# Research articles for the 2020-05-28

A RAROC Valuation Scheme for Loans and its Application in Loan Origination
Engelmann, Bernd,Pham, Ha
SSRN
In this article, a RAROC (risk-adjusted return on capital) valuation scheme for loans is derived. The critical assumption throughout the article is that no market information on a borrowerâ€™s credit quality like bond or credit default swap spreads is available. Therefore, market-based approaches are not applicable, and an alternative combining market and statistical information is needed. The valuation scheme aims to derive the individual cost components of a loan which facilitates the allocation to bank?s operational units. After its introduction, a theoretical analysis of the scheme linking the level of interest rates and borrower default probabilities shows that a bank should only originate a loan when the interest rate the borrower is willing to accept is inside the profitability range for this client. This range depends on a bankâ€™s internal profitability target and is always a finite interval only or could even be empty if a borrowerâ€™s credit quality is too low. Aside from analyzing the theoretical properties of the scheme, we show how it can be directly applied in the daily loan origination process of a bank.

A moment matching method for option pricing under stochastic interest rates
Fabio Antonelli,Alessandro Ramponi,Sergio Scarlatti
arXiv

In this paper we present a simple, but new, approximation methodology for pricing a call option in a Black \& Scholes market characterized by stochastic interest rates. The method, based on a straightforward Gaussian moment matching technique applied to a conditional Black \& Scholes formula, is quite general and it applies to various models, whether affine or not. To check its accuracy and computational time, we implement it for the CIR interest rate model correlated with the underlying, using the Monte Carlo simulations as a benchmark. The method's performance turns out to be quite remarkable, even when compared with analogous results obtained by the affine approximation technique presented in Grzelak and Oosterlee (2011) and by the expansion formula introduced in Kim and Kunimoto (1999), as we show in the last section.

A weighted finite difference method for American and Barrier options in subdiffusive Black-Scholes Model
Grzegorz Krzyżanowski,Marcin Magdziarz
arXiv

This paper is focused on American option pricing in the subdiffusive Black Scholes model. Two methods for valuing American options in the considered model are proposed. The weighted scheme of the finite difference (FD) method is derived and the main properties of the method are presented. The Longstaff-Schwartz method is applied for the discussed model and is compared to the previous method. In the article it is also shown how to valuate wide range of Barrier options using the FD approach. The proposed FD method has $2-\alpha$ order of accuracy with respect to time, where $\alpha\in(0,1)$ is the subdiffusion parameter, and $2$ with respect to space.

Accesso diretto e indiretto delle PMI alle garanzie pubbliche: un esercizio di valutazione delle normative regionali (SMEsâ€™ Direct and Indirect Access to Public Guarantees: An Evaluation of Regional Regulations)
Lavecchia, Luciano,Loschiavo, David,Leva, Luigi
SSRN
Italian Abstract: Il Fondo di garanzia per le PMI (FDG) rappresenta il principale strumento pubblico a sostegno dellâ€™accesso al credito. In questo lavoro si valuta lâ€™impatto delle norme regionali che ne limitano lâ€™operativitÃ  ai finanziamenti garantiti dai confidi, sfruttando le discontinuitÃ  regolamentari osservate lo scorso decennio in alcune regioni che hanno abolito (Lazio) o introdotto (Abruzzo e Marche) la limitazione. Attraverso unâ€™analisi differenza-nelle-differenze, vengono stimati gli effetti di questi mutamenti normativi utilizzando come controlli imprese operanti in regioni contigue (Toscana ed Emilia Romagna), dove il quadro normativo Ã¨ rimasto inalterato. I risultati indicano che limitare lâ€™operativitÃ  del FDG alle operazioni di controgaranzia ha prodotto effetti sostanzialmente negativi sullâ€™accesso al credito delle imprese. Nel Lazio, dopo la rimozione della limitazione, Ã¨ cresciuto sia il numero di imprese che hanno avuto accesso sia il volume dei finanziamenti garantiti, per tutte le classi dimensionali. Ãˆ inoltre migliorato il differenziale di tasso praticato. In Abruzzo e nelle Marche lâ€™introduzione della limitazione ha prodotto effetti prevalentemente negativi su numero, volume e costo dei finanziamenti alle imprese regionali.English Abstract:The Italian public guarantee scheme (Fondo di garanzia - FDG) is the main tool supporting SMEsâ€™ access to credit. This work evaluates the impact of the regional laws limiting the FDGâ€™s operations to loans guaranteed by mutual guarantee institutions. To this end, we exploit the regulationsâ€™ discontinuities that occurred in some Italian regions that have either abolished or introduced such a restriction. We study the effects of the regulation changes in a difference-in-differences setting where treated firms are located in regime switching regions and control firms are in neighbouring regions. We find that constraining access to the FDGâ€™s publicly funded collateral to counter-guarantee schemes hampered SMEsâ€™ access to finance overall. Removing the restriction increased both the number of firms with access to the FDGâ€™s guarantees and the total size of the loans granted to treated SMEs of any size. Moreover, the relative cost of credit improved for treated firms. Conversely, the introduction of the restriction to counter-guarantees had mostly negative effects on the number, size and cost of loans granted to treated firms.

An Analysis of Sovereign Credit Risk Premia in the Euro Area: Are They Explained by Local or Global Factors?
Cecchetti, Sara
SSRN
We study the determinants of sovereign credit risk in the euro area in a time period that includes the financial and sovereign debt crisis, as well as the unconventional monetary policy adopted by the European Central Bank. First, we detect the presence of commonality in sovereign credit spreads of different countries, justifying the search for the common factors that drive CDS prices. Building on the work of Longstaff et al. (2011), we employ the econometric model used in Cecchetti (2017) to decompose sovereign credit default swap spreads into expected default losses and risk premia, finding evidence of a significant contribution of the latter component. We use the model to understand to what extent the variations in CDS spreads and in the two embedded components of selected euro-area countries are more linked to local or euro area economic variables. The results point to the importance of both global and local factors, which have a greater impact on the risk premium component. Finally, we estimate the contribution of the objective probability and risk premium components of redenomination risk (as measured by the ISDA basis) to the related CDS spread components, detecting some differences between countries.

Asymmetric Volatility and Performance of Indian Equity Market- Comparison of SENSEX and S&P CNX Nifty
Gupta, Ravi Kumar
SSRN
Stock market volatility has its existence from the long time but its complete eradication is not possible, the only thing which can be done is just to know its behavior and pattern that how it behaves. The present study is aimed to understand the nature and different patterns of volatility in Indian equity market. The daily observations comprising of closing data of SENSEX of Bombay Stock Exchange and S&P CNX Nifty of National Stock Exchange for the period of 10 years i.e. from January 2003 to December 2012 is used for analysis. The data was collected from the websites www.bseindia.com and www.nseindia.com. The present study is attempted to examine the volatility of returns in Indian stock market. GARCH models were used to see the volatility of Indian equity market. It was found that there was spillover of information in the Indian stock market and with the significant coefficient of dummy in improved model. It was concluded that negative shocks do have greater impact on conditional volatility compared to positive shocks of the same magnitude in the Indian stock market.

Attitudes Toward Debt and Debt Behavior
Almenberg, Johan,Lusardi, Annamaria,SÃ¤ve-SÃ¶derbergh, Jenny,Vestman, Roine
SSRN
We introduce a novel survey measure of attitude toward debt. Matching our survey results with panel data on Swedish household balance sheets from registry data, we show that our debt attitude measure helps explain individual variation in indebtedness as well as debt build-up and spending behavior in the period 2004Ã¢??2007. As an explanatory variable, debt attitude compares well to a number of other determinants of debt, including education, risk-taking, and financial literacy. We also provide evidence that suggests that debt attitude is passed down along family lines and has a cultural element.

Banking Sector Performance, Profitability, and Efficiency: A Citationâ€Based Systematic Literature Review
SSRN
This study presents a citation‐based systematic literature review on banking sector performance, particularly in terms of profitability, productivity, and efficiency. Specifically, the study aims to identify the leading sources of knowledge in terms of the most influential journals, authors, and papers. The paper presents a content analysis of the 100 most cited papers. In total, 1996 peer‐review papers were found relevant in the Scopus database by using a comprehensive list of keywords. The results show that the Journal of Banking & Finance appears to be the leading journal in terms of publication count and citations. Based on total citations, Allen Berger is the most prolific author. The most cited paper is "Problem loans and cost efficiency in commercial banks" by Allan Berger and Robert DeYoung. The content analysis of the top 100 papers identifies five essential themes: determinants of efficiency, methodology, ownership, financial crises, and scale economies. In terms of estimation approaches, 74% of papers employed frontier analysis, which includes 34% parametric and 40% nonparametric methods, and remaining 26% have used financial ratio analysis. Additionally, stochastic frontier and data envelopment analysis are widely used in parametric and nonparametric methods, respectively. An intermediate approach is extensively adopted for the specification of inputs and outputs.

CEO Overconfidence and the Choice of Debt Issuance
Ge, Li,Jamil, Taher,Yu, Jin
SSRN
This paper examines how CEO overconfidence affects firmsâ€™ choice of debt issuance among private debt (i.e., bank loan and non-bank loan) and public bond. Using a sample of U.S. rated public firms, we find that firms with overconfident CEOs tend to issue more private debt and issue private debt more often than public debt compared with those with non-overconfident CEOs. Overconfident CEOs are more likely to borrow from banks instead of non-bank private sources. We further show that the effect of CEO overconfidence on debt choices is more pronounced during poor economic condition, and among firms with higher distress risk and cash flow risk. To alleviate endogeneity concerns, we investigate matched samples and a subsample with exogenous CEO turnover events. The results are supportive and statistically stronger.

COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model
Ian M. Trotter,Luís A. C. Schmidt,Bruno C. M. Pinto,Andrezza L. Batista,Jéssica Pellenz,Maritza Isidro,Aline Rodrigues,Attawan G. S. Suela,Loredany Rodrigues
arXiv

During the COVID-19 pandemic of 2019/2020, authorities have used temporary ad-hoc policy measures, such as lockdowns and mass quarantines, to slow its transmission. However, the consequences of widespread use of these unprecedented measures are poorly understood. To contribute to the understanding of the economic and human consequences of such policy measures, we therefore construct a mathematical model of an economy under the impact of a pandemic, select parameter values to represent the global economy under the impact of COVID-19, and perform numerical experiments by simulating a large number of possible policy responses. By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate. The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and reduction in the infection rate. Our experiments further suggest that the intervention should last until after the peak determined by the reduced infection rate. The model and its implementation, along with the general insights from our policy experiments, may help policymakers design effective emergency policy responses in the face a serious pandemic, and contribute to our understanding of the relationship between the economic growth and the spread of infectious diseases.

Cross-border Investments and Uncertainty: Firm-level Evidence
Cezar, Rafael,Gigout Magiorani, Timothee,Tripier, Fabien
SSRN
This paper studies the impact of uncertainty on cross-border investments. We build a data set of firm-level outward Foreign Direct Investments between 2000 and 2015. We create a time and country varying measure of uncertainty based on the dispersion of idiosyncratic investment returns. An increase in uncertainty delays cross-border flows to the affected country. Yet, this average e_ect hides strong heterogeneity. Firms with low ex-ante performance durably reduce their foreign investments. Meanwhile high-performing firms increase their investments after the initial shock. We interpret these results as the evidence of a cleansing effect of uncertainty shocks among multinational firms in the presence of financial frictions.

Deep Learning for Portfolio Optimisation
Zihao Zhang,Stefan Zohren,Stephen Roberts
arXiv

We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimise portfolio weights by updating model parameters. Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. Indices of different asset classes show robust correlations and trading them substantially reduces the spectrum of available assets to choose from. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period, from 2011 to the end of April 2020, including the financial instabilities of the first quarter of 2020. A sensitivity analysis is included to understand the relevance of input features and we further study the performance of our approach under different cost rates and different risk levels via volatility scaling.

Demand for Safety, Risky Loans: A Model of Securitization
Segura, Anatoli,Villacorta, Alonso
SSRN
We build a competitive equilibrium model of securitization in the presence of demand for safety by some investors. Securitization allows to create safe assets by pooling idiosyncratic risks from loan originators, leading to higher aggregate loan issuance. Yet, the distribution of loan risks out of their originators creates a moral hazard problem. An increase in the demand for safety leads to a securitization boom and riskier originated loans. When demand for safety is high, welfare is Pareto higher than in an economy with no securitization despite the origination of riskier loans. Aggregate lending expansions driven by demand for safety may, paradoxically, lead to riskier loan issuance than expansions driven by standard credit supply shocks.

Derivative Trading and Stock Market Volatility in India
Gupta, Ravi Kumar
SSRN
Stock prices are changed every day by the market. Buyers and sellers cause prices to change as they decide how valuable each stock is. Stock market volatility is significant and understanding it is imperative to investing in stocks that suit your investment or trading style and risk tolerance level. The derivative trading was initiated in the Indian Capital Market by the Government following L .C. Gupta Committee Report on derivatives in December 1997. The present study examined the impact of derivatives trading on the stock market volatility. The study attempted to estimate the volatility implications of the introduction of derivatives on the cash market. Through this study, we seek evidence regarding whether the listing of futures and options lead to any significant change in the volatility of the stock market in India.

Desperate times call for desperate measures: government spending multipliers in hard times
Sokbae Lee,Yuan Liao,Myung Hwan Seo,Youngki Shin
arXiv

We investigate state-dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the US economy is in a slack state. When the endogenized slack state is estimated as the period of the unemployment rate higher than about 12 percent, the estimated cumulative multipliers are significantly larger during slack periods than non-slack periods and are above unity. We also examine the possibility of time-varying regimes of slackness and find that our empirical results are robust under a more flexible framework. Our estimation results point out the importance of the heterogenous effects of fiscal policy and shed light on the prospect of fiscal policy in response to economic shocks from the current COVID-19 pandemic.

DiversitÃ  e inclusione nelle banche italiane: unâ€™analisi empirica delle misure a sostegno della presenza femminile nei board (Diversity and inclusion in Italian Banks: An Empirical Analysis of Measures to Support Gender Diversity on the Board of Directors)
Capone, Diana
SSRN
Italian Abstract Nel sistema bancario italiano a sostegno della diversitÃ  di genere coesistono misure, non prescrittive, poste dalle regole di vigilanza e misure, assistite da sanzioni in caso di inosservanza, poste dalla disciplina sulle societÃ  quotate. Si analizzano gli effetti di queste misure, rispettivamente, su banche non quotate e banche quotate, tra il 2014 e il 2018. Integrando, per le banche quotate, le informazioni disponibili negli archivi della Banca dâ€™Italia con quelle originali raccolte dallâ€™analisi dei curriculum vitae dei membri dei board e dalle relazioni sul governo societario, lâ€™analisi mostra che allâ€™aumento del numero delle donne nei board si sono associati anche un maggior coinvolgimento nelle dinamiche consiliari delle consigliere e caratteristiche piÃ¹ marcate di indipendenza e competenza, che le rendono potenzialmente in grado di essere piÃ¹ incisive nei meccanismi decisionali. Inoltre, lâ€™incremento del numero e il miglioramento del ruolo sostanziale delle consigliere incidono positivamente su alcuni profili che sono ritenuti determinanti per lâ€™efficacia dellâ€™azione dei board dalla letteratura scientifica e dalla Banca dâ€™Italia nelle Disposizioni di Vigilanza sul governo societario. CiÃ² apre futuri spunti per analisi quantitative che qualifichino la relazione tra presenza femminile nei board e performance aziendali tenendo conto delle caratteristiche dei componenti donna e del ruolo da loro svolto. Sotto il profilo delle scelte di policy, le evidenze raccolte sono a favore del rafforzamento delle misure in essere per le banche non quotate.English AbstractTwo kinds of measures aimed at fostering gender diversity are applied in Italian banks: non-prescriptive measures set by supervisory rules, and binding measures set by the law on listed companies. This study analyses the effects of these measures on the composition of banksâ€™ boards, between 2014 and 2018, comparing listed banks and unlisted banks. The analysis provides a complete picture of the impact of quotas for listed banks, by integrating supervisory information on all members of banksâ€™ boards, gathered by the Bank of Italy, with additional information on board membersâ€™ backgrounds collected from their CVs, and with information on the structure and functioning of boards collected from the reports on corporate governance. In particular, the analysis shows that, between 2014 and 2018, the increase in the number of women on banksâ€™ boards was associated with the enhanced independence and competence of female directors and with their stronger involvement in the work of the boards. Furthermore, the analysis shows that these improvements have had a positive impact on those board characteristics that the scientific literature and the Bank of Italy consider to be key for the effectiveness of the board in fulfilling its mandate. In terms of policy decisions, the results of the analysis suggest that the existing measures need to be strengthened for unlisted banks.

Dynamic Coupling and Market Instability
Christopher D. Clack,Elias Court,Dmitrijs Zaparanuks
arXiv

We examine dynamic coupling and feedback effects between High Frequency Traders (HFTs) and how they can destabilize markets. We develop a general framework for modelling dynamic interaction based on recurrence relations, and use this to show how unexpected latency and feedback can trigger oscillatory instability between HFT market makers with inventory constraints. Our analysis suggests that the modelled instability is an unintentional emergent behaviour of the market that does not depend on the complexity of HFT strategies - even apparently stable strategies are vulnerable. Feedback instability can lead to substantial movements in market prices such as price spikes and crashes.

Ecological Finance Theory: New Foundations
Lagoarde-segot, Thomas,Martinez, Enrique
SSRN
This paper puts forth Ecological Finance Theory as a valuable new vision for financial economics. Ecological Finance Theory is a mission-driven proposition seeking to re-embed financial systems within social and ecological constraints in order to ensure social resilience. Ecological Finance theory acknowledges the insufficiency of the neoclassical model to account for and respond to the socio-economic and biophysical realities of a post pandemic world. It introduces a sound ontological and epistemological framework taking into account the complex interactions between the financial, socio-economic and biophysical realms, and the impact of financial models in shaping reality. It draws on biological theory to provide insight into new metaphors and concepts for the study of financial systems. This paper paves the way for future work within this theory. We first describe its meta-theoretical hypotheses. We then introduce an initial set of metaphors. We finally develop a puzzle-solving application with a new stock-flow consistent model permitting to analyze ways to upscale the transformative power of local complementary currencies.

Equivalence between forward rate interpolations and discount factor interpolations for the yield curve construction
Jherek Healy
arXiv

The traditional way of building a yield curve is to choose an interpolation on discount factors, implied by the market tradable instruments. Since then, constructions based on specific interpolations of the forward rates have become the trend. We show here that some popular interpolation methods on the forward rates correspond exactly to classical interpolation methods on discount factors. This paper also aims at clarifying the differences between interpolations in terms of discount factors, instantaneous forward rates, discrete forward rates, and constant period forward rates.

Expansionary Yet Different: Credit Supply and Real Effects of Negative Interest Rate Policy
Bottero, Margherita,Minoiu, Camelia,Peydro , Jose-Luis,Polo, Andrea,Presbitero, Andrea,Sette, Enrico
SSRN
We show that negative interest rate policy (NIRP) has expansionary effects on bank credit supplyâ€" and the real economy â€"through a portfolio rebalancing channel, and that, by shifting down and flattening the yield curve, NIRP differs from rate cuts just above the zero lower bound. For identification, we exploit ECBâ€™s NIRP and matched administrative datasetsâ€" including the credit registerâ€" from Italy, severely hit by the Eurozone crisis. NIRP affects banks with higher ex-ante net short-term interbank positions or, more broadly, more liquid balance-sheets. NIRP-affected banks rebalance their portfolios from liquid assets to lending, especially to ex-ante riskier and smaller firmsâ€"without higher ex-post delinquenciesâ€"and cut loan rates (even to the same firm), inducing sizable firm-level real effects. By contrast, there is no evidence of a retail deposits channel associated with NIRP.

FinTech Credit: A Critical Review of Empirical Research Literature
Branzoli, Nicola,Supino, Ilaria
SSRN

Firmsâ€™ Listings: What is New? Italy Versus the Main European Stock Exchanges
Finaldi Russo, Paolo,Parlapiano, Fabio,Pianeselli, Daniele,Supino, Ilaria
SSRN
Over the last decade and a half non-financial corporationsâ€™ (NFCs) listings have displayed a heterogeneous pattern across European countries. The number of listed NFCs has increased in Italy and Spain, while it has declined in Germany, France and the United Kingdom. In Italy, the increase in the number of listed firms has been driven by SMEsâ€™ listings, leaving the stock market small by international standards. We break down the size gap of the Italian equity market (with respect to its European peers) into the share of listed companies and their relative size. We show that the lower share of listed NFCs in Italy accounts for the gap with France and the UK, while the smaller size of Italian public firms has a crucial bearing on the differences with Germany and Spain. Counterfactual exercises provide evidence that there is limited room to bridge these gaps, as the structure of the Italian economy leans towards small enterprises. Policy measures aimed at fostering SMEsâ€™ propensity to go public may be more effective in promoting the further development of the Italian stock exchange.

Forecasting the effect of COVID-19 on the S&P500
Arias-Calluari Karina,Alonso-Marroquin Fernando,Nattagh-Najafi Morteza,Harré Michael
arXiv

The outbreak of the novel coronavirus (COVID-19) has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history. However, in the past we have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor's 500 (S&P500) index will reach a minimum before rising again in the not-too-distant future. Here we present four forecast models of the S&P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA. The decline and recovery in the index is estimated for the following three months. The forecast is a projection of a prediction with fluctuations described by $q$-gaussian distributions. Our forecast was made on the premise that: (a) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S&P500 over the last 24 years.

Household Consumption, Household Indebtedness, and Inequality in Turkey: A Microeconometric Analysis
Albayrak, Ã–zlem
SSRN
This paper examines whether relative income and income inequality within reference groups affect household consumption. Using the explanations of consumption behavior based on Dusenberryâ€™s relative income hypothesis, we test if household consumption levels in Turkey are affected by the householdâ€™s relative position and inequality in the reference group between 2005â€"12 by employing cross-sectional household-level data. We find that household consumption is negatively related to the relative income indicator after controlling for absolute income, and positively related to the income inequality of the reference group, as the literature suggests. The paper also shows that household indebtedness has a positive impact on household consumption when inequality in the reference group and the relative position of households are controlled for. We confirm that the results are not sensitive to chosen relative income indicators and income inequality.

How Managerial Ownership and the Market for Corporate Control Can Improve Investment Timing
Guthrie, Graeme,Hobbs, Cameron
SSRN
We show how directors can set the level of CEO ownership to influence the investment-timing decisions of empire-building CEOs, and how boards' decisions are affected by the market for corporate control. The prospect of a future takeover means that CEOs with no ownership stake will over-invest in some types of projects and under-invest in others, but these problems are less severe when CEOs have an ownership stake. The value-maximizing level of CEO ownership depends on a firm's investment opportunities. When the takeover threat is weak, boards should not award CEOs any shares when the NPV from investment is large and positive, but should grant CEOs a substantial ownership stake when the NPV is negative. When the takeover threat is strong, boards should award CEOs a moderate ownership stake when the NPV is large and positive, but a small stake when the NPV is more modest.

IFRS 17 Insurance Contracts and Firm Value
Longoni, Philip
SSRN
I examine how IFRS 17 Insurance Contracts affects the firm value of insurers reporting under IFRS (â€œIFRS insurersâ€). Studying 427 insurersâ€™ stock returns around sixteen events affecting the chance that IFRS 17 passes, I find that the introduction of IFRS 17 is on average associated with negative abnormal returns for IFRS insurers, compared to insurers reporting under U.S. GAAP. Several robustness tests affirm my result, consistent with the hypothesis that IFRS 17 on average reduces firm value. OLS regressions indicate that the abnormal event returns correlate negatively with insurer size, suggesting that IFRS 17 is disproportionately costly for large insurers, potentially due to high implementation costs. Insurersâ€™ book-to-market ratio negatively correlates with the abnormal event returns, which indicates that a lack of growth opportunities exacerbates the negative returns associated with the introduction of IFRS 17.

Information Percolation and Informed Short Selling: Evidence from Earnings Announcements
Christophe, Stephen E.,Hsieh, Jim
SSRN
This study examines short selling in stocks of firms that reveal partial earnings-related information prior to their eventual earnings announcements (EA). By decomposing short selling into two components where the first corresponds to the final partial earnings disclosure and the second captures the subsequent incremental short selling until just before the EA, we estimate that the relative informativeness of shorting activity based on public partial versus private information accounts for approximately 80% and 20%, respectively, of the short selling-related decrease in the EA return. Importantly, the negative return predictability of short selling significantly increases the longer a firm implicitly delays its EA. Further, our evidence indicates time-varying short-sale constraints, ineffective following the release of partial information but rising markedly just prior to the EA. The overall findings support the proposition that short sellers are skilled investors who profit from both public partial and private information. The informativeness of short selling, however, depends critically on the efficacy of short-sale constraints.

Inside the Mind of a Stock Market Crash
Giglio, Stefano,Maggiori, Matteo,Stroebel, Johannes,Utkus, Stephen P.
SSRN
We analyze how investor expectations about economic growth and stock returns changed dur- ing the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.

Institutional Sector Cassifier, a Machine Learning Approach
Massaro, Paolo,Vannini, Ilaria,Giudice, Oliver
SSRN
We implement machine learning techniques to obtain an automatic classification by sector of economic activity of the Italian companies recorded in the Bank of Italy Entities Register. To this end, first we extract a sample of correctly classified corporations from the universe of Italian companies. Second, we select a set of features that are related to the sector of economic activity code and use these to implement supervised approaches to infer output predictions. We choose a multi-step approach based on the hierarchical structure of the sector classification. Because of the imbalance in the target classes, at each step, we first apply two resampling procedures â€" random oversampling and the Synthetic Minority Over-sampling Technique â€" to get a more balanced training set. Then, we fit Gradient Boosting and Support Vector Machine models. Overall, the performance of our multi-step classifier yields very reliable predictions of the sector code. This approach can be employed to make the whole classification process more efficient by reducing the area of manual intervention.

Introducere Ã®n finanÅ£ele comportamentale, Partea a doua (An Introduction to Behavioral Finance, Part 2)
Dumitriu, Ramona,Stefanescu, Razvan
SSRN
Romanian Abstract: AceastÄƒ lucrare abordeazÄƒ modalitÄƒÅ£ile prin care indivizii ÅŸi organizaÅ£iile fac faÅ£Äƒ variatelor riscuri asociate deciziilor financiare. PercepÅ£iile ÅŸi atitudinile faÅ£Äƒ de risc ale managerilor pot fi influenÅ£ate de complexitatea mediului financiar, dar ÅŸi de posibilitatea de a utiliza unele instrumente ce pot furniza reacÅ£ii eficace ÅŸi rapide la schimbÄƒrile acestui mediu. ÃŽn practicÄƒ, managerii trateazÄƒ riscurile din deciziile financiare Ã®n diferite moduri. Multe dintre acestea utilizeazÄƒ metode riguroase bazate pe distribuÅ£ii probabilistice, aÅŸa cum recomandÄƒ unele modele importante ale finanÅ£elor tradiÅ£ionale. TotuÅŸi, dupÄƒ cum au relevat unele studii din domeniul abordÄƒrilor descriptive ale proceselor decizionale, sunt ÅŸi manageri, Ã®n special cei care nu au Ã®ncredere Ã®n probabilitÄƒÅ£i sau care nu le Ã®nÅ£eleg, ce preferÄƒ metode mult mai rudimentare de a face faÅ£Äƒ riscurilor. Comportamentul acestora poate fi o provocare pentru raÅ£ionalitatea deciziilor financiare.English Abstract: This paper approaches the ways in which the individuals and organizations deal with various risks associated to the financial decisions. The managersâ€™ perceptions and attitudes to the risks could be influenced by the complexity of the financial environment, but also by the possibility to use some instruments that could provide efficient and rapid reactions to the changes from this environment. In fact, the managers treat risks from the financial decisions in several ways. Many of them use rigorous methods based on the probability distributions as some important models of the traditional finance recommend. However, as some studies from the field of descriptive approaches to decision making revealed, there are also managers, especially those who donâ€™t understand or donâ€™t trust the probabilities, who prefer much more rudimentary ways to deal with risks. Their behaviors could be a challenge for the financial decisions rationality.

Investor Inattention and Managerial Opportunism â€" Evidence from Mergers and Acquisitions
SSRN
We use the unique setting of Malaysian mergers and acquisitions (M&A) activity to test the effect of market-wide investor inattention when managers are attentive. During Ramadan, mainly Muslim investors are likely to be distracted while mainly non-Muslim managers are likely to be attentive and potentially opportunistic. We formulate hypotheses to disentangle opportunistic and non-opportunistic manager behavior. Our results support the distracted investors-opportunistic managers hypothesis. We find that during Ramadan:(1) aggregate M&A activity is higher, (2) the probability of deal completion is higher, (3) time to completion is lower, (4) managers fail to listen to the market, (5) announcement returns are higher and (6) post merger performance is lower.

Le informazioni statistiche sul rischio di credito della Banca d'Italia e la nuova rilevazione AnaCredit (Credit Risk Statistical Information of the Bank of Italy and the New AnaCredit Data Collection)
Di Noia, Maria,Moretti, Davide
SSRN
Italian Abstract Il lavoro fornisce un quadro delle informazioni statistiche sul rischio di credito gestite dalla Banca dâ€™Italia. Lâ€™Istituto ha una lunga tradizione nella raccolta sistematica e dettagliata di informazioni dal sistema finanziario. Con particolare riferimento a quelle sul rischio di credito, la Banca dâ€™Italia, sin dallâ€™inizio degli anni sessanta, gestisce il sistema di centralizzazione dei rischi e piÃ¹ di recente ha introdotto nuove raccolte di dati sul rischio di credito per rispondere in primis a specifiche esigenze di vigilanza. In un contesto internazionale in cui, anche per effetto dellâ€™aumento della domanda di dati in seguito alla crisi finanziaria globale dello scorso decennio, tendono ad affermarsi framework di raccolta di informazioni sempre piÃ¹ granulari e armonizzati tra paesi, lâ€™introduzione della rilevazione AnaCredit puÃ² costituire il volano per la diffusione di un â€œnuovo paradigmaâ€ di raccolta basato su tali caratteristiche, nonchÃ© un forte incentivo per le AutoritÃ  nazionali ed europee verso la razionalizzazione dellâ€™onere segnaletico a carico degli intermediari segnalanti.English Abstract This paper provides an overview of the statistical information on credit risk managed by the Bank of Italy. The Institute has a long tradition of systematic and detailed financial data collection. With specific reference to information on credit risk, the Bank of Italy has managed the Central Credit Register since the early 1960s and, more recently, introduced new data collections on credit risk primarily in response to specific supervisory needs. At international level, also due to the increasing demand for data subsequent to the global financial crisis of the last decade, the data and data collection systems of euro-area countries have become increasingly harmonized and granular. In this context, the introduction of the AnaCredit framework can be considered the main driver behind the spread of this â€˜new paradigmâ€™ of data collection, and may also prove to be a strong incentive for national and European Authorities to rationalize the reporting burden for reporting agents.

Measuring the Performance of Bank Loans Under Basel II / III and IFRS 9 / CECL
Engelmann, Bernd,Pham, Ha
SSRN
In the last two decades, both internal and external risk management of banks has undergone significant developments. Substantial investments into data collection have been made and this data is used for estimating internal credit risk models. The resulting risk parameters are required for various regulatory purposes. Banking supervision encourages banks to use a risk-based approach for computing minimum regulatory capital. Accounting rules have been tightened requiring more timely loss reserves for impaired loans. In this article, we propose a comprehensive scheme for calculating the profitability of a loan that could be used both for setting risk-based interest rates when originating a loan and for accurately determining the profitability of existing clients. The scheme utilizes the credit models developed for regulatory purposes and takes the impact of regulation on loan performance into account. We show that accounting loan loss provisions cannot be applied in a performance measurement scheme because they do not reflect true economic loss. In addition, we demonstrate that it is crucial to measure loan performance over the full life cycle of a loan. Restricting profitability measurement to a time horizon of one year as often observed in practice could be misleading.

Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework
Benjamin Avanzi,Greg Taylor,Bernard Wong,Alan Xian
arXiv

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed event arrivals to be naturally incorporated in a tractable manner, while the hidden Markov chain captures the effect of unobservable drivers of the data. In addition to increases in accuracy and interpretability, this method supplements analysis of the latent factors. Further, this procedure naturally incorporates data features such as over-dispersion and autocorrelation. Additional insights can be generated to assist analysis, including a procedure for iterative model improvement.

Implementation difficulties are also addressed with a focus on dealing with large data sets, where latent models are especially advantageous due the large number of observations facilitating identification of hidden factors. Namely, computational issues such as numerical underflow and high processing cost arise in this context and in this paper, we produce procedures to overcome these problems.

This modelling framework is demonstrated using a large insurance data set to illustrate theoretical, practical and computational contributions and an empirical comparison to other count models highlight the advantages of the proposed approach.

Mutual Funds' Performance: The Role of Distribution Networks and Bank Affiliation
Albareto, Giorgio,Cardillo, Andrea,Marinelli, Giuseppe,Hamaui, Andrea
SSRN
The paper investigates how the characteristics of the distribution network and the affiliation to a banking group affect mutual funds performance exploiting a unique dataset with extremely detailed information on fundsâ€™ portfolios and bank-issuer relationships for the period 2006-2017. We find that bank-affiliated mutual funds underperform independent ones. The structure of the distribution channels is a key-factor affecting mutual funds' performance: when bank platforms become by far the prevalent channel for the distribution of fundsâ€™ shares, asset management companies are captured by banks. As for bank affiliation, results show a positive bias of bank-controlled mutual funds towards securities issued by their own banking group clients (of the lending and investment banking divisions) and by institutions belonging to their own banking group; this last bias is exacerbated for mutual funds belonging to undercapitalized banking groups. The structure of the distribution channels explains two thirds of bank-affiliated mutual funds underperformance, whereas investment biases explain one fourth of the observed differential in returns with independent mutual funds.

No arbitrage in insurance and the QP-rule
Philippe Artzner,Karl-Theodor Eisele,Thorsten Schmidt
arXiv

This paper is an attempt to study fundamentally the valuation of insurance contracts. We start from the observation that insurance contracts are inherently linked to financial markets, be it via interest rates, or -- as in hybrid products, equity-linked life insurance and variable annuities -- directly to stocks or indices. By defining portfolio strategies on an insurance portfolio and combining them with financial trading strategies we arrive at the notion of insurance-finance arbitrage (IFA). A fundamental theorem provides two sufficient conditions for presence or absence of IFA, respectively. For the first one it utilizes the conditional law of large numbers and risk-neutral valuation. As a key result we obtain a simple valuation rule, called QP-rule, which is market consistent and excludes IFA.

Utilizing the theory of enlargements of filtrations we construct a tractable framework for general valuation results, working under weak assumptions.

The generality of the approach allows to incorporate many important aspects, like mortality risk or dependence of mortality and stock markets which is of utmost importance in the recent corona crisis. For practical applications, we provide an affine formulation which leads to explicit valuation formulas for a large class of hybrid products.

Non-concave expected utility optimization with uncertain time horizon: an application to participating life insurance contracts
arXiv

We examine an expected utility maximization problem with an uncertain time horizon, a classical example being a life insurance contract due at the time of death. Life insurance contracts usually have an option-like form leading to a non-concave optimization problem. We consider general utility functions and give necessary and sufficient optimality conditions, deriving a computationally tractable algorithm. A numerical study is done to illustrate our findings. Our analysis suggests that the possible occurrence of a premature stopping leads to a reduced performance of the optimal portfolio compared to a setting without time-horizon uncertainty.

On bid and ask side-specific tick sizes
Baldacci Bastien,Bergault Philippe,Derchu Joffrey,Rosenbaum Mathieu
arXiv

The tick size, which is the smallest increment between two consecutive prices for a given asset, is a key parameter of market microstructure. In particular, the behavior of high frequency market makers is highly related to its value. We take the point of view of an exchange and investigate the relevance of having different tick sizes on the bid and ask sides of the order book. Using an approach based on the model with uncertainty zones, we show that when side-specific tick sizes are suitably chosen, it enables the exchange to improve the quality of liquidity provision.

On the Bound of Cumulative Return in Trading Series and the Verification Using Technical Trading Rules
Can Yang,Junjie Zhai,Helong Li
arXiv

Although there is a wide use of technical trading rules in stock markets, the profitability of them still remains controversial. This paper first presents and proves the upper bound of cumulative return, and then introduces many of conventional technical trading rules. Furthermore, with the help of bootstrap methodology, we investigate the profitability of technical trading rules on different international stock markets, including developed markets and emerging markets. At last, the results show that the technical trading rules are hard to beat the market, and even less profitable than the random trading strategy.

Optimal Investing after Retirement Under Time-Varying Risk Capacity Constraint
Weidong Tian,Zimu Zhu
arXiv

This paper explores an optimal investing problem for a retiree facing longevity risk and living standard risk. We formulate the optimal investing problem as an optimal portfolio choice problem under a time-varying risk capacity constraint. Under the specific condition on model parameters, we show that the value function is a $C^2$ solution of the HJB equation and derive the optimal investment strategy in terms of second-order ordinary differential equations. The optimal portfolio is nearly neutral to the stock market movement if the portfolio's value is at a sufficiently high level; but, if the portfolio is not worth enough to sustain the retirement spending, the retiree actively invests in the stock market for the higher expected return. In addition, we solve an optimal portfolio choice problem under a leverage constraint and show that the optimal portfolio would lose significantly in stressed markets. This paper shows that the time-varying risk capacity constraint has important implications for asset allocation in retirement.

Optimal periodic dividend strategies for spectrally positive L\'evy risk processes with fixed transaction costs
Benjamin Avanzi,Hayden Lau,Bernard Wong
arXiv

We consider the general class of spectrally positive L\'evy risk processes, which are appropriate for businesses with continuous expenses and lump sum gains whose timing and sizes are stochastic. Motivated by the fact that dividends cannot be paid at any time in real life, we study $\textit{periodic}$ dividend strategies whereby dividend decisions are made according to a separate arrival process.

In this paper, we investigate the impact of fixed transaction costs on the optimal periodic dividend strategy, and show that a periodic $(b_u,b_l)$ strategy is optimal when decision times arrive according to an independent Poisson process. Such a strategy leads to lump sum dividends that bring the surplus back to $b_l$ as long as it is no less than $b_u$ at a dividend decision time. The expected present value of dividends (net of transaction costs) is provided explicitly with the help of scale functions. Results are illustrated.

Pricing and Hedging Performance on Pegged FX Markets Based on a Regime Switching Model
Samuel Drapeau,Yunbo Zhang
arXiv

This paper investigates the hedging performance of pegged foreign exchange market in a regime switching (RS) model introduced in a recent paper by Drapeau, Wang and Wang (2019). We compare two prices, an exact solution and first order approximation and provide the bounds for the error. We provide exact RS delta, approximated RS delta as well as mean variance hedging strategies for this specific model and compare their performance. To improve the efficiency of the pricing and calibration procedure, the Fourier approach of this regime-switching model is developed in our work. It turns out that: 1 -- the calibration of the volatility surface with this regime switching model outperforms on real data the classical SABR model; 2 -- the Fourier approach is significantly faster than the direct approach; 3 -- in terms of hedging, the approximated RS delta hedge is a viable alternative to the exact RS delta hedge while significantly faster.

Public Credit Guarantees and Financial Additionalities Across SME Risk Classes
Ciani, Emanuele,Gallo, Marco,Rotondi, Zeno
SSRN
In this paper we study the functioning of the Italian public guarantee fund (â€œFondo Centrale di Garanziaâ€, FCG) for small and medium enterprises (SMEs). Using an instrumental variable strategy, based on FCG eligibility, we investigate whether the guarantee generated additional loans and/or lower interest rates for SMEs. Unlike previous literature, by focusing on the lending activity of a single large Italian lender, we control for the probability of default as assessed by the bankâ€™s internal rating model, and we examine whether the effects of the guarantee differ across firms belonging to different classes of risk. We find that guaranteed firms receive an additional amount of credit equal to 7-8 percent of their total banking exposure. We also estimate a reduction of about 50 basis points in interest rates applied to term loans granted to guaranteed firms. The effects on credit availability are concentrated in the intermediate class of solvent firms, i.e. those that are neither too safe nor too risky. Conversely, interest rate effects are present in all classes, except for the least risky firms. Finally, we observe a stronger impact of the guarantee for solvent firms with a longer relationship with the bank, questioning the ability of very young firms to reduce financial frictions.

Quality Checks on Granular Banking Data: An Experimental Approach Based on Machine Learning?
Zambuto, Fabio,Buzzi, Maria Rosaria,Costanzo, Giuseppe,Di Lucido, Marco,La Ganga, Barbara,Maddaloni, Pasquale,Papale, Fabio,Svezia, Emiliano
SSRN
We propose a new methodology, based on machine learning algorithms, for the automatic detection of outliers in the data that banks report to the Bank of Italy. Our analysis focuses on granular data gathered within the statistical data collection on payment services, in which the lack of strong ex ante deterministic relationships among the collected variables makes standard diagnostic approaches less powerful. Quantile regression forests are used to derive a region of acceptance for the targeted information. For a given level of probability, plausibility thresholds are obtained on the basis of individual bank characteristics and are automatically updated as new data are reported. The approach was applied to validate semi-annual data on debit card issuance received from reporting agents between December 2016 and June 2018. The algorithm was trained with data reported in previous periods and tested by cross-checking the identified outliers with the reporting agents. The method made it possible to detect, with a high level of precision in term of false positives, new outliers that had not been detected using the standard procedures.

Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation
Samudra Dasgupta,Arnab Banerjee
arXiv

The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum annealing algorithm in QUBO form for a dynamic asset allocation problem using expected shortfall constraint. It was motivated by the need to refine the current quantum algorithms for Markowitz type problems which are academically interesting but not useful for practitioners. The algorithm is dynamic and the risk target emerges naturally from the market volatility. Moreover, it avoids complicated statistics like generalized pareto distribution. It translates the problem into qubit form suitable for implementation by a quantum annealer like D-Wave. Such QUBO algorithms are expected to be solved faster using quantum annealing systems than any classical algorithm using classical computer (but yet to be demonstrated at scale).

Repo Market and Leverage Ratio in the Euro Area
Pasqualone, Filippo,Baldo, Luca,Scalia, Antonio
SSRN
This paper provides new evidence on the effect of the leverage ratio (LR) on repo market activity in the euro area. The share of trades with central counterparties has increased in recent years as a result of greater regulatory efficiency. After controlling for factors that may affect participation in the repo market, banks are found to exert market power towards non-bank financial institutions by applying lower rates and larger bid-ask spreads. While there is a permanent rate differential between transactions conducted via CCPs â€" which can easily be netted for LR purposes - and those with non-banks, on average this differential and the bid-ask spread do not increase at quarter-end. The widening of the bid-ask spread at year-end is sizeable, but this is not necessarily due to the LR, since other important factors enter into play. This evidence lessens the concern that the additional LR reporting and disclosure requirements based on daily averages, which will take effect on June 2021, might cause a contraction in repo volume and greater rate dispersion.

Rocking the Boat: How Relative Performance Evaluation Affects Corporate Risk Taking
Do, Truc (Peter),Zhang, Huai,Zuo, Luo
SSRN
We argue that relative performance evaluation (RPE) contracts introduce a tournament among the focal firm and peer firms. We test whether a firmâ€™s riskiness is altered by its CEOâ€™s incentive to win the tournament. We find that a firm with an interim losing CEO takes more risk in the remainder of the tournament period than a firm with an interim winning CEO. This effect is stronger when the interim period is closer to the end of the evaluation period and when winning the competition is more important to the CEO. Further analysis of the Sharpe ratio reveals that the increased risk as a result of the tournament incentive reflects a poor risk-return trade-off, an unintended consequence of RPE. In summary, our results suggest that a firmâ€™s risk is determined by the incentive of its management to outperform peer firms in an RPE contract.

Sentiment Risk Premia in the Cross-Section of Global Equity
FÃ¼ss, Roland,Guidolin, Massimo ,Koeppel, Christian
SSRN
This paper introduces a new sentiment-augmented asset pricing model in order to provide a comprehensive understanding of the role of this new type of risk factors. We find that news and social media search-based indicators are significantly related to excess returns of international equity indices. Adding sentiment factors to both classical and more recent pricing models leads to a significant increase in model performance. Following the Fama-MacBeth procedure, our modified pricing model obtains positive estimates of the risk premium for positive sentiment, while being negative for negative sentiment. Our results contribute to the explanation of global cross-sectional average excess returns and are robust for fundamental factors, momentum, idiosyncratic volatility, skewness, kurtosis, and international currencies.

Servizi bancari online e dinamica degli sportelli bancari (Online Banking Services and Branch Networks)
Carmignani, Amanda,Manile, Marco,Orame, Andrea,Pagnini, Marcello
SSRN
Italian Abstract: Il lavoro analizza lâ€™impatto che lâ€™eterogeneitÃ  nel ricorso da parte della clientela bancaria ai servizi on line nei vari mercati locali ha avuto sulla successiva dinamica degli sportelli bancari. Lâ€™analisi, relativa al periodo 2012-15, si concentra sulla clientela retail, il segmento maggiormente interessato dallo sviluppo dei canali digitali. I principali risultati indicano che le chiusure degli sportelli sono state piÃ¹ intense per le banche e le province ove maggiore era la diffusione presso la clientela di servizi bancari online.English Abstract: Notwithstanding internet banking is now widely used by retail customers, little is known about its effect on the banking industry. In this paper we study how internet banking relates to branching policies in Italian local credit markets. Focusing on the period 2012-2015, we show that branch closures were more intense for those local markets and banks where the diffusion of digital banking services was higher.

Krueckeberg, Sinan
SSRN

Smart Money Flows Into ETFs and Investor Sentiment
SSRN
ETFs attract a larger proportion of institutional investors than do the underlying markets. The price of an ETF will deviate from the price of the underlying, if institutional investors are less prone to investor sentiment-driven mispricing, than are retail investors. We employ a unique identification strategy to differentiate between the response of liquidity traders, long-term and short-term arbitrageurs to sentiment measures. Liquidity traders respond positively to sentiment, which results in weaker returns in the following 3- to 6-month period. Long-term arbitrageurs who go long the ETF, and short the underlying asset benefit from this mid-term return reversal. Finally, short-term arbitrageurs respond negatively to the Baker and Wurgler (2006) sentiment measure. Their actions are profitable in the long-run as ETFs that experience higher short-term arbitrage activity experience weaker reversals.

Social Proximity to Capital: Implications for Investors and Firms
Kuchler, Theresa,Li, Yan,Peng, Lin,Stroebel, Johannes,Zhou, Dexin
SSRN
We use social network data from Facebook to show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties. This effect of social proximity on investment behavior is distinct from the effect of geographic proximity. Social connections have the largest influence on investments of small investors with concentrated holdings as well as on investments in firms with a low market capitalization and little analyst coverage. We also find that the response of investment decisions to social connectedness affects equilibrium capital market outcomes: firms in locations with stronger social ties to places with substantial institutional capital have higher institutional ownership, higher valuations, and higher liquidity. These effects of social proximity to capital on capital market outcomes are largest for small firms with little analyst coverage. We find no evidence that investors generate differential returns from investments in locations to which they are socially connected. Our results suggest that the social structure of regions affects firms' access to capital and contributes to geographic differences in economic outcomes.

Stock Prices and Risk-free Rate: An 'Internal Rationality' Approach
Zhang, Tongbin
SSRN
The co-movement between stock and short-term bond markets in US is weak in terms of the correlation between asset prices, variance decomposition and impulse response. It is essential to investors and policy makers to understand it, especially when several well-known asset pricing models imply a much stronger relationship than empirically observed. To explain this inconsistency, this paper presents a model with "Internally Rational" agents, who optimally update their subjective beliefs on stock prices. Compared with risk-free rate, agents' subjective beliefs are essential in generating stock market volatility. Quantitatively our model can jointly match the asset markets moments and the weak co-movement.

Tackling False Positives in Business Research: A Statistical Toolbox with Applications
Kim, Jae H.
SSRN
Serious concerns have been raised that false positive findings are widespread in empirical research in business disciplines. This is largely because researchers almost exclusively adopt the 'p‐value less than 0.05' criterion for statistical significance; and they are often not fully aware of large‐sample biases which can potentially mislead their research outcomes. This paper proposes that a statistical toolbox (rather than a single hammer) be used in empirical research, which offers researchers a range of statistical instruments, including a range of alternatives to the p‐value criterion such as the Bayesian methods, optimal significance level, sample size selection, equivalence testing and exploratory data analyses. It is found that the positive results obtained under the p‐value criterion cannot stand, when the toolbox is applied to three notable studies in finance.

The Carbon Footprint of Italian Loans
Faiella, Ivan,Lavecchia, Luciano
SSRN
This article presents a first insight on the carbon content of business loans in Italy, using three different methods to identify the sectors more exposed to transition risks. According to our estimates, the loansâ€™ carbon footprint of Italian banks is small compared to other European peers and the outstanding loans exposed to transition risk can be estimated in a range between 37 and 53 percent of total loans as of 2018 data, according to the methodology used. This information can be used as a starting point to evaluate, within a climate-scenario framework, how different climate policies influence the stability of the banking sector.

The Climate Risk for Finance in Italy
Faiella, Ivan,Malvolti, Danila
SSRN
The increasing attention paid to the possible consequences of climate change for the financial sector has strengthened international cooperation on green finance, with initiatives from both the industry and the institutions. International surveys show that so far there has been no adequate growth in awareness of the risks linked to climate change and the opportunities linked to the transition towards a low carbon economy. Evidence acquired on Climate-Related Financial Risk (CRFR) disclosure in Italy has confirmed the same conclusions. We have therefore identified three steps with the aim of encouraging financial institutions to take CRFR into account in their corporate risk management strategies: 1) create a information hub to gather the information required for assessing the CRFR; 2) compile a list of the information not yet available; 3) define standard methodologies that allow the climate scenarios to be part of the decision-making processes of financial institutions.

The Drivers of Cyber Risk
Aldasoro, IÃ±aki,Gambacorta, Leonardo,Giudici, Paolo,Leach, Thomas
SSRN
Cyber incidents are becoming more sophisticated and their costs difficult to quantify. Using a unique database of more than 100,000 cyber events across sectors, we document the characteristics of cyber incidents. Cyber costs are higher for larger firms and for incidents that impact several organisations simultaneously. The financial sector is exposed to a larger number of cyber attacks but suffers lower costs, on average, thanks to proportionately greater investment in information technology (IT) security. The use of cloud services is associated with lower costs, especially when cyber incidents are relatively small. As cloud providers become systemically important, cloud dependence is likely to increase tail risks. Crypto-related activities, which are largely unregulated, are particularly vulnerable to cyber attacks.

The Impact of COVID-19 on Exchange Rate Volatility: Evidence Through GARCH Model
Benzid, Lamia,Chebbi, Kaouther
SSRN
This paper employs a GARCH (1,1) model to investigate the impact of COVID-19 cases and related deaths in the US exchange rate volatility. Results show that an increase of the number of cases and the deaths (both in logs) in the US has a positive impact on the USD/EUR, USD/Yuan and USD/LivreSterling. Moreover, this paper uses GARCH (1,1) model to forecast the daily volatility of three exchange rates series with respect to American dollar. These results are useful for anyone needing forecasts of exchange rate futures volatility.

The Impact of Monetary Policy Announcements on the Volatility of Australian Banking Stock Prices
Atwood, Nathan J.,Karpavicius, Sigitas
SSRN
This paper examines the effects of monetary policy announcements, made by the Reserve Bank of Australia, on the stock price volatility of Australian commercial banks. The results suggest that the announcements of the new target cash rate increase the volatility of Australian banking stocks. In contrast, the release of the explanatory meeting minutes is not associated with any dominant impact on stock price volatility. We find that the volatility is greater during the Global Financial Crisis (GFC). However, both types of eventsâ€"the announcements of the target cash rate and the releases of the explanatory meeting minutesâ€"are not generally associated with higher volatility of banking stock prices during the GFC.

The Shortâ€Time Behavior of Vixâ€Implied Volatilities in a Multifactor Stochastic Volatility Framework
Barletta, Andrea,Nicolato, Elisa,Pagliarani, Stefano
SSRN
We consider a modeling setup where the volatility index (VIX) dynamics are explicitly computable as a smooth transformation of a purely diffusive, multidimensional Markov process. The framework is general enough to embed many popular stochastic volatility models. We develop closed‐form expansions and sharp error bounds for VIX futures, options, and implied volatilities. In particular, we derive exact asymptotic results for VIX‐implied volatilities, and their sensitivities, in the joint limit of short time‐to‐maturity and small log‐moneyness. The expansions obtained are explicit based on elementary functions and they neatly uncover how the VIX skew depends on the specific choice of the volatility and the vol‐of‐vol processes. Our results are based on perturbation techniques applied to the infinitesimal generator of the underlying process. This methodology has previously been adopted to derive approximations of equity (SPX) options. However, the generalizations needed to cover the case of VIX options are by no means straightforward as the dynamics of the underlying VIX futures are not explicitly known. To illustrate the accuracy of our technique, we provide numerical implementations for a selection of model specifications.

The What, When and Where of Limit Order Books
Bleher, Johannes,Dimpfl, Thomas,Bleher, Michael
SSRN
We model the limit order book (LOB) as a continuous Markov process and develop an algebra to describe its dynamics based on the fundamental events of the book: order arrivals and cancellations. We show how all observables (prices, returns, and liquidity measures) are governed by the same variables which also drive arrival and cancellation rates.The sensitivity of our model is evaluated in a simulation study and an empirical analysis. We estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts on several frequencies. The in-sample results based on contemporaneous information suggest that our model describes up to 90% of the variation of close-to-close returns, the adjusted $R^2$ still ranges at around 80%. In the more realistic setting where only past information enters the model, we still observe an adjusted $R^2$ in the range of 15%. The direction of the next return can be predicted, out-of-sample, with an accuracy of over 75\% for short time horizons below 10 minutes. Out-of-sample, on average, we obtain $R^2$ values for the Mincer-Zarnowitz regression of around 2-3% and an $RMSPE$ that is 10 times lower than values documented in the literature.

Three Machine Learning Solutions to the Bias-Variance Dilemma (Seminar Slides)
SSRN
Classical statistics (e.g., Econometrics) relies on assumptions that are often unrealistic in finance. Two critical assumptions are that the researcher has perfect knowledge about the modelâ€™s specification, and that the researcher knows all the variables involved in a phenomenon (including all interaction effects). When those assumptions are incorrect, classical estimators are not guaranteed to be unbiased, or to be the most efficient among the unbiased, leading to poor performance.In this presentation we explore why machine learning algorithms are generally more appropriate for financial datasets, how they outperform classical estimators, and how they solve the bias-variance dilemma.

Trading Algorithms with Learning in Latent Alpha Models
Casgrain, Philippe,Jaimungal, Sebastian
SSRN
Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyzes how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem. We provide a verification theorem, and a methodology for calibrating the model by deriving a variation of the expectation–maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it to strategies that ignore learning in the latent factors. We also provide calibration results for a particular model using Intel Corporation stock as an example.

Understanding the Evolution in Mortgage Pricing Strategy: Do Neighborhood Factors Matter?
Agarwal, Sumit,Deng, Yongheng ,He, Jia,Wang, Yonglin
SSRN
This paper analyzes mortgage lenders' strategy in pricing loans intended for sale to prime market. Using the Single-Family Loan-Level Data set obtained from Freddie Mac, we find evidence that lenders adjust their pricing behavior according to the changing lending environment. In particular, conditional on a rich set of loan and borrower characteristics, lenders heavily base the pricing decision on some unexplored neighborhood heterogeneities that cannot be explained by observed macro- and socio-economic factors in most of the year cohorts. Such regional disparities in mortgage rates attenuate remarkably in the subprime securitization boom, during which loan originators begin to shift their reliance from borrowerâ€™s neighborhood to variables reported to investors in setting mortgage rates. By contrast, the regional variations in mortgage rates become particularly pronounced in the run-up and the aftermath of the expansion of subprime securitization, when mortgage rates depend largely on borrowerâ€™s neighborhood and the reliance on reported variables is alleviated. These findings are indicative of a â€œspillover effectâ€ of the differential pricing strategy from the subprime market to prime market in the sense that lender's reliance on borrower's neighborhood and hard information temporally complement each other. Moreover, the unexplored regional factors endowed with significant power in explaining the variations in mortgage rates are suggestive of regional-based discrimination in loan price.

Universal Ownership Theory and the Double Hermeneutic: Norms and feedback loops in the Age of COVID-19
Quigley, Ellen
SSRN
The COVID-19 crisis has laid bare the preexisting conditions of modern society: inequality, workersâ€™ rights violations, air pollution, and biodiversity loss, to name a few. All of these have been revealed as systemic risks that threaten the health and wellbeing of humans and the planet on which we depend. For universal owners, this is their belief made manifest â€" proof that externalities generated in one part of the system can add outsized costs to the rest. What has changed is that this concept â€" of system-wide threats that can only be addressed at a system-wide level â€" has become much more widely known and understood by politicians and the public. For good or ill, crises present opportunities; in this metacrisis, systemic risks, externalities, and inequalities have been exposed, and with that comes a chance to counter the attendant harms. Through an exploration of the relevant evidence and theory, this paper lays out some of the systemic risks the COVID-19 crisis has revealed; identifies a number of shifting norms that are already in evidence; quantifies the opportunity that unprecedented fiscal stimulus measures present in countering these systemic risks; and explores the theory and practice of norm-building, feedback loops, and the double hermeneutic. It ends by proposing a set of five experiments for asset owners to test whether universal ownership theory could become self-fulfilling â€" and therefore whether universal owners might be capable of mitigating the systemic risks this crisis has revealed.

Using Machine Learning to Forecast Future Earnings
Xinyue Cui,Zhaoyu Xu,Yue Zhou
arXiv

In this essay, we have comprehensively evaluated the feasibility and suitability of adopting the Machine Learning Models on the forecast of corporation fundamentals (i.e. the earnings), where the prediction results of our method have been thoroughly compared with both analysts' consensus estimation and traditional statistical models. As a result, our model has already been proved to be capable of serving as a favorable auxiliary tool for analysts to conduct better predictions on company fundamentals. Compared with previous traditional statistical models being widely adopted in the industry like Logistic Regression, our method has already achieved satisfactory advancement on both the prediction accuracy and speed. Meanwhile, we are also confident enough that there are still vast potentialities for this model to evolve, where we do hope that in the near future, the machine learning model could generate even better performances compared with professional analysts.

Using Network Interbank Contagion in Bank Default Prediction
Riccardo Doyle
arXiv

Interbank contagion can theoretically exacerbate losses in a financial system and lead to additional cascade defaults during downturn. In this paper we produce default analysis using both regression and neural network models to verify whether interbank contagion offers any predictive explanatory power on default events. We predict defaults of U.S. domiciled commercial banks in the first quarter of 2010 using data from the preceding four quarters. A number of established predictors (such as Tier 1 Capital Ratio and Return on Equity) are included alongside contagion to gauge if the latter adds significance. Based on this methodology, we conclude that interbank contagion is extremely explanatory in default prediction, often outperforming more established metrics, in both regression and neural network models. These findings have sizeable implications for the future use of interbank contagion as a variable of interest for stress testing, bank issued bond valuation and wider bank default prediction.

Valueâ€Atâ€Risk Bounds with Twoâ€Sided Dependence Information
Lux, Thibaut,RÃ¼schendorf, Ludger
SSRN
Value‐at‐Risk (VaR) bounds for aggregated risks have been derived in the literature in settings where, besides the marginal distributions of the individual risk factors, one‐sided bounds for the joint distribution or the copula of the risks are available. In applications, it turns out that these improved standard bounds on VaR tend to be too wide to be relevant for practical applications, especially when the number of risk factors is large or when the dependence restriction is not strong enough. In this paper, we develop a method to compute VaR bounds when besides the marginal distributions of the risk factors, two‐sided dependence information in form of an upper and a lower bound on the copula of the risk factors is available. The method is based on a relaxation of the exact dual bounds that we derive by means of the Monge–Kantorovich transportation duality. In several applications, we illustrate that two‐sided dependence information typically leads to strongly improved bounds on the VaR of aggregations.

Vestigial Tails: Floor Brokers at the Close in Modern Electronic Markets
Hu, Edwin,Murphy, Dermot
SSRN
Closing auctions determine daily closing prices and trillions of dollars of net asset values. We study closing auction market quality on NYSE and Nasdaq as measured by the accuracy of indicative closing auction prices, volume, and order imbalances. Closing auction market quality is significantly lower on NYSE compared to Nasdaq due to the existence of a second-stage auction period on NYSE which is exclusive to floor brokers. When NYSE closes its floor during the COVID-19 pandemic, we find that NYSE auction quality improves. We use exogenous shocks to impatient liquidity trader volume stemming from end-of-month portfolio rebalancing periods and "triple witching" days to show that closing auction market quality is worse on NYSE because impatient liquidity traders pool their orders in the first-stage auction period. Consistent with this view, NYSE auction quality deteriorates during our 2011-2018 sample period as passive investing overtakes active management.