# Research articles for the 2020-08-04

A Note on Yield Curve Reduction
Tawfik, Maged
SSRN
To ensure accurate market compliance, yield curves used for marking-to-market are typically constructed of as many liquid fixed income instruments as possible. This can result in curves of very high cardinality. The high cardinality associated with such curves renders them impractical for use during simulation or for the calculation of risk metrics. Thus, reduction of such curves is highly desirable. An approach is presented for the optimal reduction of market compliant yield curves. The optimality condition is portfolio specific, in order to minimize pricing errors for the portfolio whose risk metrics are being computed.

Accuracy of European Stock Target Prices
Almeida, Joana,Gaspar, Raquel M.
SSRN
Equity researches are conducted by professionals, who also provide buy/hold/sell recommendations to investors. Nowadays, target prices determined by financial analysts are publicly available to investors, who may decide to use them for investment purposes. Studying the accuracy of such analysts' forecasts is, thus, of paramount importance.Based upon empirical data on 50 of the biggest (larger capitalisation) European stocks over a 15 year period, from 2004 to 2019 and using a panel data approach, this is the first study looking at overall accuracy in European stock markets.We find that Bloomberg's 12-month consensus target prices have no predictive over future market prices. Panel results are robust to company fixed effects and sub-period analysis. These results are in line with the (mostly US-based) evidence in the literature.Extending common practice, we perform a comparative accuracy analysis, comparing the accuracy of target prices with that of simple capitalisations of current prices. It turns out target prices are not better in forecasting, than simple capitalisations. More interestingly, by analysing also the relationship between both measures â€" target prices and capitalised prices â€" we find evidence that capitalised prices partially explain how target prices are determined.Even when considering individual regressions, accuracy is still very low, but varies considerably across stocks.

Anxiety for the pandemic and trust in financial markets
arXiv

The COVID-19 pandemic has generated disruptive changes in many fields. Here we focus on the relationship between the anxiety felt by people during the pandemic and the trust in the future performance of financial markets. Precisely, we move from the idea that the volume of Google searches about "coronavirus" can be considered as a proxy of the anxiety and, jointly with the stock index prices, can be used to produce mood indicators -- in terms of pessimism and optimism -- at country level. We analyse the "very high human developed countries" according to the Human Development Index plus China and their respective main stock market indexes. Namely, we propose both a temporal and a global measure of pessimism and optimism and provide accordingly a classification of indexes and countries. The results show the existence of different clusters of countries and markets in terms of pessimism and optimism. Moreover, specific regimes along the time emerge, with an increasing optimism spreading during the mid of June 2020. Furthermore, countries with different government responses to the pandemic have experienced different levels of mood indicators, so that countries with less strict lockdown had a higher level of optimism.

Central Bank Funding and Credit Risk-Taking
Bednarek, Peter,Dinger, Valeriya,te Kaat, Daniel,von Westernhagen, Natalja
SSRN
This paper examines the relationship between central bank funding and credit risk-taking. Employing comprehensive bank-firm-level data from the German credit registry during 2009:Q1-2014:Q4, we find that borrowing from the central bank is associated with rebalancing of bank portfolios towards ex-ante riskier firms. We further establish that this relationship is associated with the ECB's maturity extensions and that the risk-taking sensitivity of banks borrowing from the ECB is independent of idiosyncratic bank characteristics. Finally, we highlight that these shifts in bank lending might lead to an ex-post deterioration of bank balance sheets, but increase firm-level investment and employment.

Citizensâ€™ Attitudes Towards the ECB, the Euro and Economic and Monetary Union
Bergbauer, Stephanie,Hernborg, Nils,Jamet, Jean-Francois,Persson, Eric,SchÃ¶lermann, Hanni
SSRN
Building on the literature on trust in institutions, the article looks at the state, evolution and sociodemographic breakdown of citizensâ€™ trust in the ECB and support for the euro. Drawing on a novel typology of attitudes towards Economic and Monetary Union (EMU) and using microdata from Eurobarometer surveys since the introduction of the single currency, the analysis tracks the prevalence of supporters and sceptics of EMU over time and across euro area countries. It further explores the sociodemographic characteristics, economic perceptions and, more broadly, European sentiments within these groups. In this way, it provides insights into the factors shaping citizensâ€™ attitudes towards the ECB, the euro and EMU, and helps identify possible avenues for enhancing trust. The analysis indicates that popular support for EMU â€" in particular, trust in the ECB â€" hinges to a large extent on citizensâ€™ perceptions of their personal situation and the overall economic context, as well as their broader attitudes towards the European Union, while other sociodemographic indicators seem to be less relevant.

Climate Risk and Beliefs in New York Floodplains
Gibson, Matthew,Mullins, Jamie
SSRN
Applying a difference-in-differences framework to a census of residential property transactions in New York City 2003-2017, we estimate the price effects of three flood risk signals: 1) the Biggert-Waters Flood Insurance Reform Act, which increased premiums; 2) Hurricane Sandy; and 3) new floodplain maps reflecting three decades of climate change. Estimates are negative for all three signals and some are large: properties included in the new floodplain after escaping flooding by Sandy experienced 11 percent price reductions. We investigate possible mechanisms, including selection of properties into the market and residential sorting. Finding no evidence for these, we develop a parsimonious theoretical model that allows decomposition of our reduced-form estimates into the effects of insurance premium changes and belief updating. Results suggest the new maps induced belief changes comparable to those from insurance reform.

Conservative TV and Corporate Social Responsibility
Kaviani, Mahsa,Li, Lily,Maleki, Hosein
SSRN
We study the impact of exposure to conservative media on firms' CSR activities using the quasi-natural expansion of Sinclair Broadcast Group: the largest conservative broadcasting network in the U.S. local TV markets. In a difference-in-differences setting, we find that firms significantly reduce CSR activities after exposure to Sinclair TV in all three dimension: environmental, social, and governance. Consistent with ideology as a driver of our results, the effect is stronger when there is more propensity for the audience to accept Sinclair messaging: for example, for firms belonging to gun, tobacco and gambling industries, when Sinclair acquires more Fox-affiliated TV stations, and for those headquartered in Republican-leaning counties. Cross-sectional evidence also suggests stronger effects associated with lower institutional ownership, higher litigation risk, younger CEOs, or a higher percentage of female executives. Related, we find no impact of Sinclair exposure on firms' accounting performance measured by ROA, but a negative impact on their Tobin's Q and stock returns.

Deep Learning Credit Risk Modeling
Manzo, Gerardo,Qiao, Xiao
SSRN
This paper demonstrates how deep learning can be used to price and calibrate models of credit risk. Deep neural networks can learn structural and reduced-form models with high degrees of accuracy. For complex credit risk models, whose closed-form solutions are not available, deep learning offers a conceptually simple and more efficient alternative solution. We propose an approach that combines deep learning with the unscented Kalman filter to calibrate credit risk models on historical data, which attains an in-sample R-squared of 98.5 percent for the reduced-form model and 95 percent for the structural model.

Eunjung Noh
arXiv

We study how transaction cost affects to the equilibrium return and optimal stock holdings in equilibrium. To this end, we develop a continuous-time risk-sharing model where heterogenous agents trade toward terminal target holdings subject to a quadratic transaction cost. The equilibrium stock holdings and trading rate under transaction cost are characterized by a unique solution to a forward-backward stochastic differential equation (FBSDE). The equilibrium return is also characterized as the unique solution of a system of coupled but linear FBSDEs.

FinTech and the COVID-19 Pandemic: Evidence from Electronic Payment Systems
Tut, Daniel
SSRN
This paper investigates the effects of the COVID-19 pandemic on financial institutions and consumersâ€™ adoption of FinTech in payments. We find that the pandemic: [1] Initially had a negative impact on the adoption of FinTech, but favorable short-term regulatory changes have reversed some of the negative effects [2] The use of all electronic payment cards has significantly declined during the pandemic except for charge cards. We find an increase in the use of charge cards as consumers shift towards cheaper forms of payment [3] The pandemic has magnified interbank contagion and liquidity risks and has reduced both domestic and international electronic fund transfers via RTGS. The pandemic has also resulted in a deterioration in the quality of commercial banksâ€™ assets and balance sheets [4] Remittance inflows via FinTech platforms have significantly declined reflecting contractions in global economic activities.

First to React Is Last to Forgive: Evidence from the Stock Market Impact of COVID 19
Hassan, Sherif Maher,Riveros, John
SSRN
COVID 19 has had parallel and uneven economic shocks across countries since its outbreak in December 2019. Stock markets as usual were the first to react, with drop rates as much as the Global Financial crises of 2008. This study uses daily data to model the dynamic impact of COVID 19 pandemic on returns of selected stock market indices and globally-traded commodities. The overall panel least squares VAR estimation results indicate a negative short termed impact of 2.3% on the performances of the stock markets when the spread rate of corona-virus increases by 1% across countries ceteris paribus. While The COVID 19 contamination rate is not statistically significant to explain the changes in the exchange rate and gold prices in the countries of analysis, yet the virus spread rate is found to be significant in steering prices of platinum, silver, WTI, and Brent crude oil.

Forecasting Skills in Experimental Markets: Illusion or Reality?
Corgnet, Brice,Deck, Cary,DeSantis, Mark,Porter, David
SSRN
Using experimental asset markets, we study the situation of a financial analyst who is trying to infer the fundamental value of an asset by observing the marketâ€™s history. We find that such capacity requires both standard cognitive skills (IQ) as well as social and emotional skills. However, forecasters with high emotional skills tend to perform worse when market mispricing is high as they tend to give too much emphasis to the noisy signals from market data. By contrast, forecasters with high social skills perform especially well in markets with high levels of mispricing in which their skills could help them detect possible manipulation attempts. Finally, males outperform females in the forecasting task after controlling for a large number of relevant individual characteristics such as risk attitudes, cognitive skills, emotional intelligence, and personality traits.

Fragmentation in the Bitcoin Market: Evidence From Multiple Coexisting Order Books
Jeon, Yoontae,Samarbakhsh, Laleh,Hewitt, Kenji
SSRN
We explore the consolidated order book of five major exchanges to investigate how fragmentation affects the Bitcoin market. Using intraday snapshots of the order book data, we find that the Bitcoin market is quite fragmented, wherein the exchange liquidity measure is significantly negative, even for small transactions. Our results suggest that consolidation tools, such as smart order routers, can be effective in reducing the cost of trading, and that further development of the Bitcoin marketâ€™s trading and regulation is needed.

Generalized Autoregressive Score asymmetric Laplace Distribution and Extreme Downward Risk Prediction
Shao-Peng Hong
arXiv

Due to the skessed distribution, high peak and thick tail and asymmetry of financial return data, it is difficult to describe the traditional distribution. In recent years, generalized autoregressive score (GAS) has been used in many fields and achieved good results. In this paper, under the framework of generalized autoregressive score (GAS), the asymmetric Laplace distribution (ALD) is improved, and the GAS-ALD model is proposed, which has the characteristics of time-varying parameters, can describe the peak thick tail, biased and asymmetric distribution. The model is used to study the Shanghai index, Shenzhen index and SME board index. It is found that: 1) the distribution parameters and moments of the three indexes have obvious time-varying characteristics and aggregation characteristics. 2) Compared with the commonly used models for calculating VaR and ES, the GAS-ALD model has a high prediction effect.

Going Green Means Being in the Black
Conen, Ralf,Hartmann, Stefan,Rudolf, Markus
SSRN
We revisit the relationship between ESG and stock returns using a novel, monthly consensus rating for a global universe. Our results illustrate that how well or bad a firm does along the different dimensions of corporate social responsibility does affect the return of its shares. Fund managers constructing portfolios using information on a firms' corporate social performance generally outperform, however may underperform in markets, where social responsibility is not as widely accepted. Secondly, excess performance of portfolios tilted towards corporate social responsibility is not always fully explained by the interaction with common risk factors such as value, size or momentum suggesting that ESG has a systematic effect on stock returns beyond those factors. This enables active fund managers to harvest risk-adjusted alpha. Thirdly, the effect of ESG on portfolio performance is asymmetric and does not appear to be constant over time. Fourth, markets reward short and long-term performance along ESG dimensions differently. Lastly, ESG is not a globally integrated factor. Rather it differs across regions with regard to direction, magnitude and statistical significance. We do not find a scenario in which investing in stocks with high ESG ratings leads to negative risk adjusted performance, suggesting that investors can greenwash portfolios without sacrificing performance.

La Crisi Covid-19. Impatti E Rischi Per Il Sistema Finanziario Italiano in Una Prospettiva Comparata (The Covid-19 Crisis. Impacts And Risks For The Italian Financial System In A Comparative Perspective)
SSRN

Machine Learning approach for Credit Scoring
A. R. Provenzano,D. Trifirò,A. Datteo,L. Giada,N. Jean,A. Riciputi,G. Le Pera,M. Spadaccino,L. Massaron,C. Nordio
arXiv

In this work we build a stack of machine learning models aimed at composing a state-of-the-art credit rating and default prediction system, obtaining excellent out-of-sample performances. Our approach is an excursion through the most recent ML / AI concepts, starting from natural language processes (NLP) applied to economic sectors' (textual) descriptions using embedding and autoencoders (AE), going through the classification of defaultable firms on the base of a wide range of economic features using gradient boosting machines (GBM) and calibrating their probabilities paying due attention to the treatment of unbalanced samples. Finally we assign credit ratings through genetic algorithms (differential evolution, DE). Model interpretability is achieved by implementing recent techniques such as SHAP and LIME, which explain predictions locally in features' space.

Market Valuation and Corporate Investment in India
Dhananjaya, K
SSRN
The paper examines the impact of stock market valuation on corporate investment. Specifically, it aims at understanding the impact of both fundamental and non-fundamental components of the stock price on firmsâ€™ investment decisions. The study decomposes market-to-book ratio (MB) into three components, i.e., firm level mispricing, industry mispricing, and growth component to understand the impact of each of these components on corporate investment decisions. Based on the literature review, four major testable hypotheses concerning the relationship between market valuation and corporate investment have been generated. These hypotheses have been tested using panel data of Indian Public Limited Manufacturing Firms using pooled data regression model. The study shows that both fundamental and non-fundamental component of the market valuation influence the investment decisions along with cash flow variable. The research also establishes that the market valuation-investment linkage is more pronounced in the case of equity-dependent firms, suggesting that stock valuation affects corporate investment predominantly through the equity transaction channel. Thirdly, the industry mispricing is found to be significantly influencing investment decision, which indicates that corporate investment is partly influenced by the market sentiments reflected in the stock price. In succinct, the study shows that stock market valuation plays an important role in corporate investment decisions, along with the cash flow variable.

Multi-stream RNN for Merchant Transaction Prediction
Zhongfang Zhuang,Chin-Chia Michael Yeh,Liang Wang,Wei Zhang,Junpeng Wang
arXiv

Recently, digital payment systems have significantly changed people's lifestyles. New challenges have surfaced in monitoring and guaranteeing the integrity of payment processing systems. One important task is to predict the future transaction statistics of each merchant. These predictions can thus be used to steer other tasks, ranging from fraud detection to recommendation. This problem is challenging as we need to predict not only multivariate time series but also multi-steps into the future. In this work, we propose a multi-stream RNN model for multi-step merchant transaction predictions tailored to these requirements. The proposed multi-stream RNN summarizes transaction data in different granularity and makes predictions for multiple steps in the future. Our extensive experimental results have demonstrated that the proposed model is capable of outperforming existing state-of-the-art methods.

Navigating the Changing Landscape of Community Solar in Delaware: Policy Designs and Governance Frameworks to Support Community-Owned Sustainable Energy
Byrne, John,Nyangon, Joseph,Hegedus, Steven,Taminiau, Job,Li, Pengyu,de Paz, Oscar,Redhead, Karice
SSRN
Using case studies of community-owned solar generating facilities in 11 states (namely California, Colorado, Connecticut, Hawaii, Maryland, Massachusetts, Minnesota, New Jersey, New York, Oregon, and Rhode Island) and the District of Columbia, this report investigates policy and governance processes for designing community solar strategies. For this report, we use the term â€˜subscriber-basedâ€™ when the passive role of bill payers mostly defines the nature of the â€˜communityâ€™ in a project. We use the term â€˜community-activeâ€™ when communities and their members have active roles in governance and administration, normally expressed through local government sponsors. Our research finds that most programs and projects underway in the U.S. have a â€˜passiveâ€™ concept of community. Importantly, while most states deploying community solar projects have adopted subscriber-based policies, installed community solar capacity based on community-active forms of development is fast-growing and appears to already exceed the volume of solar power generation created by nationwide subscriber-based community solar. In just three years (2016-18), the community-active model has grown by more than 350%. California has been a leader in the design and spread of this model with its community choice aggregation programs having procured over 6.8 million MWh and 14.2 million MWh of solar in 2017 and 2018, respectively, which is annually equal to the output of 2.7 GWp and 5.7 GWp, respectively, of solar power in the state (EIA, 2020). Massachusetts' Community Choice Energy programs have caused 1.8 million MWh of community-governed solar electricity generation to have been sold during 2017-18, or 1.4 GWp of solar power. Thus, community-active solar initiatives in just two states have outpaced solar power development by a factor of five in 36 states using subscriber-based approaches. Additionally, solar programs, including community-active solar, create a material number of jobs. Using experience in the community-active solar market to date, we estimate that if Delaware Senate Bill 250â€™s target of 40% renewable energy generated electricity, with 10% from solar that includes a community-active solar carveout, is met by 2035, it will result in 21,000 job years in 15 years of development. In other words, a new continuing solar workforce of about 1,400 employees statewide will exist as a result of the bill's new [solar + community solar] portfolio carveout.

Negative Monetary Policy Rates and Systemic Banks' Risk-Taking: Evidence from the Euro Area Securities Register
SSRN
We show that negative monetary policy rates induce systemic banks to reach-for-yield. For identification, we exploit the introduction of negative deposit rates by the European Central Bank in June 2014 and a novel securities register for the 26 largest euro area banking groups. Banks with more customer deposits are negatively affected by negative rates, as they do not pass negative rates to retail customers, in turn investing more in securities, especially in those yielding higher returns. Effects are stronger for less capitalized banks, private sector (financial and non-financial) securities and dollar-denominated securities. Affected banks also take higher risk in loans.

Neural Network Pricing of American Put Options
Gaspar, Raquel M.,Lopes, Sara,Sequeira, Bernardo
SSRN
In this study, we use Neural Networks (NNs) to price American put options. We propose two NN modelsâ€"a simple one and a more complex oneâ€"and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for four large U.S. companiesâ€"Procter and Gamble Company (PG), Coca-Cola Company (KO), General Motors (GM), and Bank of America Corp (BAC). Our dataset is composed of all options traded within the period December 2018 until March 2019. Although on average, both NN models perform better than LSM, the simpler model (NN Model 1) performs quite close to LSM.Moreover, the second NN model substantially outperforms the other models, having an RMSE ca. 40% lower than the presented by LSM. The lower RMSE is consistent across all companies, strike levels, and maturities. In summary, all methods present a good accuracy; however, after calibration, NNs produce better results in terms of both execution time and Root Mean Squared Error (RMSE).

On Pathâ€"dependency of Constant Proportion Portfolio Insurance Strategies
Carvalho, JoÃ£o,Gaspar, Raquel M.,Beleza Sousa, JoÃ£o
SSRN
This paper evaluates the path-dependency/independency of most widespread Portfolio Insurance strategies. In particular, we look at Constant Proportion Portfolio Insurance(CPPI) structures and compare them to both the classical Option Based Portfolio Insurance(OBPI) and naive strategies such as Stop-loss Portfolio Insurance (SLPI) or a CPPI with a multiplier of one.The paper is based upon conditional Monte Carlo simulations and we show that CPPI strategies with a multiplier higher than 1 are extremely path-dependent and that they can easily get cash-locked, even in scenarios when the underlying at maturity can be worth much more than initially. The likelihood of being cash-locked increases with the size of the multiplier and the maturity of the CPPI, as well as with properties of the risky underlyingâ€™s dynamics.To emphasize the path-dependency of CPPIs, we show that even in scenarios where the investor correctlyâ€œguessesâ€ a higher future value for the underlying, CPPIs can get cash-locked, losing the linkage to the risky asset. Thiscash-lock problem is specific of CPPIs, it goes against its European style nature of traded CPPIs, and it introduces into the strategy risks not related to the underlying risky asset â€" a design risk. Design risk does not occur for path-independent portfolio insurance strategies, like the classical case of OBPI strategies, nor in naive strategies.This study contributes to reinforce the idea that CPPI strategies suffer from a serious design problem.

Online Appendix for Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models
Bryzgalova, Svetlana,Huang, Jiantao,Julliard, Christian
SSRN
The Online Appendix presents additional derivations, simulations, and empirical findings that support and extend the results in the main text of the paper.

Optimal semi-static hedging in illiquid markets
Teemu Pennanen,Udomsak Rakwongwan
arXiv

We study indifference pricing of exotic derivatives by using hedging strategies that take static positions in quoted derivatives but trade the underlying and cash dynamically over time. We use real quotes that come with bid-ask spreads and finite quantities. Galerkin method and integration quadratures are used to approximate the hedging problem by a finite dimensional convex optimization problem which is solved by an interior point method. The techniques are extended also to situations where the underlying is subject to bid-ask spreads. As an illustration, we compute indifference prices of path-dependent options written on S&P500 index. Semi-static hedging improves considerably on the purely static options strategy as well as dynamic trading without options. The indifference prices make good economic sense even in the presence of arbitrage opportunities that are found when the underlying is assumed perfectly liquid. When transaction costs are introduced, the arbitrage opportunities vanish but the indifference prices remain almost unchanged.

Peer Effects in Equity Research
Phua, Kenny,Wei, Chishen,Tham, T. Mandy
SSRN
We study the importance of peer effects among sell-side analysts who work at the same brokerage house, but cover different firms. By mapping the information network within each brokerage, we identify analysts who occupy central positions in the network. Central analysts incorporate more information from their coworkers and produce better research. Using shocks to network structures around brokerage mergers, we identify the influence of peer effects and the importance of industry expertise on analysts' performance. A portfolio strategy that exploits the forecast revisions of central analysts earns up to 24% per annum.

Predicting ETF Liquidity
Pham, Son Duy,Marshall, Ben R.,Nguyen, Nhut (Nick) Hoang,Visaltanachoti, Nuttawat
SSRN

Pricing Options Under Rough Volatility with Backward SPDEs
Christian Bayer,Jinniao Qiu,Yao Yao
arXiv

In this paper, we study the option pricing problems for rough volatility models. As the framework is non-Markovian, the value function for a European option is not deterministic; rather, it is random and satisfies a backward stochastic partial differential equation (BSPDE). The existence and uniqueness of weak solution is proved for general nonlinear BSPDEs with unbounded random leading coefficients whose connections with certain forward-backward stochastic differential equations are derived as well. These BSPDEs are then used to approximate American option prices. A deep leaning-based method is also investigated for the numerical approximations to such BSPDEs and associated non-Markovian pricing problems. Finally, the examples of rough Bergomi type are numerically computed for both European and American options.

Procyclical Asset Management and Bond Risk Premia
Barbu, Alexandru,Fricke, Christoph,Moench, Emanuel
SSRN
Institutional funds have concentrated ownership by a few institutional investors, infrequent outflows and essentially no leverage. Yet using unique granular data on the bond holdings of institutional funds, we show that their trading behavior is strongly procyclical: they actively move into higher yielding, longer duration and lower rated securities in response to lower in-terest rates, and vice versa. Institutional funds' risk-taking increases when interest rates turn negative, particularly in funds with explicit minimum return guarantees. Their trading has large and persistent price impact. We provide evidence that this procyclical behavior is driven by career concerns among institutional fund managers.

Short-Termism, Managerial Talent, and Firm Value
Thakor, Richard T.
SSRN
This paper examines how the firm's choice of investment horizon interacts with rent-seeking by privately-informed, multi-tasking managers and the labor market. There are two main results. First, managers prefer longer-horizon projects that permit them to extract higher rents from firms, so short-termism involves lower agency costs and is value-maximizing for some firms. Second, when firms compete for managers, firms practicing short-termism attract better managerial talent when talent is unobservable, but larger firms that invest in long-horizon projects hire more talented managers when talent is revealed.

Sovereign Wealth Funds and the COVID-19 shock: Economic and Financial Resilience in Resource-Rich Countries
Bortolotti, Bernardo,Fotak, Veljko
SSRN
Commodity (primarily oil) funds are facing today the most severe adverse shock of their history. The COVID-19 pandemic has accelerated the crisis in oil-rich nations, already hit by low oil prices and declining hydrocarbon revenues. Governments of all stripes are tapping sovereign wealth and foreign exchange reserves to stabilize their budgets and mitigate the effects of the ensuing recession. The future of SWFs is at risk. In this article, we provide anecdotal evidence about SWFsâ€™ behavior during the COVID-19 crisis. We subsequently quantify, using updated national official statistics, the economic and financial resilience of the main resource-producing nations and link it to possible future trends in sovereign investment. We conclude that the COVID-19 crisis may induce profound changes in the industry. In the future, we expect SWFs to become more leveraged, to favor domestic over overseas investment, and to focus on broader economic and social impact than purely financial returns.

Stock Market Valuation and Output Growth in India
Dhananjaya, K
SSRN
The paper examines the relationship between stock market valuation and output growth at the firm level. Specifically, it aims at understanding the impact of firmsâ€™ stock market valuation and stock liquidity on the growth of real output. The sample for the study includes panel data of Indian public limited manufacturing firms. The study covers the period from March 2004 to March 2017. Firms with at least two consecutive years of data have been included in the sample. The full sample includes firm-year observations of 877 firms. The finding shows that both stock market valuation and turnover ratio have a significant positive impact on the growth of output, even after controlling for other important determinants of output. Further, both stock market variables and bank credit significantly influence the growth of output. This suggests that banks and stock market provide complementary financial services required for the growth and the development of the stock market will not undermine the role of the institution based financial system.

The Belt and Road Initiative and Chinese Cross-border Mergers & Acquisitions
ZHANG, Chi,Kandilov, Ivan T.,Walker, Mark D.
SSRN
The Belt and Road Initiative was introduced by President Xi in an attempt to further economic connections with partner countries and foster development within China. We examine the effect of the Initiative on Chinese cross-border M&As. We find that the Initiative significantly increases the probability and the transaction amount of M&A deals in target countries. Moreover, the market reacts more positively to these deals. We find that the effect is entirely driven by state-owned enterprises and it is more pronounced in firms that are located on the more-developed eastern coast of China. The evidence suggests that the announcement of the Belt and Road Initiative was followed by economically meaningful cross-border M&A in targeted countries.

The Canadian Pension Fund Model: A Quantitative Portrait
Beath, Alexander,Betermier, Sebastien,Flynn, Chris,Spehner, Quentin
SSRN
This paper presents a quantitative portrait of the Canadian pension fund model. We show that, between 2004 and 2018, Canadian pension funds outperformed their international peers both in terms of asset performance and liability hedging. We find that a central factor driving this success is the implementation of a three-pillar business model that consists of i) managing assets in-house to reduce costs, ii) redeploying resources to investment teams for each asset class, and iii) channeling capital toward growth assets that increase portfolio efficiency and hedge liability risks. This model works best for funds whose pension liabilities are indexed to inflation.

The Credit Spread Puzzle - Model-Free Evidence from a Natural Experiment
Claussen, Catharina,Kriebel, Johannes,Pfingsten, Andreas
SSRN
Prior literature mostly finds bond yield spreads to be insufficiently explained by credit risk (the 'credit spread puzzle'). Recently, FeldhÃ¼tter and Schaefer (2018) and Bai et al. (2020) revived this debate. We utilize the removal of sovereign guarantees for savings banks and state banks in Germany as a unique natural experiment allowing model-free identification of the credit risk component. During a transition period of over ten years, bonds of the same issuer with and without credit risk could be directly compared. Interestingly, less than 20% of the yield spread is due to credit risk for these bonds.

The Hansen ratio in mean--variance portfolio theory
Aleš Černý
arXiv

It is shown that the ratio between the mean and the $L^2$-norm leads to a particularly parsimonious description of the mean-variance efficient frontier and the dual pricing kernel restrictions known as the Hansen-Jagannathan (HJ) bounds. Because this ratio has not appeared in economic theory previously, it seems appropriate to name it the Hansen ratio. The initial treatment of the mean-variance theory via the Hansen ratio is extended in two directions, to monotone mean-variance preferences and to arbitrary Hilbert space setting. A multiperiod example with IID returns is also discussed.

The Multiplicative Chaos of $H=0$ Fractional Brownian Fields
Paul Hager,Eyal Neuman
arXiv

We consider a family of fractional Brownian fields $\{B^{H}\}_{H\in (0,1)}$ on $\mathbb{R}^{d}$, where $H$ denotes their Hurst parameter. We first define a rich class of normalizing kernels $\psi$ such that the covariance of $$X^{H}(x) = \Gamma(H)^{\frac{1}{2}} \left( B^{H}(x) - \int_{\mathbb{R}^{d}} B^{H}(u) \psi(u, x)du\right),$$ converges to the covariance of a log-correlated Gaussian field when $H \downarrow 0$.

We then use Berestycki's good points'' approach in order to derive the limiting measure of the so-called multiplicative chaos of the fractional Brownian field $$M^{H}_\gamma(dx) = e^{\gamma X^{H}(x) - \frac{\gamma^{2}}{2} E[X^{H}(x)^{2}] }dx,$$ as $H\downarrow 0$ for all $\gamma \in (0,\gamma^{*}(d)]$, where $\gamma^{*}(d)>\sqrt{\frac{7}{4}d}$. As a corollary we establish the $L^{2}$ convergence of $M^{H}_\gamma$ over the sets of good points'', where the field $X^H$ has a typical behaviour. As a by-product of the convergence result, we prove that for log-normal rough volatility models with small Hurst parameter, the volatility process is supported on the sets of good points'' with probability close to $1$. Moreover, on these sets the volatility converges in $L^2$ to the volatility of multifractal random walks.

Towards a Sustainable Agricultural Credit Guarantee Scheme
Reason Lesego Machete
arXiv

Since 1986, Government of Botswana has been running an Agricultural Credit Guarantee Scheme for dry-land arable farming. The scheme purports to assist dry-land crop farmers who have taken loans with participating banks or lending institutions to help them meet their debt obligations in case of crop failure due to drought, floods, frost or hailstorm. Nonetheless, to date, the scheme has focused solely on drought. The scheme has placed an unsustainable financial burden on Government because it is not based on sound actuarial principles. This paper argues that the level of Government subsidies should take into account the gains made by farmers during non-drought years. It is an attempt to circumvent the challenges of correlated climate risks and recommends a quasi self-financing mechanism, assuming that the major driver of crop yield failure is drought. Moreover, it provides a novel subsidy and premium rate setting method.

Weighted Accuracy Algorithmic Approach In Counteracting Fake News And Disinformation
arXiv

As the world is becoming more dependent on the internet for information exchange, some overzealous journalists, hackers, bloggers, individuals and organizations tend to abuse the gift of free information environment by polluting it with fake news, disinformation and pretentious content for their own agenda. Hence, there is the need to address the issue of fake news and disinformation with utmost seriousness. This paper proposes a methodology for fake news detection and reporting through a constraint mechanism that utilizes the combined weighted accuracies of four machine learning algorithms.

Were Stay-at-Home Orders During Covid-19 Harmful for Business? The Marketâ€™s View
Chen, Chen,Dasgupta, Sudipto,Huynh, Thanh,Xia, Ying
SSRN
We study the market reactions following staggered implementations of lockdowns across U.S. states during Covid-19. We find that returns on firms located in lockdown states are higher following the lockdown. We interpret these market reactions as reflecting updated beliefs of market participants in the light of events that follow the lockdowns, such as compliance with stay-at-home orders. The effect is (a) concentrated among counties with a high number of infections, (b) larger for firms in essential industries, and (c) larger for Democratic states. These findings suggest that the market perceives Non-Pharmaceutical Interventions, when effective, to be beneficial for businesses.

When the Rainy Day is the Worst Hurricane Ever: The Effect of Governmental Policies on SMEs During COVID-19