Research articles for the 2020-04-14
SSRN
Multiplication is the elementary process for computing the butterfly in Fast Fourier Transform. A formal multiplication task requires an extensively additional hardware means and processing time in multiplication operation to a certain degree more than in addition and subtraction. In this work, architecture for multiplier is proposed which requires less space and works faster comparatively with other basic multiplier structures. The “UrdhvaTiryakbhyam†scheme offered by Vedic Mathematics is utilized to speed up the multiplication process. In addition, the reduction in transistor count is achieved by substrate biasing. Further the architecture is simulated in ORCAD software to analyse the area requirements of the Butterfly structure for computing Fast Fourier Transforms. The simulated results show that there is considerable reduction in transistor count and operating speed which makes the proposed multiplier a competent one with the standard multiplier architecture.
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Siegel (2014), Shilling (2015) and others rely on the work of Alfred C. Cowles to capture US stock market returns before 1926. Cowles in turn relied on Frederick Macaulay’s work for data on railroad stocks during this era. This study attempts to re-construct Cowles’ index from the ground up, by revisiting his sources and compiling anew the trade price, dividends, and capitalization of the individual stocks that make up Cowles’ index. I pursued two questions: did Cowles exclude any substantial number of stocks, such that their inclusion would change his estimates of market return? Second, do Cowles’ many adjustments for capital changes, such as rights issues, check out? I succeeded in reconstructing the railroad stock returns obtained by Cowles from Macaulay, and partially succeeded in reconstructing the utility and industrial stock returns estimated by Cowles himself. But I also found numerous excluded railroads, and an assortment of errors made by Cowles and Macaulay. I also found a set of industrial firms excluded because of judgment calls that Cowles made. Net of additions and corrections, I conclude that Cowles slightly over-estimated stock market returns in the period from 1871 to 1897. The effect is to reinforce the findings in McQuarrie (2019a): contra Siegel, stocks did not out-perform bonds during the 19th century.
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On May 14, 2018, the U.S. Supreme Court issued its seminal ruling in Murphy v. NCAA, which held that the Professional and Amateur Sports Protection Act (PASPA) violated the Tenth Amendment of the United States Constitution. This ruling, in conjunction with other societal changes, has opened the floodgates for states to liberalize laws on sports betting. In less than two years since the Supreme Court’s Murphy decision, nineteen U.S. states, in addition to Washington D.C., have legalized sports betting in some form. Meanwhile, eleven states have specifically legalized online sports betting. This article (or, perhaps more accurately stated, short treatise) is the first of its kind to provide a detailed analysis of how the United States regulates sports gambling in the aftermath of Murphy v. NCAA. The article examines closely the history of sports gambling, seminal legal decisions involving the sports betting industry, new state regulatory systems that have emerged since the Supreme Court’s Murphy decision, newfound legal risks for companies that operate in sports gaming markets, and important matters of public policy related to regulating America’s most lucrative vice.
arXiv
China's policy interventions to reduce the spread of the coronavirus disease 2019 have environmental and economic impacts. Tropospheric nitrogen dioxide indicates economic activities, as nitrogen dioxide is primarily emitted from fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric nitrogen dioxide vertical column densities from the 20 days averaged before the 2020 Lunar New Year to the 20 days averaged after. This is 20% larger than that from recent years. We relate to this reduction to two of the government's actions: the announcement of the first report in each province and the date of a province's lockdown. Both actions are associated with nearly the same magnitude of reductions. Our analysis offers insights into the unintended environmental and economic consequences through reduced economic activities.
arXiv
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.
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Stress tests have become a key tool for banks, supervisors and macro prudential authorities. An aspect of these exercises is the need for statistical models to obtain risk measurements under an adverse scenario and a fundamental question is who should develop such models. If models are developed by the authorities (top-down approach), homogeneity of treatment among banks and more control over results are achieved, but the authorities do not necessarily have all the information that individual banks have. Banks’ models (bottom-up approach) may be more accurate. However, banks may have incentives to underestimate the impact of a shock, thus reducing the supervisory reaction. In this paper, we focus on bottom-up stress tests and suggest creating a system of monetary penalties (charges) proportional to the difference between the expected and the realised losses of a portfolio. The charges would aim to induce model developers to reveal their best forecasts. We show that this approach can be seen as an adaptation of the pre-commitment approach (PCA) developed and promoted by the US Federal Reserve in the 1990s but also as an application of the penalty criterion proposed by the Italian mathematician de Finetti as the foundation of the subjectivist definition of probability. We explain how the PCA could be adapted to bottom-up stress testing and provide a practical example of the application of our proposal to the banking book. What emerges is that the PCA can indeed mitigate banks’ incentives to provide underestimated measures of risk under the adverse scenario and thus better align the incentives of banks and supervisors.
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We examine market responses to changes in the Nasdaq 100 index membership and find asymmetric stock price and investor awareness reactions. Stocks added to the Nasdaq 100 index for the first time experience permanent price gains and significant increases in investor awareness, whereas repeated additions and new deletions exhibit temporary stock price changes and no significant changes in investor awareness. Stock liquidity improves for new additions and repeated additions but worsens for new deletions. Importantly, investor awareness proxies are significantly related to cumulative abnormal returns around the Nasdaq 100 reconstitution even in the presence of liquidity and other controlling factors. Taken together, the observed results are consistent with the investor awareness hypothesis.
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Bitcoin is the oldest and most widely established cryptocurrency network with the highest market capitalization among all cryptocurrencies. Although bitcoin (with lowercase b) is increasingly viewed as a digital asset belonging to a new asset class, the Bitcoin network (with uppercase B) is a decentralized financial market infrastructure (dFMI) that clears and settles transactions in its native asset without relying on the conventional financial market infrastructures (FMIs). To be a reliable asset class as well as a dFMI, however, Bitcoin needs to have robust governance arrangements; whether such arrangements are built into the protocol (i.e., on-chain governance mechanisms) or relegated to the participants in the Bitcoin network (i.e., off-chain governance mechanisms), or are composed of a combination of both mechanisms (i.e., a hybrid form of governance).This paper studies Bitcoin governance with a focus on its alleged shortcomings. In so doing, after defining Bitcoin governance and its objectives, the paper puts forward an idiosyncratic governance model whose main objective is to preserve and maximize the main value proposition of Bitcoin, i.e., its censorship-resistant property, which allows participants to transact in an environment with minimum social trust. Therefore, Bitcoin governance, including the processes through which Bitcoin governance crises have been resolved and the standards against which the Bitcoin Improvement Proposals (BIPs) are examined, should be analyzed in light of the prevailing narrative of Bitcoin as a censorship-resistant store of value and payment infrastructure. Within such a special governance model, this paper seeks to identify the potential shortcomings in Bitcoin governance by reference to the major governance crises that posed serious threats to Bitcoin in the last decade. It concludes that the existing governance arrangements in the Bitcoin network have been largely successful in dealing with Bitcoin’s major crises that would have otherwise become existential threats to the Bitcoin network.
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Boards hire and fire CEOs based on imperfect information. Using comprehensive data on 28 cohorts in Sweden, we analyze the role of a potentially important unobserved attribute â€" CEO health â€" in corporate governance. We find CEOs are significantly healthier than the population and other high-skill professionals, in particular in mental health. Health at appointment predicts turnover, suggesting boards respond to health problems and correct mismatches that occurred at the time of appointment. Health-related corporate governance appears to work imperfectly, however, as we find CEO health also associates with firm policies requiring an active CEO role.
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The COVID-19 or coronavirus pandemic which has affected the global economy has also affected the African economy through spillovers to African countries. Many African countries have taken bold quarantine and lockdown measures to control the spread of COVID-19 although this has come at a cost such as the collapse of health systems and a painful economic crisis or recession. A coordinated and bold response by African authorities is needed. First, public funds should be provided to improve the capacity of health systems in African countries. Second, financial support should be provided to individuals, entrepreneurs and corporations to help them cope with the adverse effect of the coronavirus crisis. Third, employers should be granted incentives to preserve employment during the crisis to avoid mass layoff of workers. Finally, the Central bank in African countries should provide liquidity and credit support as well as asset purchase programs to prevent credit and liquidity crunch in domestic financial markets.
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Today, several factors contribute to the working capital management are difficult to examine. Data collected on these factors often has several variables. It is a non-trivial exercise to determine which of the factors that significantly influences the working capital. This paper adopts the use of Principal Component Analysis (PCA) on the several variables expected to influence the working capital management of Tata Motors. The principal component analyses have identified the factors and are expected to assist the mangers to identify areas where they might improve financial performance of their operation. Finally the variables ROA, DTO, ITO, CSR, CR are having communalities greater than 0.5 are incorporated and Each variable is loaded on a single component, thereby enhancing the interpretability of the factors. The variables can be broadly classified in to four viz, profitability (ROA), efficiency (DTO and ITO), absolute liquidity(CSR) and liquidity factor(CR). The above identified variables can be used for multiple regression analysis.
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I model the strategic interaction of underwriters' decisions of accepting IPO mandates of firms with correlated values. Underwriters act as certifiers and increase the perceived value of issuing firms. Investors, however, take the agency conflict associated with the fee-paying structure of IPOs into account and discount the offer price accordingly. By timely clustering of related IPOs across different underwriters, investment banks expose themselves to the outcome of other concurrent IPOs which results in a mutual disciplining effect. In this way, underwriters can credibly commit themselves to the marketing of high-value firms only. The model suggests that underpricing levels might be a function of underwriter syndicate composition and provides an agency based rationale for the observed cyclicality in IPOs.
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Joint densities for a sequential pair of returns with weak autocorrelation and strong correlation in squared returns are formulated. The marginal return densities are either variance gamma or bilateral gamma. Two dimensional matching of empirical characteristic functions to its theoretical counterpart is employed for dependency parameter estimation. Estimations are reported for 3920 daily return sequences of a thousand days. Path simulation is done using conditional distribution functions. The paths display levels of squared return correlation and decay rates for the squared return autocorrelation function that are comparable to these magnitudes in daily return data. Regressions of log characteristic functions at different time points are used to estimate time scaling coefficients. Regressions of these time scaling coefficients on squared return correlations support the view that autocorrelation in squared returns slows the rate of passage of economic time.
arXiv
The hidden-action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed. However, the hidden-action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included in the hidden-action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and about feasible actions for the agent. We follow an approach to transfer closed-form mathematical models into agent-based computational models and show that the extent of information about feasible options to carry out a task only has an impact on performance if decision makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, on the contrary, appears to be crucial in almost all situations.
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This paper examines whether the network ties help explain variation in analysts’ forecast accuracy and how the relationship between network ties and analysts’ forecast accuracy varies depending on the nature and context of those ties. We posit that the analysts could develop a valuable relationship with the chairman or CEO through their covering process, and thus acquire information of those managers’ concurrent-post companies through this concurrent-post ties. We find that the analysts who cover both companies provided formal job for chairman or CEO (called “focus firmsâ€) and companies provided concurrent-post job for those managers (called “peer firmsâ€) during the same year issue more accurate forecasts for the peer firms. This phenomenon is more significant especially when the chairman or CEO of the focus firms has worked more than two years in the peer firms, the peer firms’ earnings quality is low, institutional investors hold more shareholdings in the peer firms, analysts offer more efforts for the focus firms, and the managers of the focus firms hold the managers’ posts in the peer firms. Further research finds that the higher the proportion of focus-peer firm pairs in analyst portfolio, the less accuracy of the other firms covered by the analysts who follow both the focus firms and the peer firms simultaneously, suggesting that those analysts need invest more efforts to maintain a relationship with the focus firms’ managers. Finally, this paper also finds that analysts tend to issue optimistic forecasts for the focus firms during the litigation, in order to reward managers who offer private information of peer firms to analysts though their social network formed in the experience of concurrent posts.
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The number of listed firms in the U.S. has fallen by half since the late 1990s. Our paper examines whether and to what extent the costs of shareholder litigation have contributed to this trend. We find that higher litigation threat induce firms to delist from stock exchanges. The effect remains robust to controlling for the endogeneity problem between litigation risk and delisting probability. The litigation effect exacerbates for firms with severe information asymmetry and lightens for firms with high capital requirements. We also show that reduced litigation threat, triggered by the Ninth Circuit Ruling event does not prompt excessive managerial engagement of earning management. Instead, we observe a positive stock price reaction to the event for firms with high institutional ownership. Taken together, our findings suggest that the pressure imposed by shareholder litigation may partially explain for the recent fading attractiveness of the US public stock market.
arXiv
This paper proposes and motivates a dynamical model of the Chinese stock market based on a linear regression in a dual state space connected to the original state space of correlations between the volume-at-price buckets by a Fourier transform. We apply our model to the price migration of executed orders by the Chinese brokerages in 2009-2010. We use our brokerage tapes to conduct a natural experiment assuming that tapes correspond to randomly assigned, informed and uninformed traders. We did not notice any spike of illiquidity transmitting from the US Flash Crash in May 2010 to trading in China.
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The lack of transparency in cryptocurrency markets means that investors must assess a project’s quality on the basis of public information. This paper examines how initial coin offering (ICO) characteristics affect cross-listing returns, i.e. whether or not the available information is a valuable signal of quality. For this purpose, we analyze 250 cross-listings of 135 different tokens issued via ICOs and calculate abnormal returns for specific samples using event study methodology. We find that cross-listing returns are driven by success in terms of token performance and project funding, as well as by jurisdiction-specific characteristics like the extent of regulation and domestic market size. Other characteristics like the choice or change of blockchain infrastructure, token distribution across investors and the project team, campaign duration and whitepaper characteristics also seem to influence perceived project quality and thus cross-listing returns. The results provide insights for the literature on cross-listings, cryptocurrency markets and entrepreneurial finance in the form of ICOs. They also make it possible to interpret the information available on the market and enable investors, project teams and crypto currency exchanges to evaluate probable market reactions to cross-listings.
arXiv
Modelling multivariate systems is important for many applications in engineering and operational research. The multivariate distributions under scrutiny usually have no analytic or closed form. Therefore their modelling employs a numerical technique, typically multivariate simulations, which can have very high dimensions. Random Orthogonal Matrix (ROM) simulation is a method that has gained some popularity because of the absence of certain simulation errors. Specifically, it exactly matches a target mean, covariance matrix and certain higher moments with every simulation. This paper extends the ROM simulation algorithm presented by Hanke et al. (2017), hereafter referred to as HPSW, which matches the target mean, covariance matrix and Kollo skewness vector exactly. Our first contribution is to establish necessary and sufficient conditions for the HPSW algorithm to work. Our second contribution is to develop a general approach for constructing admissible values in the HPSW. Our third theoretical contribution is to analyse the effect of multivariate sample concatenation on the target Kollo skewness. Finally, we illustrate the extensions we develop here using a simulation study.
SSRN
Balance of payment crises, characterized by Sudden Stops, are not a phenomenon exclusive to emerging economies. This paper identifies 16 and 50 crises in advanced and emerging economies, respectively. Further, decomposing the Financial Account uncovers important differences between both groups of economies in the Foreign Direct Investment (FDI) flows: the average net FDI in advanced economies is close to zero and in emerging economies is negative, and during Sudden Stop episodes, net FDI in emerging economies shows large contractions while advanced economies flows do not move at all. To quantify the FDI’s channel effect on the dynamics of a crisis episode we develop a model with incomplete markets and an endogenous collateral constraint that generates endogenous Sudden Stops. The results from the model suggest that an emerging economy that increases the outflow FDI and eliminates the expropriation risk would reduce the long-run probability of a Sudden Stop from 2.9 to 1.3 percent.
SSRN
This article analyse several indicators of financial inclusion in Nigeria. The findings reveal that people with at least a secondary education and unemployed people had higher levels of debit card ownership, higher levels of account ownership of any type, and higher levels of account ownership in a financial institution. Also, people with at least a secondary education had higher levels of borrowings from a bank or another type of financial institution, and had lower levels of savings at a financial institution. On the other hand, savings using a savings club or persons outside the family decreased among females, poor people and among people with a primary education or less. Furthermore, there were fewer credit card ownership by unemployed people while credit card ownership increased among employed people, the richest people and among people with at least a secondary education. Also, borrowings from family or friends decreased for most categories in 2014 and 2017. Finally, the econometric estimation shows that borrowings and savings outside financial institutions (using family, friends or saving clubs) significantly contributed to economic growth than borrowing and savings through financial institutions. The findings have implications.
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This paper studies banks' lending to shadow banks and its impact on mortgage market competition. I collect shadow bank call reports through FOIA requests and document that most of shadow banks' warehouse funding is obtained from the banks that compete with them in the mortgage market. I provide evidence that banks trade off information advantage in warehouse lending against the loss in profits from increased mortgage market competition: (i) warehouse lending is clustered between competitors in local mortgage markets, especially in regions where public information of local housing value is less reliable; (ii) shadow banks cannot easily substitute to alternative funding sources if their relationship banks exogenously reduce warehouse lending; and (iii) a bank lends less to shadow banks in regions where it has greater market share in mortgage origination. To study the net effect on mortgage market competition in equilibrium, I calibrate a quantitative model that links warehouse lending and mortgage market competition. Warehouse lending market power is substantial. Banks charge 30% extra markups to the competing shadow banks relative to non-competitors. In the counterfactual, a faster GSE loan purchase program, which changes the warehouse lending market structure, would increase mortgage market competition, improving consumer welfare by $3.5 billion.
arXiv
We study the problem of stopping a Brownian motion at a given distribution $\nu$ while optimizing a reward function that depends on the (possibly randomized) stopping time and the Brownian motion. Our first result establishes that the set $\mathcal{T}(\nu)$ of stopping times embedding $\nu$ is weakly dense in the set $\mathcal{R}(\nu)$ of randomized embeddings. In particular, the optimal Skorokhod embedding problem over $\mathcal{T}(\nu)$ has the same value as the relaxed one over $\mathcal{R}(\nu)$ when the reward function is semicontinuous, which parallels a fundamental result about Monge maps and Kantorovich couplings in optimal transport. A second part studies the dual optimization in the sense of linear programming. While existence of a dual solution failed in previous formulations, we introduce a relaxation of the dual problem that exploits a novel compactness property and yields existence of solutions as well as absence of a duality gap, even for irregular reward functions. This leads to a monotonicity principle which complements the key theorem of Beiglb\"ock, Cox and Huesmann [Optimal transport and Skorokhod embedding, Invent. Math., 208:327-400, 2017]. We show that these results can be applied to characterize the geometry of optimal embeddings through a variational condition.
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We develop and estimate an equilibrium model of marketplace lending to quantify its welfare implications relative to traditional lending. We have access to the universe of data from one of the leading Chinese peer-to-peer lending platforms that allows for both direct and platform-intermediated lending through portfolio products. The data set includes detailed information on borrowers, loans, lenders’ portfolio and platform investment. We use our structural model to quantify the value of financial disintermediation, one of the key differences between marketplace and traditional lending. We do so simulating counterfactual scenarios where the platform resembles a bank in its intermediated lending by making maturity transformation and bearing liquidity risk. We find different welfare effects, measured as borrowers’ and lenders’ surplus and platform profits, depending on the level of liquidity and on the composition of lenders on the platform. While marketplace lending provides higher welfare with high liquidity and return oriented investors, traditional lending leads to better outcomes with low liquidity and liquidity oriented investors.
SSRN
We use market data on corporate bonds and equities to measure the value of U.S. corporate assets and their payouts to investors. In contrast to per share equity dividends, total corporate payouts are very volatile, turn negative when corporations raise capital, and are acyclical. This challenges the notion of risk and return since the risk premium on corporate assets is comparable to the standard equity premium. To reconcile this evidence, we show empirically that aggregate net issuances are acyclical and highly volatile, and mask a strong exposure of total payouts' cash components to low-frequency growth risks. We develop an asset-pricing framework to quantitatively assess this economic channel.
SSRN
This study argues the likely misallocation of capital in Pakistan associated with the perverse incentives faced by banking institutions to provide additional loans to weak businesses and households. Businesses and households are more likely to receive additional bank financing if they are financially constrained, because: (a) financial intermediaries in Pakistan have an incentive to allocate funds to weakened borrowers in order to avoid the realization of non-performing loans on their financial statements; and (b) the financial regulator’s pressure on commercial banks to sidestep deleveraging that will hamper economic growth arising from COVID-19 pandemic. This study hypothesizes that a potential ‘ever-greening’ behavior would be more observable among financial institutions that have capital adequacy ratios close to regulatory minimum requirement; and the policy of forbearance on extending funds by banks will be compounded by the existence of deposit insurance scheme. However, the evidence suggests that banking institutions in Pakistan have not adopted the policy of forbearance despite announcement of incentives by central bank and commercial banks prefer to park their excess funds in government securities in the face of global pandemic. Moreover, the findings of this study are not conclusive due to the availability of aggregate data and short sample period.
arXiv
Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks (often called 'primitives') for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers, which includes popular market makers such as Uniswap and Balancer. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset. We also derive several other useful properties including liquidity provider returns in the path independent case.
SSRN
We develop a dynamic credit-risk model to study how information acquisition affects the liquidity in the secondary bond market and the capital structure. In this model, creditors of a firm can acquire costly information about the firm's recovery value and exploit the information advantage by selling their bonds to uninformed buyers. When a firm's fundamental deteriorates, creditors have higher incentives to acquire information, resulting in more severe adverse selection in the secondary market. The model predicts that the trading volume and illiquidity discount of bonds are decreasing in the firm's fundamental. Further, we show that a firm's optimal leverage is non-monotonic in the information-acquisition cost.
arXiv
Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of big-data phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned- ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions.
Keywords: Information; Big Data; Electronic Markets; Analytics; Behavior
SSRN
Systemic risk management has increased in importance and policymakers have put greater emphasis on this issue with the global crises. Although there are a lot of reports and scientific papers on systemic risk management or financial stability, these works mostly focus on market actors and their interconnectedness. In this article, I change the concept from market actors to public authorities of a financial system and focus on the efficient design of a system to be able to have better systemic risk management to sustain financial stability. Throughout the article, I work under a hypothesis: "The architecture of the financial system of a country or a region is one of the pillars of the success of the systemic risk management and financial stability." As a result, I introduce the idea that instead of having an advisory committee/board/council, each country should have a financial stability "agency".
SSRN
This paper studies the effect of interest rates on market selection. Consistent with the evidence, I demonstrate that busts exhibit adverse selection, whereas booms ac- companied by ultra-low rates exhibit advantageous selection. Interestingly, booms accompanied by intermediate interest rates simultaneously exhibit adverse and ad- vantageous selection. Changes in interest rates affect selection and, hence, the quan- tity and quality of loans. When interest rates are endogenous, multiple equilibria arise, although higher interest rate equilibria imply more and better loans. Adverse selection attenuates the stimulatory effects of monetary policy, and zero interest rates can lead banks to hoard cash. Several extensions and robustness checks are applied.
SSRN
Research Question/Issue: The existing literature documents the possibility that investors may consider female Chief Executive Officers (CEOs) less valuable investment targets due to the prejudice against women. This study examines Female CEOs’ contributions to company value in the stock market, measured by Tobin’s Q, based on their management performances.Research Findings/Insights: Using a sample of 1,725 nonfinancial U.S. companies, mainly in the S&P 1500, for the period 2007â€"2018, we find that female CEOs have a negative relationship with Tobin’s Q, although they have no significant relationship with aspects of management performance that is significantly related to Tobin’s Q. However, female CEOs are negatively related to internal control material weakness as companies’ situations become riskier. Correspondingly, the relationship between female CEOs and Tobin’s Q becomes positive in riskier situations, meaning that female CEOs contribute to firm value with their risk management performance.Theoretical/Academic Implications: Our study contributes to the previous studies that investors may have negative prejudices regarding females’ risk-averse tendencies. Interestingly, our findings imply that female CEOs overcome investors’ adverse valuations with their excellent risk management ability in crises. Finally, it also shows that a tendency toward risk aversion does not drive female CEOs’ risk management ability.Practitioner/Policy Implications: The empirical evidence from this study can support regulators and boards to understand female CEOs’ management performance more deeply. Specifically, our findings suggest that female CEOs do not create inferior management performances that negatively affect company value. In contrast, their risk management ability, which does not stem from risk avoidance tendencies, can be better than male CEOs in crises.
RePEC
The ability to make a beneficial financial choice varies greatly among individuals. One of the ways this variability may present itself is through probability weighting, where on aggregate small probabilities are overweighted and large probabilities are underweighted. This paper investigates if the combination of risk literacy and the neuro-biological concept of interoception plays a role in mediating the over and under-weighting of a prospects likelihood. I find that high risk literacy increases the perception of changes in probability and as such reduces underweighting/overweighting while high interoceptive ability reduces overoptimism towards gambles in males but induces pessimism towards gambles for females.
arXiv
Equity research analysts at financial institutions play a pivotal role in capital markets; they provide an efficient conduit between investors and companies' management and facilitate the efficient flow of information from companies, promoting functional and liquid markets. However, previous research in the academic finance and behavioral economics communities has found that analysts' estimates of future company earnings and other financial quantities can be affected by a number of behavioral, incentive-based and discriminatory biases and systematic errors, which can detrimentally affect both investors and public companies. We propose a Bayesian latent variable model for analysts' systematic errors and biases which we use to generate a robust bias-adjusted consensus estimate of company earnings. Experiments using historical earnings estimates data show that our model is more accurate than the consensus average of estimates and other related approaches.
SSRN
Bank liquidity shortages during the global financial crisis of 2007-2009 led to the introduction of liquidity regulations, the impact of which has attracted the attention of academics and policymakers. In this paper, we investigate the impact of liquidity regulation on bank lending. As a setting, we use the Netherlands, where a Liquidity Balance Rule (LBR) was introduced in 2003. The LBR was imposed on Dutch banks only and did not apply to other banks operating elsewhere within the Eurozone. Using this differential regulatory treatment to overcome identification concerns, we investigate whether there is a causal link from liquidity regulation to the lending activities of banks. Using a difference-in-differences approach, we find that stricter liquidity requirements following the implementation of the LBR did not reduce lending. However, the LBR did lead Dutch banks to modify the structure of loan portfolios by increasing corporate lending and reducing mortgage lending. During this period Dutch banks experienced a significant increase in deposits and issued more equity. Overall, the findings of this study have relevance for policymakers tasked with monitoring the impact of post-crisis liquidity regulations on bank behavior.
SSRN
Public companies in the United States are required to file annual reports (Form 10-K) that, among other things, disclose the risk factors that might negatively affect the price of their stock. The risk of a pandemic was well known before the current crisis and we now know the impact for shareholders is, for almost all companies, significant and negative. To what extent did managers forewarn their shareholders of this valuation risk? We examine all 10-K filings from 2018, well before any knowledge of the current pandemic, and find that less than 21% of the filings contain any reference to pandemic-related terms. Given management’s presumably deep understanding of their business and general awareness that, for at least the past decade, pandemics have been identified as a significant global risk, it seems that this number should have been higher.
SSRN
Blockchain technology has a significant impact in multiple fields. Bitcoin was the first of its applications to rise to widespread attention. Due to its source code being publicly available (open source), it was followed quickly by a number of other cryptocurrencies as developers caught on to the opportunities it presented. The chief innovation introduced by Bitcoin was the ability to pay in a moderately secure fashion without third parties, such as banks.In this work we examine the mathematical and economic foundations upon which cryptocurrencies are built. We include a discussion of some cryptographic tools used in cryptocurrencies, such as hash functions that enable hiding information and committing to specific values; and the elliptic curve digital signature algorithm (ECDSA) used to implement Bitcoin's digital signature scheme.After covering the mathematical background we move on to the cryptocurrencies themselves. Bitcoin offers an immutable bookkeeping system based on blockchain technology, implemented using hash functions. To add transactions to the blockchain, miners calculate suitable hashes by trial and error. In order to change a transaction after it has been written onto the blockchain would require a practically infeasible amount of computation. Bitcoin transactions rely on ECDSA for authorisation. Having discussed the technical implementation of Bitcoin to some level of detail, we move on to describe two other cryptocurrencies, Zcash and Monero. These were developed to address some limitations in Bitcoin, especially when it comes to privacy considerations. We finish with a comparison of the cryptocurrencies introduced previously and a discussion of the relationship between cryptocurrencies and the society at large.
arXiv
This study explores the time-varying structure of market efficiency of the prewar Japanese stock market based on Lo's (2004) adaptive market hypothesis (AMH). In particular, we measure the time-varying degree of market efficiency using new datasets of the stock price index estimated by Hirayama (2017a,b, 2018, 2019a, 2020). The empirical results show that (1) the degree of market efficiency in the prewar Japanese stock market varied with time and that its variations corresponded with major historical events, (2) Lo's (2004) the AMH is supported in the prewar Japanese stock market, (3) the differences in market efficiency between the old and new Tokyo Stock Exchange (TSE) shares and the equity performance index (EQPI) depends on the manner in which the price index is constructed, and (4) the price control policy beginning in the early 1930s suppressed price volatility and improved market efficiency.
SSRN
How does the stock market react to the media coverage about a firm’s efforts in addressing non-financial stakeholder claims during Covid-19 pandemic, when contracting economic opportunities and resources are aggravating potential conflicts among stakeholders? I have examined the relationship between daily abnormal returns of US listed companies and independent media sentiments related to a firm’s efforts in delivering wellbeing for customers, employees, community, and the environment. I find that abnormal returns started to fluctuate more greatly as the number of confirmed cases in the US started to take off in late February. Merging Compustat security daily data and TruValue Labs daily media sentiment data, I constructed a balanced panel data of 317,075 firm-day observations, representing 4,929 listed firms in the US. Panel regressions suggest that a firm’s daily abnormal returns are relatively non-responsive to its efforts in delivering customer and community wellbeing. But there is some evidence showing that the stock market reacts positively to a firm’s efforts in delivering employee wellbeing and weakly negatively to its efforts in delivering environmental wellbeing.
arXiv
This paper studies optimal consumption, investment, and healthcare spending under Epstein-Zin preferences. Given consumption and healthcare spending plans, Epstein-Zin utilities are defined over an agent's random lifetime, partially controllable by the agent as healthcare reduces mortality growth. To the best of our knowledge, this is the first time Epstein-Zin utilities are formulated on a controllable random horizon, via an infinite-horizon backward stochastic differential equation with superlinear growth. A new comparison result is established for the uniqueness of associated utility value processes. In a Black-Scholes market, the stochastic control problem is solved through the related Hamilton-Jacobi-Bellman (HJB) equation. The verification argument features a delicate containment of the growth of the controlled morality process, which is unique to our framework, relying on a combination of probabilistic arguments and analysis of the HJB equation. In contrast to prior work under time-separable utilities, Epstein-Zin preferences largely facilitate calibration. In four countries we examined, the model-generated mortality closely approximates actual mortality data; moreover, the calibrated efficacy of healthcare is in broad agreement with empirical studies on healthcare across countries.
arXiv
We study portfolio selection in a model with both temporary and transient price impact introduced by Garleanu and Pedersen (2016). In the large-liquidity limit where both frictions are small, we derive explicit formulas for the asymptotically optimal trading rate and the corresponding minimal leading-order performance loss. We find that the losses are governed by the volatility of the frictionless target strategy, like in models with only temporary price impact. In contrast, the corresponding optimal portfolio not only tracks the frictionless optimizer, but also exploits the displacement of the market price from its unaffected level.
arXiv
A recent model for the stock market calculates future price distributions of a stock as a wave function of a quantum particle confined in an infinite potential well. In such a model the question arose as to how to estimate the classical potential needed for solving the Schrodinger equation. In the present article the method used in that work for evaluating the potential is described, in the simplest version to implement, and more sophisticated implementations are suggested later.
arXiv
This paper enhances the pricing of derivatives as well as optimal control problems to a level comprising risk. We employ nested risk measures to quantify risk, investigate the limiting behavior of nested risk measures within the classical models in finance and characterize existence of the risk-averse limit. As a result we demonstrate that the nested limit is unique, irrespective of the initially chosen risk measure. Within the classical models risk aversion gives rise to a stream of risk premiums, comparable to dividend payments. In this context, we connect coherent risk measures with the Sharpe ratio from modern portfolio theory and extract the Z-spread - a widely accepted quantity in economics to hedge risk. By involving the Z-spread we demonstrate that risk-averse problems are conceptually equivalent to the risk-neutral problem.
The results for European option pricing are then extended to risk-averse American options, where we study the impact of risk on the price as well as the optimal time to exercise the option.
We also extend Merton's optimal consumption problem to the risk-averse setting.
SSRN
Recent regulatory amendments aimed at modernizing disclosures and enhancing their usefulness focus on repetition and interactivity within firms’ disclosure filings. We use an experiment to provide empirical evidence on the effects of disclosure repetition (the repeating of information in the filing) and disclosure interactivity (user involvement in directing the form or content of the information displayed) on investors’ decision processes. Our results show that repetition and interactivity jointly influence investors’ reliance on other, non-repeated information within the filing. When the disclosure filing is less interactive, the presence of repeated information reduces investors’ reliance on non-repeated information within the filing. This evidence corroborates concerns that repetition can obscure value-relevant information from investors. However, when the filing is more interactive, investors’ reliance on non-repeated information actually increases in the presence of repetition. Thus, our evidence suggests that disclosure interactivity is an important presentation feature that counteracts the potentially harmful effects of repetition on non-repeated information reliance.
arXiv
Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance" produced not only by the variation of the topology of the system but also as a consequence of the external levels of risk to which the network as a whole is submitted. Starting from the "Susceptible-Infected" (SI) model of epidemics and its relation to the communicability functions of networks we develop a series of risk-dependent centralities for nodes in (financial and economic) networks. We analyze here some of the most important mathematical properties of these risk-dependent centrality measures. In particular, we study the newly observed phenomenon of ranking interlacement, by means of which two entities may interlace their ranking positions in terms of risk in the network as a consequence of the change in the external conditions only, i.e., without any change in the topology. We test the risk-dependent centralities by studying two real-world systems: the network generated by collecting assets of the S\&P 100 and the corporate board network of the US top companies, according to Forbes in 1999. We found that a high position in the ranking of the analyzed financial companies according to their risk-dependent centrality corresponds to companies more sensitive to the external market variations during the periods of crisis.
arXiv
We show how the approach to equilibrium in Kirman's ants model can be fully characterized in terms of the spectrum of a Schr\"odinger equation with a P\"oschl-Teller ($\tan^2$) potential. Among other interesting properties, we have found that in the bimodal phase where ants visit mostly one food site at a time, the switch time between the two sources only depends on the ``spontaneous conversion" rate and not on the recruitment rate. More complicated correlation functions can be computed exactly, and involve higher and higher eigenvalues and eigenfunctions of the Schr\"odinger operator, which can be expressed in terms of hypergeometric functions.
SSRN
There are now a large and rapidly growing number of studies that test the constancy of stock return anomalies. In this study, we produce new and convincing evidence that the standard constancy test is heavily influenced by selection bias. Backed by a carefully designed Monte Carlo simulation, we show that selection bias predisposes the standard constancy test to reject the null by a factor of five to 12 times more than normally expected. Failure to recognize this bias can result in publication of the type of pseudo discoveries that Harvey (2017) warns about in his Presidential Address to the American Finance Association. We then describe the Quandt/Andrews test, a correct and unbiased test for anomalies and changes in anomalies, and apply it to test the constancy of 15 well-known stock return anomalies.
SSRN
The paper aims to analyse the current situation of insurance services offered by different institutions in Nepal and explore the opportunities and challenges of insurance sector. It also discusses the regulatory and development issues in insurance sector. The paper has been prepared based on desk review. The study concludes that Nepalese insurance industry experienced slow growth, limited coverage and low penetration and density over the long period prior to 2001 which is gradually increased thereafter. There is the domination of commercial insurers in insurance industry, domain of social insurance and social security programs also growing immensely but deposit insurance and insurance services by non-insurance organizations is in small scale. The regulatory issues in insurance sector is crucial as the industry has been faced poor corporate governance practices, poor quality of services, rising of fraudulent activities, low insurance coverage and penetration, under insurance, mis-selling and force selling, lack of qualified insurance personnel. Insurance is a pillar of the financial system, permanent sources of fund to banking sector and capital market. For the financial stability and sustainable economic growth, there should be sound coordination among the regulatory authorities and market players. The ultimate goal of these organizations should be to ensure the protection of rights of the customers.
SSRN
Recent influential work finds large increases in inequality in the U.S., based on measures of wealth concentration that notably exclude the value of social insurance programs. This paper revisits this conclusion by incorporating Social Security retirement benefits into measures of wealth inequality. Wealth inequality has not increased in the last three decades when Social Security is accounted for. When discounted at the risk-free rate, real Social Security wealth increased substantially from $5.6 trillion in 1989 to just over $42.0 trillion in 2016. When we adjust for systematic risk coming from the covariance of Social Security returns with the market portfolio, this increase remains sizable, growing from over $4.6 trillion in 1989 to $34.0 trillion in 2016. Consequently, by 2016, Social Security wealth represented 58% of the wealth of the bottom 90% of the wealth distribution. Redistribution through programs like Social Security increases the progressivity of the economy, and it is important that our estimates of wealth concentration reflect this.
SSRN
Using a unique, quasi-natural experiment, we evaluate the recruitment of statutory external auditors for tax inspection purposes. In 2011, statutory external auditors were assigned to certify the tax compliance of firms subject to a regulatory change enacted by a state directive in Greece. Under this new directive, besides the audit report for financial statements, auditors were responsible to prepare the “Tax Compliance Report†(TCR) to assure that the audited firm complies with official tax rules. We investigate whether this proactive measure had any effect on firms’ tax avoidance behavior. Using a difference-in-differences research design, our empirical results suggest that non-conforming tax avoidance for treated firms (i.e. firms subject to tax audits) significantly decreased in the post-TCR period compared to that of the control sample (i.e. firms not subject to tax audits). Conversely, conforming tax avoidance increased. This evidence suggests that treated firms switched from non-conforming to conforming tax avoidance activities.
SSRN
The purpose of this paper is to examine the connected dynamics of the affected Asian financial markets and global financial market in relation to the outbreak of the coronavirus (COVID-19) pandemic. We particularly examine the temporal dependence and connectedness of the affected markets with the global financial market by using the time-varying dependence approach in a time-frequency space under COVID-19. Our findings indicate a strong, positive dependence among the investigated markets’ due to the outbreak of COVID-19. In addition, we report an increased tendency of co-movements over the higher horizon which is documented by COVID-19. These findings are of significant interest for market participants, policymakers, and international investors.
SSRN
This study examines whether the flow volatility experienced by institutional investors affects firms’ financing costs. Using Greenwood and Thesmar’s (2011) stock price fragility, a proxy for firm exposure to its institutional investors’ flow volatility, we find that firms with high stock price fragility pay higher bank loan costs than firms with low fragility. This effect on cost is partially mediated through board monitoring and most pronounced when lenders rely more on institutional shareholders to discipline corporate management, suggesting that unstable flows might weaken institutional investors’ monitoring effectiveness. The paper adds to the evidence that non-fundamental risks (institutional investors’ flow shocks) can have real impact on firms.
SSRN
I study optimal voluntary disclosure by a firm manager where the stock market provides valuable feedback for investment making. The manager uses her disclosure decision to maximize stock price informativeness and therefore her ability to learn from prices. This information eliciting disclosure strategy results in profit-maximizing investment, however, only conditional on the information the manager received internally. While the equilibrium disclosure policy elicits market feedback optimally for a privately informed manager, it does not internalize an informational externality it imposes on other managerial information states. With the strategic use of voluntary disclosure, basic principles of managerial decision making may fail to hold: First, better internal information provision for the manager may result in lower expected profits; second, a higher success probability of the investment project may decrease efficient investment; and third, the ex-ante efficient disclosure policy may be implemented by giving the manager pure short-term incentives.
arXiv
The multivariate conditional probability distribution quantifies the effects of a set of variables onto the statistical properties of another set of variables. In the study of systemic risk in the financial system, the multivariate conditional probability distribution can be used for stress-testing by quantifying the propagation of losses from a set of `stressing' variables to another set of `stressed' variables. Here it is described how to compute such conditional probability distributions for the vast family of multivariate elliptical distributions, which includes the multivariate Student-t and the multivariate Normal distributions. Simple measures of stress impact and systemic risk are also proposed. An application to the US equity market illustrates the potentials of this approach.
SSRN
We propose a new method, VASA, based on variable subsample aggregation of model predictions for equity returns using a large-dimensional set of factors. To demonstrate the effectiveness, robustness, and dimension reduction power of VASA, we perform a comparative analysis between state-of-the-art machine learning algorithms. As a performance measure, we explore not only the global predictive but also the stock-specific R2's and their distribution. While the global R2 indicates the average forecasting accuracy, we find that high variability in the stock-specific R2's can be detrimental for the portfolio performance, due to the higher prediction risk. Since VASA shows minimal variability, portfolios formed on this method outperform the portfolios based on more complicated methods like random forests and neural nets.
SSRN
[enter Abstract Body]Sustainability has become one of the challenges of today’s banks. Since sustainable business models are responsible for the environment and society along with generating economic benefits, they are an attractive approach to sustainability. Sustainable business models also offer banks competitive advantages such as increasing brand reputation and cost reduction. However, no framework is presented to evaluate the sustainability of banking business models. To bridge this theoretical gap, the current study using A Delphi-Analytic Hierarchy Process method, firstly, developed a sustainable business model to evaluate the sustainability of the business model of banks. In the second step, the sustainability performance of sixteen banks from eight European countries including Norway, The UK, Poland, Hungary, Germany, France, Spain, and Italy, assessed. The proposed business model components of this study were ranked in terms of their impact on achieving sustainability goals. Consequently, the proposed model components of this study, based on their impact on sustainability, are respectively value proposition, core competencies, financial aspects, business processes, target customers, resources, technology, customer interface, and partner network. The results of the comparison of the banks studied by each country disclosed that the sustainability of the Norwegian and German banks’ business models is higher than in other counties. The studied banks of Hungary and Spain came in second, the banks of the UK, Poland, and France ranked third, and finally, the Italian banks ranked fourth in the sustainability of their business models.
arXiv
The PCL framework provides a comprehensive climate risk management approach grounded in the assessment of societal values of financial and non-financial loss tolerability. The framework optimizes response action across three main clusters, namely preemptive adaptation (P) or risk reduction, contingent arrangements (C), and loss acceptance (L); without a predetermined hierarchy across them. The PCL Framework aims at including the three clusters of outlay within a single continuum, and with the main policy outcome being a balanced portfolio of actions across the three clusters by way of an optimization module, such that the aggregate outlay is optimized in the long-term. It is proposed that the approach be applied separately for each hazard to which the target community is exposed. While it is currently applied to climate-related risk management, the methodology can be repurposed for use in other contexts where societal buy-in is central.
SSRN
We extend prior research examining the relation between aggregate recommendation changes and future returns by documenting that this relation varies over time as a function of the predictability of future earnings growth. When industry-level earnings growth is more predictable, we find that recommendation changes relate negatively to future returns. Our evidence suggests that this negative relation results from analysts revising recommendations upward for higher expected earnings growth but failing to adjust downward for a related decrease in investor risk aversion and demand for risk premia leading to lower expected returns. In contrast, when industry-level earnings growth is less predictable, we find that recommendation changes relate positively to future returns. However, this positive relation results from analysts and investors similarly underestimating earnings growth persistence. Overall, the evidence fails to support the claim that analysts’ recommendation changes incorporate aggregate information in a manner that adds value to investors by predicting future returns.
arXiv
In March and April 2020, public health authorities in the United States acted to mitigate transmission of COVID-19. These actions were not coordinated at the national level, which creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy. We combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States (N = 132,048). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. Mean county-level daily growth in COVID-19 infections peaked at 17.2% just before stay-at-home orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.8 percentage point (95% CI 0.7 to 8.6) reduction in the growth rate after one week and an 8.6 percentage point (3.0 to 14.1) reduction after two weeks. By day 22 the reduction (18.2 percentage points, 12.3 to 24.0) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13, 2020 when a national emergency was declared might have reduced cumulative infections by 62.3%, and might have helped to reverse exponential growth in the disease by April 5. The results here suggest that a coordinated nationwide stay-at-home order may have reduced by hundreds of thousands the current number of infections and by thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level, especially for diseases for which local spread has already occurred and testing availability is delayed.
arXiv
Our goal in this paper is to study and characterize the interdependency structure of the Mexican Stock Exchange (mainly stocks from BMV) in the period 2000-2019 and provide visualizations which in a one shot provide a big-picture panorama. To this end, we estimate correlation/concentration matrices from different models and then compute metrics from network theory including eigencentralities and network modularity
SSRN
In this paper, we use U.S. commercial banks' data to investigate whether the effect of unexpected deposit flows on loan production depends on banks' exposure to off-balance sheet funding liquidity risk. We find that lending is sensitive to deposit shocks at small banks but not at large ones. Furthermore, for small banks, the increase in lending explained by unexpected deposit inflows depends on how much they are exposed to funding liquidity risk stemming from their off-balance sheets, as measured by the level of unused commitments. Small banks more exposed to such funding liquidity risk tend to extend fewer new loans. Our results indicate that unexpected deposit inflows from, for instance, the failure of other banks or market disruptions might not as easily be fueled again to borrowers.
SSRN
Since 2014, a number of U.S. states have legalized business activities related to the production, distribution, and use of recreational cannabis. These activities remain illegal at the U.S. federal level, creating a dual regulatory environment. The uncertainty related to the enforcement of federal cannabis laws affects businesses located in legalizing states, particularly federally-insured banks. Applying a difference-in-differences approach to a matched sample of banks in legalizing and non-legalizing states, we document an increase in audit fees incurred by banks located in legalizing states after cannabis legalization. This finding is consistent with increased auditor effort and engagement risk being an unintended consequence of state-level recreational cannabis legalization. In supplemental analysis, we find that the relation between banks’ audit fees and cannabis legalization was greater for banks having larger increases in banking activity, suggesting that audit fees increased primarily for banks that may be engaging in relationships with cannabis-related businesses.
SSRN
We study the effect of U.S. macroeconomic news releases on equity markets, bond markets, and exchange rates of 27 countries from 1997 to 2019. Looking at changes in 60-minute windows around announcements, we document the following findings: First, asset prices respond overwhelmingly symmetric across countries to news. Positive news about real activity lead to an increase in equity prices, a rise in long-term interest rates, and a depreciation of the local currency against the U.S. dollar. Second, some countries respond systematically stronger to U.S. macroeconomic news releases than others. Third, the size of the response co-varies with macroeconomic fundamentals. For instance, stock prices of countries with greater trade and financial linkages respond stronger to news about the real economy and weaker to news on prices. Fourth, U.S. macro news explain a sizable fraction of the quarterly variation in foreign stock and bond markets. Overall, our findings support international business cycle models, in which shocks lead to cross-country co-movement of asset prices and in which linkages affect the transmission of shocks.
