Research articles for the 2020-09-29

A generative adversarial network approach to calibration of local stochastic volatility models
Christa Cuchiero,Wahid Khosrawi,Josef Teichmann

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analysis on many samples of implied volatility smiles, showing the accuracy and stability of the method.

Corporate Taxes and Corporate Social Responsibility
Chang, Xin (Simba),Jin, Yaling,Yang, Endong,Zhang, Wenrui
This paper examines the impact of corporate taxes on firms’ corporate social responsibility (CSR), by exploiting the staggered changes in corporate taxes across U.S. states as a quasi-natural experiment. Firms enhance CSR performance significantly following tax cuts, but they do not reduce CSR in response to tax increases, consistent with the rigidity of CSR. The effect of tax decrease on CSR is more pronounced for firms that are more affected by state-level tax changes, more financially constrained, headquartered in more CSR-friendly states, owned by more socially responsible institutional investors, in more competitive industries, or led by CEOs who are more long-term oriented. The evidence suggests that tax cuts alleviate financing constraints, thereby enabling firms to be more socially responsible. Overall, our findings shed light on how fiscal policy shapes companies’ incentives to be socially responsible.

Do Checks on Bureaucrats Improve Firm Value? Evidence from a Natural Experiment
An, Jiafu,Armitage, Seth,Hou, Wenxuan,Liu, Xianda
This paper studies the impact on firm value of tighter checks on bureaucrats’ behavior. We use as a natural experiment the revision in 2015 by the Communist Party of China (CPC) of its regulations on disciplinary actions. We document a positive and substantial market reaction following this unexpected policy change that tightened and formalized constraints on bureaucrats’ misconduct. The impact is less pronounced for firms with state ownership, firms having CEOs or directors with CPC membership, and firms that operate in provinces with better institutional quality. However, the subsequent revision in 2018 that enforced political obedience is not associated with a positive market reaction.

Do Stop-loss Rules Add Value in International Equity Market Allocation?
Dai, Bochuan,Marshall, Ben R.,Nguyen, Nhut H.,Visaltanachoti, Nuttawat
We consider the performance of stop-loss rules in international equity market allocation. Our results indicate that stop-loss rules, which involve closing positions that decline by a pre-specified percentage, are important determinants in the parametric portfolio policy. They generate portfolios that have superior mean and risk-adjusted returns for investors. This result holds in general but is economically stronger in declining markets. The out-performance is robust to the inclusion of transaction costs.

Does Stock Manipulation Distort Corporate Investment? The Role of Short Selling Costs and Share Repurchases
Campello, Murillo,Matta, Rafael,Saffi, Pedro A. C.
We characterize the effect of short selling costs on interactions between informed and uninformed speculators, showing how this dynamic impacts corporate decisions such as investment and stock repurchases. Low shorting costs allow for manipulation to coexist with informed trading, reducing price informativeness and investment. Manipulation becomes less profitable as shorting costs increase, making prices more informative and boosting investment when speculators are less likely to be informed. At high shorting costs, informed shorting is unprofitable even in the absence of manipulation threats, resulting in low price informativeness and constraining firms' access to financing. Our model shows that the ability to pre-commit funds before prices reflect speculators' information yields a negative relation between investment and shorting costs. Critically, it demonstrates how managers can stop manipulative shorts through stock repurchases, leading to efficient investment. Stock liquidity, cash flow uncertainty, and management â€" creditor agency problems are shown to shape the impact of short selling costs on corporate policies.

Dynamic Spatial Network Quantile Autoregression
Xu, Xiu,Wang, Weining,Shin, Yongcheol
This paper proposes a dynamic spatial autoregressive quantile model. Using predetermined network information, we study dynamic tail event driven risk using a system of conditional quantile equations. Extending Zhu et. al. (2019), we allow the contemporaneous dependency of nodal responses by incorporating a spatial lag to our model. For example, this is to allow a firm's tail behavior to be connected with a weighted aggregation of the simultaneous returns of the other firms. In addition, we also control for the common factor effects. The instrumental variable quantile regressive method is used for our model estimation, and the associated asymptotic theory for estimation is also provided. Simulation results show that our model performs well at various quantile levels with different network structure, especially when the node size increases. Finally we illustrate our method with an empirical study. We uncover significant network effects in the spatial lag among financial institutions.

FSA in an ETF World
Lundholm, Russell J.
This paper models the value of conducting financial statement analysis (FSA) in the presence of an electronically traded fund (ETF) that gives exposure to the firm’s systematic value. FSA is characterized as a costly process that yields a private signal about the idiosyncratic portion of a firm’s future payoffs. The value of this signal depends on how efficiently price transmits information to uninformed traders. A popular argument is that ETFs are attracting noise traders away from the underlying firm, making prices more informative and private information less valuable. While I find that prices are more informative after the introduction of an ETF, I show that this isn’t because of a change in the amount or location of noise trading. Holding noise trading constant, ETFs allow informed investors to hedge out exposure to the portion of firm value that they are uninformed about, which causes them to place larger bets on their private information, and this is what causes firm prices to be more informative. The introduction of an ETF into an economy thus presents two competing forces on the value of conducting FSA. On the one hand, prices are more informative after the arrival of an ETF, making private information less valuable, but on the other hand, informed traders can use the ETF to hedge, making private information more valuable. I characterize how these forces trade off as a function of the exogenous noise in the economy. These results are unavailable in previous theoretical papers about ETFs because they modeled investors as being risk neutral, thus eliminating their desire to hedge out uncertainty.

Financial Inclusion, At What Cost? : Quantification of Economic Viability of A Supply Side Roll Out
Markose, Sheri M.,Arun, Thankom,Ozili, Peterson K
The Prime Minster Jan-Dhan Yojna (PMJDY), started in 2014, follows in a long line of drives for financial inclusion in India, marked only by a much greater scope and ambition than previous roll outs. This top down approach to close the gap on the unbanked of India relies primarily on public sector banks with targets set for rural outreach. We develop an innovative approach using cross sectional bank level data from 2014 till 2017 to quantify the incentives and costs involved in targeting unbanked households. This gives a monetary estimate of the economic shortfalls or surpluses for participating banks, measured as bank balances relative to outlay costs and subsidies per PMJDY beneficiary. We model the double bind problem faced by banks to achieve economies of scale that arise from spreading the fixed infrastructure costs over the number of below poverty line (BPL) customers when there is a dearth of balances in these accounts. This lack of economic viability of PMJDY accounts is found in most public sector banks, a matter which is problematic in view of their extant financial fragility in India. We provide evidence for cross subsidization of rural bank accounts by urban accounts. We give estimates using fixed effects panel methods as to what cost public sector banks bear and also quantify the extent to which account ineffectiveness is ameliorated with exogenous factors, primarily the tie up of PMJDY accounts with bio-metric Aadhar cards and electronic direct benefit transfer of G2P payments.

Forecasting Short-term load using Econometrics time series model with T-student Distribution
Kasun Chandrarathna,Arman Edalati,AhmadReza Fourozan tabar

By significant improvements in modern electrical systems, planning for unit commitment and power dispatching of them are two big concerns between the researchers. Short-term load forecasting plays a significant role in planning and dispatching them. In recent years, numerous works have been done on Short-term load forecasting. Having an accurate model for predicting the load can be beneficial for optimizing the electrical sources and protecting energy. Several models such as Artificial Intelligence and Statistics model have been used to improve the accuracy of load forecasting. Among the statistics models, time series models show a great performance. In this paper, an Autoregressive integrated moving average (SARIMA) - generalized autoregressive conditional heteroskedasticity (GARCH) model as a powerful tool for modeling the conditional mean and volatility of time series with the T-student Distribution is used to forecast electric load in short period of time. The attained model is compared with the ARIMA model with Normal Distribution. Finally, the effectiveness of the proposed approach is validated by applying real electric load data from the Electric Reliability Council of Texas (ERCOT). KEYWORDS: Electricity load, Forecasting, Econometrics Time Series Forecasting, SARIMA

Hidden in Plain Sight: The Role of Corporate Board of Directors in Public Charity Lobbying
Ahn, Changhyun,Houston, Joel F.,Kim, Sehoon
Using IRS tax filings by public charities linked to lobbying disclosure and corporate board data, we show that charities with corporate directors on their boards spend more money on lobbying for the connected firms' industry interests. Firms with greater exposure to political risk and lobbying activities more often seek board connections with charities, and the effects of connections are stronger when charities are connected to such firms or when charities are constrained on funding. We rule out assortative matching between directors and charities by controlling for firm-charity pair fixed effects, and address concerns of reverse causality using director turnovers as shocks to firm-charity connections. Consistent with quid-pro-quo relationships between firms and charities, we find that connected firms benefit from increased procurement contracts, and that connected charities receive more grants and donations. Our results highlight executive charitable engagement as a hidden avenue for corporate political activities.

Historical Determinants of Modern Finance: Evidence from Initial Coin Offerings
An, Jiafu,Hou, Wenxuan,Liu, Xianda
This paper examines the impact of historical endowment on the function of Initial Coin Offering (ICO) market. We find that a country’s legal origin, disease environment, exposure to slave trade, and proximity to ancient trade routes are important determinants of the total amount of capital raised through ICOs. Further analyses suggest that the impact of historical endowment operates through legal and information sharing institutions. This finding suggests that history plays an important role in shaping modern finance, even after addressing the simultaneity and reverse causality concerns.

Hoarding Bad News: When Non-financial Firms Hold Financial Assets
Ni, Xiaoran,Peng, Yuchao,Shen, Ji
This paper theoretically illustrates and empirically examines how listed non-financial firms use financial assets as an earnings manipulation tool with bad-news-hoarding motives. China created its first accounting standards for financial instruments in 2007, which classify financial assets based on the highly subjective “managerial holding intention” criterion, and financial assets accounted by fair value methods can affect current profits and losses. We find that during the years 2007 through 2016, financial asset holding is positively associated with the likelihood of stock price crashes for Chinese listed firms. The effect is more pronounced for firms with a CEO from a finance background, high information asymmetries, and weak governance. In addition, we identify accrual earnings management as a channel through which financial asset holding increases stock price crash risk. Our evidence indicates that in typical emerging markets such as China, fair value accounting may have unintended consequences, inducing non-financial firms to employ financial assets as a tool to hoard bad news.

How Do Investors Trade R&D-intensive Stocks? Evidence from Hedge Funds and Other Institutional Investors
Alldredge, Dallin,Caglayan, Mustafa Onur,Celiker, Umut
We examine how institutional investors trade stocks with high research and development (R&D) expenses and investigate whether they can detect value-relevant R&D. We document significant differences between hedge funds and other institutional investors in terms of their trading in high R&D stocks. We find that hedge funds (other institutional investors) invest more (less) in high R&D stocks compared to low R&D stocks. Moreover, while hedge funds have strong stock-picking ability in high R&D stocks, other institutional investors do not show any evidence of stock-picking ability. Our findings suggest that hedge funds have differential skillset to better identify the value-relevant R&D.

Improved Estimation of Dynamic Models of Conditional Means and Variances
Wang, Weining,Wooldridge, Jeff,Xu, Mengshan
Modelling dynamic conditional heteroscedasticity is the daily routine in time series econometrics. We propose a weighted conditional moment estimation to potentially improve the efficiency of the QMLE (quasi maximum likelihood estimation). The weights of conditional moments are selected based on the analytical form of optimal instruments, and we nominally decide the optimal instrument based on the third and fourth moments of the underlying error term. This approach is motivated by the idea of general estimation equations (GEE). We also provide an analysis of the efficiency of QMLE for the location and variance parameters. Simulations are conducted to show the better performance of our estimators.

Intangibles to Tangible: In Search of Firm Value Creation
Liao, Cunfei,Jiang, Fuwei,Jin, Fujing,Tang, Guohao
In this paper, we propose a comprehensive intangible assets-related measure, I-SCORE, with the purpose to explain the cross sectional returns in the U.S. stock market. We apply the partial least squares method to construct I-SCORE from 15 firm characteristics. The results show that firms with high I-SCORE generate substantially higher future returns than those with low I-SCORE. The predictability of I-SCORE is robust after controlling for innovation or R&D-related factors, along with some famous asset pricing factors. I-SCORE is positively related to future profitability and cash flow growth. The positive relation between intangible assets and future returns is stronger for firms with higher limits-to-arbitrage and higher valuation uncertainty, which is consistent with behavioral mispricing explanations. The risk-based theory also explains the intangible asset score strategy, for firms with higher cash flow have more powerful I-SCORE premium.

Killer Acquisitions and Beyond: Policy Effects on Innovation Strategies
Letina, Igor,Schmutzler, Armin,Seibel, Regina
This paper provides a theory of strategic innovation project choice by incumbents and start-ups. We show that prohibiting killer acquisitions strictly reduces the variety of innovation projects. By contrast, we find that prohibiting other acquisitions only has a weakly negative innovation effect, and we provide conditions under which the effect is zero. Furthermore, for both killer and other acquisitions, we identify market conditions under which the innovation effect is small, so that prohibiting acquisitions to enhance competition would be justified.

On the Pricing of Currency Options under Variance Gamma Process
Azwar Abdulsalam,Gowri Jayprakash,Abhijeet Chandra

The pricing of currency options is largely dependent on the dynamic relationship between a pair of currencies. Typically, the pricing of options with payoffs dependent on multi-assets becomes tricky for reasons such as the non-Gaussian distribution of financial variable and non-linear macroeconomic relations between these markets. We study the options based on the currency pair US dollar and Indian rupee (USD-INR) and test several pricing formulas to evaluate the performance under different volatility regimes. We show the performance of the variance gamma and the symmetric variance gamma models during different volatility periods as well as for different moneyness, in comparison to the modified Black-Scholes model. In all cases, variance gamma model outperforms Black-Scholes. This can be attributed to the control of kurtosis and skewness of the distribution that is possible using the variance gamma model. Our findings support the superiority of variance gamma process of currency option pricing in better risk management strategies.

Portfolio Efficiency Tests with Conditioning Information - Comparing GMM and GEL Estimators
Vigo Pereira, Caio,Laurini, Márcio
We evaluate the use of Generalized Empirical Likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. Estimators from GEL family present some optimal statistical properties, such as robustness to misspecification and better properties in finite samples. Unlike GMM, the bias for GEL estimators do not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. By means of Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. An extensive empirical analysis shows the properties of the estimators for different sample sizes and portfolios types for two asset pricing models.

Portfolio Efficiency with High-Dimensional Data As Conditioning Information
Vigo Pereira, Caio
In this paper, we build efficient portfolios using different frameworks proposed in the literature with several datasets containing an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate several approaches to impose sparsity and dimensionality reduction, as well as possible latent factors driving the returns of the risky assets. In contrast to previous studies that made use of naive OLS and low-dimension information sets, we find that (i) accounting for large conditioning information sets, and (ii) the use of variable selection, shrinkage methods and factors models, such as the principal component regression and the partial least squares provide better out-of-sample results as measured by Sharpe ratios.

Relationship between Macroeconomic Indicators and Capital Markets Performance in selected Southeastern European Countries
Dodig, Ante
This study tests the weak form of the efficient capital markets theorem in five transition economies in Southeast Europe between 2005 and 2016. A panel pooled mean group estimator is used to examine the relationship between macroeconomic indicators and the performance of stock market indexes. This is a suitable estimator for these young frontier markets, given that they have yet to develop the breadth and depth of an advanced marketâ€"such as ample liquidity and tradersâ€"to aggregate cross-country data and use level series prime data instead of differentials of the same.These frontier capital markets are found to be weak form inefficient, meaning that stock prices do not reflect available current public information. In other words, when a market is transparent and investor behavior is rational, the macroeconomic data should be included in the value of the stock indexes. The five countries may benefit from bringing their capital markets legislation in line with those of developed countries and by improving corporate governance and transparency. This would boost investor trust and liquidity. The coverage of this research can be extended to find more standardized data values and develop additional factors not captured by this model.

Smart Derivatives: On-Chain Forwards for Digital Assets
Delgado De Molina Rius, Alfonso,Gashier, Eamonn
In this paper, we present a framework for the development of on-chain forwards (and futures). This utilizes smart contracts to automate the custody of collateral and settlement of payouts on expiry. Importantly, our framework also enables forwards to be traded without counter party risk or reliance on off-chain assets (such as fiat currencies). To achieve this, we build on our previous work on on-chain options and demonstrate how the relevant mathematical guarantees can be extended to forwards. In addition, we discuss recent trends in cryptoasset derivatives, capital requirements, and other design considerations (such as the use of split contracts). This paper will be of interest to academics and practitioners interested in financial smart contracts.

Spot Asset Carry Cost Rates and Futures Hedge Ratios
Leistikow, Dean,Chen, Ren-Raw,Xu, Yuewu
Since the 1970s, futures hedge ratios have traditionally been calculated ex-post using an economically structure-less statistical analysis. This paper proposes an ex-ante, more efficient, less computationally demanding, general “carry cost rate” based hedge ratio. Though the proposed hedge ratio is biased, its bias is stationary and mitigable via a one-time calculation effort. Thus, unlike the traditional hedge ratio, the proposed unadjusted and bias-adjusted “carry cost rate” hedge ratios are trivial to update. Finally, the paper shows that each has higher hedge-effectiveness than either the “traditional” or “naïve” (1 for 1) futures hedge ratios in both diverse real and simulated markets.

Testing Forecast Rationality for Measures of Central Tendency
Timo Dimitriadis,Andrew J. Patton,Patrick Schmidt

Rational respondents to economic surveys may report as a point forecast any measure of the central tendency of their (possibly latent) predictive distribution, for example the mean, median, mode, or any convex combination thereof. We propose tests of forecast rationality when the measure of central tendency used by the respondent is unknown. We overcome an identification problem that arises when the measures of central tendency are equal or in a local neighborhood of each other, as is the case for (exactly or nearly) symmetric distributions. As a building block, we also present novel tests for the rationality of mode forecasts. We apply our tests to survey forecasts of individual income, Greenbook forecasts of U.S. GDP, and random walk forecasts for exchange rates. We find that the Greenbook and random walk forecasts are best rationalized as mean, or near-mean forecasts, while the income survey forecasts are best rationalized as mode forecasts.

The Impact of CEO/CFO Outside Directorships on Auditor Selection and Audit Quality
Yu, Jaeyoon,Kwak, Byungjin,Park, Myung Seok,Zang, Yoonseok
We examine whether outside directorships of chief executive officer/chief financial officer (CEO/CFO) and resulting network ties to auditors affect auditor selection decisions and subsequent audit quality. The network ties arise when the CEO/CFO of a firm (home firm) serves as an outside director of another firm that hires an auditor (connected auditor). Using a sample of firms that switch auditors in the post-Sarbanes-Oxley Act period, we find that home firms are more likely to appoint connected auditors. We also find that home firms hiring connected auditors experience a significant decline in subsequent audit quality, compared to those hiring non-connected auditors. Specifically, the increases in the likelihood of misstatements, the magnitude of absolute discretionary accruals, and the propensity to meet or beat earnings benchmarks after home firms appoint connected auditors are significantly greater, compared to those for other firms switching to non-connected auditors. We further find that the decline in audit quality is more pronounced when the network is established at the local office level.

The Progress of Global Financial Transparency: Evidence from The Financial Secrecy Index 2009â€"2018
Jansky, Petr,Palanský, Miroslav
While financial secrecy has recently risen on the agenda of policy makers and scholars alike, much remains unknown about its development since the global financial crisis. To show how financial secrecy evolved over time on average, by category, and across countries, we combine the five Financial Secrecy Index editions from 2009 to 2018 to create a financial secrecy panel data set. We present four main findings. First, financial secrecy has decreased on average â€" i.e. that financial transparency has improved â€" by at least 2â€"9% between 2011 and 2018. Second, most of the observed improvement comes from international standards and cooperation, one of four key financial secrecy areas recognized by the Financial Secrecy Index. Third, we observe a convergence across countries between 2011 and 2018 â€" many of the most secretive have become less so while the opposite is true of some formerly less secretive countries. For example, the Seychelles are now only slightly more secretive than the Netherlands. Fourth, we find that changes in contributions to global financial secrecy over time are not tied to geographical regions and that it is thus worth studying changes at the individual country-level. For example, we find that the United Arab Emirates, the Netherlands and Malta have become substantially more important providers of financial secrecy, though they are still less important than the current leaders, i.e. Switzerland, the United States and the Cayman Islands. Having documented changes in financial secrecy over the past decade, we conclude with how the data set may be used as a tool for studying and perhaps even curbing financial secrecy â€" a policy objective which has thus far been only moderately met.

Time your hedge with Deep Reinforcement Learning
Eric Benhamou,David Saltiel,Sandrine Ungari,Abhishek Mukhopadhyay

Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions? The standard approach based on Markowitz or other more or less sophisticated financial rules aims to find the best portfolio allocation thanks to forecasted expected returns and risk but fails to fully relate market conditions to hedging strategies decision. In contrast, Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. In this paper, we present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging strategy. Our experiment for an augmented asset manager interested in sizing and timing his hedges shows that our approach achieves superior returns and lower risk.

When Local Governments' Stay-at-Home Orders Meet the White House's "Opening Up America Again"
Reza Mousavi,Bin Gu

On April 16th, The White House launched "Opening up America Again" (OuAA) campaign while many U.S. counties had stay-at-home orders in place. We created a panel data set of 1,563 U.S. counties to study the impact of U.S. counties' stay-at-home orders on community mobility before and after The White House's campaign to reopen the country. Our results suggest that before the OuAA campaign stay-at-home orders were effective in decreasing time spent at retail & recreation places and in increasing time spent at home. These stay-at-home orders were less effective in more conservative counties. We further find that the OuAA campaign significantly increased time spent at retail & recreation places and decreased time spent at home particularly in conservative counties. However, in conservative counties with stay-at-home orders in place, OuAA campaign was less effective when compared to conservative counties without stay-at-home orders. These findings signal promising news for local (county and state) authorities. That is, even when the federal government is reopening the country, the local authorities that enforced stay-at-home restrictions were to some extent effective.