per se to discharge adequate liquidity management as a form of self-regulation; the third part explains some of the key features of the principles; and the fourth part looks at the features of the principles with specific reference to the crisis prevention measures to improve banks’ responses to liquidity risks. This ‘nuts and bolts’ approach surprisingly lacks reference to bank corporate culture as an important all-encompassing matter when it comes to banks and risk-taking. The final section reflects on these preventive elements and highlights the importance of the ECB combining a risk-based and forward-looking ‘judgement’ based approach to supervision.
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Using a sample of 1,215 US retail investors, we provide evidence on the effect of investment literacy and of investment literacy overconfidence on the likelihood of purchasing securities on margin, and also, among those who had purchased securities on margin, the likelihood of experiencing a margin call. Based on multivariate analyses, the likelihood of buying on margin decreased with investment literacy, and also increased with overconfidence. The likelihood of buying on margin decreased with age, and increased with risk tolerance and with the amount of investment assets. Among those who had bought securities on margin, the likelihood of experiencing a margin call decreased with investment literacy, without control variables, and increased with risk tolerance. However, with control variables, no variable had a significant relationship with having a margin call. This study provides important insights for researchers as well as practitioners on the relationship between margin trading and market quality.
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Researchers in accounting have recently provided evidence of a striking increase in the usefulness of earnings announcements based on stock market price and volume reactions (Beaver et al., 2018; Barron et al., 2018). Price reactions, however, are unable to capture investor disagreement and volume reactions capture both the resolution of prior disagreement and newfound disagreement generated by earnings announcements. Thus, it remains to be determined if earnings announcements have become increasingly useful in leveling the informational playing field, a key public policy objective of financial reporting. To address this possibility, we examine changes in disagreement around annual earnings announcements over the last forty years using analyst forecast measures found in the literature. First, we show that forecast dispersion is reduced around earnings announcements and this reduction has increased over time. Next, we use a forecast measure of informedness from Barron et al. (1998) to show that analysts as a group are more informed by earnings announcements in recent time periods. Finally, we use Barron et al.’s forecast measure of consensus to show that the ability of earnings announcements to make analysts more commonly informed has increased over time.
arXiv
We study how personalized news aggregation for rational inattentive voters (NARI) affects policy polarization and public opinion. In a two-candidate electoral competition game, an attention-maximizing infomediary aggregates information about candidates' valence into news. Voters decide whether to consume news, trading off the expected gain from improved expressive voting against the attention cost. NARI generates policy polarization even if candidates are office-motivated. Personalized news serves extreme voters with skewed signals and makes them the disciplining entities of policy polarization. Analysis of disciplining voters' identities and policy latitudes yields insights into the political effects of recent regulatory proposals to tame tech giants.
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How to tax capital income is a critical issue today. The realization ruleâ€"requiring that property usually must be sold before gains are taxedâ€"is central to taxing capital income, but often decreases the efficiency, equity, and simplicity of the tax system. Estimates suggest that the realization rule costs the government over $2 trillion over 10 years. Given these problems, it is unclear why the rule exists for assets that are easy to value and sell. Scholars have long speculated about the role of the public’s views here, but little is known empirically about them. We conduct the first survey experiment to understand the psychology of the realization rule, which has broad implications for the taxation of capital income. We have three main findings. First, respondents strongly prefer to wait to tax gains on stocks until sale: 75% to 25%. This pattern persists across a variety of other assets and policy framings: indeed, nearly half of those without stock prefer raising everyone’s taxes (including their own) to taxing unsold stock gains. But the flip side is that there is surprisingly strong support for taxing gains on assets at sale or transfer, including at death, in areas where current law never taxes those gains. Second, these views change only modestly after randomized participants observe a policy debate composed of videos explaining both the pros and cons of taxing before sale, though the pro and con treatments have large effects individually. And, third, among many possible explanations of these attitudes, we find particular evidence for four: using a different mental account for unsold gains than other ways of getting richer; a tendency to support the status quo; concerns about complexity; and a desire to tax consumption, not income, in the context of capital gains.
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The COVID-19 pandemic, declared on March 11, 2020 by the World Health Organisation (WHO), has had a severe economic and financial impact on every economy around the world. This paper aims to analyze the short-term impact of COVID-19 on global financial stock market indices. We study the impact of six different WHO announcements regarding COVID-19 on five different sectors (Pharma, Healthcare, Information Technology, Hotel & Airline) based on the indices of three different economies (World, Developed and Emerging economy). We also study the movement of stock prices and volume of nine different global stock market indices (classified as developed & emerging) based on the number of new cases and deaths due to COVID-19. The study’s findings suggest that there is a significant effect of COVID-19 on global financial stock markets. However, the effect is varied for developed and emerging economies.
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Prior studies find that delayed earnings announcements tend to communicate unfavorable news, and investors react negatively when firms delay earnings announcements. However, these findings do not explain why investors discount delayed earnings, even after controlling for the earnings news, and why firms sometimes announce good news late. Motivated by theory from Trueman (1990) that attempts to explain these phenomena, we examine whether announcement delays indicate earnings management. We predict and find that good news firms with higher discretionary accruals are more likely to announce earnings late. Consistent with post fiscal year-end activities driving announcement delays, we fail to find a relation between measures of real earnings management and late announcements. Using a last-chance earnings management measure based on tax expense manipulation, we also predict and find strong evidence that good news firms engaging in last-chance earnings management are more likely to delay earnings announcements. Consistent with Trueman’s (1990) theory that earnings management explains why investors discount delayed earnings announcements, we find that, on average, earnings announcement returns are 1.4% lower for late announcers relying on last-chance earnings management to report good news. Overall, our findings suggest that announcement delays provide information about not only the sign of the earnings news but also the potential for earnings management.
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In this paper, we propose ex-ante characteristics that predict the drop in risk-adjusted performance out-of-sample for a large set of stock anomalies published in finance and accounting academic journals. Our set of predictors is generated by hypotheses of OOS decay put forward by McLean and Pontiff (2016): arbitrage capital flowing into newly published strategies and in-sample overfitting linked to multiple hypothesis testing. The year of publication aloneâ€"compatible with both hypothesesâ€"explains 30% of the variance of Sharpe decay across factors: Every year, the Sharpe decay of newly-published factors increases by 5ppt. The other important variables are directly related to overfitting: the number of operations required to calculate the signal and two measures of sensitivity of in-sample Sharpe to outliers together add another 15% of explanatory power. Some arbitrage-related variables are statistically significant, but their predictive power is marginal.
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This paper investigates the behavior of assisted UAE banks four years after specific government intervention programs that were applied to them in 2009-10 using panel corrected standard error (PCSE) model estimation and artificial neural network (ANN). The study results demonstrate that government interventions in the UAE banking sector in 2009 crisis negatively affected banking sector stability in the long-run. The economic significance of these effects is that government intervention is found to decrease a bank’s stability (increase riskiness) in the range of almost 14% to 37%, because of reduced market discipline and less efficient banking structure. The coefficient for banks’ credit activities is negatively related with bank stability, which implies that banks with higher proportions of loans in their portfolio face lower stability and a higher risk level. This is supported by the fact that, (though statistically less significant), banks that are less efficient (ER) tend to engage in riskier activities thus reducing banks’ stability. ANN was found to be superior to PCSE analysis in predicting Stability (riskiness) of the UAE banks post-bailout in terms of correct classification percentage lowest RMSE, AIC in holdout sample, and key factors of importance identified in ANN that were not significant in PCSE. The methodological implication is that ANN study results can also be used by decision makers and policy makers to prioritize different factors to adjust the weight of these factors in their policy strategies.Index of Acronyms ADSE Abu Dhabi Security ExchangeAED Arab Emirate DirhamAI Artificial IntelligenceANN Artificial neural network CAGR Compound annual growth rateCDs Certificates of Deposit CVH Charter value hypothesisDFM Dubai Financial MarketDiD Difference in difference DIFC Dubai International Financial Centre ETF Exchange-traded funds GDP Gross Domestic ProductHHITA Concentration measure - Herfindahl Index - Total AssetsIMF International Monetary FundLLP/R Loan loss provisions/reserves MSTA Market share to Total AssetsOECD Organization of Economic Cooperation & DevelopmentPCSE Panel corrected standard error RBS Royal Bank of ScotlandROA Return on Assets ROE Return on EquitySCP Structure, Conduct and PerformanceSWFs Sovereign Wealth FundsUAE United Arab EmiratesY-o-Y Year on Yearσ Standard deviation
