# Research articles for the 2021-03-01

arXiv

The rapid growth of the e-commerce market in Indonesia, making various e-commerce companies appear and there has been high competition among them. Marketing intelligence is an important activity to measure competitive position. One element of marketing intelligence is to assess customer satisfaction. Many Indonesian customers express their sense of satisfaction or dissatisfaction towards the company through social media. Hence, using social media data provides a new practical way to measure marketing intelligence effort. This research performs sentiment analysis using the naive bayes classifier classification method with TF-IDF weighting. We compare the sentiments towards of top-3 e-commerce sites visited companies, are Bukalapak, Tokopedia, and Elevenia. We use Twitter data for sentiment analysis because it's faster, cheaper, and easier from both the customer and the researcher side. The purpose of this research is to find out how to process the huge customer sentiment Twitter to become useful information for the e-commerce company, and which of those top-3 e-commerce companies has the highest level of customer satisfaction. The experiment results show the method can be used to classify customer sentiments in social media Twitter automatically and Elevenia is the highest e-commerce with customer satisfaction.

SSRN

We analyze auctions of unsecured money market deposits of firms to banks via a FinTech platform. In each auction, only the firm observes the banks and their interest rate bids and decides where to deposit its funds. We observe that deposit interest rate bids increase monotonically with bank risk and that firms in general prefer higher deposit interest rates. However, our results show that firmsâ€™ selection of banks in which to deposit is concave in the bid interest rate in line with the general notion of credit rationing. We find this confirmed on the intensive as well as on the extensive margin. Risky banks eventually exit the market, and re-enter when their risk decreases again. Risky banks exit when the bid-interest rate increases above central bank policy rates suggesting that central bank funding crowds out deposits thereby reducing monitoring by short-term creditors. This has important implications for banksâ€™ access to unsecured corporate funding, financial stability and the understanding of deposit markets more broadly.

RePEC

Starting from the Cholesky-GARCH model, recently proposed by Darolles, Francq, and Laurent (2018), the paper introduces the Block-Cholesky GARCH (BC-GARCH). This new model adapts in a natural way to the asset pricing framework. After deriving conditions for stationarity, uniform invertibility and beta tracking, we investigate the finite sample properties of a variety of maximum likelihood estimators suited for the BC-GARCH by means of an extensive Monte Carlo experiment. We illustrate the usefulness of the BC-GARCH in two empirical applications. The first tests for the presence of beta spillovers in a bivariate system in the context of the Fama and French (1993) three factor framework. The second empirical application consists of a large scale exercise exploring the cross-sectional variation of expected returns for 40 industry portfolios.

SSRN

This paper examines the effects for client firms of changes in the investment banking industry that affect its structure and the business relationships between investment banks and client firms. Using an event study approach and a sample of consolidation activities in the industry, we show that firms that rely more on services provided by their investment banks experience significant negative abnormal returns upon announcement of a merger involving their bank as the target. The adverse effect is more pronounced for firms that are more likely to be financially constrained and is attenuated when the acquiring bank is more reputable or has larger underwriting capacity. We also show that the value effects are complemented by significant changes in the economic activity of client firms in terms of reduced investment and employment. We conduct several tests to establish the effects are indeed relationship-specific and not a reflection of unobservable deal or client characteristics.

SSRN

Using firm-level R&D and patent data for 88 countries, we find that country climate vulnerability negatively affects firmsâ€™ R&D investment and innovation performance. This effect operates through the decreased responsiveness of R&D investment to investment opportunities (i.e., investment efficiency), reduced incentives to innovate, and lower private value of new innovations. The effect is more pronounced for firms with longer product development cycles and more attentive to climate change and exists in both developed and developing countries. The silver lining is that climate vulnerability increases the ratio of patents on climate change mitigation technologies (CCMTs) in innovations. Finally, we find similar results when using climate-related natural disasters as an identification strategy. Overall, our findings suggest that climate vulnerability hinders corporate innovation activities in general, but it also facilitates innovation in CCMTs.

arXiv

This study aims to examine the challenges and applications of machine learning for financial research. 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 the underlying paradigm of machine learning and the research philosophy in financial economics. Given the peculiar features of financial markets and the empirical framework within social science, 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 research to become a robust complement to the econometrician's toolbox. Moreover, we discuss the various applications of machine learning in the research process such as estimation, empirical discovery, testing, causal inference and prediction.

arXiv

The indirect environmental impacts of transport disruptions in urban mobility are frequently overlooked due to a lack of appropriate assessment methods. Consequential Life Cycle Assessment (CLCA) is a method to capture the environmental consequences of the entire cause and effect chain of these disruptions but has never been adapted to transportat disruption at the city scale. This paper proposes a mathematical formalization of CLCA applied to a territorial mobility change. The method is applied to quantify the impact on climate change of the breakthrough of free-floating e-scooters (FFES) in Paris. A FFES user survey is conducted to estimate the modal shifts due to FFES. Trip substitutions from all the Parisian modes concerned are considered - personal or shared bicycles and motor scooters, private car, taxi and ride-hailing, bus, streetcar, metro and RER (the Paris metropolitan area mass rapid transit system). All these Parisian modes are assessed for the first time using LCA. Final results estimate that over one year, the FFES generated an extra thirteen thousand tons of CO2eq under an assumption of one million users, mainly due to major shifts coming from lower-emitting modes (60% from the metro and the RER, 22% from active modes). Recommendations are given to enhance their carbon footprint. A scenario analysis shows that increasing the lifetime mileage is insufficient to get a positive balance: reducing drastically servicing emissions is also required. A sensitivity analysis switching the French electricity mix for eleven other country mixes suggests a better climate change effect of the FFES in similar metropolitan areas with higher electricity carbon intensity, such as in Germany and China. Finally, the novelty and the limits of the method are discussed, as well as the results and the role of e-scooters, micromobility, and shared vehicles towards a sustainable mobility.

SSRN

This paper studies the conflict between ESG funds and their investors. Funds trade-off greater short-term financial performance against long-term sustainability. This conflict results in ESG funds voting against their stated pro-social mandate, even when supported by proxy advisors. Lower returns to sustainable proposals result in funds actively managing returns, while flow tests suggest that investors do not respond to contradictory voting. Simulating a correction to this voting pattern suggests an increase in passage of proposals, and greater sustainability disclosures. While investors delegate their pro-social preferences onto socially responsible funds, financial returns ultimately determine a funds' stance towards such issues.

arXiv

Bitcoin is a peer-to-peer electronic payment system that popularized rapidly in recent years. Usually, we need to query the complete history of Bitcoin blockchain data to acquire variables with economic meaning. This becomes increasingly difficult now with over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in social science. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort, which enables us to create datasets and visualizations for some key indicators of Bitcoin transactions, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the accumulated unspent transaction output (UTXO). We provide a computationally feasible approach to characterize Bitcoin transactions, which paves the way for the future economic studies of Bitcoin.

arXiv

Machine learning methods for solving nonlinear partial differential equations (PDEs) are hot topical issues, and different algorithms proposed in the literature show efficient numerical approximation in high dimension. In this paper, we introduce a class of PDEs that are invariant to permutations, and called symmetric PDEs. Such problems are widespread, ranging from cosmology to quantum mechanics, and option pricing/hedging in multi-asset market with exchangeable payoff. Our main application comes actually from the particles approximation of mean-field control problems. We design deep learning algorithms based on certain types of neural networks, named PointNet and DeepSet (and their associated derivative networks), for computing simultaneously an approximation of the solution and its gradient to symmetric PDEs. We illustrate the performance and accuracy of the PointNet/DeepSet networks compared to classical feedforward ones, and provide several numerical results of our algorithm for the examples of a mean-field systemic risk, mean-variance problem and a min/max linear quadratic McKean-Vlasov control problem.

SSRN

Using data from the United States and Canada, we quantify consumersâ€™ net pecuniary cost of using cash, credit cards, and debit cards for purchases across income cohorts. The net cost includes fees paid to financial institutions, rewards received from credit or debit card issuers, and the merchant cost of accepting payments that is passed on to consumers as higher retail prices. Even though credit cards are more expensive for merchants to accept compared with other payment methods, merchants typically do not differentiate prices at checkout, but instead pass through their costs to all consumers. As a result, credit card transactions are cross-subsidized by cheaper debit and cash payments. Card rewards and consumer fees paid to financial institutions are additional sources of cross-subsidies. We find that consumers in the lowest-income cohort pay the highest net pecuniary cost as a percentage of transaction value, while consumers in the highest-income cohort pay the lowest. This result is robust under various scenarios and assumptions, suggesting payment card pricing and merchant cost pass-through have regressive distributional effects in the United States and Canada.

SSRN

We study the impact of lender concentration on household credit access. An extensive literature has found little to no relationship between local lender concentration and mortgage interest rates. Consequently, federal regulators regard mortgage markets as national and view local concentration as irrelevant to financial regulation and monetary policy. We argue this conclusion is incomplete, showing that local concentration strongly affects lending standards and upfront fees. Application rejection rates are higher in more concentrated areas, and the effects are particularly pronounced for low-income, female, and racial minority applicants. The pool of originated mortgages is less risky in more concentrated areas both in terms of ex-ante credit scores and ex-post default. On the intensive margin, lenders charge higher fees in more concentrated markets: non-interest fees are on average 35 basis points higher in the 10% most concentrated markets than in the 10% least concentrated markets. Again, these effects are strongest among minority applicants. Our findings suggest that contrary to current policy, regulators concerned with credit access should regard mortgage markets as local when making policy decisions such as bank merger approvals.

SSRN

We posit that active options trading provides managerial incentives to invest in corporate social responsibility (CSR) because managers can better hedge the downside risks of long-term and uncertain CSR investment. Our results show that firms with more options trading have better CSR performance. We also find stronger results for firms with more product market competition, larger takeover risks, shorter CEO career horizon, and higher reputation and goodwill. We use a quasi-natural experiment that reduces options trading costs for treated firms to address endogeneity concerns. A subsample analysis of insider hedging transactions corroborates our main findings. Overall, we provide the first evidence that active derivative markets have real effects on firmsâ€™ CSR performance.

arXiv

We study equilibrium distancing during epidemics. Distancing reduces the individual's probability of getting infected but comes at a cost. It creates a single-peaked epidemic, flattens the curve and decreases the size of the epidemic. We examine more closely the effects of distancing on the outset, the peak and the final size of the epidemic. First, we define a behavioral basic reproduction number and show that it is concave in the transmission rate. The infection, therefore, spreads only if the transmission rate is in the intermediate region. Second, the peak of the epidemic is non-monotonic in the transmission rate. A reduction in the transmission rate can lead to an increase of the peak. On the other hand, a decrease in the cost of distancing always flattens the curve. Third, both an increase in the infection rate as well as an increase in the cost of distancing increase the size of the epidemic. Our results have important implications on the modeling of interventions. Imposing restrictions on the infection rate has qualitatively different effects on the trajectory of the epidemics than imposing assumptions on the cost of distancing. The interventions that affect interactions rather than the transmission rate should, therefore, be modeled as changes in the cost of distancing.

arXiv

Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part because they lack transparency and explainability which are important factors in establishing reliable technology and the research on this topic with a specific focus on applications in credit risk management. In this paper, we implement two advanced post-hoc model agnostic explainability techniques called Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine learning (ML)-based credit scoring models applied to the open-access data set offered by the US-based P2P Lending Platform, Lending Club. Specifically, we use LIME to explain instances locally and SHAP to get both local and global explanations. We discuss the results in detail and present multiple comparison scenarios by using various kernels available for explaining graphs generated using SHAP values. We also discuss the practical challenges associated with the implementation of these state-of-art eXplainabale AI (XAI) methods and document them for future reference. We have made an effort to document every technical aspect of this research, while at the same time providing a general summary of the conclusions.

SSRN

Financial wealth inequality and long-term real interest rates track each other closely over the post-war period. Faced with lower returns on financial wealth, households with high levels of financial wealth must increase savings to afford the consumption that they planned before the decline in rates. Lower rates beget higher financial wealth inequality. Inequality in total wealth, the sum of financial and human wealth and the relevant concept for household welfare, rises much less than financial wealth inequality and even declines at the top of the wealth distribution. A standard Bewley model produces the observed increase in financial wealth inequality in response to a decline in real interest rates, when high financial-wealth households have a financial portfolio with high duration.

SSRN

In contrast to traditional forms of production, gig economy workers provide their own physical capital. This organization of production creates a tradeoff: On one hand, households can utilize assets for both durable consumption and production. On the other hand, it is often difficult for households to obtain financing. In the context of ride share, a difference-in-difference analysis around ride share entry shows the dual asset use benefit leads households to purchase more cars, increase utilization, and obtain employment, but financial constraints dampen these effects. I quantify this tradeoff with a structural model featuring alternate forms of production and costly finance. The estimated model shows that relative to traditional taxi firms with otherwise identical regulation and technology, gig economy production increases ride quantities by 20%, decreases prices by 30\%, and increases annual welfare by $5 billion. However, the gig economy is uniquely sensitive to household borrowing constraints on the extensive margin: When finance is unavailable to low-income households, these gains evaporate. In contrast, the gig economy is less sensitive to increases in interest rates on the intensive margin: Unlike traditional firms, gig economy production can shift to wealthier households that do not require financing.

SSRN

We examine the impact of the COVID-19 pandemic and subsequent monetary and fiscal policy actions on municipal bond market pricing. Using high-frequency trading data, we estimate key policy events at the peak of the crisis by focusing on a sample of bonds within a very narrow window of just before and just after each event. We find that policy interventions, in particular those with explicit credit backstops, were effective in stabilizing the municipal bond market. In addition, by exploiting daily variation in traded municipal bonds and virus exposure across U.S. counties, we find that prior to the policy interventions, COVID-related credit risks were a significant component of elevated short-term bond yields. Following the interventions, however, the pricing of localized credit risks declined for short-maturity bonds, but became more notable for longer-maturity bonds. The shift in credit risk pricing reflects policy interventions being targeted on short-term bonds, as well as investorsâ€™ expectation of long-lasting recessional impacts on state and local government budgets.

arXiv

This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead prices. Traditional time series issues, e.g. ARIMA order selection, stationarity, together with potential financial applications are covered in the exploratory data analysis, which pave paths to the adaptive learning model. By designing and running the learning model, we found it to perform well compared to the top fixed models, and some could improve the forecasting accuracy by being more stable and resilient to non-stationarity. Applications to hypothesis testing are shown with a rolling window, and further potential applications to finance and statistics are outlined.

arXiv

With the emergence of Bitcoin and recently proposed stablecoins from BigTechs, such as Diem (formerly Libra), central banks face growing competition from private actors offering their own digital alternative to physical cash. We do not address the normative question whether a central bank should issue a central bank digital currency (CBDC) or not. Instead, we contribute to the current research debate by showing how a central bank could do so, if desired. We propose a token-based system without distributed ledger technology and show how earlier-deployed, software-only electronic cash can be improved upon to preserve transaction privacy, meet regulatory requirements in a compelling way, and offer a level of quantum-resistant protection against systemic privacy risk. Neither monetary policy nor financial stability would be materially affected because a CBDC with this design would replicate physical cash rather than bank deposits.

SSRN

We show that information complementarities play an important role in the spillover of transparency shocks. We exploit staggered revelation of financial misconduct by S&P500 firms and find that the implied cost of capital increases for â€œcloseâ€ industry peers relative to â€œdistantâ€ peers. Disclosure also increases. The effects are particularly strong when the close peers share common analysts and institutional ownership with the fraudulent firm. While disclosure remains high for the next four years, with sustained disclosure, the cost of equity starts to decrease. Firmsâ€™ financing patterns tilt more towards debt financing initially at the expense of equity, but eventually revert.

SSRN

I present analytical pricing formulae for derivatives of compounded rates. Since the announced replacement of LIBOR, the compounded overnight rate has become the new market standard for floating-rate loans and notes. Many contracts contain a zero-based floor. The compounded rate is a time average of a series of benchmark rates. Floors and caps on compounded rates are thus Asian types of options. I prove that even if the rate process is non-Gaussian, the Gaussian process is asymptotically the correct model for pricing derivatives due to the central limit theorem. The approximation's maximum mispricing is bounded by the Berry-Esseen inequality.

SSRN

We introduce a novel covariance estimator that exploits the heteroskedastic nature of financial time series by employing exponential weighted moving averages and shrinking the in-sample eigenvalues through cross-validation. Our estimator is model-agnostic in that we make no assumptions on the distribution of the random entries of the matrix or structure of the covariance matrix. Additionally, we show how Random Matrix Theory can provide guidance for automatic tuning of the hyperparameter which characterizes the time scale for the dynamics of the estimator. By attenuating the noise from both the cross-sectional and time-series dimensions, we empirically demonstrate the superiority of our estimator over competing estimators that are based on exponentially-weighted and uniformly-weighted covariance matrices.

arXiv

This paper presents a model addressing welfare optimal policies of demand responsive transportation service, where passengers cause external travel time costs for other passengers due to the route changes. Optimal pricing and trip production policies are modelled both on the aggregate level and on the network level. The aggregate model is an extension from Jokinen (2016) with flat pricing model, but occupancy rate is now modelled as an endogenous variable depending on demand and capacity levels. The network model enables to describe differences between routes from the viewpoint of occupancy rate and efficient trip combining. Moreover, the model defines the optimal differentiated pricing for routes.

arXiv

We study the general problem of Bayesian persuasion (optimal information design) with continuous actions and continuous state space in arbitrary dimensions. First, we show that with a finite signal space, the optimal information design is always given by a partition. Second, we take the limit of an infinite signal space and characterize the solution in terms of a Monge-Kantorovich optimal transport problem with an endogenous information transport cost. We use our novel approach to: 1. Derive necessary and sufficient conditions for optimality based on Bregman divergences for non-convex functions. 2. Compute exact bounds for the Hausdorff dimension of the support of an optimal policy. 3. Derive a non-linear, second-order partial differential equation whose solutions correspond to regular optimal policies. We illustrate the power of our approach by providing explicit solutions to several non-linear, multidimensional Bayesian persuasion problems.

arXiv

To hedge investments made in different economies, investors make use of cross-currency derivatives to manage risks associated with fluctuations of exchange rates. In this paper, we set forth the discussion of extending the random field LIBOR market model (RFLMM) to the cross-currency setting. We assume that forward LIBOR rates of the domestics and foreign economies are lognormal and derive an approximate closed-form pricing solution for a Quanto cap written on a foreign forward LIBOR rate. Further, we derive an exact pricing formula for an option written on the spot exchange rate between the domestic and a foreign economy.

SSRN

When the government commits to a debt policy, the future value of government primary surpluses at all horizons is dictated by the debt dynamics under the risk-neutral measure. We compare the present discounted value of future surpluses implied by the U.S. federal government debt dynamics in a no-arbitrage bond pricing model to the PDV of actual government surpluses. Since the late 1990s, the debt-implied PDV of surpluses have consistently and persistently exceeded realized surpluses. They have also exceeded surplus forecasts resulting from tax and spending policy rules. U.S. Treasury investors appear to have been overly optimistic when assessing future surpluses.

arXiv

We prove a rate of convergence of order 1/N for the N-particle approximation of a second-order partial differential equation in the space of probability measures, like the Master equation or Bellman equation of mean-field control problem under common noise. The proof relies on backward stochastic differential equations techniques.

SSRN

The Secured Overnight Financing Rate (SOFR) is on a finishing path to replace US dollar LIBOR. The lack of well-formed, termed rates equivalent to one and three month LIBOR rates remains a key issue concerning the financial industry. Sticking to SOFRâ€™s root in the Treasury repo market, this paper predicts SOFR term rates by pricing Treasury repos across different tenors. We show that the tri-party component of SOFR can experience one year repo spread of 18 basis points during the global financial crisis. The bilateral component of SOFR can go even higher, to 35 bps at 1 year term. This pinpoints the need for SOFR term rates producing methods to capture the heterogeneous nature of SOFR composition.

arXiv

A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500.

Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions. However, returns for all three assets are heavy tailed and second moments (variance, standard deviation) non-convergent. In the case of Bitcoin, lower sampling frequencies (monthly vs weekly, weekly vs daily) drastically reduce the Kurtosis of log-returns and increase the convergence of empirical moments to their true value. The opposite effect is observed for Gold and S&P 500. These properties suggest that Bitcoin's volatility is essentially an intra-day and intra-week phenomenon that is strongly attenuated on a weekly time-scale, and make it an attractive store of value to investors and speculators, but its high standard deviation excludes its use a currency.

arXiv

The relationship between set-valued risk measures for processes and vectors on the optional filtration is investigated. The equivalence of risk measures for processes and vectors and the equivalence of their penalty function formulations are provided. In contrast with scalar risk measures, this equivalence requires an augmentation of the set-valued risk measures for processes. We utilize this result to deduce a new dual representation for risk measures for processes in the set-valued framework. Finally, the equivalence of multiportfolio time consistency between set-valued risk measures for processes and vectors are provided; to accomplish this, an augmented definition for multiportfolio time consistency of set-valued risk measures for processes is proposed.

arXiv

One of the greatest contributors of the 20th century among all academician in the field of statistical finance, M. F. M. Osborne published in 1956 [6] an essential paper and proposed to treat the question of stock market motion through the prism of both the Law of Weber-Fechner [1, 4] and the branch of physics developed by James Clerk Maxwell, Ludwig Boltzmann and Josiah Willard Gibbs [3, 5] namely the statistical mechanics. He proposed an improvement of the known research made by his predecessor Louis Jean-Baptiste Alphonse Bachelier, by not considering the arithmetic changes of stock prices as means of statistical measurement, but by drawing on the Weber-Fechner Law, to treat the changes of prices. Osborne emphasized that as in statistical mechanics, the probability distribution of the steady-state of subjective change in prices is determined by the condition of maximum probability, a statement close to the Gibbs distribution conditions. However, Osborne also admitted that the empirical observation of the probability distribution of logarithmic changes of stock prices was emphasizing obvious asymmetries and consequently could not perfectly confirm his prior theory. The purpose of this paper is to propose an explanation to what we could call the Osborne paradox and then address an alternative approach via Bayesian inference regarding the description of the probability distribution of changes in logarithms of prices that was thenceforth under the prism of frequentist inference. We show that the stock market returns are locally described by equilibrium statistical mechanics with conserved statistics variables, whereas globally there is yet other statistics with persistent flowing variables that can be effectively described by a superposition of several statistics on different time scales, namely, a superstatistics.

SSRN

This paper documents that the occurrence of firm-specific stock price crashes has grown puzzling from 6.5 percent in 1950 to an astonishing 27 percent in 2018 for US-listed firms. A burgeoning literature attributes stock price crashes to agency-related problems attributed to managerial opportunism that seeks to camouflage bad news through the channels of financial reporting opacity and overinvestment. Our study offers empirical evidence suggesting that these agency-originated channels offer a limited role in explaining this increasing frequency of stock price crashes. We show, especially in the post-SOX period, that there is a notable absence of any statistical relationship between the two prominent channels and future stock price crashes. This study contributes to the literature by bringing to the fore the stock price crash risk puzzle, for which a prominent explanation in the post-SOX era remains undetermined. Further, the study discusses possible explanations wherein future research can look for answers.

arXiv

We propose in this paper to consider the stock market as a physical system assimilate to a fluid evolving in a macroscopic space subject to a Force that influences its movement over time where this last is arising from the collision between the supply and the demand of Financial agents. In fluid mechanics, this Force also results from the collisions of fluid molecules led by its physical property such as density, viscosity, and surface tension. The purpose of this article is to show that the dynamism of the stock market behavior can be explained qualitatively and quantitatively by considering the supply & demand collision as the result of Financial agents physical properties defined by Stokes Law. The first objective of this article is to show theoretically that fluid mechanics equations can be used to describe stock market physical properties. The second objective based on the knowledge of stock market physical properties is to propose an Econophysics analog of the stock market viscosity and Reynolds number to measure stock market conditions, whether laminar, transitory, or turbulent. The Reynolds Number defined in this way can be applied in research into the study and classification of stock market dynamics phases through for instance the creation of Econophysics analog of Moddy diagram, this last could be seen as a physical way to quantify asset and stock index idiosyncratic risk. The last objective is to present evidence from a computer simulation that the stock market behavior can be a priori, and posteriori explained by physical properties (viscosity & density) quantifiable by fluid mechanics law (Stokes law) and measurable with the stock market Reynolds Number.

arXiv

The Atoyac River is among the two most polluted in Mexico. Water quality in the Upper Atoyac River Basin (UARB) has been devastated by industrial and municipal wastewater, as well as from effluents from local dwellers, that go through little to no treatment, affecting health, production, ecosystems and property value. We did a systematic review and mapping of the costs that pollution imposes on different sectors and localities in the UARB, and initially found 358 studies, of which 17 were of our particular interest. We focus on estimating the cost of pollution through different valuation methods such as averted costs, hedonic pricing, and contingent valuation, and for that we only use 10 studies. Costs range from less than a million to over $16 million dollars a year, depending on the sector, with agriculture, industry and tourism yielding the highest costs. This exercise is the first of its kind in the UARB that maps costs for sectors and localities affected, and sheds light on the need of additional research to estimate the total cost of pollution throughout the basin. This information may help design further research needs in the region.

SSRN

A huge interest has emerged, in the mutual fund industry, in socially responsible investing (SRI). Central to this development is whether SRI funds underperform conventional funds. Using a novel approach, we decompose mutual fund portfolios into socially responsible (green) and non-socially responsible (brown) components. We find that, in comparison to the non-socially responsible component, the socially responsible part exhibits a lower raw return, lower risk-adjusted return, lower Sharpe ratio, and similar degree of performance reversal. The magnitudes of these underperformances are, however, small and align with SRI having a limited negative impact on fund performance yet offering some diversification benefits.

SSRN

This paper finds that a zero-investment strategy that goes long (short) in the highest (lowest) quintiles of firm-specific risk earns overall positive excess returns across twenty-one emerging markets. Interestingly, in previous studies such returns were found to be negative for the US and developed markets. Nevertheless, the risk-adjusted alphas of the capital asset pricing model, the Fama and French model and the Carhart model are mostly negative for a number of emerging markets. Thus, the puzzling negative premiums associated with firm-specific risks are ultimately reconciled across global equity markets. The impetus for such negative premiums is primarily given by the firms with the lowest firm-specific risk, as these firms are hedged against market based risks and have significant positive alphas.

arXiv

The quanto option is a cross-currency derivative in which the pay-off is given in foreign currency and then converted to domestic currency, through a constant exchange rate, used for the conversion and determined at contract inception. Hence, the dependence relation between the option underlying asset price and the exchange rate plays an important role in quanto option pricing.

In this work, we suggest to use empirical copulas to price quanto options. Numerical illustrations show that the flexibility provided by this approach, concerning the dependence relation of the two underlying stochastic processes, results in non-negligible pricing differences when contrasted to other models.

SSRN

We define accounting engagement as stakeholdersâ€™ actions taken with the ultimate intention to influence corporate reporting. Against this definition, we reflect on the role of user engagement, review existing literature on such actions and discuss potential avenues for research. The evidence reviewed suggests accounting engagement is rare. We reflect on the reasons underpinning this observation, given evidence on increasing overt engagement on other corporate issues such as governance, social, and environmental responsibility, or managerial compensation. Both information production and information acquisition costs have decreased over time further questioning why engagement has not increased. We discuss potential reasons linked with concerns over whether financial reporting meets usersâ€™ information needs, particularly, given the emergence of new users and the role of new technologies in the diffusion and processing of information. These concerns go together with claims of increasing complexity of financial accounting, and the threat of information overload.

arXiv

Ergodicity economics is a new branch of economic theory that notes the conceptual difference between time averages and expectation values, which coincide only for ergodic observables. It postulates that individual agents maximise the time average growth rate of wealth, known widely as growth optimality. This contrasts with the dominant behavioural model in economics, expected utility theory, in which agents maximise expectation values of changes in psychologically transformed wealth. Historically, growth optimality was explored for additive and multiplicative gambles. Here we apply it to a general class of wealth dynamics, extending the range of economic situations where it may be used. Moreover, we show a correspondence between growth optimality and expected utility theory, in which the ergodicity transformation in the former is identified as the utility function in the latter. This correspondence offers a theoretical basis for choosing utility functions and predicts that wealth dynamics are strong determinants of risk preferences.

arXiv

We consider issues of time in automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. In particular, we explore two effects: (i) reaction speed - the time taken for trading strategies to calculate a response to market events; and (ii) trading urgency - the sensitivity of trading strategies to approaching deadlines. Much of the literature on trading agents focuses on optimising pricing strategies only and ignores the effects of time, while real-world markets continue to experience a race to zero latency, as automated trading systems compete to quickly access information and act in the market ahead of others. We demonstrate that modelling reaction speed can significantly alter previously published results, with simple strategies such as SHVR outperforming more complex adaptive algorithms such as AA. We also show that adding a pace parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a sense of urgency that significantly improves profitability.

arXiv

The economic approach to determine optimal legal policies involves maximizing a social welfare function. We propose an alternative: a consent-approach that seeks to promote consensual interactions and deter non-consensual interactions. The consent-approach does not rest upon inter-personal utility comparisons or value judgments about preferences. It does not require any additional information relative to the welfare-approach. We highlight the contrast between the welfare-approach and the consent-approach using a stylized model inspired by seminal cases of harassment and the #MeToo movement. The social welfare maximizing penalty for harassment in our model can be zero under the welfare-approach but not under the consent-approach.

SSRN

Exploiting the COVID-19 outbreak as an exogenous shock, this paper attempts to shed light on the closed-end fund (CEF) discount puzzle. CEF discounts increased substantially after COVID-19, and I identify a sentiment channel underlying this effect. I show that COVID-19 reduced individual investor sentiment. Using the difference-in-differences approach, I find that CEFs with higher sentiment beta or higher retail ownership experienced a larger increase in discounts after the COVID-19 outbreak. These results are unlikely to be driven by alternative channels such as the liquidity, expense, payout, and leverage channels. Overall, the results support the sentiment-based explanation of CEF discounts.

SSRN

Chinese Abstract: ç¾Žå›½çš„æŒè‚¡æƒç›ŠæŠ«éœ²è§„åˆ™ï¼ˆç®€ç§°æŠ«éœ²è§„åˆ™ï¼‰è¢«ç§»æ¤åˆ°ä¸å›½ï¼Œå¹¶æ²¡æœ‰è§£å†³è¿è§„ä¸¾ç‰Œç‰æŠ•èµ„è€…ä¿æŠ¤é—®é¢˜ã€‚å·²æœ‰å¦æœ¯ç "ç©¶é›†ä¸åœ¨æ³•å¾‹è§£é‡Šè§'åº¦ï¼Œå¯¹äºŽæŠ«éœ²è§„åˆ™çš„ç†è®ºå'Œå®žè·µéš¾é¢˜æ²¡æœ‰æä¾›ä»¤äººæ»¡æ„çš„è§£å†³æ–¹æ¡ˆã€‚æœ¬æ–‡ç³»ç»Ÿæ¯"è¾ƒäº†ä¸å›½å'Œç¾Žå›½ã€æ—¥æœ¬ã€éŸ©å›½ã€æ–°åŠ å¡ã€ä¸å›½é¦™æ¸¯å'Œå°æ¹¾çš„æŠ«éœ²è§„åˆ™ã€‚ç "ç©¶å'çŽ°ä¸å›½çš„ç»éªŒè¿èƒŒäº†å›½é™…å¦è€…çš„ä¸€èˆ¬ç†è®ºå‡è®¾ã€‚æœ¬æ–‡æå‡ºä¸€ç³»åˆ—å›½æƒ…å› ç´ æ¥è§£é‡Šä¸å›½çš„åˆ¶åº¦ç‰¹ç‚¹ï¼Œä¾‹å¦‚å›½å®¶æˆ˜ç•¥ã€è‚¡æƒç»"æž„ã€åˆ©ç›Šé›†å›¢ç‰ï¼Œå¹¶åœ¨æ¤åŸºç¡€ä¸Šæå‡ºäº†å®Œå–„å»ºè®®ã€‚English Abstract: The shareholding disclosure rules of the United States transplanted to China did not solve investor protection problems such as illegal listing. The existing academic research focuses on the perspective of legal interpretation, and does not provide a satisfactory solution to the theoretical and practical problems of shareholding disclosure rules. This paper systematically compares the shareholding disclosure rules of mainland China with those of the United States, Japan, South Korea, Singapore, Hong Kong and Taiwan. China's experience refutes some general theoretical assumptions of international scholars. This paper puts forward a series of national conditions to explain the characteristics of China's system, such as national strategy, ownership structure and interest groups. On this basis, it puts forward some suggestions for the improvement of the Chinese shareholding disclosure regime.