Miracle-dz/UniFAR-FTUs huggingface dataset:
Foreign Capital Inflows, Exchange Rates, and Government Stability

In theory, changes in a host country exchange rate can be a cause or consequence of changes in its level of foreign direct investment (FDI), and recent incidences suggest that government stability may have sizable implications for the interactions between FDI and the exchange rate. This paper uses a semiparametric system of simultaneous equations to empirically characterize the relationship between FDI and the exchange rate, with each country's level of government stability serving as a moderator. The results suggest that across developed and developing economies the most prevalent type of symbiosis between FDI and the exchange rate is a positive effect of FDI on the exchange rate, but no effect of the exchange rate on FDI. This significant FDI effect is heterogeneous, with an interquartile range of 1.241. At the median, a 10 percent increase in FDI inflows relative to GDP causes approximately a 13.29 percent increase in the annual change in the exchange rate. Government stability acts as a moderator variable by strengthening the relationship between FDI and the exchange rate in some countries, but eliminates the relationship in other countries.
I Might Be a Liar, but Not a Thief: An Experimental Distinction between the Moral Costs of Lying and Stealing

In this paper, we shed light on the different moral costs of dishonesty and stealing. To accomplish this, we set up a die-rolling task which allowed participants to increase their own payout through dishonesty or theft. The results show that participants have fewer reservations about dishonesty compared to stealing, which implies higher intrinsic costs for stealing. We found that gender contributes to this effect, as women distinguish significantly between lying and stealing, while men do not.
Tail Risk in Credit Markets: A Dynamic Power-Law Model

I provide a measure of time-varying tail risk in credit markets based on a dynamic power-law model. Credit tail risk is estimated from extreme price fluctuations of credit default swaps (CDS) on government debt. Tail returns are described by a power-law for core and peripheral countries within the Economic and Monetary Union. I find that the tail distributions of returns for short- and long-term debt insurances exhibit the cubic law of returns on different time-scales, which implies the existence of a finite second moment. Sellers of short-term CDS protection bear a higher tail risk of more extreme returns than long-term CDS, especially on shorter time scales. Cross-sectionally, countries in the core region that have a lower probability of credit default imply a greater tail risk than in the peripheral region. This phenomenon can be explained by the significant impact of volatility on the tail distribution. My evidence suggests that credit default swaps of core countries with cheap insurance prices pay off in high-risk states and thus are valuable hedges against extreme events. I show that these findings have important implications for risk management, portfolio allocation, and asset pricing.
Does Corporate Governance Matter for Financial Analysts? Evidence from Tunisia

This article examines the link between corporate governance mechanisms with both analyst forecasts accuracy and recommendations within the Tunisian Stock Exchange. Based on agency and signaling theories, good governance mechanisms aim to mitigate agency conflicts, and improve corporate transparency. As a result, they can serve as mediators in the forecasting process. Using a sample of 357 firm-year observations, there is a significant relation between governance quality, target price accuracy and recommendations, respectively. One of the most important results is that CEO compensation is an effective mechanism on which analysts can rely when setting their forecasts, especially within the financial sector.
Job Search with Financial Information: Theory and Evidence

This paper examines whether, when, and why job seekers use ﬁrms’ ﬁnancial information in the job search process. We ﬁnd ﬁrst evidence of ﬁnancial information’s relevance to job seekers by documenting a substantial increase in job search activity around earnings announcements in the spirit of Beaver (1968). We also ﬁnd that ﬁnancial information acquisition by job seekers is positively related to both job postings and interviews at the ﬁrm-county-month level. Spurred by this ﬁnding, we develop a theoretical model of job search paired with ﬁrms’ heterogeneous earnings to better understand job seeker’s information acquisition behavior. Our model predicts that job seekers trade oﬀ the probability of an oﬀer with the value of the employment contract and intensify information acquisition as the number of available positions shrinks relative to the pool of job seekers. Consistent with these predictions, we ﬁnd that ﬁrm performance is positively correlated with both job seekers’ search activities and employers’ posted wages. We also ﬁnd that the positive association between ﬁnancial information acquisition and interviews is stronger when the job market is more competitive. Overall, these results indicate that, like capital market participants, job seekers value and use ﬁnancial reporting.
The Measurement of Corporate Tax Avoidance in the Presence of Net Operating Losses

This article addresses the question whether net operating loss (NOL) observations can be implemented in the measurement of corporate tax avoidance and how the handling of losses affects the measurement outcome. I find that the implementation of NOLs in the measurement of tax avoidance is feasible but only under certain restrictions. If researchers control for the confounding effect of firm-profitability per asset and heterogeneous characteristics of non-prospering firms, losses can meaningfully be included in the analysis by the use of asset-scaled proxies. When researchers decide to remove net operating losses from their sample, they should be aware of misleading tax expense before and after loss-years that remain in the sample and do not signal tax planning. In view of this, I re-examine trends in aggregate U.S. corporate tax avoidance documented by Dyreng, Hanlon, Maydew and Thornock (2017) who omit NOLs. I document that their findings are robust to misleading tax expenses of firms around loss years but that the loss structure in the data partly conceals the true development of U.S. domestic firms’ corporate tax planning. More precisely, I show that profitable U.S. domestic firms have become more tax-disfavored over time relative to profitable U.S. multinationals when the dynamic Compustat data coverage is taken into account.
What Do the Portfolios of Individual Investors Reveal About the Cross-Section of Equity Returns?

We construct a parsimonious set of equity factors by sorting stocks according to the sociodemographic characteristics of the individual investors who own them. The analysis uses administrative data on the stockholdings of Norwegian investors in 1997-2018. Consistent with financial theory, a mature-minus-young factor, a high wealth-minus-low wealth factor, and the market factor price stock returns. Our three factors span size, value, investment, profitability, and momentum, and perform well in out-of-sample bootstrap tests. The tilts of investor portfolios toward the new factors are driven by wealth, indebtedness, macroeconomic exposure, age, gender, education, and investment experience. Our results are consistent with hedging and sentiment jointly driving portfolio decisions and equity premia.
Systemic Risk in the Chinese Banking Sector

We examine the evolution and factors of systemic risk in the Chinese banking sector over the last decade from the perspective of international investors. We apply the SRISK measure of systemic risk to a representative sample of listed Chinese institutions that captures 50-60% of total banking assets and utilize the Granger-causality network-based approach to demonstrate interlinkages among Chinese banks beyond the largest financial institutions. Firstly, we show a dramatic increase in systemic risk after 2011 and the increased contribution of small- and medium-sized banks. Then, we identify causality relationships from housing prices, economic policy uncertainty and shadow banking towards systemic risk and causality from shadow banking to housing prices. According to our results, international concerns about the stability of the Chinese banking system are well justified and a systemic event with international impact could be caused by distress in a Chinese financial institution outside of the group of the largest banks.
Female CFOs, Leverage and the Moderating Role of Board Diversity and CEO Power

Abstract This paper investigates the extent to which female Chief Financial Officers (CFOs) affect corporate leverage. We examine female CFOs in UK firms and find that they significantly reduce the leverage of the firm; however, a female CFO's ability to influence corporate leverage is moderated by the senior decision-making environment in the firm. In particular, female CFOs are more effective in reducing leverage in firms with boards that are diverse with respect to gender, nationality and age, and in firms where the Chief Executive Officer (CEO) is not overly powerful. In addition, we find that externally appointed female CFOs reduce leverage more, in line with them having less allegiances to other top managers and greater scope to deviate from existing policies. Our work contributes to the literature by examining the conditions under which gender-specific risk-taking preferences lead to observable effects on corporate outcomes.
The Linear Effect of Bank Liquidity on Profitability in Selected African Economies

Given the importance attached to sound liquidity management in the banking industry, this paper investigates the effect of bank liquidity on profitability. The study employs the bankruptcy cost and risk-return hypotheses to examine the linear effect of bank liquidity on bank profit. Annual bank-specific data from commercial banks in Kenya, Nigeria and South Africa, for the period 2000-2014, are used in the study. The two-step system generalised method of moments technique of analysis, an instrumental variable technique that addresses issues such as endogeneity, reverse causality and auto-correlation, is used for the investigation. The results revealed a statistically significant and positive relationship between liquidity and bank profit indicating the applicability of the bankruptcy cost hypothesis. This implies that banks in the study benefit from reduced financial distress and funding costs thereby increasing profits. The study thus recommends that commercial banks in the selected countries hold higher levels of liquidity to mitigate the risk of failure and increase profit.
Financial Frictions and the Non-Distortionary Effects of Delayed Taxation

Financially constrained agents discount future cash flows at above-market rates. In this paper, we present the hypothesis that delaying tax payments can materially reduce distortions when agents are credit constrained. We test this hypothesis in the context of the labor supply decisions of young workers in Norway, where a kinked income-contingent student-debt conversion scheme replicates an income tax with delayed payments. Bunching analyses reveal elasticities that are an order of magnitude below those we find at a regular income tax threshold, and which are increasing in ex-ante financial resources. These findings underline the potential for delayed taxation to be a powerful new component of optimal tax policy.
Estimating Production Functions in Differentiated-Product Industries with Quantity Information and External Instruments

This paper develops a new method for estimating production-function parameters that can be applied in differentiated-product industries with endogenous quality and variety choice. We take advantage of data on physical quantities of outputs and inputs from the Colombian manufacturing survey, focusing on producers of rubber and plastic products. Assuming constant elasticities of substitution of outputs and inputs within firms, we aggregate from the firm-product to the firm level and show how quality and variety choices may bias standard estimators. Using real exchange rates and variation in the "bite" of the national minimum wage, we construct external instruments for materials and labor choices. We implement a simple two-step instrumental-variables method, first estimating a difference equation to recover the materials and labor coefficients and then estimating a levels equation to recover the capital coefficient. Under the assumption that the instruments are uncorrelated with firms' quality and variety choices, this method yields consistent estimates, free of the quality and variety biases we have identified. Our point estimates differ from those of existing methods and changes in our preferred productivity estimator perform relatively well in predicting future export growth.
Differential Effects by Mental Health Status of Filling the Medicare Part D Coverage Gap

Supplemental Digital Content is available in the text. Objective: The objective of this study was to study how changes in insurance benefit design affect medication use of older adults with mental disorders. Data Sources: US Medicare claims data from 2007 to 2018. Study Design: Exploiting the gradual elimination of the Medicare prescription drug coverage gap beginning in 2011, we examine the effects on medication use and out-of-pocket spending by drug type with a difference-in-differences approach. We identify subpopulations by mental disorder and compare the estimates across mental health groups and to the general Medicare population. Principal Findings: Closing the gap substantially reduced individuals’ out-of-pocket spending, and the reduction was larger for those with more severe mental disorders. The policy led to a statistically significant increase in branded drugs used for the Medicare population (0.91; P<0.01; 12.12% increase), beneficiaries with severe mental disorders (2.71; P<0.01; 11.13% increase), and common mental disorders (2.63; P<0.01; 11.62% increase), whereas such effect for beneficiaries with Alzheimer disease and dementia (AD) is substantially smaller (0.44; P<0.01; 1.83% increase). In contrast, the policy decreased generic drugs used by about 3%–5% for all groups. Overall, beneficiaries without mental health illness have a statistically significant increase in total medication use (2.05%) following the coverage gap closure, while all 3 mental health groups have either no statistically significant changes or a small reduction in total mediation use (AD, −1.26%). Conclusions: Patients’ responses to price changes vary across mental disorders and by drug type. The impact on branded drug utilization among those with AD is particularly small. Our findings suggest that lowering medication costs has differential impacts across diseases and may not be sufficient to improve adherence for all conditions, in particular those with severe mental health disorders such as AD.
Dynamic Portfolio Management with Machine Learning

We present a structured portfolio optimization framework with sparse inverse covariance estimation and an attention-based LSTM network that exploits machine learning (deep learning) techniques. We shrink Wishart volatility towards a Graphical Lasso initial covariance estimator and solve the portfolio optimization using a fast coordinate descent algorithm with regularization determined using a genetic algorithm. We further introduce a novel portfolio shrinkage rule using an attention-based Long-Short-Term-Memory (LSTM) network, allowing a formal selection of reference portfolios where the network forecasts future performance based on predetermined out-of-sample monthly certainty equivalent return. We reduce the dimension of successful candidates and then linearly combine them. When nested within a minimum-variance, Bayes-Stein shrinkage, Black-Litterman portfolio framework with four types of weight constraints based on no-short-selling, upper, lower-generalized variance-based restrictions, our approach delivers a clear improvement over the baseline sample-based minimum-variance portfolio and claims superiority over 11 GARCH models used to forecast covariances, as well as a minimum-variance combination of all dynamic optimization models. We provide an illustrative example based on optimal diversification across hedge fund strategies. Robustness checks show our application of sparse covariance dominates the use of a dimension reduction algorithm for Wishart covariance forecasting.
Supplementary Appendix to Beta-Adjusted Covariance Estimation

In this supplementary appendix, we first provide a brief R and Python tutorial for the proposed BAC estimator. Then, we describe the implementation of the BAC estimator in case of microstructure noise and jumps. We further present more detailed empirical results for the BAC estimation applied to the XLF ETF components. Finally, we provide additional proofs for results presented in the main paper.
The Role of Employee’s Mindfulness and Human Resource Development Climate Towards Public Leadership Behavior-Analyzing the Moderating Impact of Emotional Capital

Present study examined the relationship between Human Resource Development (HRD) climate, mindfulness and public leadership behavior and how emotional capital increases the role of Human Resource Development Climate (HRDC) and mindfulness in public leadership behavior. Data were gathered from 252 respondents of 18 public universities of Islamabad and Rawalpindi. Random sampling technique was used. To analyze the collected data, correlation and regression test were used. Findings of the study give clear picture of Public Leadership Behavior (PLB) in public universities of Pakistan. Study results proved that HRDC, mindfulness and PLB have the positive relationship which would further get enhanced by the role of emotional capital. For future researchers, this study also provides valuable findings and recommendations.
Tax Avoidance, Tax Risk, and the Volatility of Stock Returns

We explore the effects of tax avoidance and tax risk on stock return volatilities of U.S. firms. We find that firms with very low and very high levels of tax avoidance and firms with high levels of tax risk have more volatile stock returns. We observe that tax avoidance primarily affects stock return volatility through changes in investors’ cash flow expectations; in contrast, tax risk seems to affect stock returns through cash flow and discount rate channels. Furthermore, we find that changes in expected cash flows and discount rates are less offsetting for firms with extreme levels of tax avoidance and high levels of tax risk.
Accounting, Intangible Assets, Stock Market Activity, and Measurement and Disclosure Policy: Views from the UK

This paper considers the impact of UK practices with respect to the measurement and disclosure of intangible assets, focusing on RD activities. We first update prior UK work relating RD activities to market prices. Second, given the clearly identified role of disclosure outside of the financial statements in helping market participants value RD expenditures, we consider whether market forces are generally sufficient to ensure adequate disclosures with respect to intangibles by considering the cases of two biotechnology firms involved in the issuance of misleading disclosures. Within this context, we consider how disclosure regulation and enforcement mechanisms have evolved in recent years, and how this evolution has likely been affected by our ‘scandal’ cases. Our conclusions are that the case of the UK does not give rise to any wide-scale concerns about the economic ill-effects caused by the current state of recognition and disclosure with respect to expenditures on intangibles. Further, market forces are unlikely to be sufficient in ensuring honest and timely disclosures with respect to intangibles, but the combination of official regulation and voluntary self-regulation appears to have stemmed the tide of any such disclosure scandals in the UK.
Practical smart contract sharding with ownership and commutativity analysis

Sharding is a popular way to achieve scalability in blockchain protocols, increasing their throughput by partitioning the set of transaction validators into a number of smaller committees, splitting the workload. Existing approaches for blockchain sharding, however, do not scale well when concurrent transactions alter the same replicated state component—a common scenario in Ethereum-style smart contracts. We propose a novel approach for efficiently sharding such transactions. It is based on a folklore idea: state-manipulating atomic operations that commute can be processed in parallel, with their cumulative result defined deterministically, while executing non-commuting operations requires one to own the state they alter. We present CoSplit—a static program analysis tool that soundly infers ownership and commutativity summaries for smart contracts and translates those summaries to sharding signatures that are used by the blockchain protocol to maximise parallelism. Our evaluation shows that using CoSplit introduces negligible overhead to the transaction validation cost, while the inferred signatures allow the system to achieve a significant increase in transaction processing throughput for real-world smart contracts.
Local Product Market Competition and Bank Loans

Abstract We investigate the influences of local product market competition on the cost of private debt. Our evidence suggests that the cost of bank loans is significantly higher for firms headquartered in states with greater local product market competition measured by the Herfindahl-Hirschman Index for resident industries. To establish causality, we examine the recognition of the Inevitable Disclosure Doctrine and firm relocations to identify exogenous shocks to local product market competition. We find that the cost of bank loans is lower for firms facing less intense local product market competition after the adoption of IDD and higher for firms relocated to states with more competitive product markets. The results imply that banks value the characteristics of a firm's local product market when approving loan contracts.
Combining Penalization and Adaption in High Dimension with Application in Bond Risk Premia Forecasting

The predictability of a high-dimensional time series model in forecasting with large information sets depends not only on the stability of parameters but also depends heavily on the active covariates in the model. Since the true empirical environment can change as time goes by, the variables that function well at the present may become useless in the future. Combined with the instable parameters, finding the most active covariates in the parameter time-varying situations becomes difficult. In this paper, we aim to propose a new method, the Penalized Adaptive Method (PAM), which can adaptively detect the parameter homogeneous intervals and simultaneously select the active variables in sparse models. The newly developed method is able to identify the parameters stability at one hand and meanwhile, at the other hand, can manage of selecting the active forecasting covariates at every different time point. Comparing with the classical models, the method can be applied to high-dimensional cases with different sources of parameter changes while it steadily reduces the forecast error in high- dimensional data. In the out-of-sample bond risk premia forecasting, the Penalized Adaptive Method can reduce the forecasting error(RMSPE and MAPE) around 24% to 50% comparing with the other forecasting methods.