Research articles for the 2021-04-12

A Fast Evidential Approach for Stock Forecasting
Tianxiang Zhan,Fuyuan Xiao
arXiv

In the framework of evidence theory, data fusion combines the confidence functions of multiple different information sources to obtain a combined confidence function. Stock price prediction is the focus of economics. Stock price forecasts can provide reference data. The Dempster combination rule is a classic method of fusing different information. By using the Dempster combination rule and confidence function based on the entire time series fused at each time point and future time points, and the preliminary forecast value obtained through the time relationship, the accurate forecast value can be restored. This article will introduce the prediction method of evidence theory. This method has good running performance, can make a rapid response on a large amount of stock price data, and has far-reaching significance.

A New Macro-Financial Condition Index for the Euro Area
Morana, Claudio
SSRN
In this paper, we introduce a new time-domain decomposition for weakly stationary or trend stationary processes, based on trigonometric polynomial modelling of the underlying component of an economic time series. The method is explicitly devised to disentangle medium to long-term and short-term fluctuations in macroeconomic and financial series, in order to accurately measure the financial cycle and the concurrent long swings in economic activity. The implementation of this decomposition is straightforward and relies on standard regression analysis and general to specific model reduction. Full support to the proposed method is provided by Monte Carlo simulation. In the paper, we also provide a multivariate extension, involving sequential univariate decompositions and Principal Components Analysis. Based on this multivariate approach, we introduce a set of new composite indexes of macro-financial conditions for the euro area and assess their information content. In particular, with reference to the current pandemics, the indicators suggest that most of the GDP contraction has been of short-term, cyclical nature. This is likely due to the prompt monetary and fiscal policy responses. Yet our evidence suggests that the financial cycle might have currently achieved a peak area. Hence, the risk for further, deeper disruptions is high, particularly in so far as a new sovereign/corporate debt crisis were not eventually avoided.

Cobandag Guloglu, Zeynep,Ekinci, Cumhur
SSRN
We analyze the performance of five different methods appearing in the market microstructure literature in predicting effective and quoted bid-ask spreads (Roll, LOT Mixed, Effective Tick, High-Low and Closing Percent Quoted Spread proxies). With data from index futures, currency futures and gold futures traded in Borsa Istanbul and taking percent effective and percent quoted spreads obtained from intraday trade and quote data as benchmarks, we calculate and compare the correlations and root mean square errors of the spread measures. Results show that none of the proxies is successful enough in estimating effective or quoted spread although under normal market conditions, Effective Tick appears to perform best.

Pascal Michaillat,Emmanuel Saez
arXiv

This paper develops a new model of business cycles. The model is economical in that it is solved with an aggregate demand-aggregate supply diagram, and the effects of shocks and policies are obtained by comparative statics. The model builds on two unconventional assumptions. First, producers and consumers meet through a matching function. Thus, the model features unemployment, which fluctuates in response to aggregate demand and supply shocks. Second, wealth enters the utility function, so the model allows for permanent zero-lower-bound episodes. In the model, the optimal monetary policy is to set the interest rate at the level that eliminates the unemployment gap. This optimal interest rate is computed from the prevailing unemployment gap and monetary multiplier (the effect of the nominal interest rate on the unemployment rate). If the unemployment gap is exceedingly large, monetary policy cannot eliminate it before reaching the zero lower bound, but a wealth tax can.

An algorithm for the pricing and timing of the option to make a two-stage investment with loan guarantees
Dong, Linjia,Yang, Zhaojun
SSRN
We develop a jump-diffusion model for a guarantee-investment combination financing mode (G-I mode) that is recently popular in financial practice. We assume that a borrower has exclusively an option to invest in a project in two stages. The project's cash flow follows a double exponential jump-diffusion process and it is increased by a growth factor once the second-stage investment is exercised. The first-stage investment cost is financed by a bank loan with the guarantee provided by an insurer, who promises to provide the second-stage investment cost as well as take the lender's all default losses. In return for the guarantee and investment, the borrower pays the insurer a guarantee fee upon first investment and a fraction of equity upon second investment. The fraction of equity depends on the uncertain cash flow level when the first investment is exercised, which makes the timing and pricing of the option to invest in a project interesting and challenging. We provide closed-form solutions and produce a numerical algorithm for the timing and pricing of the real option.

Analysis of bank leverage via dynamical systems and deep neural networks
Fabrizio Lillo,Giulia Livieri,Stefano Marmi,Anton Solomko,Sandro Vaienti
arXiv

We consider a model of a simple financial system consisting of a leveraged investor that invests in a risky asset and manages risk by using Value-at-Risk (VaR). The VaR is estimated by using past data via an adaptive expectation scheme. We show that the leverage dynamics can be described by a dynamical system of slow-fast type associated with a unimodal map on [0,1] with an additive heteroscedastic noise whose variance is related to the portfolio rebalancing frequency to target leverage. In absence of noise the model is purely deterministic and the parameter space splits in two regions: (i) a region with a globally attracting fixed point or a 2-cycle; (ii) a dynamical core region, where the map could exhibit chaotic behavior. Whenever the model is randomly perturbed, we prove the existence of a unique stationary density with bounded variation, the stochastic stability of the process and the almost certain existence and continuity of the Lyapunov exponent for the stationary measure. We then use deep neural networks to estimate map parameters from a short time series. Using this method, we estimate the model in a large dataset of US commercial banks over the period 2001-2014. We find that the parameters of a substantial fraction of banks lie in the dynamical core, and their leverage time series are consistent with a chaotic behavior. We also present evidence that the time series of the leverage of large banks tend to exhibit chaoticity more frequently than those of small banks.

Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis
Marco Dueñas,Víctor Ortiz,Massimo Riccaboni,Francesco Serti
arXiv

By interpreting exporters' dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms' trade status. We focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms' outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.

Assessing the practicability of the condition used for dynamic equilibrium in Pasinetti theory of distribution
A Jayakrishnan,Anil Lal S
arXiv

In this note an assessment of the condition $$K_w/K=S_w/S$$ is made to interpret its meaning to the Passineti's theory of distribution\cite{pasinetti1962rate}. This condition leads the theory to enforce the result $$s_w\rightarrow0$$ as $$P_w\rightarrow 0$$, which is the Pasinetti's description about behavior of the workers. We find that the Pasinetti's claim, of long run worker's propensity to save as not influencing the distribution of income between profits and the wage can not be generalized. This claim is found to be valid only when $$W>>P_w$$ or $$P_w=0$$ with $$W\ne0$$. In practice, the Pasinetti's condition imposes a restriction on the actual savings by one of the agents to a lower level compared to its full saving capacity. An implied relationship between the propensities to save by workers and capitalists shows that the Passineti's condition can be practiced only through a contract for a constant value of $$R=s_w/s_c$$, to be agreed upon between the workers and the capitalists. It is showed that the Passineti's condition can not be described as a dynamic equilibrium of economic growth. Implementation of this condition (a) may lead to accumulation of unsaved income, (b) reduces growth of capital, (c)is not practicable and (d) is not warranted. We have also presented simple mathematical steps for the derivation of the Pasinetti's final equation compared to those presented in \cite{pasinetti1962rate}

Board Committees and Director Departures
Jagannathan, Murali,Krishnamurthy, Srinivasan,Spizman, Joshua D.
SSRN
We examine whether directors utilize private information obtained through their committee memberships to depart from firms prior to the revelation of their poor performance. Such departures raise the concern that directors leave the firm when they are most needed. Utilizing private information to make decisions in their personal interest may also violate the directorsâ€™ fiduciary duties. We focus on departures of audit committee members since information regarding earnings quality should be available to them prior to public release. The departure of audit committee members who serve on multiple boards is coincident with a deterioration in earnings quality. Other directors do not appear to time their departure based on declines in earnings quality. Results from examining the reasons behind this finding are consistent with the directorâ€™s preference to lead a â€œquiet lifeâ€ and a desire to lower their exposure to litigation risk rather than to protect their reputation in the director market.

Changes in the DJIA: Market Reactions and Impact of Estimation Window
Ryan, Patricia A,Villupuram, Sriram V.
SSRN
Changes in the DJIA from 1929-2019 are examined to evaluate the immediate and long-term market reaction after a component change in the DJIA. Using multiple event study methodologies, there is a clear increase in wealth when a firm is added to the DJIA and a decrease in wealth around the time of deletion from the DJIA. Additions earn positive abnormal returns regardless of estimation window. The choice of estimation window is critical for deletions as we show that this is the reason for the difference in results in the literature. Using a post-estimation window, deletions have a more significant negative wealth effect. Using pre-estimation window, returns are negative post announcement, but not at the announcement. Long term, firms added to the DJIA have positive abnormal returns in the second year after inclusion. Deletions from the DJIA after the Great Depression have negative returns three years after removal thus implying a potential investment opportunity upon DJIA changes.

Closed-form option pricing for exponential LÃ©vy models: a residue approach
Aguilar, Jean-Philippe,Kirkby, Justin
SSRN
Exponential L\'evy processes provide a natural and tractable generalization of the classic Black-Scholes-Merton model which are capable of capturing observed market implied volatility skews. In the existing literature, closed-form option pricing formulas are sparse for exponential L\'evy models, outside of special cases such as Merton's jump diffusion, and complex numerical techniques are required even to price European options.To bridge the gap, this work provides a comprehensive and unified pricing framework for vanilla and exotic European options under the Variance Gamma (VG), Finite Moment Log Stable (FMLS), one-sided Tempered Stable (TS), and Normal Inverse Gaussian (NIG) models. We utilize the Mellin Transform and residue calculus to obtain closed-form series representations for the price of several European options, including vanillas, digitals, power, and log options. These formulas provide nice theoretical representations, but are also efficient to evaluate in practice, as numerous numerical experiments demonstrate. The closed-form nature of these option pricing formulas makes them ideal for adoption in practical settings, as they do not require complicated pricing methods to achieve high accuracy prices, and the resulting pricing error is reliably controllable.

Combining dimensionality reduction with neural networks for realized volatility forecasting
He, Lidan,Bucci, Andrea,Liu, Zhi
SSRN
The application of artificial neural networks to finance has received a great deal of attention from both investors and researchers, especially as a forecasting method. When the number of predictors is high, these methods suffer from the so-called "curse of dimensionality" and produce biased forecasts. In this paper, we relied on dimensionality reduction methods to alleviate such issue when a wide set of financial and macroeconomic variables is considered in the prediction of stock market volatility. Specifically, we combined Bayesian Model Averaging (BMA), Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF) and Least Absolute Shrinkage and Selection Operator (LASSO) with hybrid artificial neutral networks to forecast realized volatility. The results showed that reduced models were able to perform in a similar way or even outperforms the compared full models in terms of predictive accuracy.

Crash Probability Anomaly in the Chinese Stock Market
Fang, Yi,Niu, Hui,Tong, Xiangda
SSRN
This study investigates the cross-sectional relationship of stock price crash probability in the Chinese stock market. We find that there is a negative cross-sectional correlation between crash probability and stock return. Meanwhile, we discover that the anomaly of crash probability is affected by market-wide sentiment, which is stronger in high-priced stocks, but not related to company size. Those above findings are diametrically opposite of those of the U.S. market.

Demand-pull and technology-push: What drives the direction of technological change? -- An empirical network-based approach
Kerstin Hötte
arXiv

Demand-pull and technology-push are linked to an empirical two-layer network-based on coupled cross-industrial input-output (IO) and patent citation links among 155 4-digit (NAICS) US-industries in 1976-2006 to study the evolution of industry hierarchies and link formation. Both layers co-evolve, but differently: The patent network became denser and increasingly skewed, while market hierarchies are balanced and sluggish in change. Industries became more similar by patent citations, but less by IO linkages. Having similar R&D capabilities as other big industries is positively related to innovation and growth, but relying on the same market inputs is unfavorable but may incite industries to explore other technological pathways. A tentative interpretation is the non-rivalry of intangible knowledge. This may strengthen existing R&D trajectories. Growth in the market is constrained by competition and market pressure may trigger a re-direction in both layers. This work is limited by its reliance on endogenously evolving classifications.

Discount Rate Risk in Private Equity: Evidence from Secondary Market Transactions
Boyer, Brian H.,Nadauld, Taylor,Vorkink, Keith,Weisbach, Michael S.
SSRN
Standard measures of PE performance based on cash flows overlook discount rate risk. An index constructed from prices paid in secondary market transactions indicates that PE discount rates vary considerably. While the standard alpha for our index is zero, measures of performance based on cash flow data for funds in our index are large and positive. To illustrate that results are not driven by idiosyncrasies of PE secondary markets, we obtain similar results using cash flows and returns of synthetic funds that invest in small cap stocks. Ignoring variation in PE discount rates can lead to a misallocation of capital.

Disposition effect across distinct investor categories in a tax-free context: are team dynamics and cognitive dissonance behind it?
de Groot, Alexander,NÃºÃ±ez-Letamendia, Laura,de Groot, Olivier
SSRN
Building on recent literature and employing survival analysis on trading data, we document differences in the strength and direction of the disposition effect on distinct categories of investors: (i) individuals not receiving professional advice; (ii) individuals receiving it; (iii) professional managers of delegated retail portfolios; (iv) professional managers of funds/institutional portfolios. We also find that the disposition effect is contingent upon paper gain-loss magnitudes in a more complex way than the V-shape proposed by the literature, and that market ups-downs do not exert influence on the propensity to this bias. We interpret our results using alternative explanations of investor behavior.

Earning management, agency cost and value: Non-rent seeking and rent seeking prone firms - Indian evidence
Ganguli, Santanu K.
SSRN
The study explores and contrasts the earning management and its nature of non-rent seeking and rent seeking prone firms followed by their respective valuation implication in the light of agency problem (cost) in India, where rent seeking is central to the economy. The empirical results suggest that the cash flow management by the non-rent seeking firms having lesser agency cost, is likely to be beneficial from valuation point as the same might aid in addressing information asymmetry as to future growth potential. Opposed to the existing literature, accrual management is considered detrimental from valuation perspective for these firms. For rent seeking prone firms where agency cost is documented to be higher, both cash flow management and cost of production management are opportunistic because they are likely to be aimed at reaping private benefit, as such, both have an inverse relation with the value. The findings of the study are robust and consistent.

Environmental assessment of a new generation battery: The magnesium-sulfur system
Claudia Tomasini Montenegro,Jens F. Peters,Manuel Baumann,Zhirong Zhao-Karger,Christopher Wolter,Marcel Weil
arXiv

As environmental concerns mostly drive the electrification of our economy and the corresponding increase in demand for battery storage systems, information about the potential environmental impacts of the different battery systems is required. However, this kind of information is scarce for emerging post-lithium systems such as the magnesium-sulfur (MgS) battery. Therefore, we use life cycle assessment following a cradle-to-gate perspective to quantify the cumulative energy demand and potential environmental impacts per Wh of the storage capacity of a hypothetical MgS battery (46 Wh/kg). Furthermore, we also estimate global warming potential (0.33 kg CO2 eq/Wh) , fossil depletion potential (0.09 kg oil eq / Wh), ozone depletion potential (2.5E-08 kg CFC-11/Wh) and metal depletion potential (0.044 kg Fe eq/Wh), associated with the MgS battery production. The battery is modelled based on an existing prototype MgS pouch cell and hypothetically optimised according to the current state of the art in lithium-ion batteries (LIB), exploring future improvement potentials. It turns out that the initial (non-optimised) prototype cell cannot compete with current LIB in terms of energy density or environmental performance, mainly due to the high share of non-active components, decreasing its performance substantially. Therefore, if the assumed evolutions of the MgS cell composition are achieved to overcome current design hurdles and reach a comparable lifespan, efficiency, cost and safety levels to that of existing LIB; then the MgS battery has significant potential to outperform both existing LIB, and lithium-sulfur batteries.

Financial Heterogeneity and the Dynamics of Credit Rationing in Japan
Mizobata, Hirokazu
SSRN
A perceptual gap between banks and firms exists in Japan, preventing the credit channel of monetary policy. Banks believe that bankable customers are scarce, while firms believe that banks do not issue loans without collateral or guarantees. To explain this gap, I focus on the dispersion in the degree of financial constraints across listed Japanese firms from FY1991 to FY2015. I construct a firm-specific and time-varying measure of financial constraints through the structural estimation, and investigate its distribution over time. The results reveal a right-skewed distribution for the index of financial constraints, indicating that many firms face minor financial constraints, while a few face severe financial constraints. The spread between the 75th and 25th percentiles of the index of financial constraints increased after the bubble burst, indicating that Japan's financial heterogeneity is becoming outstanding recently. Finally, decomposing financial heterogeneity into within- and between-industry effects shows that the observed financial inequality is due to the increase in inequality among firms within narrowly defined industries.

Financial Markets Prediction with Deep Learning
Jia Wang,Tong Sun,Benyuan Liu,Yu Cao,Degang Wang
arXiv

Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.

Financial vulnerability and seeking expert advice: Evidence from a survey experiment
Delis, Manthos D.,Galariotis, Emilios C.,Monne, Jerome
SSRN
The role of a bank advisor is especially important for guiding and counseling financially distressed individuals. Using a randomized controlled survey experiment conducted on a representative sample of French individuals and priming the financial vulnerability of half the respondents, we examine attitudes toward bank advisors. We find that priming deters low-income individuals from showing an extremely negative attitude toward seeking banking advice (positive effect); it also deters them from showing an extremely positive attitude (negative effect). We also find that acute financial distress partially drives the positive effect, and a lack of financial literacy partially drives the negative effect.

Frequency-Dependent Higher Moment Risks
BarunÃ­k, Jozef,Kurka, Josef
SSRN
Based on intraday data for a large cross-section of individual stocks and exchange traded funds, we show that short-term as well as long-term fluctuations of realized market and average idiosyncratic higher moments risks are priced in the cross-section of asset returns. Specifically, we find that market and average idiosyncratic volatility and kurtosis are significantly priced by investors mainly in the long-run even if controlled by market moments and other factors, while skewness is mostly short-run phenomenon. A conditional pricing model capturing the time-variation of moments confirms downward-sloping term structure of skewness risk and upward-sloping term structure of kurtosis risk, moreover the term structures connected to market skewness risk and average idiosyncratic skewness risk exhibit different dymanics.

Geographic income diversification of large European banks: better or worse?
Gerek, Caner,Tuncez, Ahmet M.
SSRN
This study examines the impact of geographic income diversification of large European banks on performance by using unique hand-collected European banking data. By dividing the total operating income into three regions as home country, the rest of Europe and the rest of the world, we find evidence that geographic income diversification reduces bank performance. Moreover, we separately analyze the net effects of shifting operations from home country to the rest of Europe and the rest of world income and find that they reduce the bank performance except for the banks that are already more concentrated in these regions. We also analyze only two regions (home and foreign) and control the effect of board nationality diversity, and show that our results hold.

Global pathways to sustainable development to 2030 and beyond
Enayat A. Moallemi,Sibel Eker,Lei Gao,Michalis Hadjikakou,Jan Kwakkel,Patrick M. Reed,Michael Obersteiner,Brett A. Bryan
arXiv

Progress to-date towards the UN Agenda 2030 has fallen short of expectations. We undertake a model-based global integrated assessment to project future progress by 2030, 2050, and 2100 and to characterise the transformations needed to deliver the global Sustainable Development Goals and an increasingly ambitious 21st century sustainability agenda. Our results quantify the scale and pace of transformations required through eight key entry points: increasing education access, powering sustainable economic development, controlling global population growth, lowering energy intensity across sectors, decarbonising energy systems, promoting healthy food diets, limiting agricultural land expansion, and reducing global emissions intensity. Our findings indicate many actions that appear to make a limited contribution to initial progress are in fact vital for accelerating change towards sustainable development later in the century.

Google Search and Stock Returns: A Study on Bist 100 Stocks
Bulut, Ali Eray,Ekinci, Cumhur
SSRN
This study investigates whether there is a relationship between Google search and stock returns after we account for market, size, and value. We analyze weekly data on BIST 100 stocks from 2012 to 2017. Our results reveal that Google search is associated with positive returns, especially in small-capitalization stocks, but high search volume in the current period does not predict positive returns in the next period. The relationship is stronger (weaker) for sports and real estate (commercial and banking) firms. We provide additional evidence for market, size, and value factors. Institutional interest in the stock, more than firm size, can explain the relation between search volume and stock returns.

Higher Realized Moments and Stock Return Predictability
Rehman, Seema,Sharif, Saqib,Ullah, Wali
SSRN
This study exploits information contained in high frequency sample data by computing higher realized moments of individual firms in the emerging stock market of Pakistan. Furthermore, the relation of higher moments with future stock returns is examined by constructing decile portfolios based on weekly realized volatility, skewness and kurtosis to predict next week return of the trading strategy that takes long position for portfolio of stocks having high realized moment and takes short position for portfolio of stocks having low realized moment. The long short spread is significant for equal weighted weekly returns based on realized volatility. The long short weekly return is positive and highly significant for realized skewness, 1.659 and 1.969 (in bps) with t-statistics of 7.92 and 14.027 for value and equal weighted portfolios respectively. The result for realized skewness is also supported by Carhartâ€™s Alphas. Similar results are obtained for realized kurtosis, 0.427 and 0.664 (in bps) of long short return, with t-statistics of 2.079 and 4.049 for value and equal weighted portfolios respectively. The evidence suggests that realized skewness and kurtosis can predict the next weekâ€™s moment based cross sectional stock returns.

Influences on Brand Loyalty Among Thai Female Cosmetic Consumers
Taghipour, Amirhossein
SSRN
This study aims to examine and identify variables and factors which influence Thai womenâ€™spurchasing decision of Korean cosmetics. The data were compiled from questionnaires givento 400 female respondents living in Bangkok, who were between the ages of 14-30 and whohad purchased Korean cosmetics in the past. The methodology used in this study is thecorrelation coefficient relationship and ANOVA to test the hypotheses. The results illustratedthat the country of origin (COO) has a relationship with the perceived quality of thecosmetics and consequently, to brand equity. There are differences between packaging, price,and perceived quality for customers, in which packaging has more influence on satisfaction.In addition, customer loyalty was affected indifferently by brand equity and customersatisfaction.

Least Squares Monte Carlo applied to Dynamic Monetary Utility Functions
Hampus Engsner
arXiv

In this paper we explore ways of numerically computing recursive dynamic monetary risk measures and utility functions. Computationally, this problem suffers from the curse of dimensionality and nested simulations are unfeasible if there are more than two time steps. The approach considered in this paper is to use a Least Squares Monte Carlo (LSM) algorithm to tackle this problem, a method which has been primarily considered for valuing American derivatives, or more general stopping time problems, as these also give rise to backward recursions with corresponding challenges in terms of numerical computation. We give some overarching consistency results for the LSM algorithm in a general setting as well as explore numerically its performance for recursive Cost-of-Capital valuation, a special case of a dynamic monetary utility function.

Long Term Bias, Incentives, and Agency Costs
Kastiel, Kobi
SSRN
The problem of managerial short-termism has long preoccupied policymakers, researchers, and practitioners. These groups have given much less attention, however, to the converse problem of managerial long-termism. Michal Barzuza and Eric Talley fill this gap in their pioneering article, Long-Term Bias. Relying on the behavioral finance and psychology literatures, the authors provide a novel and thought-provoking analysis of managerial long-term bias, which may be just as detrimental as the more widely condemned short-term bias. This invited Comment to Barzuza and Talleyâ€™s article advances three claims. First, it argues that proper incentivesâ€"created by executive compensation, heightened risk of early termination, market responses and shareholder pressuresâ€"are likely to turn most managers more realistic and thus to mitigate their long-term biases. Second, it explains how, in reality, it could be almost impossible to distinguish between long-term bias and traditional agency theories of empire building and pet projects. Ultimately, both long-termist and self-interested managers systematically harm shareholders; both choose to ignore shareholder interests and waste free cash flow on inferior business investments. This also explains why the cure to both long-term bias and agency costs is similar: reducing the relative insulation of the board from shareholdersâ€™ disciplinary power. Finally, this Comment expresses strong support for most of Barzuza and Talleyâ€™s normative conclusions, with one important exception: their acceptance of the use of dual-class stock. With a perpetual lock on control and a limited equity stake, corporate leaders will be immune to any â€œinstitutional brakeâ€ on all forms of long-termist overinvestment. If anything, the analysis of Barzuza and Talley provides an additional strong justification to oppose the use of perpetual dual-class stock.

Modelling uncertainty in financial tail risk: a forecasting combination and weighted quantile approach
Giuseppe Storti,Chao Wang
arXiv

A novel forecasting combination and weighted quantile based tail risk forecasting framework is proposed, aiming to reduce the impact of modelling uncertainty in financial tail risk forecasting. The proposed approach is based on a two-step estimation procedure. The first step involves the combination of Value-at-Risk (VaR) forecasts at a grid of different quantile levels. A range of parametric and semi-parametric models is selected as the model universe which is incorporated in the forecasting combination procedure. The quantile forecasting combination weights are estimated by optimizing the quantile loss. In the second step, the Expected Shortfall (ES) is computed as a weighted average of combined quantiles. The quantiles weighting structure used to generate the ES forecast is determined by minimizing a strictly consistent joint VaR and ES loss function of the Fissler-Ziegel class. The proposed framework is applied to six stock market indices and its forecasting performance is compared to each individual model in the model universe and a simple average approach. The forecasting results based on a number of evaluations support the proposed framework.

Momentum Effect in the Oman Stock Market Over the Period of 2005-2018
Gharaibeh, Omar
SSRN
The purpose of this paper is to investigate the profitability of the momentum effects on the Oman Stock Market (OSM). This study uses the monthly returns of all stocks listed on the OSM, with a total of 107 companies used in the study for the period from 2005 to 2018. According to the methodology developed by Jegadeesh and Titman (1993), this study builds momentum portfolios based on various sizes. Moreover, the January effect is also examined to recognize if this effect is related to the momentum effect. The results find that there is evidence of momentum returns and these returns are statistically and economically significant. The sub-periods confirmed the profitability of the momentum strategy. This paper shows that momentum returns are evident at different sizes; big, medium, and small-sized portfolios. Besides, the result shows that the classic January effect does not play an important role in the momentum returns. Thus, the implication is that the momentum should not take into account the annual, seasonal, and size returns. The capital asset pricing model (CAPM) or the three-factor model cannot explain momentum returns generated by individual stocks in the Oman Stock Market. These results are useful to academia and investors alike.

Monetary Policy and Asset Valuation
Bianchi, Francesco,Lettau, Martin,Ludvigson, Sydney C.
SSRN
We document large, longer-term, joint regime shifts in asset valuations and the real federal funds rate-r* spread. To interpret these findings, we estimate a novel macro-finance model of monetary transmission and find that the documented regimes coincide with shifts in the parameters of a policy rule, with long-term consequences for the real interest rate. Estimates imply that two-thirds of the decline in the real interest rate since the early 1980s is attributable to regime changes in monetary policy. The model explains how infrequent changes in the monetary policy stance can generate persistent changes in asset valuations and the equity premium.

Monetary policy, Twitter and financial markets: evidence from social media traffic
Masciandaro, Donato,Romelli, Davide,Rubera, Gaia
SSRN
How does central bank communication affect financial markets? This paper shows that the monetary policy announcements of three major central banks, i.e. the European Central Bank, the Federal Reserve and the Bank of England, trigger significant discussions on monetary policy on Twitter. Using machine learning techniques we identify Twitter messages related to monetary policy around the release of monetary policy decisions and we build a metric of the similarity between the policy announcement and Twitter traffic before and after the announcement. We interpret large changes in the similarity of tweets and announcements as a proxy for monetary policy surprise and show that market volatility spikes after the announcement whenever changes in similarity are high. These findings suggest that social media discussions on central bank communication are aligned with bond and stock market reactions.

Mortality Forecasting Using Stacked Regression Ensembles
SSRN
We present a stacked regression ensemble method that optimally combines different mortality models to reduce the mean squared errors of mortality rate forecasts and mitigate model selection risk. Stacked regression uses a supervised machine learning algorithm to approximate the horizon-specific weights by minimizing the cross-validation criterion for each forecasting horizon. The horizon-specific weights facilitate the development of a mortality model combination customized to each horizon. Unlike other model combination methods, stacked regression simultaneously solves model selection and estimates model combinations to improve model forecasts. Our numerical illustrations based on 44 populations from the Human Mortality Database demonstrate that stacking mortality models increases predictive accuracy. Using one-year-ahead to 15-year-ahead out-of-sample mean squared errors, we find that stacked regression improves mortality forecast accuracy by 13% - 49% and 19% - 90% over the individual mortality models for males and females, respectively. Therefore, combining the mortality rate forecasts provides lower out-of-sample point forecast errors than selecting the single best individual mortality method. Stacked regression ensemble also achieves better predictive accuracy than other model combination methods, namely Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. Our results support the stacked regression ensemble approach over individual mortality models and other model combination methods in forecasting mortality rates. We also provide a user-friendly open-source R package, CoMoMo, that combines multiple mortality rate forecasts using different model combination techniques.

Multivariate Systemic Optimal Risk Transfer Equilibrium
Alessandro Doldi,Marco Frittelli
arXiv

A Systemic Optimal Risk Transfer Equilibrium (SORTE) was introduced in: "Systemic optimal risk transfer equilibrium", Mathematics and Financial Economics (2021), for the analysis of the equilibrium among financial institutions or in insurance-reinsurance markets. A SORTE conjugates the classical B\"{u}hlmann's notion of a Risk Exchange Equilibrium with a capital allocation principle based on systemic expected utility optimization. In this paper we extend such a notion to the case when the value function to be optimized is multivariate in a general sense, and it is not simply given by the sum of univariate utility functions. This takes into account the fact that preferences of single agents might depend on the actions of other participants in the game. Technically, the extension of SORTE to the new setup requires developing a theory for multivariate utility functions and selecting at the same time a suitable framework for the duality theory. Conceptually, this more general framework allows us to introduce and study a Nash Equilibrium property of the optimizer. We prove existence, uniqueness, and the Nash Equilibrium property of the newly defined Multivariate Systemic Optimal Risk Transfer Equilibrium.

Oil-US Stock Market Nexus: Some insights about the New Coronavirus Crisis
Claudiu Albulescu,Michel Mina,Cornel Oros
arXiv

We provide a new investigation of the relationship between oil and stock prices in the context of the outbreak of the new coronavirus crisis. Specifically, we assess to what extent the uncertainty induced by COVID-19 affects the interaction between oil and the United States (US) stock markets. To this end, we use a wavelet approach and daily data from February 18, 2020 to August 15, 2020. We identify the lead-lag relationship between oil and stock prices, and the intensity of this relationship at different frequency cycles and moments in time. Our unique findings show that co-movements between oil and stock prices manifest at 3-5-day cycle and are stronger in the first part of March and the second part of April 2020, when oil prices are leading stock prices. The partial wavelet coherence analysis, controlling for the effect of COVID-19 and US economic policy-induced uncertainty, reveals that the coronavirus crisis amplifies the shock propagation between oil and stock prices.

On ESG Investing: Heterogeneous Preferences, Information, and Asset Prices
Goldstein, Itay,Kopytov, Alexandr,Shen, Lin,Xiang, Haotian
SSRN
We study how environmental, social and governance (ESG) investing reshapes information aggregation and price formation. We develop a rational expectations equilibrium model in which traditional and green investors are informed about monetary and non-monetary risks but have distinct preferences over them. Because of the preference heterogeneity, traditional and green investors trade in opposite directions based on the same information and make the price noisier to each other. We show that an increase in the share of green investors and an improvement in the quality of non-monetary information can reduce overall price informativeness and increase firm's cost of capital. Our analyses provide a rich set of testable implications.

Online Appendix: The Impact of Cryptocurrency Regulation on Trading Markets
Feinstein, Brian D.,Werbach, Kevin
SSRN
This file contains additional analyses to support Feinstein & Werbach, "The Impact of Cryptocurrency Regulation on Trading Markets," Journal of Financial Regulation (forthcoming).

Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China
Yue Liu,Lixin Tian,Zhuyun Xie,Zaili Zhen,Huaping Sun
arXiv

Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene.

Realization Utility with Path-Dependent Reference Points
Kong, Linghui,Qin, Cong,Yue, Xingye
SSRN
In this paper, we propose a tractable model to study the impact of path-dependent reference points on optimal trading strategies of a realization utility investor. We find that when reference points are adaptive to prior paper gains and losses, two interesting effects arise endogenously: (a) Discount effect, i.e., a constant subjective discount factor in effect becomes a stochastic one; and (b) Mean-reverting effect, i.e., a constant investment opportunity in effect becomes a stochastic one with expected return being mean-reverting, which provides a new interpretation of belief in mean reversion. In addition, these two effects offset each other in the state of paper gains while get reinforced in the state of paper losses, leading to a more salient disposition effect. The model can be easily extended to incorporate other factors such as asymmetric adaptation of reference points, jump risks in the underlying stocks, and liquidation shocks, yielding new interesting trading behaviors.

Relationship between Framing Bias and Big Five Personality Traits of Individual Investors
Sachan, Abhishek,Chugan, Pawan K.
SSRN
Returns depend upon decisions of investors, but investors biases challenge the ability to take rational decisions. Study of biases and their relationships with personality traits helps to understand how biases originate, the way in which they possibly effect investors, and which personality types could be more susceptible to them. There are evidences that biases have relationships with personality traits of investors and this study focuses on one such relationship between framing bias and personality traits. Given the qualitative nature of variables under study, the relationship was established by statistically significant coefficients of logistic regression equation, where bias-variable was dependent and big five personality traits were independent. The score of personality trait, which had significant relationship, was cross tabulated with bias variable, the chi square test indicated a statistically significant relationship. The results lead to conclusion that an investor with higher score of agreeableness has higher probability of having framing bias. It is also discussed that an agreeable person may demonstrate irrationality discussed in prospect theory, more as compared to others, as the framing effects were measured using gain and loss frames. Since the study deals with frames of communication, it indicates towards the effects of personality traits on communication between portfolio manager and clients. The study contributes for portfolio managers that an agreeable client may not actually agree for rational decision if the communication is not in right frame.

Resource Mobilization by Listed Entities in International Capital Markets
Gosavi, Chinmay
SSRN
Raising funds from international markets is very key factor for any listed entity as it does not only cater the need of funds but also increase the goodwill and reputation of company on internationalplatform. It helps the company in broadening itâ€™s shareholder base and to enhance investor quality.Nowadays many Indian listed entities attract towards international capital markets as the underlying instruments are listed and traded in international stock exchanges hence free from delivery and settlement problems. Further the foreign investors are not required to comply with rigid formalities and regulation which otherwise they would require in case of investment through other Foreign Direct Investment (FDI) routes. The present research paper undertakes critical study of various methods and instruments of tapping international capital markets. This paper examines the regulatory provisions to issue instruments in international capital markets.

Risk transference constraints in optimal reinsurance
BalbÃ¡s, Alejandro,BalbÃ¡s, Beatriz,BalbÃ¡s, Raquel,Heras, Antonio
SSRN
This paper deals with the optimal reinsurance problem and involves the goals of both insurer and reinsurer. An important novelty may be the incorporation of the background risk that the reinsurer uses in order to diversify (or hedge) the risk ceded by the insurer. Accordingly, general methods to prevent the reinsurer moral hazard must be extended, and a new constraint must be satisfied by the selected reinsurance contract, namely, "the reinsurer increment of risk must be lower than the contract premium". Simultaneously, since the contract must be attractive to the insurer too, "the contract premium must be lower than the insurer risk reduction". Integrating both ideas, "the contract premium must be higher than the reinsurer risk growth and lower than the insurer risk mitigation". Bearing in mind both requirements, that is, the protection against the moral hazard and the spread containing the contract premium, the optimal reinsurance problem is studied under very general conditions about the involved risk measures and premium principles, general solutions are provided, and a practical illustrative example is presented.

Road safety for fleets of vehicles
Dionne, Georges,Desjardins, Denise,Angers, Jean-François
RePEC
Road safety for fleets of vehicles has been neglected in the insurance literature, mainly because appropriate data is not available. This paper makes a threefold contribution: 1) Produce statistics on current fleets' road safety offences using a panel of 20 years of data on truck fleets; 2) relate these offences to fleets' accidents; and 3) identify and classify the riskiest fleets for insurance ratemaking based on past experience in managing road safety. Our results show a substantial heterogeneity between fleets in terms of road safety.

Selfish Shareholders: Corporate Donations During COVID-19
Michele Fioretti,Victor Saint-Jean,Simon C. Smith
arXiv

During the onset of the COVID-19 pandemic, conflicting incentives caused most shareholders to adverse corporate social responsibility (CSR) -- measured by firms' charitable donations -- since it would further burden firms' already strained finances. Those shareholders that favored donations, large individual investors, did so to bolster their own images as they are typically synonymous with the donating firms. Image gains do not pass through to institutional shareholders, who instead preferred to donate themselves rather than having the firms they invested in donate. Taken together, our results cast doubts on large corporations' willingness to demand costly CSR measures across firms in their portfolios.

Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall
SSRN
In this paper new semiparametric GARCH models with long memory are introduced.The estimation of the nonparametric scale function is carried out by anadapted version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on therevised recommendations by the Basel Committee to measure market risk in thebanks' trading books (Basel Committee on Banking Supervision, 2013), the semi-parametric GARCH models are applied to obtain rolling one-step ahead forecastsfor the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets.In addition, standard regulatory traffic light tests (Basel Committee on BankingSupervision, 1996) and a newly introduced traffic light test for the ES are carriedout for all models. The practical relevance of our proposal is demonstrated by acomparative study. Our results indicate that semiparametric long memory GARCHmodels are an attractive alternative to their conventional, parametric counterparts.

Short Term Trading Models Using Hurst Exponent and Machine Learning
Sidhu, Gursewak Singh,Ibrahim Ali Metwaly, Ali,Tiwari, Animesh,Bhattacharyya, Ritabrata
SSRN
Predicting the direction of Stock Indices has always been an appealing topic which has motivated researchers over the years to develop better predictive models. Recently, Machine learning (ML) based models have been frequently deployed to forecast the direction of classic financial time series data. In the 1950s, Hurst Exponent was introduced as a statistical measure to classify various Time Series. This research analyzes the effectiveness of using Machine Learning and Hurst Exponent along with popular Technical Indicators for short term trading predictions. In this study we explore the use of Hurst Exponent to segment data for a short-term machine learning model in order to improve trading strategy. A comparative analysis has been carried out between the performance of a standalone short-term model, and a Segmented model (Segments based on hurst exponent cut off) in S&P 500, SSE Composite Indices, Gold SPDR Shares and Bitcoin. This new approach is being introduced in order to reach the optimum integration between Machine learning & Hurst Exponent.

Social Networks and Market Reactions to Earnings News
Hirshleifer, David A.,Peng, Lin,Wang, Qiguang
SSRN
Using social network data from Facebook, we show that earnings announcements made by firms located in counties with higher investor social network centrality attract more attention from both retail and institutional investors. For such firms, the immediate price and volume reactions to earnings announcements are stronger, and post-announcement drift is weaker. Such firms have lower post-announcement persistence of return volatility but higher persistence in investor attention and trading volume. These effects are stronger for small firms, firms with poor analyst and media coverage, and for stocks with salient returns. Our evidence suggests a dual role of social networks---they facilitate the incorporation of public information into prices, but also trigger persistent excessive trading.

Squeezing Shorts Through Social News Platforms
Tengulov, Angel,Allen, Franklin,Nowak, Eric,Pirovano, Matteo
SSRN
At the end of January 2021, a group of stocks listed on US stock exchanges experienced sudden surges in their stock prices, which - coupled with high short interest â€" led to brief short squeeze episodes. We argue that these short squeezes were the result of coordinated trading by investors, who discussed their trading strategies on social news platforms. In addition, option markets played a central role in these events. Using hand-collected data we provide the first rigorous academic study of these short-squeezes and show that they significantly impeded market quality not only of the stocks at issue but also of their competitors. This evidence calls for tighter monitoring of social news platforms and a better understanding of the interlinkages between these platforms, derivatives markets and equity markets.

Strategic Choice of Presentation Format: The Case of ETR Reconciliations
Chychyla, Roman,Falsetta, Diana,Ramnath, Sundaresh
SSRN
To minimize costs related to unfavorable perceptions of their tax-related activities, firms with low effective tax rates (ETR) could avoid, where possible, explicit mentions of their effective tax rates. Using this reputational cost perspective we study an item of required disclosure in the income tax footnote of the 10-K, the ETR reconciliation table, where firms can choose a presentation format that reveals the tax rate (the percentage format) or one that avoids explicit mention of the effective tax rate (the dollar format). We find that firms with low ETRs are 24 percent more likely to use the dollar format, and are also less likely to mention their tax rates elsewhere in their disclosures, consistent with the choice of dollar format reflecting a firm's overall tax disclosure strategy. Analysts' tax expense forecasts are less accurate for dollar format firms, suggesting higher processing costs associated with tax-related disclosures for these firms.

THE RELIABILITY OF GEOMETRIC BROWNIAN MOTION FORECASTS OF S&P 500 INDEX VALUES
Sinha, Amit K.
SSRN
This manuscript extends the literature on the application of geometric Brownian motion. Forecasted drift and diffusion terms estimated separately and recursively are plugged into the framework to forecast S&P 500 index values. Expected index values are estimated from one hundred thousand simulated index values and probabilities. The results of comparing expected index values to actual values, indicate that while reliable predictions of S&P 500 index values can be obtained at monthly, quarterly and annual frequencies, the reliability may decrease in that order.

The Distribution of Investor Beliefs, Stock Ownership and Stock Returns
Hardouvelis, Gikas A.,Karalas, Georgios,Vayanos, Dimitri
SSRN
We study theoretically and empirically the relationship between investor beliefs, ownershipdispersion and stock returns. We find that high dispersion, measured by high breadth or lowHerfindahl index, forecasts returns positively for large stocks, as in Chen, Hong, and Stein (2002),but negatively for small stocks. We explain that relationship in a difference-of-opinion modelin which stocks differ in the size of investor disagreements and the extent of belief polarization.These differences are characterized by range and kurtosis, respectively. Proxying investor beliefsby analyst forecasts, we find that range and kurtosis affect ownership dispersion in the way thatour model predicts.

The EU Sustainable Governance Consultation and the Missing Link to Soft Law
Ferrarini, Guido ,Siri, Michele,Zhu, Shanshan
SSRN
In this paper, we investigate whether reform of EU company law is needed to make corporate governance more sustainable through an analysis of some of the key questions found in the European Commission's questionnaire in its public consultation on sustainable corporate governance. We also consider some issues, which the Commission paid scant attention to in its questionnaire, such as the role of corporate governance codes and other types of soft law, mainly of international origin, in promoting sustainable governance. In addition, we underline that the EU legislator has adopted several measures in recent years, which offer better prospects for sustainable governance than the reform of directors' duties the Commission is currently planning. We conclude that the failure to take corporate governance codes and the existing regulatory framework into account could seriously impair pending reforms of directors' duties and their link to sustainability.

The Effect of Sport in Online Dating: Evidence from Causal Machine Learning
Daniel Boller,Michael Lechner,Gabriel Okasa
arXiv

Online dating emerged as a key platform for human mating. Previous research focused on socio-demographic characteristics to explain human mating in online dating environments, neglecting the commonly recognized relevance of sport. This research investigates the effect of sport activity on human mating by exploiting a unique data set from an online dating platform. Thereby, we leverage recent advances in the causal machine learning literature to estimate the causal effect of sport frequency on the contact chances. We find that for male users, doing sport on a weekly basis increases the probability to receive a first message from a woman by 50%, relatively to not doing sport at all. For female users, we do not find evidence for such an effect. In addition, for male users the effect increases with higher income.

The Efficient Hedging Frontier with Deep Neural Networks
Zheng Gong,Carmine Ventre,John O'Hara
arXiv

The trade off between risks and returns gives rise to multi-criteria optimisation problems that are well understood in finance, efficient frontiers being the tool to navigate their set of optimal solutions. Motivated by the recent advances in the use of deep neural networks in the context of hedging vanilla options when markets have frictions, we introduce the Efficient Hedging Frontier (EHF) by enriching the pipeline with a filtering step that allows to trade off costs and risks. This way, a trader's risk preference is matched with an expected hedging cost on the frontier, and the corresponding hedging strategy can be computed with a deep neural network.

We further develop our framework to improve the EHF and find better hedging strategies. By adding a random forest classifier to the pipeline to forecast market movements, we show how the frontier shifts towards lower costs and reduced risks, which indicates that the overall hedging performances have improved. In addition, by designing a new recurrent neural network, we also find strategies on the frontier where hedging costs are even lower.

The Growth of Passive Indexing and Smart-Beta: Competitive Effects on Actively Managed Funds
Densmore, Michael
SSRN
Using a sample of US equity funds, I investigate the extent to which competition from low-cost index funds affects fees, performance, and survival rates of actively managed funds. I measure the intensity of competition using the market value of holdings overlap between the portfolios of index entrants and active incumbents. Disentangling the competitive effects of traditional index funds (market index) from smart-beta index funds (factor index), I provide evidence that factor index fund entry is negatively related to changes in actively managed net fees but no significant impact of market index fund entry. Additionally, I find that both factor and market index entry are negatively related to active incumbent survival rates and that this effect is most pronounced for relatively expensive active incumbents. Importantly, I show that entry of index funds has had an attenuating effect on dispersion in fees across actively managed funds. Lastly, I find evidence that factor index entry has had an attenuating effect on active incumbent future performance.

The Impact of Investor Sentiment on Catering Incentives around the World
Kim, Kihun,Byun, Jinho,Liao, Rose C.,Pan, Carrie H.
SSRN
This study tests catering theory of dividend policies in twenty-one countries from 1991 to 2017. First, we show that there are important differences in corporate dividend policies across countries. Second, we find that the catering incentive is stronger when investor sentiment is low. Third, firms domiciled in countries with strong legal protections for investors are more likely to catering to investors, especially when investor sentiment is low. Our findings shed light to the factors contributing to the fluctuations in dividend catering around the world.

The Kuroda Bazooka
Gunji, Hiroshi
SSRN
In this study, we examine the impact of the monetary easing policy announced by the Bank of Japan on October 31, 2014 on householdsâ€™ willingness to borrow. This policy, called the Kuroda Bazooka, was not anticipated by the private sector, so it can be regarded as an exogenous shock. We use an interrupted time-series analysis to estimate the effects of the Kuroda Bazooka, a technique often used in medicine but not yet widely used in economics. The analysis of the data before and after the shock show that the Kuroda Bazooka increased household borrowing intention by about 10%.

The Mechanisms of Loan Market Efficiency
SSRN
This article develops an account of the mechanisms of efficiency of corporate loan markets or the secondary markets in which loans made to corporate borrowers are traded. In our account: 1) professionally informed trading incorporating information about the quality of the loan terms offered to borrowers, is the primary source of corporate loan market efficiency, and 2) antitrust law is among the principal policy tools that can foster loan market efficiency by policing market participants' efforts to restrict activist loan market investors from accessing information in the loan market. The main objective of fostering loan market efficiency is to allow activist investors incorporate information about the erosion of the quality of the underwritten terms into loan prices and prompt corrections in mispricing in primary markets thereby contributing to the tightening of the terms subsequently offered in primary markets. From a policy perspective, efficient loan markets can help alleviate the concerns around the erosion of underwriting standards that have become widespread in recent years.

The Microstructure of Cointegrated Assets
Stoikov, Sasha,Decrem, Peter,Hua, Yikai,Shen, Anne
SSRN
We define the micro price of multiple cointegrated assets. This yields a notion of fair prices, as a function of the observable state of multiple order books. We compute the microprices of two highly cointegrated assets, using Level-1 data collected on Interactive Brokers. We design an execution algorithm based on this two dimentional microprice and show that it can save half of the bid-ask spread cost.The code for this paper is available here: https://github.com/xhshenxin/Micro_Price

The link between unemployment and real economic growth in developed countries
Ivan Kitov
arXiv

Ten years ago we presented a modified version of Okun law for the biggest developed economies and reported its excellent predictive power. In this study, we revisit the original models using the estimates of real GDP per capita and unemployment rate between 2010 and 2019. The initial results show that the change in unemployment rate can be accurately predicted by variations in the rate of real economic growth. There is a discrete version of the model which is represented by a piece wise linear dependence of the annual increment in unemployment rate on the annual rate of change in real GDP per capita. The lengths of the country-dependent time segments are defined by breaks in the GDP measurement units associated with definitional revisions to the nominal GDP and GDP deflator (dGDP). The difference between the CPI and dGDP indices since the beginning of measurements reveals the years of such breaks. Statistically, the link between the studied variables in the revised models is characterized by the coefficient of determination in the range from R2=0.866 (Australia) to R2=0.977 (France). The residual errors can be likely associated with the measurement errors, e.g. the estimates of real GDP per capita from various sources differ by tens of percent. The obtained results confirm the original finding on the absence of structural unemployment in the studied developed countries.

Uncovering commercial activity in informal cities
Daniel Straulino,Juan C. Saldarriaga,Jairo A. Gómez,Juan C. Duque,Neave O'Clery
arXiv

Knowledge of the spatial organisation of economic activity within a city is key to policy concerns. However, in developing cities with high levels of informality, this information is often unavailable. Recent progress in machine learning together with the availability of street imagery offers an affordable and easily automated solution. Here we propose an algorithm that can detect what we call 'visible firms' using street view imagery. Using Medell\'in, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. Applying spatial analysis to our dataset we detect a polycentric structure with five distinct clusters located in both the established centre and peripheral areas. Comparing the density of visible and registered firms, we find that informal activity concentrates in poor but densely populated areas. Our findings highlight the large gap between what is captured in official data and the reality on the ground.

Using Social Media to Identify the Effects of Congressional Partisanship on Asset Prices
Bianchi, Francesco,Gomez Cram, Roberto,Kung, Howard
SSRN
We measure the individual and collective viewpoints of US Congress members on various economic policies by scraping their Twitter accounts. Tweets that criticize (support) a particular company are associated with a significant negative (positive) stock price reaction in a narrow time window around the tweet. A sharp partisan divide emerges, with Republicans and Democrats coordinated in both their support and opposition for different industries emanating from disparate legislative agendas. Members of congress coordinate within parties to push legislation through their social media accounts. As an illustrative and relevant example, we analyze the Tax Cuts and Jobs Act of 2017 and document significant aggregate stock market responses to the real-time evolution of partisan viewpoints about the bill.

Vaccine allocation to blue-collar workers
László Czaller,Gergő Tóth,Balázs Lengyel
arXiv

Vaccination may be the solution to the pandemic-induced health crisis, but the allocation of vaccines is a complex task in which economic and social considerations can be important. The central problem is to use the limited number of vaccines in a country to reduce the risk of infection and mitigate economic uncertainty at the same time. In this paper, we propose a simple economic model for vaccine allocation across two types of workers: white-collars can work from home; while blue-collars must work on site. These worker types are complementary to each other, thus a negative shock to the supply of either one decreases the demand for the other that leads to unemployment. Using parameters of blue and white-collar labor supply, their infection risks, productivity losses at home office during lock-down, and available vaccines, we express the optimal share of vaccines allocated to blue-collars. The model points to the dominance of blue-collar vaccination, especially during waves when their relative infection risks increase and when the number of available vaccines is limited. Taking labor supply data from 28 European countries, we quantify blue-collar vaccine allocation that minimizes unemployment across levels of blue- and white-collar infection risks. The model favours blue-collar vaccination identically across European countries in case of vaccine scarcity. As more vaccines become available, economies that host large-shares of employees in home-office shall increasingly immunize them in case blue-collar infection risks can be kept down. Our results highlight that vaccination plans should include workers and rank them by type of occupation. We propose that prioritizing blue-collar workers during infection waves and early vaccination can also favour economy besides helping the most vulnerable who can transmit more infection.

Chen, Jun,Ewens, Michael
SSRN
Although an extensive literature shows that startups are financially constrained and that constraints vary by geography, the source of these constraints is still relatively unknown. We explore intermediary financing constraints, a channel studied in the banking literature, but only implicitly addressed in the venture capital (VC) literature. Our empirical setting is the VC fundraising and startup financing environment around the passage of the Volcker Rule, which restricted banks' ability to invest in venture capital funds as limited partners (LPs). The rule change disproportionately impacted regions of the U.S. historically lacking in VC financing. We find that a one standard deviation increase in VCs' exposure to the loss of banks as LPs led to an 18% decline in fund size and about a 10% decrease in the likelihood of raising a follow-on fund. Startups were not completely cushioned from the additional constraints on their VCs: capital raised fell and pre-money valuations declined. Overall, VC financing constraints manifest as fewer, smaller funds that change investment strategy and experience increases in bargaining power. Last, we show that the rule change increased the likelihood startups moved out of impacted states, thus exacerbating the geographic disparity in high-growth entrepreneurship.

WTO GPA and Sustainable Procurement as Tools for Transitioning to a Circular Economy
Sareesh Rawat
arXiv

We live in an age of consumption with an ever-increasing demand of already scarce resources and equally fast growing problems of waste generation and climate change. To tackle these difficult issues, we must learn from mother nature. Just like waste does not exist in nature, we must strive to create circular ecosystems where waste is minimized and energy is conserved. This paper focuses on how public procurement can help us transition to a more circular economy, while navigating international trade laws that govern it.

What is a Protection Gap? Homeowners Insurance as a Case Study
Feinman, Jay M.
SSRN
In the past few years, the insurance community has paid increasing attention to the â€œprotection gapâ€â€"the extent to which significant losses are not covered by insurance. The Geneva Association, the insurersâ€™ global think tank, has pioneered the concept, and it has become widely adopted. Insurance always presents gaps in coverage; not all risks are insured or indeed insurable. The protection gap concept necessarily embodies a normative componentâ€"that insureds with limited coverage, potential insureds who lack insurance, and society as a whole suffer when certain gaps in insurance exists. It is this normative component of the protection gap concept that has not been fully developed and is the subject of this article. Part I of the article explains the commonly used definitions of the protection gap. The most commonly used definitionâ€"the â€œrisk protection gapâ€â€"is purely empirical, measuring the difference between total losses and insured losses. Analytically superior but harder to operationalize is the â€œinsurance protection gap,â€ which is the difference between the amount of insurance that is economically beneficial and the amount of insurance in place. The insurance protection gap properly introduces a normative element to the concept, but it does not capture all of the considerations at stake. Part I offers a different definition: In a particular context, the protection gap is the difference between the amount of insurance that is in place and the amount of insurance that should be in place. Part II of the article expands on the definition and discusses how much insurance â€œshould beâ€ in place. The method begins by defining a particular insurance context and then constructs policyholder expectations in that context. To define a baseline against which a protection gap should be measured, however, policyholder expectations must be reasonable. Therefore, the risks at issue must be insurable, the insurance must not be undermined by other effectiveness issues, and the social effects of coverage or its absence must be taken into account. Part III illustrates how the articleâ€™s definition of the protection gap can be applied by analyzing several issues in homeowners insurance. A major problem, and a clear instance of the protection gap, is the extent to which homeowners frequently are underinsured for their losses. The most frequently discussed protection gap involves disaster losses, so this part applies the analysis to flood losses. The part concludes by considering whether several more mundane issues constitute protection gaps, damage caused by rain runoff, and matching of damaged and undamaged property.

When the banking gets tough, the large get going: How capital regulation is driving consolidation
Maragopoulos, Nikos
SSRN
In response to the Global Financial Crisis (2007-2009), the capital regulation was significantly enhanced through the adoption of regulatory measures aimed to improve the resilience of banks, among others, by increasing the required quantity and quality of capital held. Particular emphasis was placed on addressing the â€œtoo-big-to-failâ€ problem seeking to reduce the incentives for large banks to become ever larger and more systemically relevant. The present paper examines whether the banksâ€™ size affects the level of applicable capital requirements and the amount of (CET1) capital that banks are required to hold. Based on an analysis of the capital requirements for 108 ECB-supervised banks, it is demonstrated that the arrangements governing the calibration of the bank-specific elements of capital requirements (i.e. Pillar 2 Requirement, systemic buffer, Pillar 2 Guidance), as well as other bank-specific characteristics relevant to capital requirements (i.e. banksâ€™ ability to issue AT1 and Tier 2 instruments, RW density) tend to favour G-SIBs and other large banks with assets exceeding â‚¬200bn. On average, G-SIBs are subject to a CET1 requirement of c. 3pp lower than banks with assets less than â‚¬30bn, mainly due to the fact that they take advantage of the AT1 and Tier 2 allowances granted by CRR/CRDV. Also, given that large banks have a significantly lower RW density, for every billion of assets held, the amount of CET1 capital that G-SIBs are required to hold is nearly half the amount that small banks must keep under the applicable capital requirements.The discrepancies relating to the approach for the determination of the systemic buffer (i.e. highest of G-SII buffer, O-SII buffer, or systemic risk buffer), which is still determined at national level, contribute to the creation of an unlevel playing field in the Banking Union, as banks with similar asset size and systemic relevance are treated in a different manner because they are located in different Member States. Therefore, the SSMR should be amended to transfer the competence for the determination of the systemic buffer to the ECB to ensure that the Banking Union will be treated as a single jurisdiction and the determination of the systemic buffer will be made based on uniform conditions.As demonstrated in this analysis, the capital regulation functions as an incentive, rather than as an obstacle, to the further increase of banksâ€™ size. In light of the need for further consolidation of the banking sector in the Banking Union, banks subject to lower (CET1) capital requirements, as is the case for large banks, need less CET1 capital to finance potential M&As, mostly by leveraging on their ability to tap capital markets for AT1 and Tier 2 issuances. Thus, from a capital requirements perspective, large banks have every incentive to expand their operations, either on domestic or cross-border basis, at the expense of smaller banks.

Why is Corporate Virtue in the Eye of the Beholder? The Case of ESG Ratings
Christensen, Dane M.,Serafeim, George,Sikochi, Siko
SSRN
Despite the rising use of environmental, social, and governance (ESG) ratings, there is substantial disagreement across rating agencies regarding what rating to give to individual firms. As what drives this disagreement is unclear, we examine whether a firmâ€™s ESG disclosure helps explain some of this disagreement. We predict and find that greater ESG disclosure actually leads to greater ESG rating disagreement. These findings hold using firm fixed effects, and using a difference-in-differences design with mandatory ESG disclosure shocks. We also find that raters disagree more about ESG outcome metrics than input metrics (policies), and that disclosure appears to amplify disagreement more for outcomes. Lastly, we examine consequences of ESG disagreement and find that greater ESG disagreement is associated with higher return volatility, larger absolute price movements, and a lower likelihood of issuing external financing. Overall, our findings highlight that ESG disclosure generally exacerbates ESG rating disagreement rather than resolving it.

Ãœberblick Ã¼ber die aktuelle Entwicklung von Financial Literacy unter BerÃ¼cksichtigung der MÃ¶glichkeiten der privaten Altersvorsorge
Daus, Viktoria,Krahnhof, Philippe,Zureck, Alexander
SSRN
Financial Literacy hat seit dem Jahr 2005 zunehmend an Bedeutung gewonnen, ohne dass das Bewusstsein fÃ¼r diese Eigenschaft in den KÃ¶pfen der BevÃ¶lkerung prÃ¤sent ist. Hierbei handelt es sich um den Fachterminus zur Messung der finanziellen Bildung von SchÃ¼lern bzw. von Erwachsenen. Der Artikel dient der Beantwortung der Frage: â€žWas bedeutet Financial Literacy und warum hat es so eine groÃŸe Bedeutung im Zusammenhang mit der finanziellen Vorsorge?â€œFolglich dient diese Ausarbeitung dem Ãœberblick der Financial Literacy Thematik unter besonderer BerÃ¼cksichtigung der MÃ¶glichkeiten der privaten Altersvorsorge aus Sicht von deutschen Privatkunden.Der Artikel dient u.a. der Illustration der notwendigen Investition in das Bildungssystem zum nachhaltigen Aufbau sowie Sicherstellung der Finanzbildung in Deutschland. Der Fokus dieser Arbeit liegt auf der Vermittlung allgemeiner Grundlagen im Kontext von Financial Literacy.