# Research articles for the 2021-05-24

A New Valuation Measure for the Stock Market
Andrey Sarantsev
arXiv

We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the American stock market. We split total returns into three components: earnings growth, dividend yield, and valuation change. The first two components are fundamental, the third is speculative. We treat earnings growth as exogenous. Combining the other two components gives us a new valuation measure, which fits autoregression of order 1 with Gaussian innovations, centered at 4.6%. Therefore, long-term total returns equals long-term earnings growth plus 4.6%. We compare this new valuation measure with CAPE. We confirm the classic rule: a retiree should invest in stocks and withdraw 4% of initial wealth after adjusting for inflation, this will preserve wealth and establish retirement income.

A maximum entropy model of bounded rational decision-making with prior beliefs and market feedback
Benjamin Patrick Evans,Mikhail Prokopenko
arXiv

Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack-Leibler divergence between posterior decisions and prior beliefs. When information acquisition is free, the homo economicus agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least-biased decisions based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making in the presence of market feedback, as well as importantly adding a temporal interpretation to the framework. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.

Aggressive Boards and CEO Turnover
SSRN
This study investigates a communication game between a CEO and a board of directors where the CEO's career concerns can potentially impede value-increasing informative communication. By adopting a policy of aggressive boards (excessive replacement), shareholders can facilitate communication between the CEO and the board. The results are in contrast to the multitude of models which generally find that management-friendly boards improve communication, and help to explain empirical results concerning CEO turnover. The results also provide the following novel predictions concerning variation in CEO turnover: (1) there is greater CEO turnover in firms or industries where CEO performance is relatively more difficult to assess; (2) the board is more aggressive in their replacement of the CEO in industries or firms where the board's advisory role is more salient; and (3) there is comparatively less CEO turnover in firms or industries where the variance of CEO talent is high.

Air Pollution and Media Slant: Evidence from Chinese Corporate News
Wang, Xinjie,Wu, Ge,Xiang, Zhiqiang,Zhang, Jianyu
SSRN
This paper examines the impact of air pollution on the media slant of publicly listed firms in China. Using a large panel of air quality and media data at the city level, we find that lower air quality generally leads to a more negative media slant. When the air quality falls from lightly polluted to heavily polluted, the number of negative sentences in a news article increases by about 1%. Our subsample analysis shows that the effect of air pollution on media slant is similar in news articles for large and small firms, SOE and non-SOE firms, official press, and non-official press. Furthermore, the effect of air pollution on media slant is stronger for firms in heavy pollution industries. These results suggest that air pollution affects media slant.

Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria, and Models
Xin, Xi,Huang, Fei
SSRN
With the rapid development of artificial intelligence/machine learning technologiesand insurersâ€™ extensive use of Big Data, a growing concern is that insurance companiescan use proxies or develop more complex and opaque algorithms to discriminate againstpolicyholders. A grey area has resulted from this phenomenon â€" direct discrimination isprohibited, but indirect discrimination can be tolerated without restrictions. This paperaims to establish the linkage of various insurance regulations, fairness criteria, andanti-discrimination pricing models. To this end, this paper reviews anti-discriminationlaws and regulations of different jurisdictions with a special focus on indirect discriminationof general insurance in the US.We summarise different discrimination definitionsand fairness criteria originated from both insurance and machine learning fields. Empiricalanalysis using a general insurance dataset is conducted to compare differentanti-discrimination models and their impact on insurance pricing.

Arbitrage-free neural-SDE market models
Samuel N. Cohen,Christoph Reisinger,Sheng Wang
arXiv

Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books. This paper develops a nonparametric model for the European options book respecting underlying financial constraints and while being practically implementable. We derive a state space for prices which are free from static (or model-independent) arbitrage and study the inference problem where a model is learnt from discrete time series data of stock and option prices. We use neural networks as function approximators for the drift and diffusion of the modelled SDE system, and impose constraints on the neural nets such that no-arbitrage conditions are preserved. In particular, we give methods to calibrate \textit{neural SDE} models which are guaranteed to satisfy a set of linear inequalities. We validate our approach with numerical experiments using data generated from a Heston stochastic local volatility model.

Asset Securitization and Firm Expansion in Product Markets: Evidence from the Real Estate Development Industry
Ma, Chao
SSRN
Previous studies found that through facilitating â€œbankruptcy remoteness,â€ asset-backed securitization can reduce firmsâ€™ borrowing costs and probabilities of facing credit constraints and increase their market values. However, little research has examined the real effect of securitization on firmsâ€™ product-market activities. We examine the real estate development industry and find that after securitization, developers become more aggressive in purchasing land and entering new markets. With extra funding from securitization, financially constrained developers become more likely to purchase a parcel independently, whereas developers with capacity constraints conduct more strategic alliances with other developers in purchasing a parcel to utilize more production capacities.

Banche centrali, rischi climatici e finanza sostenibile [Central Banks, Climate Risks and Sustainable Finance]
Bernardini, Enrico,Faiella, Ivan,Mistretta, Alessandro,Natoli, Filippo,Lavecchia, Luciano
SSRN
Italian Abstract: Negli ultimi anni i cambiamenti climatici in corso e la transizione verso un modello di sviluppo economico sostenibile hanno assunto una rilevanza centrale per il sistema finanziario, chiamando in causa anche le banche centrali. Queste ultime, il cui interesse Ã¨ testimoniato dai lavori del Network for Greening the Financial System (NGFS), stanno raccogliendo le sfide poste da tali fenomeni all'interno delle loro attivitÃ   istituzionali e di investimento. La Banca d'Italia, attraverso progetti di studio interni e partecipando ai maggiori tavoli di lavoro a livello nazionale e internazionale, contribuisce all'analisi dei rischi che i cambiamenti climatici comportano per il sistema economico e finanziario. Inoltre, in linea con i recenti sviluppi della finanza sostenibile, ha integrato criteri di sostenibilitÃ   nelle proprie decisioni di investimento. Questo lavoro ha lo scopo di dar conto delle evidenze finora ottenute circa i rischi e le opportunitÃ   legati ai cambiamenti climatici e alla finanza sostenibile, evidenziando quanto giÃ   fatto e quanto ancora si possa fare per includere questi temi nell'agenda dei banchieri centrali.English Abstract: In the last few years, the climate changes under way and the transition towards a sustainable economic development model have become of great importance for the financial system, involving central banks as well. The latter, whose interest is demonstrated by the work of the Network for Greening the Financial System (NFGS), are taking on the challenges posed by these events as part of their institutional and investment activities. By means of internal study projects and by taking part in the most important round tables at national and international level, the Bank of Italy is helping to analyse the risks that climate change creates for the economic and financial system. In addition, and in line with the recent developments in sustainable finance, it has also integrated sustainability criteria into its investment decisions. This paper aims to give an account of the evidence collected so far on the risks and opportunities linked to climate change and sustainable finance, highlighting what has already been done and what else can be done to put these issues on the agenda of central banks.

Bank Stress Test Disclosures, Private Information Production, and Price Informativeness
Heitz, Amanda,Wheeler, Barrett
SSRN
Pursuant to the Dodd-Frank Act, from 2015-2017, banks holding assets between $10 -$50 billion were required to disclose a portion of their company-run stress test results. We find that these disclosures are associated with a 5% reduction in analyst following, driven by more experienced analysts. Analysts that continue to follow these banks issue forecasts that are less dispersed and contain less firm-specific information. Furthermore, post-disclosure, bank equity returns become more synchronous with the entire stock market, indicating that returns contain less firm-specific information. Consistent with recent theory models, our results suggest that increased regulatory disclosures may have unintended consequences.

Big Bath Accounting in Managerial Tone Following CEO Turnovers
SSRN
Prior work has documented that incoming CEOs make accounting decisions which reduce reported firm performance. It is however unclear what motivates this so-called â€œbig bathâ€ behavior. For one, it might serve to give a more accurate representation of actual firm performance, when the prior CEO has falsely inflated reported firm performance. For another, big baths by incoming CEOs are argued to be motivated opportunistically, when new CEOs try to blame their predecessor for poor performance and set a low benchmark for their own tenure. Investigating the textual tone in earnings press releases in the years surrounding CEO turnovers, we find that incoming CEOs use negative tone to an extent that cannot be explained by current firm performance or proxies for expected future performance. Moreover, this phenomenon of â€œbig bath rhetoricâ€ is exclusive to â€œforcedâ€ CEO turnovers, where incoming CEOs have greater incentives and more opportunity to bias the perception about firm performance downwards. These results document opportunistic accounting behavior consistent with the hypothesis that incoming CEOs strategically try to bias the perception of market participants about their firmâ€™s situation downwards.

Blessing or Curse of Democracy?: Current Evidence from the Covid-19 Pandemic
Ryan P. Badman,Yunxin Wu,Keigo Inukai,Rei Akaishi
arXiv

Background: A major question in Covid-19 research is whether democracies handled the Covid-19 pandemic crisis better or worse than authoritarian countries. However, it is important to consider the issues of democracy versus authoritarianism, and state fragility, when examining official Covid-19 death counts in research, because these factors can influence the accurate reporting of pandemic deaths by governments. In contrast, excess deaths are less prone to variability in differences in definitions of Covid-19 deaths and testing capacities across countries. Here we use excess pandemic deaths to explore potential relationships between political systems and public health outcomes.

Methods: We address these issues by comparing the official government Covid-19 death counts in a well-established John Hopkins database to the generally more reliable excess mortality measure of Covid-19 deaths, taken from the recently released World Mortality Dataset. We put the comparison in the context of the political and fragile state dimensions.

Findings: We find (1) significant potential underreporting of Covid-19 deaths by authoritarian governments and governments with high state fragility and (2) substantial geographic variation among countries and regions with regard to standard democracy indices. Additionally, we find that more authoritarian governments are (weakly) associated with more excess deaths during the pandemic than democratic governments.

Interpretations: The inhibition and censorship of information flows, inherent to authoritarian states, likely results in major inaccuracies in pandemic statistics that confound global public health analyses. Thus, both excess pandemic deaths and official Covid-19 death counts should be examined in studies using death as an outcome variable.

Board Reforms and Innovation
SSRN
We study the effect of board reforms on firms' research and development investments utilizing a sample of 40 countries. Using a difference-in-differences analysis, we find that firms' invest more in research and development following corporate governance reforms. Of these, two reforms - having an independent audit committee and board independence - have a greater impact on innovation. Additionally, we show that reforms have the largest impact on research and development investment in hi-tech industries and the health sector.

Cao, Zhangfan,Lee, Edward,Harakeh, Mostafa
SSRN
We examine the influence of business strategy on dividend payouts. We find that firms following an innovation-oriented strategy (prospectors) pay significantly lower dividends than those following an efficiency-oriented strategy (defenders). Our cross-sectional analyses suggest that such relation is more pronounced for firms enjoying better growth opportunities and superior performance. Further analysis reveals that prospectors make significantly more capital investment, consistent with prospectors paying less dividend to finance investment opportunities. Furthermore, by exploiting exogenous shocks to the supplies of high-skill employees, we conduct triple-differences (DiDiD) analyses and find that prospectors increase their dividend payouts in response to the reduction of talent mobility and consequently viability of the innovation-oriented strategy. Our results are robust to a propensity-score-matched (PSM) sample, an instrumental variable (IV) approach, the inclusion of individual business strategy components and alternative measures of key variables. Overall, our findings highlight business strategy as an inherent determinant of dividend payout policies.

Can Adaptive Market Hypothesis Explain the Existence of Seasonal Anomalies? Evidence from Dhaka Stock Exchange, Bangladesh
Akhter, Tahmina,Yong, Othman
SSRN
This paper examines the behavior of seasonal anomalies in Dhaka Stock Exchange (DSE) of Bangladesh and whether the time varying nature of the anomalies is in line with Adaptive Market Hypothesis (AMH). With this aim the research investigated whether the changes in market conditions, for example: up and down market states, stock market bubbles and crashes, initiation of automated trading system and circuit breaker system can affect the behavior of calendar anomalies and therefore, can provide justification for the seasonal patterns in DSE. To achieve the stated objectives, this study utilizes daily general index values of DSE from 1993 to 2018, with GARCH (1,1) model, Markov switching model, subsample analysis and rolling window analysis. The findings support the existence of AMH at DSE in the form of time-varying nature of seasonal anomalies. However, not all seasonal anomalies examined in the study were found to grow weaker over time. The most important finding of this study is that the investors in emerging stock markets, for example DSE, may not learn from the past investment experiences and show the adapting ability towards changed market conditions in the same manner like the investors in a developed market.

Can we imitate stock price behavior to reinforcement learn option price?
Xin Jin
arXiv

This paper presents a framework of imitating the price behavior of the underlying stock for reinforcement learning option price. We use accessible features of the equities pricing data to construct a non-deterministic Markov decision process for modeling stock price behavior driven by principal investor's decision making. However, low signal-to-noise ratio and instability that appear immanent in equity markets pose challenges to determine the state transition (price change) after executing an action (principal investor's decision) as well as decide an action based on current state (spot price). In order to conquer these challenges, we resort to a Bayesian deep neural network for computing the predictive distribution of the state transition led by an action. Additionally, instead of exploring a state-action relationship to formulate a policy, we seek for an episode based visible-hidden state-action relationship to probabilistically imitate principal investor's successive decision making. Our algorithm then maps imitative principal investor's decisions to simulated stock price paths by a Bayesian deep neural network. Eventually the optimal option price is reinforcement learned through maximizing the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths of the underlying.

Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking
Brian P. Hanley
arXiv

Four radical ideas are presented. First, that the rationale for cancellation of principal can be modified in modern banking. Second, that non-cancellation of loan principal upon payment may cure an old problem of maintenance of positive equity in the non-governmental sector. Third, that crediting this money to local/state government, and fourth crediting to at-risk loans that create new utility value, creates an additional virtuous monetary circuit that ties finances of government directly to commercial activity.

Taking these steps can cure a problem I have identified with modern monetary theory, which is that breaking the monetary circuit of taxation in the minds of politicians will free them from centuries of restraint, optimizing their opportunities for implementing tyranny. It maintains and strengthens the current circuit, creating a new, more direct monetary circuit that in some respects combats inequality.

China, Coal, Calamities and Cryptos
Akyildirim, Erdinc,Corbet, Shaen,Lucey, Brian M.
SSRN
The growth of cryptocurrency mining in China, still heavily reliant on coal as a fuel for electricity generation, raises natural questions on the inter-relatedness of coal and crypto prices and volatilities. We investigate this, and the safety and thus supply stability of domestic Chinese coal mining. We find strong evidence of increased bitcoin volatility in the period post-Chinese coal mining accidents, with limited effects on global coal prices. Global coal price interrelationships do not respond to Chinese mining disaster-induced volatility but do respond to the largest Bitcoin-related price movements.

Comparative advantage and pathways to financial development: Evidence from Japanâ€™s silk-reeling industry
Hoffmann, Mathias,Okubo, Toshihiro
SSRN
We exploit the natural experiment of Japanâ€™s opening to international trade to examine how comparative advantage can shape a countryâ€™s long-run path towards financial development. In the late 19th century, many of Japanâ€™s prefectures had a natural comparative advantage in silk reeling. Producing silk for export required access to finance. At the same time, for technological reasons, borrower-quality in the silk reeling industry was notoriously hard to assess. Silk exporters overcame these frictions by forming local cooperative banks. We show that in the ancient silk prefectures, local cooperative banks continued to dominate local banking markets for over a century while bigger, country-wide banks came to dominate in other regions. By the late 20th century, the silk prefectures are indistinguishable from other regions in terms of their general level of financial development. However, our results suggest that they were effectively less financially integrated with the rest of the country. Hence, comparative advantage in silk favored the emergence of a banking-system dominated by small relationship lenders. But due to the local nature of these lenders, it also caused long-term geographical segmentation in banking markets.

Concentration in Asiaâ€™s Cross-Border Banking: Determinants and Impacts
Lapid, Ana Kristel,Mercado, Jr., Rogelio,Rosenkranz, Peter
SSRN
Cross-border bank positions in Asia and the Pacific remain highly concentrated to few counterparties, exposing the region to financial risks and policy spillovers. Consequently, assessing the determinants and impacts of the regionâ€™s cross-border banking concentration is relevant to the design of appropriate policies for promoting financial development and safeguarding financial stability. To this end, we construct cross-border bank concentration measures for 47 economies in Asia and the Pacific from 2000 to 2019. The results show that higher openness of capital account and trade, as well as better per capita income, are significantly associated with lower cross-border bank concentration. Moreover, elevated cross-border bank concentration tends to lower domestic credit growth and nonperforming loans. We find no impact on bank profitability for the region.

Confronting Machine Learning with Financial Data
Lommers, Kristof,El Harzli, Ouns,Kim, Jack
SSRN
This paper aims to examine the challenges and applications of machine learning for financial data. Machine learning algorithms have been developed for certain data environments which substantially differ from the one we encounter in finance. Not only do difficulties arise due to some of the idiosyncrasies of financial markets, there is a fundamental tension between machine learning and the conventional framework of quantitative analysis in finance. Given the peculiar features of financial markets, various adjustments have to be made to the conventional machine learning methodology. We discuss some of the main challenges of machine learning in finance and examine how these could be accounted for. Despite some of the challenges, we argue that machine learning could be unified with financial data analysis to become a robust complement to the researcherâ€™s toolbox.

Cross-asset time-series momentum: Crude oil options and global stock markets
SSRN
We examine the profitability of a cross-asset time-series momentum strategy (XTSMOM) constructed using past energy options and stock market returns as joint predictors. We show that past crude oil options straddle returns negatively predict while past stock returns positively predict future stock market returns globally. XTSMOM outperforms the single-asset time-series momentum (TSMOM) and buy-and-hold strategies with higher mean returns, lower standard deviations, and higher Sharpe ratios. XTSMOM is also able to forecast economic cycles. We contribute to the literature on cross-asset momentum spillovers as well as on the impacts of crude oil uncertainty on stock markets.

Deprivation, Crime, and Abandonment: Do Other Midwestern Cities Have 'Little Detroits'?
Scott W. Hegerty
arXiv

Both within the United States and worldwide, the city of Detroit has become synonymous with economic decline, depopulation, and crime. Is Detroit's situation unique, or can similar neighborhoods be found elsewhere? This study examines Census block group data, as well as local crime statistics for 2014, for a set of five Midwestern cities. Roughly three percent of Chicago's and Milwaukee's block groups--all of which are in majority nonwhite areas--exceed Detroit's median values for certain crimes, vacancies, and a poverty measure. This figure rises to 11 percent for St. Louis, while Minneapolis has only a single "Detroit-like" block group. Detroit's selected areas are more likely to be similar to the entire city itself, both spatially and statistically, while these types of neighborhoods for highly concentrated "pockets" of poverty elsewhere. Development programs that are targeted in one city, therefore, must take these differences into account and should be targeted to appropriate neighborhoods.

Quigley, Ellen,Bugden, Emily,Odgers, Anthony
SSRN
This report details the advantages and disadvantages of a policy of fossil fuel divestment for the University of Cambridge across social, moral, political, financial, and reputational dimensions. It comprises an analysis of the current state of the fossil fuel industry, including its transition-readiness and a set of criteria against which one might judge companies' decarbonisation plans; a complete review of the academic literature on fossil fuel divestment; and an online regularly-updated timeline of global divestment announcements. This report was peer-reviewed and edited by Cambridge University Press. The University announced its intention to divest from fossil fuels and aim for a net-zero portfolio by 2038 on October 1st 2020: https://www.cam.ac.uk/news/cambridge-to-divest-from-fossil-fuels-with-net-zero-plan.

Dividend Restrictions and Asymmetric Information
SSRN
We develop a dynamic model of banks whose insiders have superior information about the impact of a pending shock to the bankâ€™s cash holdings and can signal the bankâ€™s type through its dividend policy. Banks that will be adversely affected by the shock have incentives to pool with unaffected banks to increase their market value. To avoid being mimicked, the unaffected banks can credibly signal via a more aggressive payout strategy. Dividend payout restrictions have the potential to prevent a separating equilibrium from forming. This leads to the bad type adopting a more aggressive payout policy with a higher risk of default but mitigates the distortion of the good type's policy. We identify a number of scenarios where this trade-off presents an opportunity for regulatory intervention and some where it does not.

Do Analysts Distribute Negative Opinions Earlier?
Yang, Yanhua Sunny Sunny,Yung, Chris
SSRN
This paper examines analystsâ€™ forecast timing when issuing negative opinions. We are motivated by two findings in the literature. One finding is that management tends to withhold bad news, which would result in good news being more abundant but relatively uninformative and bad news with the opposite features. The other finding is that analysts tend to issue accurate forecasts earlier. Taken together, the two findings suggest that analysts treat observed bad news as having higher precision and respond to it by issuing forecasts more quickly. Our theoretical predictions and empirical evidence support this. We find that relative to good news forecasts 1) bad news forecasts are released earlier, and 2) holding timing fixed, bad news forecasts have lower absolute forecast errors. These results are robust to alternative sample selections and variable measurements.

Does Corporate Culture Impact Tax Behavior: Machine Learning Approach
SSRN
This study investigates whether corporate culture of a firm impacts its tax shelter behavior. We use novel machine learning methodology to measure corporate culture of a firm. The analyses show that firms with stronger corporate culture are more likely to engage in tax shelter behavior and extent of such behavior is greater in these firms. The findings also suggest that firms with stronger corporate culture engage in greater tax avoidance, which is a less controversial corporate behavior compared to tax shelter activities. The results continue to hold when we implement battery of tests to address endogeneity issues (e.g., two-stage least squares methodology and difference-in-difference analysis). Finally, we determine that engaging in tax shelter behavior as a result of stronger corporate culture increases a firmâ€™s after-tax earnings in future periods.

Does Geopolitics Have an Impact on Energy Trade? Empirical Research on Emerging Countries
Fen Li,Cunyi Yang,Zhenghui Li,Pierre Failler
arXiv

The energy trade is an important pillar of each country's development, making up for the imbalance in the production and consumption of fossil fuels. Geopolitical risks affect the energy trade of various countries to a certain extent, but the causes of geopolitical risks are complex, and energy trade also involves many aspects, so the impact of geopolitics on energy trade is also complex. Based on the monthly data from 2000 to 2020 of 17 emerging economies, this paper employs the fixed-effect model and the regression-discontinuity (RD) model to verify the negative impact of geopolitics on energy trade first and then analyze the mechanism and heterogeneity of the impact. The following conclusions are drawn: First, geopolitics has a significant negative impact on the import and export of the energy trade, and the inhibition on the export is greater than that on the import. Second, the impact mechanism of geopolitics on the energy trade is reflected in the lagging effect and mediating effect on the imports and exports; that is, the negative impact of geopolitics on energy trade continued to be significant 10 months later. Coal and crude oil prices, as mediating variables, decreased to reduce the imports and exports, whereas natural gas prices showed an increase. Third, the impact of geopolitics on energy trade is heterogeneous in terms of national attribute characteristics and geo-event types.

Does energy efficiency affect ambient PM2.5? The moderating role of energy investment
Cunyi Yang,Tinghui Li,Khaldoon Albitar
arXiv

The difficulty of balance between environment and energy consumption makes countries and enterprises face a dilemma, and improving energy efficiency has become one of the ways to solve this dilemma. Based on data of 158 countries from 1980 to 2018, the dynamic TFP of different countries is calculated by means of the Super-SBM-GML model. The TFP is decomposed into indexes of EC (Technical Efficiency Change), TC (Technological Change) and EC has been extended to PEC (Pure Efficiency Change) and SEC (Scale Efficiency Change). Then the fixed effect model and fixed effect panel quantile model are used to analyze the moderating effect and exogenous effect of energy efficiency on PM2.5 concentration on the basis of verifying that energy efficiency can reduce PM2.5 concentration. We conclude, first, the global energy efficiency has been continuously improved during the sample period, and both of technological progress and technical efficiency have been improved. Second, the impact of energy efficiency on PM2.5 is heterogeneous which is reflected in the various elements of energy efficiency decomposition. The increase of energy efficiency can inhibit PM2.5 concentration and the inhibition effect mainly comes from TC and PEC but SEC promotes PM2.5 emission. Third, energy investment plays a moderating role in the environmental protection effect of energy efficiency. Fourth, the impact of energy efficiency on PM2.5 concentration is heterogeneous in terms of national attribute, which is embodied in the differences of national development, science & technology development level, new energy utilization ratio and the role of international energy trade.

Eastern Promise: Assessing the Future of UK-India Trade
Submitter, Institute of Economic Affairs
SSRN
India is at a crossroads. As the UK Prime Minister prepares to meet Indian leaders virtually, he promises an Enhanced Trade Partnership, possibly leading to a full Free Trade Agreement (FTA). There are important commercial reasons for this agreement, but more importantly, there are powerful geopolitical reasons. India could be brought into an alignment of nations, including the CPTPP members, as a bulwark against the negative impact of Chinaâ€™s market distortions and security policies. The UK may just have the right combination of offensive and defensive flexibility to be able to do a deal with India. The contours of that deal are emerging and involve key UK asks, such as financial services and legal services access, as well as Scotch whisky tariff reduction, and key Indian asks, such as movement of natural persons supplying services and the UK committing not to impose bans on Indian agriculture in violation of the WTO SPS agreement. There is a developing alignment of nations, which collectively promote pro-competitive regulation where countries interact with each other through equivalence and mutual recognition as opposed to regulatory harmonisation. India has to choose whether to align with these nations or others, such as China, which have a very different model. There are strong geopolitical reasons for India to join this grouping â€" which could be started with an FTA with the UK â€" relating to its difficult relationship with China and its need to secure support in the Indian Ocean. However, a number of obstacles remain for this future to be reached. India has recently taken actions against the property rights of foreign investors. Property rights form the bedrock of economic systems that leverage the forces of competition to generate economic growth. But Indiaâ€™s market signals on property rights are negative and risk undermining its global reputation and potential. When the Prime Minister meets the Indian PM, he should make it clear that while the UK welcomes a deeper relationship with India, this will depend on whether India endorses, in both word and deed, property rights protection, market competition, and open trade.

Empirical Analysis of Service Quality, Reliability and End-User Satisfaction on Electronic Banking in Nigeria
Esther Enoch Yusuf,Abubakar Bala
arXiv

Today, almost all banks have adopted ICT as a means of enhancing their banking service quality. These banks provide ICT based electronic service which is also called electronic banking, internet banking or online banking etc to their customers. Despite the increasing adoption of electronic banking and it relevance towards end users satisfaction, few investigations has been conducted on factors that enhanced end users satisfaction perception. In this research, an empirical analysis has been conducted on factors that influence electronic banking user's satisfaction perception and the relationship between these factors and the customer's satisfaction. The study will help bank industries in improving the level of their customer's satisfaction and increase the bond between a bank and its customer.

Evaluating the Effect of Credit Collection Policy on Portfolio Quality of Micro-Finance Bank
Esther Yusuf Enoch,Abubakar Mahmud Digil,Usman Abubakar Arabo
arXiv

This study evaluates the effect of collection policy on portfolio quality of microfinance banks in Adamawa State, Nigeria. Real data were collected from 51 credit officers, then a multi-stage sampling method was used to select a sample of 21 respondents from the population (i.e., 51 credit officers). In addition, we used regression analysis and descriptive statistics to analyze the data collected and to also test our proposed hypothesis. Based on the evaluation performed, the results showed that collection policy has a higher effect on portfolio quality. Hence, the study showed that microfinance banks should adhere to strict or stiff debt collection policy as strictness in collection policy help the banks to recover their loans, thereby improving the portfolio quality of the bank.

Examination of the Relationship between Derivative Financial Instruments and the Economic Development of Lithuania
Garskaite-Milvydiene, Kristina,Martinkute-Kauliene, Raimonda
SSRN
Derivative financial instruments play a major role in financial markets. However, there are rather contradictory views regarding this issue. Their impact on the financial markets, their stability and the economy have not been thoroughly examined. The aim of this paper is to analyse derivatives and the economic situation in the country and to investigate the relationship between the derivatives and the macroeconomic factors which have the greatest impact on the volume of the derivatives. The paper analyses derivatives statistics and macroeconomic indicators in Lithuania. As a result, the relationship between the derivatives and the countryâ€™s macroeconomic indicators is examined by identifying the most significant factors, as the structure and volume of derivatives in different markets may be determined by different macroeconomic factors. The performed analysis and estimation have shown that foreign direct investment has the largest impact on the derivatives, their volume and structure, and average earnings have the least impact.

Extending the Heston Model to Forecast Motor Vehicle Collision Rates
Darren Shannon,Grigorios Fountas
arXiv

We present an alternative approach to the forecasting of motor vehicle collision rates. We adopt an oft-used tool in mathematical finance, the Heston Stochastic Volatility model, to forecast the short-term and long-term evolution of motor vehicle collision rates. We incorporate a number of extensions to the Heston model to make it fit for modelling motor vehicle collision rates. We incorporate the temporally-unstable and non-deterministic nature of collision rate fluctuations, and introduce a parameter to account for periods of accelerated safety. We also adjust estimates to account for the seasonality of collision patterns. Using these parameters, we perform a short-term forecast of collision rates and explore a number of plausible scenarios using long-term forecasts. The short-term forecast shows a close affinity with realised rates (over 95% accuracy), and outperforms forecasting models currently used in road safety research (Vasicek, SARIMA, SARIMA-GARCH). The long-term scenarios suggest that modest targets to reduce collision rates (1.83% annually) and targets to reduce the fluctuations of month-to-month collision rates (by half) could have significant benefits for road safety. The median forecast in this scenario suggests a 50% fall in collision rates, with 75% of simulations suggesting that an effective change in collision rates is observed before 2044. The main benefit the model provides is eschewing the necessity for setting unreasonable safety targets that are often missed. Instead, the model presents the effects that modest and achievable targets can have on road safety over the long run, while incorporating random variability. Examining the parameters that underlie expected collision rates will aid policymakers in determining the effectiveness of implemented policies.

Failed Attempt to Break Up the Oligopoly in Sovereign Credit Rating Market after Financial Crises
Malewska, Alicja
SSRN
For decades, the credit rating market has been dominated by three major agencies (Moody's, S&P and Fitch Ratings). Their oligopolistic dominance is especially strong in sovereign credit ratings industry, where they hold a collective global share of more than 99%. Global financial crisis and the Eurozone sovereign debt crisis exposed serious flaws in rating process and forced public authorities to act. This study investigates effectiveness of new regulations adopted in the United States and in the European Union after financial crises in terms of reducing oligopolistic dominance of the â€œBig Threeâ€ in sovereign credit ratings market. The study applies descriptive statistical analysis of economic indicators describing concentration rate in a market, as well as content analysis of legal acts and case study methodology. Analysis shows that the Dodd-Frank reform and new European rules on supervision of credit rating agencies were not effective enough and did not lead to the increased competition in the market. The evidence from this study is explained using two alternative perspectives â€" economic theory of natural oligopoly and hegemonic stability theory coming from international relations field.

Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning
Tim Leung,Theodore Zhao
arXiv

We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine (SVM), and long short-term memory (LSTM) neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.

Green Sentiment, Stock Returns, and Corporate Behavior
Briere, Marie,Ramelli, Stefano
SSRN
We propose a new method to estimate non-fundamental demand for green financial assets based on the arbitrage activity of exchange traded funds (ETFs). By estimating the monthly abnormal flows into environment-friendly ETFs, we construct a Green Sentiment Index capturing shifts in investors' appetite for environmental responsibility not yet priced in the value of the underlying assets. Our measure of green sentiment differs significantly from the climate-related news and attention indexes proposed by the extant literature, and it has additional explanatory power on both stock returns and corporate decisions. Over the period 2010-2020, changes in green sentiment anticipate a lasting stock out-performance by more environmentally responsible firms (of approximately 60 basis points over six months for a one-standard-deviation higher green sentiment), as well as an increase in their capital investments and cash holdings.

How to Regulate Corporate Social Responsibility Reporting - International Evidence on the Impact of Different Regulatory Approaches
Schramm, Florian,Ernstberger, JÃ¼rgen,Maniora, Janine
SSRN
This paper examines how differences in CSR reporting regulations influence their impact on information asymmetry for both the firms affected and their non-affected peers. Using an international sample of firms based in 24 countries, of which 12 countries have adopted CSR regulations, we find that the decrease in bid-ask spreads after CSR mandates is stronger for mandates issued by governments than for those issued by stock exchanges and stronger for mandates requiring standalone CSR reports than for those requiring other formats. We document that CSR mandates also mitigate the information asymmetry of non-affected firms due to comparability externalities. In cross-sectional analyses, we find that the effect on information asymmetry associated with mandates is greater for firms that use well-established reporting guidelines, such as the Global Reporting Initiative (GRI). We control for firmsâ€™ CSR performance in sensitivity analyses and find that the decrease in information asymmetry is attributable to increased disclosure rather than to a change in CSR activity.

Identifying Taste-Based Discrimination: Effect of Black Electoral Victories on Racial Prejudice and Economic Gaps
Sakong, Jung
SSRN
I test for the causal impact of Black electoral victories in local elections on White Americansâ€™ attitude toward Black Americans. Using Race Implicit Attitude Test scores as a measure of racial prejudice and close-election regression-discontinuity design for causal inference, I find Black electoral victories cause measures of racial bias to rise, by 4% of the average Black-White difference in IAT scores. Simultaneously, they widen racial gaps in unemployment and mortgage denials. Interpreting these close electoral victories as instrumental variables, I find a large causal effect of prejudice-based racial discrimination on Black-White economic gaps.

Inference for multi-valued heterogeneous treatment effects when the number of treated units is small
Marina Dias,Demian Pouzo
arXiv

We propose a method for conducting asymptotically valid inference for treatment effects in a multi-valued treatment framework where the number of units in the treatment arms can be small and do not grow with the sample size. We accomplish this by casting the model as a semi-/non-parametric conditional quantile model and using known finite sample results about the law of the indicator function that defines the conditional quantile. Our framework allows for structural functions that are non-additively separable, with flexible functional forms and heteroskedasticy in the residuals, and it also encompasses commonly used designs like difference in difference. We study the finite sample behavior of our test in a Monte Carlo study and we also apply our results to assessing the effect of weather events on GDP growth.

Learning from Revisions: A Tool for Detecting Potential Errors in Banks' Balance Sheet Statistical Reporting
Cusano, Francesco,Marinelli, Giuseppe,Piermattei, Stefano
SSRN
Ensuring and disseminating high-quality data is crucial for central banks to adequately support monetary analysis and the related decision-making process. In this paper we develop a machine learning process for identifying errors in banksâ€™ supervisory reports on loans to the private sector employed in the Bank of Italyâ€™s statistical production of Monetary and Financial Institutionsâ€™ (MFI) Balance Sheet Items (BSI). In particular, we model a â€œRevisions Adjusted â€" Quantile Regression Random Forestâ€ (RAâ€"QRRF) algorithm in which the predicted acceptance regions of the reported values are calibrated through an individual â€œimprecision rateâ€ derived from the entire history of each bankâ€™s reporting errors and revisions collected by the Bank of Italy. The analysis shows that our RA-QRRF approach returns very satisfying results in terms of error detection, especially for the loans to the households sector, and outperforms well-established alternative outlier detection procedures based on probit and logit models.

Local and Global Agglomeration Patterns in the Banking Sector: The Calm in the Mid of a Storm
Di Giacinto, Valter,Pagnini, Marcello
SSRN
We compare the spatial agglomeration of banksâ€™ branches in Italy across local areas (as identified by local labor market areas) to that of other services. Banks branches appear to be only weakly spatially agglomerated, their spatial distribution being similar to that of other services and to that of the firms from whom the demand for banking services is most likely to stem from. These findings have been stable throughout the period 1991-2015, despite the dramatic changes occurring in that time span (liberalization, ICT and the great recession). On the other hand, local areas with a higher (lower) presence of banking branches tend to be geographically clustered, displaying also a moderately decreasing pattern in this polarization. IC technologies partially contributed to this trend.

Measuring Financial Advice: Aligning Client Elicited and Revealed Risk
Thompson, John R.J.,Feng, Longlong,Reesor, R. Mark,Grace, Chuck,Metzler, Adam
SSRN
Financial advisors use questionnaires and discussions with clients to determine a suitable portfolio of assets that will allow clients to reach their investment objectives. Financial institutions assign risk ratings to each security they offer, and those ratings are used to guide clients and advisors to choose an investment portfolio risk that suits their stated risk tolerance. This paper compares client Know Your Client (KYC) profile risk allocations to their investment portfolio risk selections using a value-at-risk discrepancy methodology. Value-at-risk is used to measure elicited and revealed risk to show whether clients are over-risked or under-risked, changes in KYC risk lead to changes in portfolio configuration, and cash flow affects a client's portfolio risk. We demonstrate the effectiveness of value-at-risk at measuring clients' elicited and revealed risk on a dataset provided by a private Canadian financial dealership of over 50,000 accounts for over 27,000 clients and 300 advisors. By measuring both elicited and revealed risk using the same measure, we can determine how well a client's portfolio aligns with their stated goals. We believe that using value-at-risk to measure client risk provides valuable insight to advisors to ensure that their practice is KYC compliant, to better tailor their client portfolios to stated goals, communicate advice to clients to either align their portfolios to stated goals or refresh their goals, and to monitor changes to the clients' risk positions across their practice.

Model-Free Finance and Non-Lattice Integration
Christian Bender,Sebastian Ferrando,Alfredo Gonzalez
arXiv

Starting solely with a set of possible prices for a traded asset $S$ (in infinite discrete time) expressed in units of a numeraire, we explain how to construct a Daniell type of integral representing prices of integrable functions depending on the asset. Such functions include the values of simple dynamic portfolios obtained by trading with $S$ and the numeraire. The space of elementary integrable functions, i.e. the said portfolio values, is not a vector lattice. It then follows that the integral is not classical, i.e. it is not associated to a measure. The essential ingredient in constructing the integral is a weak version of the no-arbitrage condition but here expressed in terms of properties of the trajectory space. We also discuss the continuity conditions imposed by Leinert (Archiv der Mathematik, 1982) and K\"onig (Mathematische Annalen, 1982) in the abstract theory of non-lattice integration from a financial point of view and establish some connections between these continuity conditions and the existence of martingale measures

Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances
Kiss, Tamás,Mazur, Stepan,Nguyen, Hoang,Österholm, Pär
RePEC
In this paper we analyze how skewness and heavy tails a ect the estimated relationship between the real economy and the corporate bond-yield spread, a popular predictor of real activity. We use quarterly US data to estimate Bayesian VAR models with stochastic volatility and various distributional assumptions regarding the disturbances. In-sample, we find that after controlling for stochastic volatility innovations in GDP growth can be well-described by a Gaussian distribution. In contrast, both the unemployment rate and the yield spread appear to benefit from being modelled using non-Gaussian innovations. When it comes to real-time forecasting performance, we find that the yield spread is an important predictor of GDP growth, and that accounting for stochastic volatility matters, mainly for density forecasts. Incremental improvements from non-Gaussian innovations are limited to forecasts of the unemployment rate. Our results suggest that stochastic volatility is of first order importance when modelling the relationship between yield spread and real variables; allowing for non-Gaussian innovations is less important.

News Tone, Investor Sentiment, and Liquidity Premium
Liu, Jun,Wu, Kai,Zhou, Ming,Jin, Fujing
SSRN
Using textual media tone as investor sentiment measure, we find that in China, firms with a more pessimistic (optimistic) news tone lead to more (less) active trading, higher (lower) depth, or spread. This negative (positive) effect of investor sentiment on trading activity or stock liquidity is robust to controlling other firm characteristics and macroeconomic conditions. We observe that the liquidity premium is significant in China, which is more pronounced across firms with an optimistic sentiment. Additionally, we decompose liquidity into the sentiment-driven and non-sentiment-driven (NSD) components and find that the NSD premium is declining. This decline stems from a higher sensitivity of sentiment to liquidity. Besides, we also find that NSD premium performs better following high economic policy uncertainty and low growth of margin trading and stocks with low shareholdings of institutional investors.

On Bank Pricing of Single-family Residential Home Loans: Are Australian Households Paying Too Much?​
Shilling, James,Tiwari, Piyush
RePEC
This paper focuses on understanding the observed differences in interest rates on single-family residential mortgages during September 2008 to December 2017. Exploiting the conceptual difference in risks associated with fixed rate and variable rate mortgages for lenders, we construct a synthetic variable rate. Synthetic variables are obtained from 3-year fixed rates by adjusting them for interest rate risks premium and call options that are embedded in fixed rates. Estimated error correction model for the difference between actual and synthetic mortgage rate reveals that the unbiasedness hypothesis is rejected and that the lenders in pricing actual variable rates have attached a risk premia of 90 to 150 basis points over synthetic rates. This requires further investigation into institutional arrangements, market structures, underwriting and lending practices of banks as these remain unexplained.

Optimal Consumption with Reference to Past Spending Maximum
Shuoqing Deng,Xun Li,Huyen Pham,Xiang Yu
arXiv

This paper studies the infinite horizon optimal consumption with a path-dependent reference under the exponential utility. The performance is measured by the difference between the non-negative consumption rate and a fraction of the historical consumption maximum. The consumption running maximum process is chosen as an auxiliary state process that renders the value function two dimensional. The Hamilton-Jacobi-Bellman (HJB) equation can be heuristically expressed in a piecewise manner across different regions to take into account all constraints. By employing the dual transform and smooth-fit principle, some thresholds of the wealth variable are derived such that the classical solution to the HJB equation and the feedback optimal investment and consumption strategies can be obtained in the closed form in each region. The complete proof of the verification theorem is provided and numerical examples are presented to illustrate some financial implications.

Optimal investment and contingent claim valuation with exponential disutility under proportional transaction costs
Alet Roux,Zhikang Xu
arXiv

We consider indifference pricing of contingent claims consisting of payment flows in a discrete time model with proportional transaction costs and under exponential disutility. This setting covers utility maximisation as a special case. A dual representation is obtained for the associated disutility minimisation problem, together with a dynamic procedure for solving it. This leads to efficient and convergent numerical procedures for indifference pricing, optimal trading strategies and shadow prices that apply to a wide range of payoffs, a large range of time steps and all magnitudes of transaction costs.

Optimal system design for energy communities in multi-family buildings: the case of the German Tenant Electricity Law
Fritz Braeuer,Max Kleinebrahm,Elias Naber,Fabian Scheller,Russell McKenna
arXiv

Involving residential actors in the energy transition is crucial for its success. Local energy generation, consumption and trading are identified as desirable forms of involvement, especially in energy communities. The potentials for energy communities in the residential building stock are high but are largely untapped in multi-family buildings. In many countries, rapidly evolving legal frameworks aim at overcoming related barriers, e.g. ownership structures, principal-agent problems and system complexity. But academic literature is scarce regarding the techno-economic and environmental implications of such complex frameworks. This paper develops a mixed-integer linear program (MILP) optimisation model for assessing the implementation of multi-energy systems in an energy community in multi-family buildings with a special distinction between investor and user. The model is applied to the German Tenant Electricity Law. Based on hourly demands from appliances, heating and electric vehicles, the optimal energy system layout and dispatch are determined. The results contain a rich set of performance indicators that demonstrate how the legal framework affects the technologies' interdependencies and economic viability of multi-energy system energy communities. Certain economic technology combinations may fail to support national emissions mitigation goals and lead to lock-ins in Europe's largest residential building stock. The subsidies do not lead to the utilisation of a battery storage. Despite this, self-sufficiency ratios of more than 90% are observable for systems with combined heat and power plants and heat pumps. Public CO2 mitigation costs range between 147.5-272.8 EUR/tCO2. Finally, the results show the strong influence of the heat demand on the system layout.

Pandemic Lessons -- Devising an assessment framework to analyse policies for sustainability
arXiv

COVID-19 pandemic has sharply projected the globally persistent multi-dimensional fundamental challenges in securing general socio-economic wellbeing of the society. The problems intensify with increasing population densities and also vary with several socio-economic-geo-cultural activity parameters. These problems directly highlight the urgent need for accomplishing the interdependent United Nations Sustainable Development Goals (SDGs) to ensure that in future we do not enter into vicious loops of contracting newer zoonotic viruses and need not search for their vaccines while incurring socio-economic havoc. Behavioural changes in human activities/responses are indispensable for achieving the interdependent SDGs. Using root cause analysis approach, we have developed a yearly assessment framework for viably analysing and identifying requisite region-specific downstream/upstream socio-economic policies to reach the SDGs. The framework makes use of an infographic bar chart representation based on the normalised values of 20 human activity/impact parameters classified under three categories as - negative, limiting and positive. With a holistic view encompassing the SDGs, we illustrate through this framework the impact and urgent need of region-specific human behavioural reforms. This framework enables the foresight about policies regarding their potential in bringing down the negative parameter values to the desired zero level for accomplishing the SDGs through planetary health.

Paris Agreement requires substantial, broad, and sustained engagements beyond COVID-19 recovery packages
Katsumasa Tanaka,Christian Azar,Olivier Boucher,Philippe Ciais,Yann Gaucher,Daniel J. A. Johansson
arXiv

It has been claimed that COVID-19 public stimulus packages are sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 degree C target of the Paris Agreement. We argue that this view is short-sighted, overly reliant on public investment and misrepresents the grand challenges that climate change mitigation entails.

Perturbation analysis of sub/super hedging problems
arXiv

We investigate the links between various no-arbitrage conditions and the existence of pricing functionals in general markets, and prove the Fundamental Theorem of Asset Pricing therein. No-arbitrage conditions, either in this abstract setting or in the case of a market consisting of European Call options, give rise to duality properties of infinite-dimensional sub- and super-hedging problems. With a view towards applications, we show how duality is preserved when reducing these problems over finite-dimensional bases. We finally perform a rigorous perturbation analysis of those linear programming problems, and highlight numerically the influence of smile extrapolation on the bounds of exotic options.

Predictable Returns over the Credit Cycle
Bai, Hang
SSRN
Asset returns move predictably over the credit cycle. Using credit expansion in the nonfinancial corporate sector to capture the phase of the credit cycle, this paper shows that both equity and corporate bond returns decline as credit expansion accelerates. Impulse response analysis suggests that the stage of the credit cycle contains information about the term structure of risk premium. Moreover, this paper examines alternative indicators of the credit cycle and finds that credit flows to the government sector (i.e., government credit expansion) forecast higher future equity returns. Overall, the evidence highlights time varying risk premium as a potential driver of the credit cycle.

Predicting returns and dividend growth - the role of non-Gaussian innovations
Kiss, Tamás,Mazur, Stepan,Nguyen, Hoang
RePEC
In this paper we assess whether exible modelling of innovations impact the predictive performance of the dividend price ratio for returns and dividend growth. Using Bayesian vector autoregressions we allow for stochastic volatility, heavy tails and skewness in the innovations. Our results suggest that point forecasts are barely affected by these features, suggesting that workhorse models on predictability are sufficient. For density forecasts, however, we finnd that stochastic volatility substantially improves the forecasting performance.

Predicting the Leadership Mindset of Wildland Firefighters
Hair, Joseph F.,Gapud, Stephanie
SSRN
Leadership behaviors of wildland firefighting teams engaged in firefighting activities are â€œsharedâ€ despite the existence of a highly bureaucratic and hierarchical organizational structure. This paradoxical type of leadership appears to be based on individual perceptions of the effectiveness of shared leadership in a high reliability organization and identified in this research as Mindful Leadership. Mindful Leadership in this study is conceptualized as a higher-order construct (HOC) that explains the leadership behavior of the team members. The study empirically validates the construct and related measures. Several directions for future studies are proposed. Smart-PLS 3.3.2 was used to execute partial least square path modeling analysis of the hierarchical component model. Internal and external predictive validity metrics were established, including PLSpredict.

Pricing multivariate european equity option using gaussian mixture distributions and evt-based copulas
Hassane Abba Mallam,Diakarya Barro,Yameogo WendKouni,Bisso Saley
arXiv

In this article, we present an approach which allows to take into account the effect of extreme values in the modeling of financial asset returns and in the valorisation of associeted options. Specifically, the marginal distribution of assets returns is modeled by a mixture of two gaussiens distributions. Moreover, we model the joint dependence structure of the returns using an extremal copula which is suitable for our financial data. Applications are made on the Atos and Dassault Systems actions of the CAC40 index. Monte-Carlo method is used to compute the values of some equity options: the call on maximum, the call on minimum, the digital option and the spreads option with the basket (Atos, Dassault systems).

Quantum Speedup of Monte Carlo Integration with respect to the Number of Dimensions and its Application to Finance
Kazuya Kaneko,Koichi Miyamoto,Naoyuki Takeda,Kazuyoshi Yoshino
arXiv

Monte Carlo integration using quantum computers has been widely investigated, including applications to concrete problems. It is known that quantum algorithms based on quantum amplitude estimation (QAE) can compute an integral with a smaller number of iterative calls of the quantum circuit which calculates the integrand, than classical methods call the integrand subroutine. However, the issues about the iterative operations in the integrand circuit have not been discussed so much. That is, in the high-dimensional integration, many random numbers are used for calculation of the integrand and in some cases similar calculations are repeated to obtain one sample value of the integrand. In this paper, we point out that we can reduce the number of such repeated operations by a combination of the nested QAE and the use of pseudorandom numbers (PRNs), if the integrand has the separable form with respect to contributions from distinct random numbers. The use of PRNs, which the authors originally proposed in the context of the quantum algorithm for Monte Carlo, is the key factor also in this paper, since it enables parallel computation of the separable terms in the integrand. Furthermore, we pick up one use case of this method in finance, the credit portfolio risk measurement, and estimate to what extent the complexity is reduced.

Random concave functions
Peter Baxendale,Ting-Kam Leonard Wong
arXiv

Spaces of convex and concave functions appear naturally in theory and applications. For example, convex regression and log-concave density estimation are important topics in nonparametric statistics. In stochastic portfolio theory, concave functions on the unit simplex measure the concentration of capital, and their gradient maps define novel investment strategies. The gradient maps may also be regarded as optimal transport maps on the simplex. In this paper we construct and study probability measures supported on spaces of concave functions. These measures may serve as prior distributions in Bayesian statistics and Cover's universal portfolio, and induce distribution-valued random variables via optimal transport. The random concave functions are constructed on the unit simplex by taking a suitably scaled (mollified, or soft) minimum of random hyperplanes. Depending on the regime of the parameters, we show that as the number of hyperplanes tends to infinity there are several possible limiting behaviors. In particular, there is a transition from a deterministic almost sure limit to a non-trivial limiting distribution that can be characterized using convex duality and Poisson point processes.

Research regarding Agriculture sector by Dr.iqbal shaukat.
Shaukat, Dr. Iqbal
SSRN
Agriculture research is very important for the economist and Agriculturist.AgricultureSector is the intrgreal part of country economy.

Responding to Activist Short Sellers: Allegations, Firm Responses, and Outcomes
Brendel, Janja,Ryans, James
SSRN
This study provides descriptive evidence on how firms respond to activist short seller reports and how these responses are associated with outcomes for the targeted firms. We show that the frequency of these reports has grown substantially in recent years. Although we find that firms respond only 31% of the time, this rate increases substantially when the report is accompanied by significantly negative abnormal returns and when the report contains new evidence. Not responding is associated with a less negative stock price response at report release and fewer adverse outcomes. Firms that launch internal investigations following the report release have significantly higher subsequent rates of stock exchange delisting and SEC enforcement actions, and lower rates of being acquired. Overall, our results highlight the impact of activist short sellers on target firms and that firm responses are associated with material outcomes.

Role of Agriculture sector in the era of globalization.
Shaukat, Dr. Iqbal
SSRN
Agriculture is the important part of world economy.Agriculture sector is the part and percell of economy.

Rush to Raise: Does Fundraising Pressure Incentivize Strategic Venture Capital Deal Pricing?
Turner, Nick,Zein, Jason,Pham, Peter K.
SSRN
We provide evidence that venture capitalists (VCs) strategically enhance their current fundsâ€™ interim performance around new fundraising events. Using novel investment-round-level pricing data, we document that, immediately prior to raising another fund, VCs tend to invest in follow-on financing rounds of their existing portfolio firms at abnormally high step-ups in valuation, relative to subsequent follow-on rounds at the same firms. This pattern cannot be explained by deal and investor characteristics, and strengthens when multiple VCs in the syndicate concurrently raise new funds. Investing at high round prices translates into higher quarterly portfolio IRRs reported at the fund level. Overall, our results question the veracity of portfolio valuations based on investment-round pricing, especially when the VC is under the pressure to raise a new fund.

Safe Assets as Balance Sheet Multipliers
Ozdenoren, Emre,Yuan, Kathy,Zhang, Shengxing
SSRN
Safe assets are assets free of adverse selection but might have stochastic cashflows. We find that holding publicly issued safe assets promotes the production of assets exposed to information frictions, by lowering adverse selection in the combined asset portfolio on the balance sheet (of shadow banks). It also facilitates the issuance of private safe assets â€" debt claims backed by both assets on the balance sheet, expanding the liability side of the balance sheet. We decompose the resulting convenience yield of public safe assets into two components: safety and liquidity. We find that safe assets with less stochastic cashflows command larger price premiums when adverse selection in the economy is severe. We find that QE operations such as asset swaps between the central bankâ€™s balance sheets with those of the banks improve the fiscal capacity of central banks and are more liquidity effective.

Strategic default and optimal audit resources with costly state verification
Krause, Andreas
SSRN
I develop a model in which the ability to repay a loan is private information that can only be verified by the bank at some costs, which can be recovered from the borrower if it has reported untruthfully. The bank will optimize the resources it spends on this auditing of borrowers and the resulting equilibrium is then characterized. It is shown that in equilibrium, a significant fraction of companies default strategically, but most are captured via auditing. The failure rates of banks are also small. Finally extensions are discussed to include limited liability to banks and the partial recovery of auditing costs as well as punitive costs to borrowers.

Technical Analysis in the Stock Market: A Review
Han, Yufeng ,Liu, Yang,Zhou, Guofu,Zhu, Yingzi
SSRN
Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.

Text-Based Mutual Fund Peer Groups
Abis, Simona,Lines, Anton
SSRN
The proliferation of mutual fund strategies is a longstanding puzzle in the asset management literature. To gain new insight into this topic, we introduce a method for categorizing funds based on the strategy descriptions in their prospectuses. The resulting Strategy Peer Groups (SPGs), constructed using unsupervised machine learning, capture novel information about the funds and are more detailed than existing style categories. Where the prior literature finds that more unique funds experience greater flows, we find instead that investors prefer funds whose portfolio weights and characteristics are closer to the SPG averages. Investors also favor funds with high SPG-adjusted returns, while different investor clientelesâ€"represented by retail, institutional, and retirement share classesâ€"differ in their allocations across peer groups. Our results are consistent with a mutual fund industry that caters to distinct investor clienteles with heterogeneous marginal rates of substitution, rather than investors with a general preference for variety.

The Firm Next Door: Using Satellite Images to Study Local Information Advantage
Kang, Jung Koo,Stice-Lawrence, Lorien,Wong, Yu Ting Forester
SSRN
We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store-level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short-term trading profits.

The Impact of the COVID-19 Health Crisis on the Housing Market in Spain
Alves, Pana,San Juan, Lucio
SSRN
The residential real estate market has been affected by the COVID-19 pandemic, which broke out at a time when the cycle of this market was in a mature phase. Activity fell off sharply in the early months of the health crisis, owing to the effect of the restrictions adopted. It has since seen a slow recovery and remains highly influenced by epidemiological developments and the related impact on agentsâ€™ economic outlook. The pandemic has triggered manifest changes in the type of housing in demand, attributable to householdsâ€™ new needs arising from the lockdown and increased remote working. As compared with other crises, prices are showing greater downward rigidity, particularly in the case of new housing, although the impact of the pandemic is proving highly uneven across regions. The pandemic-induced economic crisis has not driven up the cost of financing for house purchase which has continued to decline to record lows. Nevertheless, there are some signs of a tightening of credit standards and of some of the terms and conditions applied to loans.

The Problems of Personal Income Tax on Revenue Generation in Gombe State
Abubakar Bala,Esther Yusuf Enoch,Salisu Yakubu
arXiv

This study examined the problems of personal income tax on revenue generation in Gombe state. The methodology used in data collection is survey, which utilized both primary and secondary types of data. Purposive sampling technique was adopted in selecting a sample of 150 respondents from both employees of state board of internal revenue service and tax payers in the state. The chi square statistics test was used in testing the hypotheses. The study found that tax avoidance/evasion and complete absences of information technology are serious problems affecting revenue generation in the state. It recommends that government should device strict measures in dealing and punishing individuals engage in tax avoidance and evasion. It should also employ the use of information technology as it is the only way problems experience in personal income tax collection can be reduced drastically.

The Profitability of Technical Stock Trading Has Moved from Daily To Intraday Data
Schulmeister, Stephan
SSRN
This paper investigates how technical trading systems exploit the momentum and reversal effects in the S&P 500 spot and futures market. When based on daily data, the profitability of 2580 technical models has steadily declined since 1960, and has been unprofitable since .the early 1990s. However, when based on 30-minutes-data the same models produce an average gross return of 7.2% per year between 1983 and 2007. These results do not change substantially when trading is tested over eight subperiods. In particular, there is no clear trend of a declining profitability of technical stock trading based on 30-minutes-data. Those 25 models which performed best over the most recent subperiod produce a significantly higher gross return over the subsequent subperiod than all models. Between 2001 and 2007 the 2580 models perform worse than over the 1980s and 1990s. This result could be due to stock markets becoming recently more efficient or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.

The Role of Academic Research in SEC Rulemaking: Evidence from Business Roundtable v. SEC
Geoffroy, Rachel,Lee, Heemin
SSRN
To shed light on the role that academic research plays in Securities and Exchange Commission (SEC) rulemaking, this paper examines the SEC's patterns of consumption of academic research from 2007 through 2017. We show how the Business Roundtable v. SEC ruling in 2011 increased consideration given to academic research during SEC rulemaking. We find that after the ruling, the SEC cites more papers in its proposed rules and, in particular, more papers that illustrate the costs of regulation. This change in academic citations results in fewer negative comment letters on proposed SEC regulations. We survey academics whose research was cited by the SEC, and the majority respond that the SEC's description of their work is completely or mostly accurate. When we survey general academics, their average rating of the SEC's accuracy is lower, although the rating improves regarding specific SEC quotes citing academic research. Although there is still room for a more substantive discussion of research, having a higher standard of cost-benefit analysis leads to a more balanced discussion of academic research.

The connectedness between Sukuk and conventional bond markets and the implications for investors
Samitas, Aristeidis,Papathanasiou, Spyros,Koutsokostas, Drosos
SSRN
Purpose: The purpose of this paper is to examine the connectedness across a variety of Sukuk and conventional bond indices and the implications for optimal asset allocation for the period January 1, 2010â€"April 30, 2020.Design/methodology/approach: The data set consists of five major Sukuk (Dow Jones Sukuk, Thompson Reuters BPA Malaysia Sukuk, Indonesia Government Sukuk, S&P MENA Sukuk and Tadawul Sukuk and Bonds Index) and five conventional bond indexes, one for developed (USA) and four for emerging markets (Malaysia, Indonesia, Africa and Qatar). This study investigates the connectedness and volatility spillover effects across the aforementioned indices, by following the Diebold and Yilmaz (2012) approach, based on the time-varying parameter vector autoregressive (TVP-VAR) model. In addition, this paper provides optimal hedge ratios and portfolio weights for investors.Findings:The empirical results show that Sukuk and conventional bond markets are highly integrated and that total connectedness exhibits sensitivity to exogenous shocks. The Dow Jones and the Malaysian Sukuk indices are the primary shock transmitters to other markets. However, the weak volatility spillovers between the Dow Jones and conventional bonds suggest that opportunities for optimal asset allocation may in fact exist. The highest (lowest) hedging effectiveness can be achieved by taking a short position in Malaysian (Qatarian) bonds.Originality/value: To the best of the knowledge, this is the largest sample taken into account to investigate the connectedness between Sukuk and conventional bonds.

The non-universality of wealth distribution tails near wealth condensation criticality
Sam L. Polk,Bruce M. Boghosian
arXiv

In this work, we modify the Affine Wealth Model of wealth distributions to examine the effects of nonconstant redistribution on the very wealthy. Previous studies of this model, restricted to flat redistribution schemes, have demonstrated the presence of a phase transition to a partially wealth-condensed state, or "partial oligarchy", at the critical value of an order parameter. These studies have also indicated the presence of an exponential tail in wealth distribution precisely at criticality. Away from criticality, the tail was observed to be Gaussian. In this work, we generalize the flat redistribution within the Affine Wealth Model to allow for an essentially arbitrary redistribution policy. We show that the exponential tail observed near criticality in prior work is in fact a special case of a much broader class of critical, slower-than-Gaussian decays that depend sensitively on the corresponding asymptotic behavior of the progressive redistribution model used. We thereby demonstrate that the functional form of the tail of the wealth distribution of a near-critical society is not universal in nature, but rather is entirely determined by the specifics of public policy decisions. This is significant because most major economies today are observed to be near-critical.

The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries
C. Seri,A. de Juan Fernandez
arXiv

We employ an ARDL bounds testing approach to cointegration and Unrestricted Error Correction Models (UECMs) to estimate the relationship between income and CO2 emissions per capita in 21 Latin American Countries (LACs) over 1960-2017. Using time series we estimate six different specifications of the model to take into account the independent effect on CO2 emissions per capita of different factors considered as drivers of different dynamics of CO2 emissions along the development path. This approach allows to address two concerns. First, the estimation of the model controlling for different variables serves to assess if the EKC hypothesis is supported by evidence in any of the LACs considered and to evaluate if this evidence is robust to different model specifications. Second, the inclusion of control variables accounting for the effect on CO2 emissions is directed at increasing our understanding of CO2 emissions drivers in different countries. The EKC hypothesis effectively describes the long term income-emissions relationship only in a minority of LACs and, in many cases, the effect on CO2 emissions of different factors depends on the individual country experience and on the type and quantity of environmental policies adopted. Overall, these results call for increased environmental action in the region.

Towards Artificial Intelligence Enabled Financial Crime Detection
Zeinab Rouhollahi
arXiv

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

ValoraciÃ³n de Empresas: Caso ESNOBIS S.A. (Business Valuation: Case ESNOBIS S.A.)
Vergara-Romero, Arnaldo
SSRN
Spanish Abstract: Muchas veces se tiene que tomar decisiones sobre la gestiÃ³n y el crecimiento de una empresa, la controversia es la diferencia del valor y el precio que se disputan entre los empresarios que venden y compran empresas en marcha. TambiÃ©n en esta controversia estÃ¡n las empresas nuevas en el mercado que no saben cÃ³mo valorar dichas empresas. Este trabajo explica la importancia, los mÃ©todos y los pasos para valorar una empresa, se analiza mediante el MÃ©todo de Flujo de Caja Descontados obteniendo un estudio del valor intrÃ­nseco, indicadores de valor y value drivers que crean valor de la empresa ESNOBIS S.A.English Abstract: In many occasions an entrepreneur has to make decisions about the management and growth of a company, the controversial point is the difference between the value and the price in which those who sell and buy are negotiating with on-going companies. It is found in the same situation those companies which do not know how to value companies. This paper is explaining the importance, methods and how to value a company through the Discounted Cash Flow method which gives the internal value, value indicators and value drivers that give more value to the company ESNOBIS S.A.

Valuation Uncertainty
Golubov, Andrey,Konstantinidi, Theodosia
SSRN
We develop a firm-specific measure of valuation uncertainty from the distribution of valuations predicted by an empirical multiples-based valuation model. The measure is effective in summarizing the information in existing proxies and offers substantial incremental variation. Among many possible applications, we use our measure to test the hypothesis that valuation uncertainty is conducive to valuation mistakes. A value-like long-short strategy is particularly profitable among high valuation uncertainty stocks. Stocks in the short leg earn average returns indistinguishable from the risk-free rate â€" turning negative following periods of high investor sentiment â€" and their future earnings disappoint. Insiders trade against the presumed valuation mistakes.

Value relevance of accounting information and stock price reaction: Empirical evidence from China
Rahman, Jahidur Md,Liu, Ruoling
SSRN
Research question: This study investigates whether the release of financial accounting information is related to the change in stock prices.Motivation: The relationship between accounting information and stock price reaction is well documented in the United States and other major developed economies. However, what is the relationship between share prices and accounting information in emerging economies? According to the standards of Wall Street, London, and Tokyo, the emerging capital markets in the post-communist countries are very small, but they can be a major source of capital for economic growth and development.Idea: For investors, investment decisions about stock should be made by analyzing factors that can influence the stock price. Investors need to have information to determine whether they can benefit from the shares they bought. The financial condition of companies is one of the major information that investors should know.Data: We obtained data from the A-share market in Shanghai and Shenzhen Stock Exchange of 1,272 listed companies. Data from selected companiesâ€™ annual report in 2008- 2018 and closing share prices in 2009-2019 were collected. The sample data were from the China Stock Market and Accounting Research database.Tools: This study used a stepwise regression model to select variables that can provide significant effects and analyzed the regression of added variables and stock price.Findings: We find that value relevance of accounting number, profitability, liquidity, and operational efficiency are positively related to stock price reaction. Other accounting variables, such as earnings per share, current ratio, quick ratio, and debt to equity ratio, have a more significant influence on the market share price. A linear relationship is observed between stock prices and earning per share, current ratio, and quick ratio according to the stepwise regression method.Contribution: This study provides new insights for academic research and provides additional meaningful and useful information regarding the relevance of companiesâ€™ accounting information for foreign and domestic investors, specifically domestic investors. This research has scientific and practical usefulness for investors who care about their returns on investments.

What Drives the Tail Risk Effect in the Chinese Stock Market?
Sun, Kaisi,Wang, Hui,Zhu, Yifeng
SSRN
In this paper, we primarily explore the mechanism of the negative tail risk effect in the Chinese stock market from several perspectives, including investor sentiment, underreaction to bad news, tail risk preference, and heterogeneous beliefs under the short-selling constraint. We document a significant negative tail risk effect in the lowest capital gains overhang quintile and confirm that investors prefer the tail risk when facing prior capital loss. On the basis of a quasi-natural experiment provided by the implementation of the margin trading system, we also validate that the insufficient release of pessimistic beliefs under the short-selling constraint contributes to the negative effect.

What's a Nice Company like Goldman Sachs Doing in the Supreme Court?
Booth, Richard A.
SSRN
This essay considers the issues raised in the latest securities fraud class action to reach the Supreme Court â€" Goldman Sachs v. Arkansas Teacher Retirement System â€" and finds that the claims asserted therein against Goldman Sachs on behalf of open-market buyers of its common stock are claims that should have been asserted on behalf of Goldman Sachs (by means of a derivative action) and against the individuals who caused the losses at issue. The losses suffered by individual buyers of Goldman Sachs stock during the extraordinarily long forty-month alleged fraud period are minimal if they exist at all. Moreover, the law is quite clear that claims on behalf of the company arising from the same constellation of facts should take precedence over any claims on behalf of individual buyers. Yet the practice that has evolved is the opposite: Class claims take priority and company claims are settled for non-monetary governance reforms of dubious value rather than for real money. The forces that have led to this classic example of market failure are both fascinating and sinister. But the bottom line is that ordinary investors â€" such as investors in well-diversified mutual funds and index funds â€" end up losing far more than they gain from class actions. Indeed, index fund investors effectively pay out about twenty dollars for every dollar they recover. Thus, the best hope for reforming the system is for index funds to step up and intervene to assert the interests of diversified investors in favor of litigating such claims as derivative actions rather than as class actions.

Wirecard and Greensill Scandals Confirm Dangers of Mixing Banking and Commerce
Wilmarth, Arthur E.
SSRN
The pandemic crisis has accelerated the entry of financial technology (â€œfintechâ€) firms into the banking industry. Some of the new fintech banks are owned or controlled by commercial enterprises. Affiliations between commercial firms and fintech banks raise fresh concerns about the dangers of mixing banking and commerce. Recent scandals surrounding the failures of Wirecard and Greensill Capital (Greensill) reveal the potential magnitude of those perils. The Federal Deposit Insurance Corporation (FDIC) and the Office of the Comptroller of the Currency (OCC) have encouraged commercial enterprises to acquire fintech banks. The FDIC has authorized commercial firms to acquire FDIC-insured industrial banks in reliance on a controversial loophole in the Bank Holding Company Act (BHC Act). The OCC is seeking to charter nondepository fintech national banks, which commercial firms could own under a separate exemption in the BHC Act. The FDICâ€™s and OCCâ€™s initiatives undermine â€" and could potentially destroy â€" the BHC Actâ€™s longstanding policy of separating banking and commerce. The disasters at Wirecard and Greensill demonstrate the importance of maintaining a strict separation between banking and commerce. Regulators in Germany and other countries allowed banks controlled by Wirecard and Greensill to engage in risky and abusive transactions that benefited their parent companies and other related parties, including commercial firms connected to their major investors. Wirecard Bank provided financial support to its parent company and CEO, and it also made fraudulent transfers of funds to insiders and their controlled entities. Greensill Bank made preferential and unsound loans that benefited its parent company and leading investors. Greensill Bank securitized many of its reckless loans, and Greensill Capital sold the resulting asset-backed securities as â€œsafeâ€ and â€œliquidâ€ investments to misinformed investors. Regulators failed to take timely enforcement actions against Wirecard and Greensill because they did not exercise consolidated supervisory authority over the complex international structures created by both firms. In addition, Wirecard and Greensill built extensive networks of influence that produced significant political favors and regulatory forbearance in Germany and the U.K. The collapse of Wirecard and Greensill embarrassed government agencies and inflicted massive losses on investors, creditors, and other stakeholders. The failures of Wirecard and Greensill provide clear warnings about the dangers of allowing fintechs to offer banking services while evading prudential regulatory requirements and supervisory standards that apply to traditional banks and their corporate owners. Regulators and policymakers should not allow fintechsâ€™ claims of â€œinnovationâ€ to serve as a rationale for regulatory arbitrage and as camouflage for fraud. Both disasters show that high-tech firms engaged in banking and commercial activities are likely to create the same unacceptable hazards as previous banking-and-commercial conglomerates, including toxic conflicts of interest, reckless lending, dangerous concentrations of economic power and political influence, supervisory blind spots, and systemic threats to economic and financial stability.