Research articles for the 2020-11-11
SSRN
Cryptocurrencies’ values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabelled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on www.quantlet.de.
SSRN
I incorporate the productivity risks into an investment-based q-factor asset pricing model. The productivity risks factors largely summarize the cross-sectional portfolio return, where the time-varying volatility plays an important role. A parsimonious q-factor model driven by productivity risks explains about 90% variation of return of 25 Size/BM portfolios and 75% variation of return of 160 portfolios, which is comparable to the Fama-French multifactor models, the Carhart (1997) four-factor model, and the Hou, Mo, Xue & Zhang (2020) augmented q-factor model. As such, productivity risks significantly affect asset prices and can be one of the potential forces driving investment-based factor models.
arXiv
Arresting COVID infections requires community collective action that is difficult to achieve in a socially and economically diverse setting. Using district level data from India, we examine the effects of caste and religious fragmentation along with economic inequality on the growth rate of reported cases. The findings indicate positive effects of caste homogeneity while observing limited impact of economic inequality and religious homogeneity. However, the gains from higher caste homogeneity are seen to erode with the unlocking procedure after the nationwide lockdown. We find that community cohesion through caste effect is relatively dominant in rural areas even when mobility restrictions are withdrawn. Our findings indicate planners should prioritize public health interventions in caste-wise heterogeneous areas to compensate for the absence of community cohesion. The importance of our study lies in empirically validating the causal pathway between homogeneity and infection and providing a basis for zoning infection prone areas.
SSRN
Borrowing fees set in the securities lending market contain reliable information about the cross section of short-term expected stock returns. Using securities lending data for 14 developed and emerging markets from 2011 to 2018, we find that stocks with high borrowing fees tend to underperform their peers over the short term. Moreover, stocks that remain expensive to borrow continue to underperform, but persistence of high borrowing fees is not systematically predictable. While the information in borrowing fees is fast decaying, it can still be efficiently incorporated into real-world equity portfolios.
arXiv
Community electricity storage systems for multiple applications promise benefits over household electricity storage systems. More economical flexibility options such as demand response and sector coupling might reduce the market size for storage facilities. This paper assesses the economic performance of community electricity storage systems by taking competitive flexibility options into account. For this purpose, an actor-related, scenario-based optimization framework is applied. The results are in line with the literature and show that community storage systems are economically more efficient than household storage systems. Relative storage capacity reductions of community storage systems over household storage systems are possible, as the demand and generation profiles are balanced out among end users. On average, storage capacity reductions of 9% per household are possible in the base case, resulting in lower specific investments. The simultaneous application of demand-side flexibility options such as sector coupling and demand response enable a further capacity reduction of the community storage size by up to 23%. At the same time, the competition between flexibility options leads to smaller benefits regarding the community storage flexibility potential, which reduces the market viability for these applications. In the worst case, the cannibalization effects reach up to 38% between the flexibility measures. The losses of the flexibility benefits outweigh the savings of the capacity reduction whereby sector coupling constitutes a far greater influencing factor than demand response. Overall, in consideration of the stated cost trends, the economies of scale, and the reduction possibilities, a profitable community storage model might be reached between 2025 and 2035. Future work should focus on the analysis of policy frameworks.
arXiv
In tis paper we consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. Also, we make short review of researches in forecasting based on various models of ANFIS models. Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Also, we propose our models of DL and Neuro-Fuzzy Networks for this task. Finally, we show possibility of using these models for data science tasks. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.
SSRN
US firm cash holdings have become increasingly concentrated over time withering shareholder returns and heightening agency problems associated with free cash flows. Our use of a robust regression technique (LAD) and a state-of-the-art variable selection procedure (LASSO) to identify the determinants of US corporate cash holdings, creates a sparse model which is resistant to outliers. We obtain several results. First, the median of absolute errors of the LAD-LASSO-selected variables is significantly lower than the corresponding values of basic determinants in both in-sample and out-of-sample schemes. Second, financial leverage is a key determinant of cash holdings, indicating that cash and debt policies are tightly related. Third, none of the corporate governance proxies are selected by the LAD-LASSO method challenging what we already know from previous studies regarding the role of these mechanisms in corporate cash management. Our findings help shareholders determine to what extent firm cash levels are excessive, help managers determine their cash needs, and help policymakers predict the aggregate corporate cash demand and formulate policy.
SSRN
Credence goods markets â€" like for health care or repair services â€" with their informational asymmetries between sellers and customers are prone to fraudulent behavior of sellers and resulting market inefficiencies. We present the first model that considers both diagnostic uncertainty of sellers and the effects of insurance coverage of consumers in a unified framework. We test the model’s predictions in a laboratory experiment. Both in theory and in the experiment diagnostic uncertainty decreases the rate of efficient service provision and leads to less trade. In theory, insurance also decreases the rate of efficient service provision, but at the same time it also increases the volume of trade, leading to an ambiguous net effect on welfare. In the experiment, the net effect of insurance coverage on efficiency turns out to be positive. We also uncover an important interaction effect: if consumers are insured, experts invest less in diagnostic precision. We discuss policy implications of our results.
arXiv
This article aims to combine factor investing and reinforcement learning (RL). The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and analytical properties of the performance measure. This enables the implementation of REINFORCE methods, which we perform on a large dataset of US equities. Across a large range of implementation choices, our result indicates that RL-based portfolios are very close to the equally-weighted (1/N) allocation. This implies that the agent learns to be agnostic with regard to factors. This is partly consistent with cross-sectional regressions showing a strong time variation in the relationship between returns and firm characteristics.
arXiv
Recent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analysis relies on a two-step methodology. First, it estimates the community-wise causal impact of social distancing and shelter-in-place policies adopted in Chicago via Structural Bayesian Time-Series across four crime categories (i.e., burglary, assault, narcotics-related offenses, and robbery). Once the models detected the direction, magnitude and significance of the trend changes, Firth's Logistic Regression is used to investigate the factors associated to the statistically significant crime reduction found in the first step of the analyses. Statistical results first show that changes in crime trends differ across communities and crime types. This suggests that beyond the results of aggregate models lies a complex picture characterized by diverging patterns. Second, regression models provide mixed findings regarding the correlates associated with significant crime reduction: several relations have opposite directions across crimes with population being the only factor that is stably and positively associated with significant crime reduction.
arXiv
This work examines the economic benefits of learning a new skill from a different domain: cross-skilling. To assess this, a network of skills from the job profiles of 4,810 online freelancers is constructed. Based on this skill network, relationships between 3,525 different skills are revealed and marginal effects of learning a new skill can be calculated via workers' wages. The results indicate that the added economic value of learning a new skill strongly depends on the already existing skill bundle but that acquiring a skill from a different domain is often beneficial. Likewise, the data illustrate how to reveal valuable skills required for new and opaque technology domains, such as Artificial Intelligence. As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for mastering new technologies and designing individual training pathways. This can help to increase employability and reduce labour market shortages.
arXiv
Based on previous developments of the concept of market states using correlation matrices, in the present paper we address the dynamical evolution of correlation matrices in time. This will imply minor modifications to the market states themselves, due to increased attention to the transition matrix between the states. We will introduce trajectories of the correlation matrices by considering one day shifts for the epoch used to calculate the correlation matrices and will visualize both the states and the trajectories after dimensional scaling. This approach using dynamics improves the options of risk assessment and opens the door to dynamical treatments of markets and shows noise suppression in a new light.
SSRN
Building on fractional Black-Scholes, this paper draws a connection between option implied Hurst exponent H and current market mood. H comes with several advantages over VIX and survey based sentiment measures, such as a straight forward interpretation (optimistic/pessimistic) or the highly frequent data availability. From global evidence, we find strong correlations of H with a broad range of well established sentiment gauges. Analyzing H in more detail, investor fear occurs much faster than confidence is gained back. From periodical plus rolling long-term memory analysis for eight major regions around the globe, we observe that persistence in sentiment is depended on its level: the higher the market mood, the more overreacting and fragile it becomes. Other way around, if pessimism rises, then the mood gets stable and trending.
SSRN
We establish that macroprudential policies limiting capital flows can curb risks arising from corporate foreign currency borrowing in emerging markets. Using detailed firm-level data from India, we show that propensity to issue foreign currency debt for the same firm is higher when the difference in short-term interest rates between India and the US is higher, i.e., when the dollar `carry trade' is more profitable; this behavior is driven by the period after the global financial crisis. The positive relationship between issuance and the `carry trade' breaks down once regulators institute more stringent interest-rate caps on foreign currency borrowing. Riskier borrowers such as importers and those with higher interest costs cut issuance most. Firm equity exposure to foreign exchange risk rose after issuance in favorable funding conditions and emerged as a source of external sector vulnerability during the `taper tantrum’ of 2013. Macroprudential policy action limiting capital flows is able to nullify this effect, such as during the market stress due to the COVID-19 pandemic.
SSRN
Corporate credit ratings have tightened in a very gradual but cumulatively substantial way over two decades. We examine its spillover effects in the spread of syndicated loans, the market for which features banks and institutional investors. We find that syndicated loan spreads do not fully correct for the ratings conservatism. The correction in spreads is greater for smaller, speculative borrowers, loans with fewer lenders and a greater lead bank share that resemble single lender loans, for borrower names with CDS, and in the CDS markets. The incomplete correction is also detectable in newly rated borrowers who did not earlier have ratings. Thus, even in markets without small retail participants, form, rather than substance alone, matters. The presence of large sophisticated players does not appear to guarantee outcomes that de-bias the biases built into simple aggregates such as ratings.
arXiv
Community electricity storage systems for multiple applications promise benefits over household electricity storage systems. More economical flexibility options such as demand response and sector coupling might reduce the market size for storage facilities. This paper assesses the economic performance of community electricity storage systems by taking competitive flexibility options into account. For this purpose, an actor-related, scenario-based optimization framework is applied. The results are in line with the literature and show that community storage systems are economically more efficient than household storage systems. Relative storage capacity reductions of community storage systems over household storage systems are possible, as the demand and generation profiles are balanced out among end users. On average, storage capacity reductions of 9% per household are possible in the base case, resulting in lower specific investments. The simultaneous application of demand-side flexibility options such as sector coupling and demand response enable a further capacity reduction of the community storage size by up to 23%. At the same time, the competition between flexibility options leads to smaller benefits regarding the community storage flexibility potential, which reduces the market viability for these applications. In the worst case, the cannibalization effects reach up to 38% between the flexibility measures. The losses of the flexibility benefits outweigh the savings of the capacity reduction whereby sector coupling constitutes a far greater influencing factor than demand response. Overall, in consideration of the stated cost trends, the economies of scale, and the reduction possibilities, a profitable community storage model might be reached between 2025 and 2035. Future work should focus on the analysis of policy frameworks.
SSRN
Portfolio management is confronted with climate change â€" stronger and more rapidly than expected. Risks arising from the transition process from a brown, carbon-based to a green, low-carbon economy need to be integrated into portfolio and risk management. We show how to quantify these carbon risks by using a capital markets-based approach. Our measure of carbon risk, the carbon beta, can serve as an integral part to portfolio management practices in a more comprehensive way than fundamental carbon risk measures. Apart from other studies, we demonstrate that both green and brown stocks are risky per se, but there is no adequate remuneration in the financial markets. In addition, carbon risk exposure is correlated with exposures towards other common risk factors. This requires due diligence when integrating carbon risk in investment practices. By implementing carbon risk screening and best-in-class approaches, we find that investors can gain a desired level of carbon risk exposure, but this does not come without well-hidden costs.
SSRN
We use event study approach to assess the effects of the early stages of COVID-19 on global markets. A large sample of stock markets (66) covering all key global markets and regions is included. The findings indicate that the Wuhan lockdown induces negative spillover effects on markets in Europe, North America and other global markets that have yet to introduce domestic restrictions and have minimal infections at the time. Increasing cases outside China particularly in Europe and the introduction of containment measures result in severe market decline. Our findings highlight the need for quick, globally coordinated response to contagious diseases.
SSRN
In this paper, we examine the monitoring role of government customers in emerging markets, a setting where public procurement is significant but the procurement institutions are weak. In these countries, financial statements certification could be an important mechanism for a private firm to facilitate contracting with governments. Employing a sample of private firms across 98 emerging economies, we first document in-depth private-firm audit regulations for each country. We find that firms are more likely to have financial statements certified by an external auditor when they have government contracts. We further find that the association is less pronounced when governments have weaker monitoring incentives â€" when suppliers are subject to monitoring from tax authorities or creditors, when government contracting officials receive bribes, and when government spending is less transparent. Additional analyses show that financial statements certification serves as a substitute for governments’ internal assurance procedures. We corroborate our inferences using different identification checks such as controlling for firm fixed effects, propensity-score matching, entropy balancing, changes specification, using the staggered adoption of an E-Procurement system to infer changes in governments’ monitoring incentives, and several other cross-sectional analyses.
SSRN
Following the banking sector stress events of 2008â€"09 and 2011â€"12, a new framework for resolving failing banks has been implemented in the European Union which aims to facilitate authorities imposing losses on private creditors. The new framework implements global standards requiring banks to maintain a minimum quantum of loss-absorbing (or ‘bail-in’) bonds. Using data on the credit spreads on large European banks’ bonds between 2010 and 2019, we provide evidence that the risk sensitivity of banks’ credit spreads has increased since the reforms, and that the level and risk sensitivity of spreads on senior bail-in bonds are higher than those of comparable non-bail-in bonds. These findings support the hypothesis that the reforms have increased investors’ perception of the likelihood that they will be bailed in. These results hold for both UK and euro-area banks, though they are somewhat weaker for periphery European banks. We show that the degree of progress a bank has made in issuing bail-in bonds is positively related to the level and risk sensitivity of such bonds. We show that the higher level and risk sensitivity of spreads on bail-in bonds are largely invariant to whether bail-in bonds are contractually subordinated (ie issued as non-preferred senior) or structurally subordinated (ie issued from the holding company), and the effects are also unaffected by whether or not a bank is classified as a global systemically important bank (G-SIB). Finally, we show that the results are robust to changes in the strategy or risk profile of individual banks, via the inclusion of time-varying bank-specific effects.
SSRN
Standard approaches to the theory of financial markets are based on equilibrium and efficiency. Here we develop an alternative based on concepts and methods developed by biologists, in which the wealth invested in a financial strategy is like the population of a species. We study a toy model of a market consisting of value investors, trend followers and noise traders. We show that the average returns of strategies are strongly density dependent, i.e. they depend on the wealth invested in each strategy at any given time. In the absence of noise the market would slowly evolve toward an efficient equilibrium, but the large statistical uncertainty in profitability makes this noisy and uncertain. Even in the long term, the market spends extended periods of time far from perfect efficiency. We show how core concepts from ecology, such as the community matrix and food webs, apply to markets. The wealth dynamics of the market ecology explains how market inefficiencies spontaneously occur and gives insight into the origins of excess price volatility and deviations of prices from fundamental values.
SSRN
The study reports the results of an empirical investigation of the magnitude of disclosure by listed Private Commercial Banks (PCBs) in Bangladesh and the attributes influencing it. For the purpose of the study, samples were taken following the purposive sampling approach covering about 21 percent of the population. A researcher developed unweighted disclosure checklist containing 247 information items divided into ten groups, were utilized. Applying the Dichotomous scoring procedure, the study found an average disclosure of 75.76 percent with a constant growth during the study period. Significant differences were found while comparing the disclosure level among the years and among the sample banks under study. Out of the eight variables, the regression model used in the study found Log total assets, Return on Equity (ROE), and EPS as three influential contributors to disclosure; while other five variables were found to have insignificant influence on disclosure of PCBs in Bangladesh.
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We evaluate the impact of the Federal Reserve corporate credit facilities (PMCCF and SMCCF). A third of the positive effect on prices and liquidity occurred on the announcement date. We document immediate pass through into primary markets, particularly for eligible issuers. Improvements continue as additional information is shared and purchases begin, with the impact of bond purchases larger than the impact of purchases of ETFs. Exploiting cross-sectional evidence, we see the greatest impact on investment grade bonds and in industries less affected by COVID, concluding that the improvement in corporate credit markets can be attributed both to announcement effects of Federal Reserve interventions on the economy and to the specific differential impact of the facilities on eligible issues.
SSRN
This paper studies whether borrowing firms consider informational needs of various market participants and strategically adjust their financial reporting behavior. Using a large sample of borrowing firms from 1990 to 2018, we first posit and find that borrowing firms sharing the same bank as their primary lender have higher financial statement comparability. Moreover, using the setting of bank mergers, we investigate the borrowing firms’ strategic disclosure behavior in response to an increase in banks’ demand for financial report information. We demonstrate that the financial statement comparability between borrowers of target and acquirer banks increases in the post bank merger event. Further, we find that the financial statement comparability between borrowers of a target bank and those of its industry peers, who are not affected by bank mergers decreases in the post bank merger period. The findings are consistent with our argument that, in response to bank mergers, the borrowing firms prioritize the demand from their new creditors over the demand from other market participants and make strategic disclosure decisions accordingly. We conduct additional tests to show that our results hold even after we consider target borrowers’ voluntary disclosure behavior, target borrower switching its lender in the post bank merger period, the location of the acquiring bank and the acquiring bank’s internal governance. Further, we conduct two pseudo tests to validate our results. Our study concludes that borrowers of a target bank strategically aim to favor the acquirer bank’s informational needs.
SSRN
The terms of exchange-traded stock option contracts are usually adjusted when corporate actions take place. These adjustments are made to safeguard the value of the outstanding option contracts. Recently, a new type of corporate event has appearedâ€"levered and inverse exchange-traded product issuers are reducing leverage ratios with increased frequency. While such changes directly affect option values, no contract adjustments are made, resulting in windfall transfers of wealth from outstanding long to outstanding short option holders. In one instance alone, the transfer was more than USD 100 million. To remedy the problem, we offer a simple contract adjustment procedure.
arXiv
We present the clustering analysis of the financial markets of S&P 500 (USA) and Nikkei 225 (JPN) markets over a period of 2006-2019 as an example of a complex system. We investigate the statistical properties of correlation matrices constructed from the sliding epochs. The correlation matrices can be classified into different clusters, named as market states based on the similarity of correlation structures. We cluster the S&P 500 market into four and Nikkei 225 into six market states by optimizing the value of intracluster distances. The market shows transitions between these market states and the statistical properties of the transitions to critical market states can indicate likely precursors to the catastrophic events. We also analyze the same clustering technique on surrogate data constructed from average correlations of market states and the fluctuations arise due to the white noise of short time series. We use the correlated Wishart orthogonal ensemble for the construction of surrogate data whose average correlation equals the average of the real data.
arXiv
It is argued that Marxism, being based on contradictions, is an illogical method. More specifically, we present a rejection of Marx's thesis that the rate of profit has a long-term tendency to fall.
SSRN
Turning points in financial markets are often characterized by changes in the direction and/or magnitude of market movements with short-to-long term impacts on investors' decisions. This paper develops a Bayesian technique to turning point detection in financial equity markets. We derive the interconnectedness among stock market returns from a piece-wise network vector autoregressive model. The empirical application examines turning points in global equity market over the past two decades. We also compare the COVID-19 induced interconnectedness with that of the global financial crisis in 2008 to identify similarities and the most central market for spillover propagation.
arXiv
The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier-Ricci curvature and Forman-Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci-type curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or 'business-as-usual' periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.
arXiv
We analyze novel portfolio liquidation games with self-exciting order flow. Both the N-player game and the mean-field game are considered. We assume that players' trading activities have an impact on the dynamics of future market order arrivals thereby generating an additional transient price impact. Given the strategies of her competitors each player solves a mean-field control problem. We characterize open-loop Nash equilibria in both games in terms of a novel mean-field FBSDE system with unknown terminal condition. Under a weak interaction condition we prove that the FBSDE systems have unique solutions. Using a novel sufficient maximum principle that does not require convexity of the cost function we finally prove that the solution of the FBSDE systems do indeed provide existence and uniqueness of open-loop Nash equilibria.
arXiv
The production of corrugated paper boxes accounts for roughly one third of the world's total paper production and, as a result of both COVID-19 and the rise of e-commerce, is a growing market. We provide a fresh approach to determining near-optimal stock policies for integrated paper companies. The new approach shows that existing policies can be improved by a significant margin. In a case study we saw a reduction in total waste by 9%, with a simultaneous decrease in logistics costs.
arXiv
Research attention on decentralized autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of publications and the share of these studies in the corpus of energy system modelling literature. This paper shows the status quo and future modelling needs for research on local autonomous energy systems. A total of 359 studies are roughly investigated, of which a subset of 123 in detail. The studies are assessed with respect to the characteristics of their methodology and applications, in order to derive common trends and insights. Most case studies apply to middle-income countries and only focus on the supply of electricity in the residential sector. Furthermore, many of the studies are comparable regarding objectives and applied methods. Local energy autonomy is associated with high costs, leading to levelized costs of electricity of 0.41 $/kWh on average. By analysing the studies, many improvements for future studies could be identified: the studies lack an analysis of the impact of autonomous energy systems on surrounding energy systems. In addition, the robust design of autonomous energy systems requires higher time resolutions and extreme conditions. Future research should also develop methodologies to consider local stakeholders and their preferences for energy systems.
SSRN
In this essay, which formed the basis for the luncheon keynote speech at the Rethinking Stewardship online conference presented by the Ira M. Millstein Center for Global Markets and Corporate Ownership at Columbia Law School and ECGI, the European Corporate Governance Institute, the essential, but not sufficient, role of regulation to promote more effective stewardship by institutional investors is discussed. To frame specific policy recommendations that align the responsibilities of institutional investors with the best interests of their human investors in sustainable wealth creation, environmental responsibility, the respectful treatment of stakeholders, and, in particular, the fair pay and treatment of workers, the essay: 1) explains how the corporate governance system we now have is fundamentally different than the system we had when the regulatory structures governing institutional investors were put in place; 2)identifies the suboptimal results that have ensued by increasing the power of institutional investors, and thus the stock market, over public companies, while diminishing the protections for other stakeholders and society generally; 3) discusses why leaving needed change to the industry itself is not an adequate answer; and 4) sets forth a series of specific, measured public policy changes for mutual funds, pension funds, and hedge funds. In sum, the essay explains and addresses the reality that companies that make products and deliver services cannot focus more on sustainable profitability, respectful treatment of stakeholders, and social responsibility than the powerful investors that control them permit. Like any powerful economic interest, institutional investors should be expected to be responsible citizens and faithful fiduciaries.
arXiv
Fire sales are among the major drivers of market instability in modern financial systems. Due to iterated distressed selling and the associated price impact, initial shocks to some institutions can be amplified dramatically through the network induced by portfolio overlaps. In this paper we develop a mathematical framework that allow us to investigate central characteristics that drive or hinder the propagation of distress. We investigate single systems as well as ensembles of systems that are alike, where similarity is measured in terms of the empirical distribution of all defining properties of a system. This asymptotic approach ensures a great deal of robustness to statistical uncertainty and temporal fluctuations. A characterization of those systems that are resilient to small shocks emerges, and we provide explicit criteria that regulators may exploit in order to assess the stability of any system.
We illustrate the application of these criteria for some exemplary configurations in the context of capital requirements and test the applicability of our results for systems of moderate size by Monte Carlo simulations.
SSRN
We explore the impact of the COVID-19 pandemic on the term structure of interest rates. Using data from developed and emerging countries, we demonstrate that the expansion of the disease significantly affects sovereign bond markets. The growth of confirmed cases increases the term spreads of government bonds. The effect is independent of policy responses to COVID-19 and robust to many considerations.
SSRN
Actuarial reserving techniques have evolved from the application of algorithms, like the chain-ladder method, to stochastic models of claims development, and, more recently, have been enhanced by the application of machine learning techniques. Despite this proliferation of theory and techniques, there is relatively little guidance on which reserving techniques should be applied and when. In this paper, we revisit traditional reserving techniques within the framework of supervised learning to select optimal reserving models. We show that the use of optimal techniques can lead to more accurate reserves and investigate the circumstances under which different scoring metrics should be used.
SSRN
This paper studies the equilibrium level of prudential regulation in a framework with negative borrowing externalities. A debt limit is implemented by a politician appointed through majoritarian elections. If she is committed to perfect enforcement, voting allows borrowers to internalize the externality. If politician is captured, she exempts politically connected borrowers from regulation (imperfect enforcement), distorting voters' policy preferences. Depending on the electoral power of the connected borrowers, the outcome may be an either too lax or too strict policy. Additional results highlight the impact of income inequality on the strictness of prudential regulation.
arXiv
In this paper, the solution of the equity premium puzzle was given. First, the Arrow-Pratt measure of relative risk aversion for detecting the risk behavior of investors was questioned, and then a new tool was developed to study the risk behavior of investors. This new tool in the new formulated model was tested for the equity premium puzzle for a solution. The results show that the calculated value of the coefficient of relative risk aversion is 2.201455 which is compatible with the empirical studies and as investors who invest in risk-free asset place disutility on the not sure wealth value, investors who invest in equity place utility on the not sure wealth value.
SSRN
I examine a large set of return anomalies in international equity markets. While previous studies find that anomalies are strong in international markets, I show that only few replicate when mitigating the impact of tiny stocks, accounting for multiple testing, and using factor models to adjust for expected returns. Accounting for the former two, only 19 of 132 anomalies yield significant long--short returns in the ex-U.S. world cross-section. Most of these are value anomalies. Factor models hardly seem necessary for Japan and the Middle East. In other international markets, the best U.S. factor models help shrink the cross-sections further.
SSRN
Central banks make public the results of open market operations (OMOs), which they use to adjust the liquidity available to the financial system to maintain the short-term borrowing rate in the range compatible with achieving their monetary policy objectives. This paper shows that such announcements are costly because they moderate the impact of changes in supply achieved through OMOs. Nevertheless, communication of OMOs is desirable because it improves the transparency of the funding market, which makes the price of liquidityâ€"a key input into economic decision makingâ€"more reflective of underlying demand and supply of liquidity.
