Research articles for the 2020-04-22
arXiv
Forecasts of product demand are essential for short- and long-term optimization of logistics and production. Thus, the most accurate prediction possible is desirable. In order to optimally train predictive models, the deviation of the forecast compared to the actual demand needs to be assessed by a proper metric. However, if a metric does not represent the actual prediction error, predictive models are insufficiently optimized and, consequently, will yield inaccurate predictions. The most common metrics such as MAPE or RMSE, however, are not suitable for the evaluation of forecasting errors, especially for lumpy and intermittent demand patterns, as they do not sufficiently account for, e.g., temporal shifts (prediction before or after actual demand) or cost-related aspects. Therefore, we propose a novel metric that, in addition to statistical considerations, also addresses business aspects. Additionally, we evaluate the metric based on simulated and real demand time series from the automotive aftermarket.
arXiv
A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.
SSRN
This essay will appear as a chapter in a forthcoming edited volume published by Oxford University Press. It builds on the earlier article, Beyond Intermediation: A New (FinTech) Model for Securities Holding Infrastructures, 22 U. Pa. J. Bus. L. 386 (2020), which argues that serious consideration should be given to modifications of the deeply intermediated securities holding systems in the United States and elsewhere. Many of the costs and risks imposed by the intermediated holding systems fall within the domain of the regulation of securities markets (internal costs), such as impairments of shareholder voting and bondholder claims against issuers. Others fall outside of that sphere (external costs), such as the enforcement of anti-money laundering and anti-terrorist financing regulations and tax laws.These legacy holding systems reflect a monopoly for the intermediaries (stockbrokers and banks) who benefit from the infrastructure, sustained by the path dependence resulting from the influence of the relevant intermediaries and the entrenched, sticky characteristics of the infrastructure and the regulatory environment. This essay argues that the prevailing infrastructure imposes external costs on constituencies outside the securities markets, while its beneficiaries, the intermediaries, do not internalize these costs. This represents a classic negative externality and a fundamental regulatory failure.The essay explains the broad societal impact of financial market infrastructures, which are very understudied aspects of financial markets. It emphasizes the regulatory failures in the regulation of financial markets and in regulation more generally. It challenges regulators (primarily the SEC in the United States) to establish more ambitious goals for the securities holding infrastructures and the Fintech community to meet those goals. Finally, the essay further illuminates a roadmap and framework for a cost-benefit analysis of the prevailing infrastructure and the reforms that are outlined in Beyond Intermediation.
arXiv
We propose a statistical framework to quantify location and co-location associations of economic activities using information-theoretic measures. We relate the resulting measures to existing measures of revealed comparative advantage, localization and specialization and show that they can all be seen as part of the same framework. Using a Bayesian approach, we provide measures of uncertainty of the estimated quantities. Furthermore, the information-theoretic approach can be readily extended to move beyond pairwise co-locations and instead capture multivariate associations. To illustrate the framework, we apply our measures to the co-location of occupations in US cities, showing the associations between different groups of occupations.
SSRN
Cryptocurrencies are often thought to operate out of the reach of national regulation, but in fact their valuations, transaction volumes and user bases react substantially to news about regulatory actions. The impact depends on the specific regulatory category to which the news relates: events related to general bans on cryptocurrencies or to their treatment under securities law have the greatest adverse effect, followed by news on combating money laundering and the financing of terrorism, and on restricting the interoperability of cryptocurrencies with regulated markets. News pointing to the establishment of specific legal frameworks tailored to cryptocurrencies and initial coin offerings coincides with strong market gains. These results suggest that cryptocurrency markets rely on regulated financial institutions to operate and that these markets are segmented across jurisdictions.
SSRN
This paper establishes a new decomposition of optimal dynamic portfolio choice under general incomplete-market diffusion models by disentangling the fundamental impacts on optimal policy from market incompleteness and flexible wealth-dependent utilities. We derive explicit dynamics of the components for the optimal policy, and obtain an equation system for solving the shadow price of market incompleteness, which is found to be dependent on both market state and wealth level. We identify a new important hedge component for non-myopic investors to hedge the uncertainty in shadow price due to variation in wealth level. As an application, we establish and compare the decompositions of optimal policy under general models with the prevalent HARA and CRRA utilities. Under nonrandom but possibly time-varying interest rate, we solve in closed-form the HARA policy as a combination of a bond holding scheme and a corresponding CRRA strategy. Finally, we develop a simulation method to implement the decomposition of optimal policy under the general incomplete market setting, whereas existing approaches remain elusive.
SSRN
This paper places the institution of bank deposit protection in the context of government paternalism. I apply the theories of deposit insurance, merit goods, patronized goods, government paternalism, and institutional change to the analysis of the Russian case. I rely on statistical data from Central Bank of Russia, Deposit Insurance Agency, and Rosstat. The findings are five-fold: (1) There is similarity between theoretical justification for government paternalism and intervention in household savings and the humanitarian sphere; (2) Deposit protection fits well the Russian institutional setup, due to its paternalistic nature; (3) The public choice-driven purpose of government intervention in household savings may change in the process. Patronage of small savers becomes camouflage for protection of private banks; (4) Deposit guarantee redistributes wealth from the public sector to the private one; (5) Deposit guarantee hinders the evolution of market discipline and responsibility while fostering opportunistic behavior patterns among depositors and banks. The research implication of the paper is that, in the absence of strict eligibility criteria for merit goods, one can identify goods and services that probably do not belong there but sneak into that category by means of a manipulated public choice and thus get similar treatment with traditional humanitarian sectors. The policy implication is that the authorities might wish to tackle opportunistic behavior at source, i.e. by amending the parameters of the deposit insurance scheme.
arXiv
The lead-lag relationship plays a vital role in financial markets. It is the phenomenon where a certain price-series lags behind and partially replicates the movement of leading time-series. The present research proposes a new technique which helps better identify the lead-lag relationship empirically. Apart from better identifying the lead-lag path, the technique also gives a measure for adjudging closeness between financial time-series. Also, the proposed measure is closely related to correlation, and it uses Dynamic Programming technique for finding the optimal lead-lag path. Further, it retains most of the properties of a metric, so much so, it is termed as loose metric. Tests are performed on Synthetic Time Series (STS) with known lead-lag relationship and comparisons are done with other state-of-the-art models on the basis of significance and forecastability. The proposed technique gives the best results in both the tests. It finds paths which are all statistically significant, and its forecasts are closest to the target values. Then, we use the measure to study the topology evolution of the Foreign Exchange market, as the COVID-19 pandemic unfolds. Here, we study the FX currency prices of 29 prominent countries of the world. It is observed that as the crises unfold, all the currencies become strongly interlinked to each other. Also, USA Dollar starts playing even more central role in the FX market. Finally, we mention several other application areas of the proposed technique for designing intelligent systems.
SSRN
Six weeks after becoming a pandemic, COVID-19 has caused over 150,000 deaths across 210 countries. Governments around the world have instituted universal lockdowns to curve the spread of this serious disease. While it is obvious that extended universal lockdowns have saved lives that otherwise would have been lost to COVID-19, they have also caused historical losses of livelihoods. Universal lockdowns are particularly detrimental to minorities and the working class, who have suffered the greatest job loss since the Great Depression. In some countries, unemployment carries the loss of access to health services, which is the opposite of what lockdowns intended to achieve. Hundreds of millions of citizens worldwide will endure the effects of universal lockdowns for years to come. Universal lockdowns are a blunt tool that should be used tactically, for brief periods of time. In this study, we introduce a new mathematical model (called K-SEIR) to simulate the outcomes of lockdowns, and help evaluate various exit strategies. We demonstrate that targeted lockdowns can achieve better outcomes than universal lockdowns, in terms of (1) saving lives, (2) protecting the most vulnerable in society (the elderly, the poor), and (3) preventing the depletion of medical resources.There is not one solution that fits all. National governments must devise tailored targeted lockdowns, based on their particular circumstances. We hope that the K-SEIR model will help governments learn from the mistakes of the COVID-19 crisis management, and help prepare society for COVID-20.
SSRN
This paper documents novel evidence that private debt contains value-relevant nonpublic information with significant economic value. We extract banks' private information from term loan spreads. Abnormal loan spreads significantly predict firms' future operating performance and uncertainty measures. Equity analysts and investors are not privy to banks' private information. Firms with higher abnormal loan spreads experience more negative earnings surprises over the next several quarters. Their stocks underperform on average by about 0.5% per month with no reversals in longer horizons. This result is concentrated among loans associated with better borrower-lender relationship, indicating that relationship banking facilitates valuable information acquisition. The abnormal loan spreads also negatively predict stock returns of borrowers' peer firms.
arXiv
We provide a unified treatment of pathwise Large and Moderate deviations principles for a general class of multidimensional stochastic Volterra equations with singular kernels, not necessarily of convolution form. Our methodology is based on the weak convergence approach by Budhijara, Dupuis and Ellis. We show in particular how this framework encompasses most rough volatility models used in mathematical finance and generalises many recent results in the literature.
SSRN
Increasing healthcare costs are a big concern for the well-being of liquidity-constrained households. This paper evaluates the effect of binding liquidity constraints on healthcare spending decisions. Further, the paper compares the effect of liquidity constraints on healthcare expenditure with the effect on the non-health consumption in particular on the food consumption. I extend a standard incomplete markets model with a health capital in the felicity function. Theoretically, I show that households reduce their healthcare expenditure due to the binding liquidity constraints in the current period, whereas expenditure declines in the next period due to the expected binding constraints one period ahead. I use the extended model to test the incidence of binding liquidity constraints with a linearized Euler equation. Empirically, I show that the test of liquidity constraints for healthcare expenditure reveals different implications than a standard test of liquidity constraints for nondurable consumption. In particular, current binding constraints and expected binding constraints lead to the opposite direction of bias when the liquidity constraints are omitted. The resulting overall bias depends on which constraint has a stronger effect. Moreover, the income elasticity of healthcare expenditure varies significantly between asset poor and rich families, more than the elasticity of non-health consumption among wealth quintiles. Altogether, my findings show that the effects of liquidity constraints are heterogeneous across households and across expenditure categories.
arXiv
We analyze an approach to managing the COVID-19 pandemic without shutting down the economy while staying within the capacity of the healthcare system. We base our analysis on a detailed heterogeneous epidemiological model, which takes into account different population groups and phases of the disease, including incubation, infection period, hospitalization, and treatment in the intensive care unit (ICU). We model the healthcare capacity as the total number of hospital and ICU beds for the whole country. We calibrate the model parameters to data reported in several recent research papers. For high- and low-risk population groups, we calculate the number of total and intensive care hospitalizations, and deaths as functions of time. The main conclusion is that countries, which enforce reasonable hygienic measures on time can avoid lockdowns throughout the pandemic provided that the number of spare ICU beds per million is above the threshold of about 100. In countries where the total number of ICU beds is below this threshold, a limited period quarantine to specific high-risk groups of the population suffices. Furthermore, in the case of an inadequate capacity of the healthcare system, we incorporate a feedback loop and demonstrate that quantitative impact of the lack of ICU units on the death curve. In the case of inadequate ICU beds, full- and partial-quarantine scenarios outcomes are almost identical, making it unnecessary to shut down the whole economy. We conclude that only a limited-time quarantine of the high-risk group might be necessary, while the rest of the economy can remain operational.
SSRN
This paper studies the design and effects of monetary and fiscal policy in the euro area. To do so, a stylized twoâregion model of monetary and fiscal policy rules in the EMU is built. We analyse how monetary and fiscal rules affect the adjustment dynamics in the model. Both the effects on the individual countries and on the EMU aggregate economy are studied. Three aspects play an important role in the analysis: (i) the consequences of alternative monetary and fiscal policy rules, (ii) the consequences of asymmetries between EMU countries (asymmetries in macroeconomic shocks and macroeconomic structures), and (iii) the role of alternative degrees of backwardâ and forwardâlooking behaviour in consumer decisions and inflation expectations.
SSRN
We show that negative monetary policy rates induce systemic banks to reach-for-yield. For identification, we exploit the introduction of negative deposit rates by the European Central Bank in June 2014 and a novel securities register for the 26 largest euro area banking groups. Banks with more customer deposits are negatively affected by negative rates, as they do not pass negative rates to retail customers, in turn investing more in securities, especially in those yielding higher returns. Effects are stronger for less capitalized banks, private sector (financial and non-financial) securities and dollar-denominated securities. Affected banks also take higher risk in loans.
SSRN
Is it possible to achieve almost riskless investment results in the long run through equity investments? The persistence of low interest rates is spurring research on this question, because of the need to increase yields, while limiting variability of investment results. Target date funds aim to achieve an almost riskless outcome over a long horizon. They can be managed as contingent claims when expressed as units of a stock index. To assess the robustness of target date funds we introduce a simple overfunding scheme and show its reliability through bootstrapping.
arXiv
We propose a stochastic model for a limit order book with liquidity fluctuations. Our model shows how severe intermittencies in the liquidity can affect the order book dynamics. The law of large numbers (LLN), central limit theorem (CLT) and large deviations (LD) are proved for our model. Our results allow us to satisfactorily explain the volatility and local trends in the prices, relevant empirical characteristics that are observed in this type of markets. Furthermore, it shows us how these local trends and volatility are determined by the typical values of the bid-ask spread. In addition, we use our model to show how large deviations occur in the spread as a direct result of severe liquidity fluctuations.
SSRN
This paper studies the design, effects and interactions of monetary and fiscal policies in the euro area. A stylized New Keynesian model with backward and forward looking dynamics is developed and augmented with monetary and fiscal policy rules. Numerical simulations are used to assess the effects of demand and supply shocks and the role of monetary and fiscal policies in their transmission.
SSRN
The aim is to analyse daily Bitcoin trading activity in the Bitcoin/Yen market, looking not at Bitcoinâs price or aggregate volume, but at the size and number of trades, in order to understand any changes in daily trading behaviour during different market conditions. Trade data from 2017-2019 for five Bitcoin Exchanges (Kraken, BTCBOX, Fisco, Zaif and Coincheck) is used in the analysis and split into four sub-periods based on Bitcoinâs market conditions. The results are surprising. There are no consistent patterns to trading activity. Both small and large investors continue to trade equally across all weekdays, weekends and holidays. The only factor driving changes in trading activity is the Bitcoin price itself.
SSRN
With a market share of 76.2 percent of the total German crowdfunding market with monetary consideration (German: Crowd-investing), real estate projects make up the largest part by far. Only a significantly smaller portion is used for classic start-up finance. The volume brokered, and the number of projects rose again significantly in the past year, whereas growth rates continue to level off. With this report I would like to build on last years Crowd-invest Real Estate Report 2019 and analyze the developments of the past 12 months. In this short follow-up report, the development of the most important key figures in the market is shown. These include, among others, analyses on the development of interest rates, platforms, periods and volume.
arXiv
Corporate failure resonates widely leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Firms are represented as a point cloud in a five dimensional space, one axis for each predictor. Visualising that cloud using Ball Mapper reveals failing firms are not often neighbours. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy but actually sit in characteristic spaces where failure has not occurred.
SSRN
The increasing pace of FinTech development has triggered a worldwide race among policy makers to overhaul their own regulatory landscape in order to be as innovation-friendly as possible. Consequently, a vast array of new tools and regulatory practices have emerged over the last years. The paper provides a critical systematisation of regulatory strategies and toolkits that have emerged so far (such as regulatory sandboxes and innovation hubs), stressing the increasing role played by legal marketing as a by-product of regulatory competition. Furthermore, the article describes and supports the paradigm of pro-competitive regulation underlying Open Banking projects in the EU, UK, Australia and other jurisdictions as the true game-changer approach that can unlock the potential of FinTech innovation.
SSRN
This report provides contingency tables (crosstab) analysis of security class actions (SCAs) and flags from Corporate Watchdog Reports. Watchdog Research assigns red and yellow flags to disclosures made by publicly traded firms according to their proprietary classification system. We find the following flags to be leading indicators of security class actions: red flags for financial revisions and CFO changes; and yellow flags for the Beneish M-Score, cybersecurity, and non-audit fees. We also find that a security class action in one year increases the probability of another security class action in the following year, i.e. security class actions are serially correlated over time. Additionally, we find that several flags are correlated with security class actions. The presence of a correlated flag increases the probability that a security class action will occur in the same calendar year it was awarded. The analysis is based on a panel data set of NYSE and NASDAQ companies from 2014 to 2018.
SSRN
I propose and model stock loan lotteries, a financial innovation that improves the welfare of individual investors. Stock loan lotteries are prize-linked payoffs using net rebates from securities lending. Stock loan lotteries motivate individual investors with prospect theory preferences to buy and hold risky assets with high expected returns. Stock loan lotteries provide the greatest welfare benefit to poor investors and have greater welfare benefits in a model with realistic market frictions. I propose a method for exchanges to bypass legal and regulatory hurdles by structuring stock loan lottery tickets as derivative securities.
SSRN
This study aims to evaluate the usefulness and relevance of accounting earnings disclosures, as the key determinant for share price changes. The main objective is to examine whether earnings response coefficient (ERC) behavior could explain more fully the share price changes, as to the reason why the share price change is not equal to the amount of announced earnings. The study is done with data sets from four developed countries of the Organization for Economic Co-operation and Development (OECD) group (i.e., Japan, the United Kingdom (UK), Sweden, and Switzerland) for the period 2001-2014. Two measures of abnormal returns are regressed against the size of the announced earnings. The first regression uses measures from individual events. The second regression uses a new measure; that is, from portfolios made out of all observations sorted by size of earnings into ten portfolios for each country. The portfolio method used was aimed at controlling possible idiosyncratic-errors-in-variables problem using individual event measures. The results using individual-event measures resulted in reasonable ERC sizes with high R2 explanatory power, a little higher than those reported in prior studies on other countries. Importantly, portfolio-based ERCs are close to the magnitude of the earnings in some tests, which supports the famous value relevance theory in accounting. This finding is new to this literature.
SSRN
Given the profound effects of fraudulent activities on managerial behaviors, employeesâ trust, and corporate culture, this paper investigates the impact of financial fraud on technological innovation at both firm and inventor levels. We find that the occurrence of financial fraud is negatively related to firmsâ and inventorsâ innovation outputs, both quantitatively and qualitatively. This finding is attributed to the joint effect of the negative impact of fraud on individual inventorsâ productivity and the number of inventors deployed. Moreover, cross-sectional tests suggest that the adverse impact of financial fraud on innovation is more pronounced when top-level managementâs tone is more myopic, when fraud is more observable to employees, or when trust between inventors and firms is lacking. Our results are robust to controls of corporate governance metrics and disclosure quality.
SSRN
In this study, we examine the impact of regulatory changes in Shariah screening guidelines as introduced by the Financial Services Act 2013 in Malaysia. The adoption of new regulations for Shariah-screening methodology affects the Shariah-compliance status of firms resulting in a considerable number of firms who either become non-compliant and removed from the list of Shariah-compliant firms or switched to Shariah-compliant. We investigate underlying determinants that governed such a switching behavior including the capital structure and ownership structure. Our results after controlling for size and financial performance indicate that financial leverage and ownersâ equity play a key role in explaining the switching behavior of Shariah firms. We also found that ownership structure plays a vital role in a firmâs decision to stay Shariah-compliant. Specifically, shares held by institutional investors (unit trust, endowment funds) and individual/family play an essential role for firms to stay Shariah-compliant. The empirical findings suggest that demand for Shariah-compliant investment in Malaysia emerges from the smaller investors investing in mutual funds/unit trust and overall composition of the population.
arXiv
We relook at the classic equity fund selection and portfolio construction problems from a new perspective and propose an easy-to-implement framework to tackle the problem in practical investment. Rather than the conventional way by constructing a long only portfolio from a big universe of stocks or macro factors, we show how to produce a long-short portfolio from a smaller pool of stocks from mutual fund top holdings and generate impressive results. As these methods are based on statistical evidence, we need closely monitoring the model validity, and prepare repair strategies.
arXiv
The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 35 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome.
SSRN
This paper proposes a new reduced-form model for the pricing of VIX derivatives that includes an independent stochastic jump intensity factor and co-jumps in the level and variance of VIX, while allowing the mean of VIX variance to be time-varying. I t the model to daily prices of futures and European options from April 2007 through December 2017. The empirical results indicate that the model significantly outperforms all other nested models and improves on benchmark by 21.6% in-sample and 31.2% out-of- sample. The model more accurately portrays the tail behavior of VIX risk-neutral distribution for both short and long maturities, as it successfully captures the time-varying skew found to be largely independent of the level of the VIX smile.
arXiv
The issue of climate change has become increasingly salient in the past years, the transition towards a renewable energy system is a priority in the transition to a sustainable society. In this document we explore the definition of energy transition, how it is reached, what are the driven factors to achieve it, and why in the 21st century context we can refer to it as Green energy transition. To answer that firstly, we have conducted a literature review discovering definitions from different disciplines, secondly, gathering the key factors that are drivers for energy transition, finally, a preliminary analysis of the factors is conducted within the context of European Union data preliminary finding that household net income and governmental legal actions related to environment are potential candidates to predict energy transition within countries. We intend to spark new research directions in order to get a common social and scientific understanding of energy transition.
SSRN
In late February and early March 2020, VIX futures prices were too low and observably undervalued in real-time, even as COVID-19 pandemic risks were growing. An investor who traded based on real-time signals of undervaluation would have earned significant trading profits by taking a long position in VIX futures in late February and holding it over March as the VIX reached record highs. The underreaction of VIX futures prices to growing risks was a vivid example of a broader pattern in the VIX futures market. A trading strategy based on the proposed valuation signal generates positive risk-adjusted returns.
SSRN
Recent studies argue that institutional common ownership in rival firms may reduce competition in product markets. Yet, empirical evidences are mixed. I re-examine this hypothesis, documenting that competition for flows on the ground of relative performance reduces institutional investorsâ ability to shift firm managersâ incentives to internalize product market externalities. Empirically, I show that the existence (static effect) and sustainability (dynamic effect) of the anti-competitive consequences of common ownership depend critically upon the degree of competition faced by relevant institutional investors. These findings emphasize the importance of accounting for asset managersâ incentives and strategic interactions when assessing the product market consequences of institutional common ownership.
SSRN
Our paper examines whether investor opinions expressed in social media predicted stock returns of financial firms during the 2007-2009 global financial crisis. We conduct a textual analysis of the articles published on the stock market insight website Seeking Alpha before the crisis and find that banks that were described in articles with a higher fraction of negative words experienced:(1) sharper drops in stock prices, (2) larger increases in expected default probability, and (3) greater surges in nonperforming loans during the crisis. Our evidence suggests that wisdom of crowds provides valuable information on how banks weather a forthcoming crisis.