Research articles for the 2021-03-29

A Comparative Evaluation of Predominant Deep Learning Quantified Stock Trading Strategies
Haohan Zhang

This study first reconstructs three deep learning powered stock trading models and their associated strategies that are representative of distinct approaches to the problem and established upon different aspects of the many theories evolved around deep learning. It then seeks to compare the performance of these strategies from different perspectives through trading simulations ran on three scenarios when the benchmarks are kept at historical low points for extended periods of time. The results show that in extremely adverse market climates, investment portfolios managed by deep learning powered algorithms are able to avert accumulated losses by generating return sequences that shift the constantly negative CSI 300 benchmark return upward. Among the three, the LSTM model's strategy yields the best performance when the benchmark sustains continued loss.

A Marginal Indemnity Function Approach to Optimal Reinsurance under the Vajda Condition
Boonen, Tim J.,Jiang, Wenjun
To manage the risk of insurance companies, a reinsurance transaction is among the myriad risk management mechanisms the top ranked choice. In this paper, we study the design of optimal reinsurance contracts within a risk measure minimization framework and subject to the Vajda condition. The Vajda condition requires the reinsurer to take an increasing proportion of the loss when it increases and therefore imposes constraints on the indemnity function. The distortion-risk-measure-based objective function is very generic, and allows for various constraints, an objective to minimize the risk-adjusted value of the insurer's liability, and for heterogeneous beliefs regarding the distribution function of the underlying loss by the insurer and reinsurer. Under a mild condition, we propose a backward-forward optimization method that is based on a marginal indemnification function formulation. To show the applicability and simplicity of our strategy, we provide three concrete examples with the VaR: one with the risk-adjusted value of the insurer's liability, one with an objective function that follows from imposing Pareto-optimality, and one with heterogeneous beliefs.

A New Approach to Minimal Variance Hedging of European Options (Part 2: The Pure-Jump Case)
Hess, Markus
This paper addresses the following question: How can a financial institution, which has issued a European option, optimally hedge the payoff of this option by investing into the underlying stock and into the option itself? Here, optimality is measured in terms of minimal variance and the associated optimal hedging portfolio is derived by a sufficient stochastic maximum principle. We further obtain a pricing formula for general European options by an application of Fourier transform methods and deduce the time dynamics of the stochastic option price process. We finally apply our theoretical results to several practical examples.

Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models
Jaydip Sen,Sidra Mehtab

Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem. The advocates of the classical efficient market hypothesis affirm that it is impossible to accurately predict the future prices in an efficiently operating market due to the stochastic nature of the stock price variables. However, numerous propositions exist in the literature with varying degrees of sophistication and complexity that illustrate how algorithms and models can be designed for making efficient, accurate, and robust predictions of stock prices. We present a gamut of ten deep learning models of regression for precise and robust prediction of the future prices of the stock of a critical company in the auto sector of India. Using a very granular stock price collected at 5 minutes intervals, we train the models based on the records from 31st Dec, 2012 to 27th Dec, 2013. The testing of the models is done using records from 30th Dec, 2013 to 9th Jan 2015. We explain the design principles of the models and analyze the results of their performance based on accuracy in forecasting and speed of execution.

Allocating to green bonds
Swinkels, Laurens
Green bonds are about a decade old financial instrument with cash flows earmarked to improve the environment or combat climate change. We show the spectacular growth of the asset class over time, but note that it is currently still less than 1% of the entire fixed income market. The composition of the asset class has changed considerably over time. At the start, it were mainly very safe supranational institutions issuing in a variety of currencies with relatively short maturities. They were followed by corporates, especially utilities, and more recently also governments have started issuing green bonds. This change of composition leads us to conclude that historical data from before 2015 is less representative for the future. Our returns- and characteristics-based analyses show that an investor allocating to green bonds should finance this from an aggregate fixed income allocation to reduce the impact on the risk and return characteristics of the existing portfolio.

An Agent-Based Modelling Approach to Brain Drain
Furkan Gürsoy,Bertan Badur

The phenomenon of brain drain, that is the emigration of highly skilled people, has many undesirable effects, particularly for developing countries. In this study, an agent-based model is developed to understand the dynamics of such emigration. We hypothesise that skilled people's emigration decisions are based on several factors including the overall economic and social difference between the home and host countries, people's ability and capacity to obtain good jobs and start a life abroad, and the barriers of moving abroad. Furthermore, the social network of individuals also plays a significant role. The model is validated using qualitative and quantitative pattern matching with real-world observations. Sensitivity and uncertainty analyses are performed in addition to several scenario analyses. Linear and random forest response surface models are created to provide quick predictions on the number of emigrants as well as to understand the effect sizes of individual parameters. Overall, the study provides an abstract model where brain drain dynamics can be explored. Findings from the simulation outputs show that future socioeconomic state of the country is more important than the current state, lack of barriers results in a large number of emigrants, and network effects ensue compounding effects on emigration. Upon further development and customisation, future versions can assist in the decision-making of social policymakers regarding brain drain.

Analysis of systematic risk around firm-specific news in an emerging market using high frequency data
Saleem, Shabir,Smith, Peter N.,Yalaman, Abdullah
We investigate whether the daily betas of individual stocks vary with the release of firm-specific news in an emerging market. Using intraday prices of all stocks traded on the Borsa Istanbul, Turkey over the period 2005-2013, we find evidence that average market betas increase significantly from two weeks before the earnings announcement day, and then revert to their average levels two weeks after the announcement. The increase in betas is greater for larger, positive surprise earnings announcements than for smaller, negative news. The results are consistent with features of the learning model of Patton and Verardo (2012) but not with a number of their empirical results.

Analyst skills in producing aggregate and firm-specific information: when do these skills matter?
Zhang, Shuran
This paper explores whether analyst skills in producing aggregate and firm-specific information facilitate valuable research in different states of the economy. I find that stock market reactions to forecast and recommendation revisions in downturns (upturns) increase with analyst skills in producing aggregate (firm-specific) information, measured as analysts’ prior forecast accuracy for high-cyclicality (low-cyclicality) firms. The effects are concentrated in smaller firms and those with lower analyst coverage and institutional ownership. I perform a number of robustness checks and rule out alternative explanations related to analyst efforts, analyst attention, investor sentiment, and selection effects. I also find that analyst skills in producing aggregate (firm-specific) information are associated with timelier research output and more favorable career outcomes in downturns (upturns). Overall, the evidence is consistent with the idea that investors give more weight to analysts’ aggregate information skills in downturns and to firm-specific information skills in upturns.

Analytic formula for option margin with liquidity costs under dynamic delta hedging
Kyungsub Lee,Byoung Ki Seo

This study derives the expected liquidity cost when performing the delta hedging process of a European option. This cost is represented by an integration formula that includes European option prices and a certain function depending on the delta process. We first define a unit liquidity cost and then show that the liquidity cost is a multiplication of the unit liquidity cost, stock price, supply curve parameter, and the square of the number of options. Using this formula, the expected liquidity cost before hedging can be calculated much faster than when using a Monte Carlo simulation. Numerically computed distributions of liquidity costs in special cases are also provided.

Asset Selection via Correlation Blockmodel Clustering
Tang, Wenpin,Xu, Xiao,Zhou, Xun Yu
We aim to cluster financial assets in order to identify a small set of stocks to approximate the level of diversification of the whole universe of stocks. We develop a data-driven approach to clustering based on a correlation blockmodel in which assets in the same cluster have the same correlations with all other assets. We devise an algorithm to detect the clusters, with a theoretical analysis and a practical guidance. Finally, we conduct an empirical analysis to attest the performance of the algorithm.

Beyond the Yield Curve: Understanding the Effect of FOMC Announcements on the Stock Market
Boehm, Christoph,Kroner, Niklas
A large literature uses high-frequency changes in interest rates around FOMC announcements to study monetary policy. These yield changes have puzzlingly low explanatory power for the stock market - even in a narrow 30-minute window. We propose a new approach to test whether the unexplained variation represents monetary policy news or just noise. In particular, we allow for a latent "Fed non-yield curve shock'', which we estimate via a heteroskedasticity-based procedure. Using a test for weak identification, we show that our shock is well identified, that is, the unexplained variation is not just noise. We then go on to show that the shock, signed to increase stock prices, leads to sizable declines in the equity and variance premium, an increase in the 10-year term premium, an increase in short-run inflation expectations, as well as a dollar depreciation against multiple non-safe-haven currencies. Hence, the evidence supports the interpretation that the shock affects risk-appetite and leads to a reverse "flight-to-safety'' effect. Lastly, using a method from the computational linguistics literature, we show that our shock can be linked to specific topics discussed in FOMC statements, suggesting that it reflects written communication by the Federal Reserve.

Board Composition and Workplace Diversity: A Machine Learning Approach
Ranta, Mikko,Ylinen, Mika
We examine the predictive value of board gender diversity on workplace diversity and the relative importance of various board and firm characteristics in predicting diversity. With a novel machine learning approach, we measure three workplace diversity variables using a social media data set of approximately 250,000 employee reviews. Our results show that board composition is an important predictor of workplace diversity, and board gender diversity is the most important predictor of the gender equality and inclusiveness dimensions of corporate diversity culture. However, board gender diversity is not found to have significance in predicting age diversity in a company, and overall, firm characteristics are found to be more critical in explaining age diversity. Furthermore, we find that workplace diversity is an important predictor of firm value, indicating a possible channel on how board gender diversity affects firm performance.

Bubbles in Ethereum
Bellon, Carlos,Figuerola-Ferretti, Isabel
We apply the Phillips et al. (2015) methodology to date-stamp bubbles inEthereum. The analysis of the drivers of fundamental value suggests that theexplosive behavior documented in ether prices does not constitute speculativebubbles, but reflects the abrupt rally of demand for the use of smart contractstied to the development of the decentralized application (dApp) ecosystem.

Categories and Functions of Crypto-Tokens
Cong, Lin William,Xiao, Yizhou
We discuss emerging research on digital tokens and cryptocurrencies. Specifically, we (i) provide a comprehensive categorization of crypto-tokens as observed or designed in practice, (ii) discuss major issues concerning the economics of using tokens including platform finance, user adoption, stablecoins, crowdsourcing, and agency issues, with legal and regulatory implications, and finally, (iii ) suggest future directions of digital currency applications and tokenomics research.

Coding Out Justice: Digital Platforms’ Enclosure of Public Transit in Cities
Monahan, Torin
This paper argues that when platform companies like Uber partner with cities to enfold public transit options into their apps, they establish themselves as obligatory passage points for individuals seeking city information or accessing city services. In the process, private platforms become the interfaces through which people experience the city and themselves in it. Platform enclosure of this sort threatens the long-term viability of urban transit systems and their capacity to provide equitable service.

Commercial and Residential Mortgage Defaults: Spatial Dependence with Frailty
Babii, Andrii,Chen, Xi,Ghysels, Eric
We investigate the spatial dependence between commercial and residential mortgage defaults. A new class of observation-driven frailty factor models is introduced to do so. The idea of dynamic parameters embedded in the class of GAS models is utilized to estimate dynamic models of default risk with potentially multiple factors which are driven by stratified grouping of large panels of mortgage loan records. The score dynamics in the models is driven by so-called generalized residuals, and have therefore a fairly intuitive interpretation of ARMA-like dynamics. The asymptotic analysis recognizes the fact that we deal with both cross-sectional and time series data features. The proposed models are computationally easy to implement and therefore attractive in big data applications, something that gives them a considerable advantage in comparison to the typical latent factor frailty models proposed in the literature. Our empirical analysis demonstrates strong spatial dependence between commercial default and residential defaults.

Credit ratings and capital structure: New evidence from overconfident CFOs
Khoo, Shee Yee,Vu, Huong ,Klusak, Patrycja
In this paper, we examine the impact of credit rating changes on the financing decisions of overconfident CFOs. We find that CFO overconfidence significantly increases the sensitivity of net debt issuances to the rating changes, particularly when firms have no access to low-risk debt. Specifically, we establish that speculative-grade firms with overconfident CFOs reduce net debt issuance following rating changes (i.e. upgrades and downgrades). Our results hold after controlling for CEO bias. Furthermore, we document that CEO overconfidence has explanatory power on firm financing policies as it generates the potential multiplier effect on debt conservatism, as well as on investment return. Findings of our paper are robust to model specifications and to the endogeneity bias.

Customer Relationship Management and Business Strategy in Mid-Sized Urban Co-operative Banks: Vision, Opportunities and Challenges
Srivastava, Dr Ashish
The cooperative movement is one of the successful models globally for organizing and conducting a wide cross-section of economic activities. Notwithstanding their small size in proportion to commercial banks, Urban Cooperative -Banks (UCBs) in India play an important role in the economy through their local reach and personalized services. In India, the UCB sector is quite heterogeneous in terms of size of business, and their response to business challenges is somewhat fragmented and ambiguous. Customer Relationship Management (CRM) and Business Strategy play an important role in augmenting the capacity and scope for growth of business, and ensuring its sustainability. This study focuses upon the mid-sized UCBs having a deposit size of ₹ 1 â€" 10 billion to examine their position and preparedness in the context of CRM and business strategy formulation. The study shows that the perceived competition in the banking sector did not impact all the UCBs in the same proportion and the banks with better positioning in terms of products, delivery channels, and market-base did not feel the pinch, and some of them even found good business opportunities to explore and benefit from. The study indicates certain areas requiring focused attention and also shows silver-linings as many mid-sized UCBs appeared gearing up for the challenges and were trying to use a tech-driven approaches to capitalize on their strengths to expand, grow and achieve their potential.

Dark Banking? Banks and Illicit Deposit Flows
Aldama-Navarrete, David
Do banks enable organized crime? Does bank regulation insulate financial intermediationfrom criminal activity? I address these questions using evidence from the drug trade in Mexico, finding that local drug cartel activity causes an increase in bank deposits. Branch networks grow in affected areas; this growth is not driven by increased lending opportunities. Post-election of a "law-and-order" government, effects attenuate, with liquidity flowing into branches of U.S. banks along the border. I interpret this as evidence that "finance follows crime" in weak institutional environments, and that, absent transnational policy coordination, regulatory arbitrage via cross-border liquidity flows undermines banking regulation.

Deciphering Bitcoin Blockchain Data by Cohort Analysis
Yulin Liu,Luyao Zhang,Yinhong Zhao

Bitcoin is a peer-to-peer electronic payment system that popularized rapidly in recent years. Usually, we need to query the complete history of Bitcoin blockchain data to acquire variables with economic meaning. This becomes increasingly difficult now with over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in social science. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort, which enables us to create datasets and visualizations for some key indicators of Bitcoin transactions, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the accumulated unspent transaction output (UTXO). We provide a computationally feasible approach to characterize Bitcoin transactions, which paves the way for the future economic studies of Bitcoin.

Do Firms Prefer Leverage Over Cash?
Kim, Raymond
Amid record levels of corporate cash and debt, this study finds that firms acquire cash, then subsequently increase debt instead of reducing debt after the Homeland Investment Act of 2003. Two possible motives for this change are 1) increasing tax benefits of debt and 2) the role of cash as debt collateral. Using all firm panel observations from 2003-2019 (ex. financials and utilities), I find one dollar of cash has 3x more predictive ability for future debt than one dollar of financing deficit. Larger multinationals have increasing debt preferences over cash, a convex relation predicted by the repatriation tax motive. Surprisingly, domestic-only firms also prefer debt irrespective of hedging needs, and financially constrained firms (identified by sentiment analysis and Altman's Z) increasingly benefit from using cash as debt collateral. Persistently low corporate borrowing rates likely alleviated debt and hedging costs, inducing a new trade-off paradigm where firms may increasingly prefer to finance with debt instead of cash.

Doing Well by Doing Good? Risk, Return, and Environmental and Social Ratings
Chava, Sudheer,Kim, Jeong Ho (John),Lee, Jaemin
We analyze the risk and return characteristics across portfolios of firms sorted by their environmental and social (ES) ratings. We document that ES ratings have no statistically significant relationship with average stock returns nor unconditional market risk. Firms with high ES ratings do have significantly lower downside risk than firms with low ES ratings. However, a firm's downside risk decreases by only 2â€"4% of its interdecile range for an interdecile-range increase in a firm's ES score. Our results suggest that the risk-return profile of ES firms cannot be the sole rationale for ES investing.

Doing business while holding public office: Evidence from Mozambique’s firm registry
Tarp, Finn,Jones, Sam
We link the universe of owners of businesses formally registered in Mozambique since Independence to a new database of politically exposed persons. Recreating the dynamic network of ties between firm owners, we estimate the value of party political and executive mandates to their personal business interests. We find holders of political office attain significantly faster growth not only in the number of companies they own but also in their structural power within the business-owner network, as measured by their ‘godfather centrality’. Such growth is concentrated in joint-stock firms active in trade and finance sectors and is even larger once we aggregate the analysis to the family-name level. This is consistent with politicians accumulating private sector wealth by acting as rentier-brokers.

Drivers of academic engagement in public-private research collaboration: an empirical study
Giovanni Abramo,Ciriaco Andrea D'Angelo

University-industry research collaboration is one of the major research policy priorities of advanced economies. In this study, we try to identify the main drivers that could influence the propensity of academics to engage in research collaborations with the private sector, in order to better inform policies and initiatives to foster such collaborations. At this purpose, we apply an inferential model to a dataset of 32,792 Italian professors in order to analyze the relative impact of individual and contextual factors affecting the propensity of academics to engage in collaboration with industry, at overall level and across disciplines. The outcomes reveal that the typical profile of the professor collaborating with industry is a male under age 40, full professor, very high performer, with highly diversified research, and who has a certain tradition in collaborating with industry. This professor is likely to be part of a staff used to collaborating with industry, in a small university, typically a polytechnic, located in the north of the country.

Employee-Friendly Corporate Culture and Firm Performance: Evidence from a Machine Learning Approach
Ylinen, Mika,Ranta, Mikko
This study investigates which values of an employee-friendly (EF) corporate culture are the most important predictors of firm value and operating performance using a novel social media dataset of approximately 250,000 crowdsourced employee reviews of 18 different characteristics of a firm’s corporate culture. The extreme gradient-boosting model with SHAP (Shapley additive explanations) and SAGE (Shapley additive global importance) values is used to examine the predictive value and relative importance of employee-friendly cultural values and the potential nonlinearities of these relationships. We find that several employee-friendly corporate culture features contain value-relevant information for predicting firms’ value (Tobin’s Q) and operating performance (ROA). Our findings (SAGE values) reveal three features indicating that predictive importance is clearly superior to other EF culture variables in our machine learning model: employee reviews about the overall company culture, pride in the company, and job security. Based on the SHAP values, these effects are positive, significant, and closely linear. The effects are negative for low values of an EF corporate culture and strongly positive for high values. Specifically, we find that satisfaction with the overall company culture and organizational pride are the most important characteristics for predicting Tobin’s Q, whereas job security and the overall company culture are the most crucial predictors of ROA. Other significant predictors of Tobin’s Q are the attitude towards older colleagues, workâ€"life balance, office/work environment, inclusive/diverse, and gender equality, while environmental friendliness, gender equality, workplace safety, and inclusive/diverse are important predictors of ROA.

FOMO in Digital Assets
Karkkainen, Tatja
This study examines the Fear of Missing Out (FOMO) sentiment as a motivation for retail investors to own ICOs. In the extant literature, this sentiment is suggested to be a possible factor explaining ICO investing. This study examines this empirically. Using novel OECD survey data on ICO investing in Malaysia, the Philippines and Vietnam, this study finds that FOMO is a motivational driver in ICO ownership. When FOMO is interacted with wealth, their explanatory effect of ICO ownership is high at 25%. Furthermore, the study also finds that ICO investors are also motivated by long-term investing.

Fertile LAND: Pricing non-fungible tokens
Dowling, Michael M.
The current popularity of non-fungible token (NFT) markets is one of the most notable public successes of blockchain technology. NFTs are blockchain-traded rights to any digital asset; including images, videos, music, even the parts of virtual worlds. As a first study of NFT pricing, we explore the pricing of parcels of virtual real estate in the largest blockchain virtual world, Decentraland; an NFT simply termed LAND. We show a LAND price series characterised by both inefficiency and a steady rise in value.

Financial Inclusion in British Columbia: Evaluating the Role of Fintech
Clements, Ryan
Financial technology (fintech) can help to mitigate the problem of financial exclusion in British Columbia. Individuals who participate in the traditional banking and financial system experience a variety of social and economic benefits. Yet several factors like personal hardship, financial illiteracy, high product costs, perceived eligibility, informational gaps, a lack of credit history and legal documents, bank resistance, and customer feelings of distrust and disrespect contribute to the exclusion of many from traditional financial products and services. People who are “unbanked” and “underbanked” often turn to high-cost (even predatory) substitutes like payday lenders, rent-to-own firms, cheque-cashing services, and pawn shops. This paper illustrates how some fintech innovationsâ€"highlighting numerous companies operating in British Columbia, Canada, and internationallyâ€"can benefit people who are unbanked and underbanked as an alternative to “fringe” banking. Fintech is not, however, a panacea for those excluded, marginalized, or underserved by traditional financial firms, and there are several implementation barriers and integration risks in this market development. This paper provides seven key policy recommendations to help maximize the inclusionary benefits of fintech in British Columbia while mitigating its potential risks.

Functional structure in production networks
Carolina Mattsson,Frank W. Takes,Eelke M. Heemskerk,Cees Diks,Gert Buiten,Albert Faber,Peter M.A. Sloot

Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks among networks from other domains according to their local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and a thorough understanding of their local connectivity structure will help us better reason about the micro-economic mechanisms behind their routine function, failure, and growth.

Funding Liquidity and the Valuation of Mortgage-Backed Securities
Dunn, Brett,Kargar, Mahyar
We study the relationship between funding liquidity and the valuation of mortgage-backed securities. Most of the financing for mortgage-backed securities occurs through a trade known as a dollar roll, the simultaneous sale and purchase of forward contracts on mortgage-backed securities that is analogous to a repurchase agreement. We develop a four-factor no-arbitrage model for valuing mortgage-backed securities that allows for the valuation of dollar rolls. Unlike previous models of the dollar roll, we allow for the possibility of a prepayment risk premium. We develop a new measure of mortgage specialness that is independent of prepayment risk premia and agency credit spreads. We find that specialness is related to measures of balance sheet constraints and primary dealer positions in mortgage-backed securities.

How Far Can Financial Acumen Push Positive Social Impact? The Case of the Redevelopment of Spofford in the Bronx
Phalippou, Ludovic
When Eric Clement graduated from the University of Oxford Said Business School executive MBA program on his 37th birthday, he was searching for the new Eldorado: jobs in the high-octane private equity industry. These were simply the most sought-after jobs out there â€" pay was high! The first-born son of Panamanian-American parents now had the degree, knowledge, and experience to make it happen. But then he was offered a job in a government organization, where he would hire fund managers to make investments and where he would have to focus on the social impact of his investments. He was intrigued. Growing up in a middle-class suburb of NYC, Eric knew how difficult it was to move up the socioeconomic ladder. He had been working since he was 11-years old, and because his parents were not from the U.S., he didn’t have the social contacts many of his friends did, which could open up career paths he had never heard of. Could he use the finance skills he had acquired throughout his career to improve the lives of underrepresented people so they wouldn’t have to go through what he did? What if a hard-core finance chap like himself stepped into a world where finance acumen was rare and tree-hugging plentiful? What if he took the opposite seat at the negotiation table, effectively hiring the people he wanted to join, and got them to behave and act responsively? What would happen then?

How Informed Were Non-Agency MBS Investors? Evidence from Mortgage Servicing Fees
Diop, Moussa,Zheng, Chen
This paper examines the informativeness of servicing fees regarding the quality of mortgage collateral pools in non-agency securitizations and, ultimately, the value of the mortgage securities. We find that servicing fees capture some unobservable credit risk that explains mortgage default and differentially affects the performance of various security classes. However, junior bond yield spreads at issuance account for this risk, which suggests that investors were more sophisticated than previously thought or that deal issuers were more transparent about collateral credit risk than recognized in the literature. The slow reemergence of non-prime lending as non-qualified mortgage lending makes this study still relevant.

How Stress Influences Commodity Trading Flows, Storage and Transportation
Chiu, James
This paper examines how different types of stress impacts commodity trading flows as a function of energy infrastructure having varying degrees of rigidity and resiliency related to modes of transport and ability to store. A simple model illustrating the effect of stress on cellular transport and accumulation of certain nuclear proteins is referenced as an analogue, demonstrating similar mechanisms and correspondingly, the benefits of diversification in managing risk for commodity traders and investors. In times of great stress these benefits are augmented with the ability to transport and store due to the fact that without these tools, outcomes are more volatile. The organic cellular model is an example of evolutionary flexibility in order to survive, with similar potential implications for the future success or failure of U.S. energy infrastructure as the consequences of both anticipated and unexpected system stress continues to proliferate.

How has labour market power evolved? Comparing labour market monopsony in Peru and the United States
Jorge Davalos,Ekkehard Ernst

We document the evolution of labour market power by employers on the US and Peruvian labour markets during the 2010s. Making use of a structural estimation model of labour market dynamics, we estimate differences in market power that workers face depending on their sector of activity, their age, sex, location and educational level. In particular, we show that differences in cross-sectional market power are significant and higher than variations over the ten-year time span of our data. In contrast to findings of labour market power in developed countries such as the US, we document significant market power of employers in Peru vis-\`a-vis the tertiary educated workforce, regardless of age and sector. In contrast, for the primary educated workforce, market power seems to be high in (private) services and manufacturing. For secondary educated workers, only the mining sector stands out as moderately more monopsonistic than the rest of the labour market. We also show that at least for the 2010s, labour market power declined in Peru. We contrast these findings with similar estimates obtained for the United States where we are able to show that increases in labour market power are particularly acute in certain sectors, such as agriculture and entertainment and recreational services, as well as in specific geographic areas where these sectors are dominant. Moreover, we show that for the US, the labour market power has gradually increased over the past ten years, in line with the general rise in inequality. Importantly, we show that the pervasive gender pay gap cannot be linked to differential market power as men face higher labour market power than women. We also discuss possible reasons for these findings, including for the differences of labour market power across skill levels. Especially, we discuss the reasons for polarization of market power that are specific to sectors and locations.

Indian Fintech: An Industry Perspective
Abidi, Qambar
The Indian financial ecosystem has strong growth potential. Total financial assets in India are only 1.58 times GDP, compared to the same metric being 3.14 and 6.73 for emerging and advanced economies respectively. Importantly, the total financial assets in India have grown at a faster rate of 15.32% for 2005-2019, while the growth rate for emerging and advanced economies has been 14.72% and 4.57% respectively. Fintech is expected to capture large amount of the potential value creation in the financial services sector, as evidenced by VC investments, which is considered an informed indicator of future growth. As of last year, Fintech companies received 39.7% of the VC funding in the financial sector. The amount of VC dollar into Fintech has grown at a rate of 35.6% between 2006-2020, twice the rate of growth in the BFSI sector. Notably, six out of 37 Indian Unicorns are Fintech firms. Within Fintech, Payment is the dominant vertical, receiving 59% of the cumulative 2006-2020 VC flow, and in the process contributing four Unicorns. The Insurance vertical, although modest in terms of total VC flow, has generated the remaining two Fintech Unicorns. Going forward, Fintech is expected to have more diversified growth due to recent regulatory initiatives and the enabling ecosystem for fintech businesses. The non-bank Lending vertical is the potential future star, already accounting for 43% of total Fintech funding rounds between 2015-2020. The creation of Account Aggregators and Open Credit Enhancement Networki, paves the way for strong future growth of the vertical.

Interest Arbitrage under Capital Controls: Evidence from Reported Entrepôt Trades
Hu, Jiafei,Yuan, Haishan
Capital controls segment the offshore credit market of Chinese renminbi from the onshore market. Using a novel administrative data set, we provide evidence that firms arbitrage the onshore-offshore interest differentials using bank-intermediated "entrepôt trades," which supposedly re-export imports with little or no processing. Onshore-offshore interest differentials drive renminbi inflows from entrepôt trades, which strongly predict 1-year-forward outflows to settle bank-issued letters of credit. The patterns and timing of entrepôt trade flows are consistent with lending by onshore banks and borrowing from offshore banks through bank-intermediated trade finance. A larger interest differential allows transactions with a lower value to be profitable and induces entry into arbitrages. Our findings suggest that renminbi interest arbitrages are feasible but costly under capital controls.

Intermediated Trade and Credit Constraints: The Case of Firm's Imports
Nucci, Francesco,Pietrovito, Filomena,Pozzolo, Alberto Franco
Growing evidence suggests that a large share of international trade transactions are made through intermediaries and that whether firms use them or not depends on different factors. In this paper, we investigate whether credit constraints introduce a degree of difference among firms in their mode of importing. To begin, we develop a simple analytical framework highlighting the possible links between credit constraints and reliance on import intermediaries, and then use firm-level data from 66 developing and developed countries to test the model's predictions. The results show that credit-constrained firms exhibit a higher probability of importing their inputs using an intermediary, while unconstrained firms are more likely to import directly. Our results also establish that the impact of credit constraints on the probability of indirect importing is amplified for firms with a higher distance from their international sourcing network. Moreover, if firms face other types of frictions to imports, then the probability that credit-constrained firms rely on intermediaries is estimated to be higher. The frictions we consider relate to the degree of regulatory burden and the extent of documentary compliance, time to import and other costs involved in import activities.

Investors' Reactions to CSR News in Family vs. Nonfamily Firms: A Study on Signal (In)Credibility
Sekerci, Naciye,Jaballah, Jamil,Essen, Marc van,Kammerlander, Nadine
We study family firm status as an important condition in signaling theory; specifically, we propose that the market reacts more positively to positive, and more negatively to negative, CSR news (i.e., signals) from family firms than to similar news from nonfamily firms. Moreover, we propose that during recessions, the direction of these relationships reverses. Based on an event study of 1,247 positive and negative changes in the CSR ratings for all firms listed on the French SFB120 stock market index (2003-2013), we find support for our hypotheses. Moreover, a post hoc analysis reveals that the relationships are contingent on whether a family CEO leads the firm.

Localization, Transcreation, Transadaptation, Transculturation: New Types of Translation or Trendy Names?
Timko, Natalia
This article proves that localization, transcreation, transculturation, etc. are not new types of translation but its varieties. Adaptation of a text, carried out in the process of its "localization" and "transcreation", is in fact cultural-pragmatic adaptation, which has long been well known to practitioners and theorists of translation. The depth and degree of cultural and pragmatic adaptation (strong, weak, lack of adaptation) are all factors that matter here. In any case, the translator resolves the key issue - ensuring that the resulting text meets the expectations and needs of the translation consumer, and the technologies used in the translator's activities are tools that help to solve this problem.

Managerial behavior in fund tournamentsâ€"the impact of TrueSkill
Swade, Alexander,Köchling, Gerrit,Posch, Peter N.
Measuring mutual fund managers’ skills by Microsoft’s TrueSkill algorithm, we find highly skilled managers to behave self-confident resulting in higher risk-taking in the second half of the year compared to less skilled managers. Introducing the TrueSkill algorithm, which is widely used in the e-sports community, to this branch of literature, we can replicate previous findings and theories suggesting overconfidence for mid-years winners.

Measuring the Benefits of Diversification Across Portfolios
Madan, Dilip B.,Wang, King
A portfolio diversification index is defined as the ratio of an equivalent number of independent assets to the number of assets. The equivalence is based on either attaining the same diversification benefit or spread reduction. The diversification benefit is the difference in value of a value maximizing portfolio and the maximum value of the components. The spread reduction is the percentage reduction attained by a spread minimizing portfolio relative to the smallest spread for the components. Asset values, bid and ask, are given by conservative valuation operators from the theory of two price economies. The diversification indices fall with the number of assets in the portfolio and it they are explained by a measure of concentration applied to normalized eigenvalues of the correlation matrix along with the average level of correlation. Diversification across global indices is not as strong as diversification across an equal number of domestic assets, but rises substantially for longer horizons of up to three years.

Measuring the alignment of real economy investments with climate mitigation objectives: The United Kingdom's buildings sector
Jachnik, Raphaël,Dobrinevski, Alexander
This paper explores data and methods to assess the alignment or misalignment with climate mitigation objectives of investments in the construction and refurbishment of residential and non-residential buildings. It takes the United Kingdom (UK) as a case study, where such investments reached GBP 162 billion (EUR 184 billion) in 2019 or 39% of UK gross fixed capital formation. The analysis trials different reference points that lead to varying results and each currently come with limitations in terms of coverage or granularity. Sector-level greenhouse gas (GHG) trajectories indicate that, in aggregate, investments in UK buildings have been insufficient, delayed or not aligned enough with caps set by UK Carbon Budgets, but such trajectories currently lack disaggregation for a more granular and insightful matching with investment data. Energy performance certificates (EPCs) allow for asset-level analyses: for instance, 79% of 2010-2019 investments in new built residential were in relatively energy efficient buildings but only 1% were consistent with more demanding recommendations towards the UK's objective of reaching net-zero GHG in 2050. The coverage and reliability of EPCs, however, needs to be improved for older buildings, whose deep retrofitting is a major financing challenge. Applying Climate Bonds Initiative criteria for low-carbon buildings identifies investments eligible for green bond financing, but such criteria have partial sectoral coverage and are based on currently most efficient buildings within the existing stock, which makes them relatively easy to meet for investments in new built.Producing more complete and policy relevant assessments of aligned and misaligned investments at national and sectoral levels requires the availability of and access to comparable and granular data on decarbonisation targets and pathways consistent with the Paris Agreement temperature goals, GHG performance of assets, corporate and household investments, as well as underlying sources of financing.

Modeling Stock Returns Using Asymmetric Garch-Icapm with Mixture and Heavy-Tailed Distributions: An Application to COVID-19 Pandemic Forecasts
Khanthaporn, Rewat,Wichitaksorn, Nuttanan
COVID-19 pandemic is an extreme event that created a turmoil in stock markets around the world. This unexpected circumstance poses a critical question whether the prevailing models can help predict the plummets of indices, hence the returns. In this study, we model the stock returns using univariate classical and asymmetric generalized autoregressive conditional heteroskedastic (GARCH) with the innovation following (1) mixture of generalized Pareto and Gaussian distributions and (2) generalized error distribution.We also employ the parallel griddy Gibbs (GG) sampling, which is a Markov chain Monte Carlo method, to facilitate the parameter estimation. Our simulation study shows that the GG estimation method outperforms the benchmark quasi-maximum likelihood estimation method. We then proceed to the empirical study of seven stock markets where the results from the in-sample period before the COVID-19 pandemic justify the use of the proposed GARCH models. The out-of-sample forecasts during the early COVID-19 period also show satisfactory results.

Monte Carlo algorithm for the extrema of tempered stable processes
Jorge Ignacio González Cázares,Aleksandar Mijatović

We develop a novel Monte Carlo algorithm for the vector consisting of the supremum, the time at which the supremum is attained and the position of an exponentially tempered L\'{e}vy process. The algorithm, based on the increments of the process without tempering, converges geometrically fast (as a function of the computational cost) for discontinuous and locally Lipschitz functions of the vector. We prove that the corresponding multilevel Monte Carlo estimator has optimal computational complexity (i.e. of order $\epsilon^{-2}$ if the mean squared error is at most $\epsilon^{2}$) and provide its central limit theorem (CLT). Using the CLT we construct confidence intervals for barrier option prices and various risk measures based on drawdown under the tempered stable (CGMY) model calibrated/estimated on real-world data. We provide non-asymptotic and asymptotic comparisons of our algorithm with existing approximations, leading to rule-of-thumb guidelines for users to the best method for a given set of parameters, and illustrate its performance with numerical examples.

Municipal Bond Insurance and the U.S. Drinking Water Crisis
Agrawal, Ashwini K.,Kim, Daniel
The alarming rise in drinking water pollution across the U.S. is often attributed to cost cutting pressures faced by local officials. We know little, however, about why these pressures are so severe for some cities compared to others. In this paper, we argue that one of the root causes of recent drinking water emergencies is the collapse of the municipal bond insurance industry. Public water infrastructure has traditionally been financed using municipal debt partly backed by a small number of monoline insurers. Starting in the 90’s, some of these insurers became increasingly involved with structured financial products unrelated to municipal water bonds, such as residential mortgage backed securities. We show that when these products crashed in value in 2007, municipalities that had relied more heavily on these insurers for water infrastructure financing subsequently faced higher borrowing costs. These municipalities then reduced their borrowing and scaled back investments in water infrastructure, which in turn, has led to elevated levels of water contamination. Our findings thus reveal how the U.S drinking water crisis can be partly traced back to financial market failures.

Optimal Stopping under Model Ambiguity: a Time-Consistent Equilibrium Approach
Yu-Jui Huang,Xiang Yu

An unconventional approach for optimal stopping under model ambiguity is introduced. Besides ambiguity itself, we take into account how ambiguity-averse an agent is. This inclusion of ambiguity attitude, via an $\alpha$-maxmin nonlinear expectation, renders the stopping problem time-inconsistent. We look for subgame perfect equilibrium stopping policies, formulated as fixed points of an operator. For a one-dimensional diffusion with drift and volatility uncertainty, we show that every equilibrium can be obtained through a fixed-point iteration. This allows us to capture much more diverse behavior, depending on an agent's ambiguity attitude, beyond the standard worst-case (or best-case) analysis. In a concrete example of real options valuation under volatility uncertainty, all equilibrium stopping policies, as well as the best one among them, are fully characterized. It demonstrates explicitly the effect of ambiguity attitude on decision making: the more ambiguity-averse, the more eager to stop -- so as to withdraw from the uncertain environment. The main result hinges on a delicate analysis of continuous sample paths in the canonical space and the capacity theory. To resolve measurability issues, a generalized measurable projection theorem, new to the literature, is also established.

Panjer class revisited: one formula for the distributions of the Panjer (a,b,n) class
Fackler, Michael
The loss count distributions whose probabilities ultimately satisfy Panjer’s recursion were classified between 1981 and 2002; they split into six types, which look quite diverse. Yet, the distributions are closely related â€" we show that their probabilities emerge out of one formula: the Binomial series. Further we propose a parameter change that leads to very intuitive representations of the distributions and their parameter space.

Passenger Transportation Policy and the Right Effectiveness of Social Restrictions during the COVID-19 Pandemic in Indonesia PT O
Sabeilai, Agusnawati
COVID-19 has hit all sectors, including the transportation sector. Passenger transportation, goods and logistics, to lease or charter are quite directly affected. Especially those in the Greater Jakarta area. This impact occurred with a decrease in passengers as of January 2020 with the following rates: MRT 94.11%, Integrated Rail (LRT) and 93.05%, KRL (Commuter Line) 78.69%. Meanwhile, in the air sector, there was a decrease of 44% for domestic passengers and 45% for international passengers. The decrease in passengers was caused by the government's recommendation to stop all public activities and limit going out of the house, such as school, college, work as well as worship all done from home. Meanwhile, BPTJ places restrictions on the operational time of public transport during the PSBB period, namely DKI Jakarta from 6:00 to 18:00 WIB, while the Bodetabek area has status 2 PSBB starts at 05.00-19.00 WIB, meanwhile, LRT Jakarta since March 23, 2020 has started by changing the policy on operating hours from 06.00-20.00 WIB and limiting service by changing the headway from 10 minutes to 30 minutes as of March 1, 2020. In the airline air sector Garuda Indonesia flights are also restructuring to find better route schedules in stopping harmful routes, in contrast to what Lion Air airlines still operate normally with strict health protocol boundarie.

Pitching Research® for $Commercialisation: A Prototype Tool
Faff, Robert W.
In this paper I present a prototype “3G” companion for two prior generation Pitching Research® tools: (1) generation 1 (1G) â€" Faff’s (2015, 2021a) original scholarly pitching research tool; and (2) generation 2 (2G) â€" Faff and Kastelle’s (2016) PR4EI (pitching research for engagement and impact) tool. Importantly, the resultant 3-dimensional pitching research framework proposed herein, aimed at strategically creating a plan for commercialising research, uncompromisingly rests on the 1G foundation stone (i.e., 1G is designed to provide a succinct and methodical approach to pitching a new scholarly research proposal to an academic expert). The paper then showcases an illustrative example of the new prototype tool for pitching research for commercialization (PR4Com), via a fully integrated worked pitch across all three dimensions: 1G, 2G and 3G. I end the paper, calling out for your help … feedback, yes please. BUT more radically in this paper (potentially) I am pitching for a collaborating partner to help significantly develop the tool and bring it into a more polished state â€" to take it to the next level and beyond. Ideally, this research partner is someone who can bring relevant demonstrated expertise and experience and, thereby, also deliver critical credibility and authenticity in the “research commercialisation” space. Is that person you …?

Portfolio Optimization with Sparse Multivariate Modelling
Pier Francesco Procacci,Tomaso Aste

Portfolio optimization approaches inevitably rely on multivariate modeling of markets and the economy. In this paper, we address three sources of error related to the modeling of these complex systems: 1. oversimplifying hypothesis; 2. uncertainties resulting from parameters' sampling error; 3. intrinsic non-stationarity of these systems. For what concerns point 1. we propose a L0-norm sparse elliptical modeling and show that sparsification is effective. The effects of points 2. and 3. are quantifified by studying the models' likelihood in- and out-of-sample for parameters estimated over train sets of different lengths. We show that models with larger off-sample likelihoods lead to better performing portfolios up to when two to three years of daily observations are included in the train set. For larger train sets, we found that portfolio performances deteriorate and detach from the models' likelihood, highlighting the role of non-stationarity. We further investigate this phenomenon by studying the out-of-sample likelihood of individual observations showing that the system changes significantly through time. Larger estimation windows lead to stable likelihood in the long run, but at the cost of lower likelihood in the short-term: the `optimal' fit in finance needs to be defined in terms of the holding period. Lastly, we show that sparse models outperform full-models in that they deliver higher out of sample likelihood, lower realized portfolio volatility and improved portfolios' stability, avoiding typical pitfalls of the Mean-Variance optimization.

Preservation and Conservation of Information Resources in Special Libraries: A Peep into Selected Law Libraries in Nigeria
Law library serves as a legal information resource repository. Its vitality in innovative research and training of legal professionals is extensive. This study reports preservation and conservation practices of law libraries. The survey design was espoused; census sampling imbibed, questionnaire was developed and administered to 113 respondents (with 93% return rate) drawn from selected universities. The study revealed that deterioration of resources includes book spines breakage, thorn book pages due to regular photocopying and frequent usage. Sand buckets and fire extinguishers were the only adopted ways of remediating natural outbreaks. Amidst these, there was a remarkably low adoption of most preservation techniques apart from dusting/cleaning, proper shelving of books and physical security. Digital preservation is yet to be explored as it is constrained by lack of practicable policies, poor electricity supply and funding. The intervention of parent institutions and concerned organisations is crucial at this point.

Public transport users versus private vehicle users: differences about quality of service, satisfaction and attitudes toward public transport in Madrid (Spain)
Juan de Oña,Esperanza Estévez,Rocio de Oña

This paper aims to further understand the main factors influencing the behavioural intentions (BI) of private vehicle users towards public transport to provide policymakers and public transport operators with the tools they need to attract more private vehicle users. As service quality, satisfaction and attitudes towards public transport are considered the main motivational forces behind the BI of public transport users, this research analyses 26 indicators frequently associated with these constructs for both public transport users and private vehicle users. Non-parametric tests and ordinal logit models have been applied to an online survey asked in Madrid's metropolitan area with a sample size of 1,025 respondents (525 regular public transport users and 500 regular private vehicle users). In order to achieve a comprehensive analysis and to deal with heterogeneity in perceptions, 338 models have been developed for the entire sample and for 12 users' segments. The results led to the identification of indicators with no significant differences between public transport and private vehicle users in any of the segments being considered (punctuality, information and low-income), as well as those that did show significant differences in all the segments (proximity, intermodality, save time and money, and lifestyle). The main differences between public transport and private vehicle users were found in the attitudes towards public transport and for certain user segments (residents in the city centre, males, young, with university qualification and with incomes above 2,700EUR/month). Findings from this study can be used to develop policies and recommendations for persuading more private vehicle users to use the public transport services.

Public-private research collaborations: longitudinal field-level analysis of determinants, frequency and impact
Giovanni Abramo,Francesca Apponi,Ciriaco Andrea D'Angelo

This study on public-private research collaboration measures the variation over time of the propensity of academics to collaborate with colleagues from private companies. It also investigates the change in weights of the main drivers underlying the academics' propensity to collaborate, and whether the type profile of the collaborating academics changes. To do this, the study applies an inferential model on a dataset of professors working in Italian universities in consecutive periods, 2010-2013 and 2014-2017. The results, obtained at overall and field levels, support the formulation of policies aimed at fostering public-private research collaborations, and should be taken into account in post-assessment of their effectiveness.

RegSafe© manifesto - an agile management control methodology for regulatory - driven programs
Beerbaum, Dirk
We investigate existing methodologies for running large scale agile regulatory-driven programs for globally distributed corporations: Scaled Agile Framework (SAFe©), Large-scale Scrum (LeSS) and Disciplined Agile Delivery (DAD). Based on the specific requirements of regulatory-driven programs, high-complexity, equally business value, high level of audit engagement, dispersed customer need, principal-agent conflict caused information asymmetries, we conclude that the existing three methodologies SAFe©, LeSS and DAD do not fully adhere to the business needs for regulatory-driven programs for globally distributed corporations. Based on the identified gaps the methodology is refined and enhanced. This leads to the creation of RegSafe©, the agile management control methodology to scale agile regulatory-driven programs for globally distributed corporations. RegSafe© is based on the agile framework and bundles in a mixed-method approach management control system, stakeholder and behavioral economics underpinnings. The regulatory tsunami caused by the Global Financial Crisis in the Financial Sector and increasing regulatory intervention and less laissez-faire incorporating ad-hoc deliveries, large scale framework metric changes, mandatory global distributed system changes lead to multiple complex large-scale programs. Service industry such as Banks spend more than 60% on their change management program for regulatory compliance. The RegSafe© framework aims at providing a tool for global alignment of such large scale regulatory-driven programs, to enhance efficiency, maximise output, enable continuous delivery and control costs by applying hybrid agile and waterfall-driven planning leading to comprehensive management control system. It has implications for practitioners and researchers on the transformation of agile organizations and program managers of regulatory-driven programs.

Replicating Market Makers
Guillermo Angeris,Alex Evans,Tarun Chitra

We present a method for constructing Constant Function Market Makers (CFMMs) whose portfolio value functions match a desired payoff. More specifically, we show that the space of concave, nonnegative, nondecreasing, 1-homogeneous payoff functions and the space of convex CFMMs are equivalent; in other words, every CFMM has a concave, nonnegative, nondecreasing, 1-homogeneous payoff function, and every payoff function with these properties has a corresponding convex CFMM. We demonstrate a simple method for recovering a CFMM trading function that produces this desired payoff. This method uses only basic tools from convex analysis and is intimately related to Fenchel conjugacy. We demonstrate our result by constructing trading functions corresponding to basic payoffs, as well as standard financial derivatives such as options and swaps.

Research on Portfolio Liquidation Strategy under Discrete Times
Qixuan Luo,Yu Shi,Handong Li

This paper presents an optimal strategy for portfolio liquidation under discrete time conditions. We assume that N risky assets held will be liquidated according to the same time interval and order quantity, and the basic price processes of assets are generated by an N-dimensional independent standard Brownian motion. The permanent impact generated by an asset in the portfolio during the liquidation will affect all assets, and the temporary impact generated by one asset will only affect itself. On this basis, we establish a liquidation cost model based on the VaR measurement and obtain an optimal liquidation time under discrete-time conditions. The optimal solution shows that the liquidation time is only related to the temporary impact rather than the permanent impact. In the simulation analysis, we give the relationship between volatility parameters, temporary price impact and the optimal liquidation strategy.

Should Retail Investors Listen to Social Media Analysts? Evidence from Text-Implied Beliefs
Dim, Chukwuma
Social media is increasingly affecting financial markets, with important implications for market efficiency. This paper uses machine learning to construct beliefs of nonprofessional social media investment analysts (SMAs) from opinions expressed about individual stocks on social media. On average, SMA beliefs are informative about future stock abnormal returns and earnings surprise. However, there exists important heterogeneity in belief formation ability. Only a small fraction, 10%, of SMAs form beliefs that produce an economically meaningful abnormal return of 56 bps over a 5-day window. SMA characteristics such as specialization, skin in the game, effort, popularity, and disagreement matter for belief formation skill. SMAs herd in belief statements. Herding is less pronounced in bad times and when the consensus is more optimistic, but more pronounced when the consensus is correct ex-post.

Simulation of the drawdown and its duration in L\'{e}vy models via stick-breaking Gaussian approximation
Jorge González Cázares,Aleksandar Mijatović

We develop a computational method for expected functionals of the drawdown and its duration in exponential L\'evy models. It is based on a novel simulation algorithm for the joint law of the state, supremum and time the supremum is attained of the Gaussian approximation of a general L\'evy process. We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications and analyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carlo estimators. Monte Carlo methods for L\'evy processes (using Gaussian approximation) have been analysed for Lipschitz payoffs, in which case the computational complexity of our algorithm is up to two orders of magnitude smaller when the jump activity is high. At the core of our approach are bounds on certain Wasserstein distances, obtained via the novel SBG coupling between a L\'evy process and its Gaussian approximation. Numerical performance, based on the implementation in the dedicated GitHub repository, exhibits a good agreement with our theoretical bounds.

Star-shaped Risk Measures
Erio Castagnoli,Giacomo Cattelan,Fabio Maccheroni,Claudio Tebaldi,Ruodu Wang

In this paper monetary risk measures that are positively superhomogeneous, called star-shaped risk measures, are characterized and their properties studied. The measures in this class, which arise when the controversial subadditivity property of coherent risk measures is dispensed with and positive homogeneity is weakened, include all practically used risk measures, in particular, both convex risk measures and Value-at-Risk.

From a financial viewpoint, our relaxation of convexity is necessary to quantify the capital requirements for risk exposure in the presence of competitive delegation or robust aggregation mechanisms.

From a decision theoretical perspective, star-shaped risk measures emerge from variational preferences when risk mitigation strategies can be adopted by a rational decision maker.

The Best Strategies for Inflationary Times
Neville, Henry,Draaisma, Teun,Funnell, Ben,Harvey, Campbell R.,Van Hemert, Otto
Over the past three decades, a sustained surge in inflation has been absent in developed markets. As a result, investors are faced with the challenge of having little evidence on how to reposition their portfolios in the face of heighted inflation risk. We provide some guidance by analyzing both passive and active strategies across a variety of asset classes for the U.S., U.K., and Japan over the past 95 years. Unexpected inflation is bad news for traditional assets, such as bonds and equities, with local inflation mattering most. Commodities and futures trend performance is strong during inflationary periods, with US regimes particularly relevant, and the most pronounced effect when the US, UK and Japan experience inflation simultaneously. Among dynamic equity factors, momentum does best during inflationary times. We also provide analysis of alternative asset classes such as fine art and discuss the economic rationale for including cryptocurrencies as part of an inflation-protect strategy.

The Indian Bankruptcy Law Experience
Abidi, Qambar
The Insolvency and Bankruptcy Code, 2016 (IBC) introduces a unied bankruptcycode in India. Under IBC, the corporate insolvency resolution process aims to improvethe eciency of the bankruptcy code and strengthen creditor protection in an under-developed credit market. In this paper we track evolution of the Indian bankruptcycode and provide an overview of corporate insolvency resolution process under IBC.Subsequently, we conduct a preliminary, empirical examination of the eect of IBC oncost of debt and amount of debt, for BSE and NSE listed rms using panel data regression,controlling for rm nancials and macro-economic covariates. We fail to nd evidence ofdesired eect of improvement of rm credit characteristics with strengthening of creditorrights. Our results are however in line with the liquidation bias observed by Vig (2013)for the SARFESIA reform in India.

The Use of Deep Reinforcement Learning in Tactical Asset Allocation
Katongo, Musonda,Bhattacharyya, Ritabrata
The Tactical Asset Allocation (TAA) problem is a problem to accurately capture short to medium term market trends and anomalies in order to allocate the assets in a portfolio so as to optimize its performance by increasing the risk adjusted returns. This project seeks to address the Tactical Asset Allocation problem by employing Deep Reinforcement Learning (DRL) Algorithms in a Machine Learning Environment as well as employing Neural Network Autoencoders for selection of portfolio assets. This paper presents the implementation of this proposed methodology applied to 30 stocks of the Dow Jones Industrial Average (DJIA). In (1), the Introduction to the project objectives is done with the Problem Description presented in (2). Part (3) presents the literature review of similar studies in the subject area. The methodology used for our implementation is presented in (4) whilst (5) and (6) presents the benchmark portfolios and the DRL portfolios development respectively. The evaluation of the performance of the models is presented in (7) and we present our conclusions and the future works in (8).

The ultimate prediction uncertainty in the chain-ladder model of Mack: An unbiased estimator
Siegenthaler, Filippo
In this paper we derive in a straightforward fashion and in a unified framework the main known results related to the estimation of the ultimate prediction uncertainty within the famous Mack's distribution free chain-ladder model (Mack and BBMW formulas) and explain the deviation between the two formulas in terms of estimation principles applied.In particular we show that the BBMW formula can also be derived without making use of the conditional resampling approach, namely by applying an estimation principle which is well defined for estimating the quadratic difference between estimated and true development factors.Moreover, we highlight that both estimators suffer from the same deficiency of not being unbiased for estimating the true uncertainty and that even given the fact that the Mack estimator can be proved to be (slightly) less overestimated (on the average given the first triangle column only) than the BBMW estimator, it does not hold true that for each and any claims development triangle the Mack formula does provide the most accurate estimate.In addition by applying an enhanced estimation principle we derive a new formula for quantifying the ultimate prediction uncertainty which can be proved to be unbiased.However, we also make the actuarial community aware that the new estimator as well as Mack and BBMW formulas can materially fail to estimate the true uncertainty.

Valuating consumer credit portfolios
Piccoli, Pedro
This paper proposes a model in which the borrower credit risk is associated with the cash flow method to assess the economic value of a consumer credit portfolio. A Monte Carlo simulation applying the method in an illustrative loan reveals that the lending standards of the institution, captured in the model by the expected and unexpected losses of the contract according to the Basel II Internal Rating Based Approach, is a key driver for the intrinsic value of the portfolio, lending support to the evidence that a bank’s credit policy and bank valuation are associated

[Access to Bank Credit and SME Financing. (Case of Kosovo) Paper Title]
Nure, Gazmend
This study explored the impact of firm and entrepreneurship characteristics such as age, size, manager’s education, business plan, the form of business and gender, on influencing SMEs' access to funding. There are different researchers through their study, evidence that some SMEs may still face difficulties in accessing bank finance. This paper reports an in-depth study into demand issues relating to access to bank finance by Kosovo SMEs and whether there is still a market failure. This paper discusses the ability of SMEs to access debt finance from the commercial banks in Kosovo obtained through questionnaires with 500 SMEs , it reports findings that SMEs had reported having difficulty raising finance and secondary data obtained from magazines, books, and the Internet to review literature. The results show that there is a mutual correlation between firms' ages, business plans, and size and access to credit in Kosovo banks. These characteristics of firms are very important and influential factors in the banks' decision to approve loans required by SMEs. This paper provides some important conclusions for the Kosovo financial system and policymakers to implement SME support and policies.