# Research articles for the 2020-10-19

'Big Bang' in Micro Consumption and Its Implications for Asset Pricing
Ju, Gaosheng,Li, Qi
SSRN
We establish some facts about micro consumption that open an avenue toward fully solving various consumption-based asset pricing puzzles. We find that top quantiles of consumption growth of the majority people are positively correlated with asset returns; at low quantiles the correlations for many people are negative. This partial negative correlation accounts for the low time-series correlation between asset returns and the growth rates of aggregated consumption, which is at the heart of many pricing anomalies. Our findings suggest that a large proportion of individualsâ€™ preference toward consumption is risk-seeking at low consumption-growth states, while most individuals are risk-averse at high consumption-growth states. Both risk-seeking and risk-averse individuals demand a positive equity premium at their respective states. The equity premium puzzle arises from modeling risk-seeking behaviors as risk aversion, which partially generates a negative equity premium.

A Cautionary Tale of Two extremes:The Provision of Government Liquidity Support in the Banking Sector
Bui, Christina,Scheule, Harald (Harry),Wu, Eliza
SSRN
Using U.S. bank holding company data, we study the impact of the crisis liquidity programs initiated by the U.S. Federal Reserve on bank-specific information production. We find empirical evidence that following the receipt of liquidity support there was a pervasive decrease in bank stock price informativeness that increased market synchronicity and crash risk. Our findings further suggest that these effects are mainly driven by bank participation in the DW and TAF programs. On the bright side, we confirm that the liquidity programs served their purpose in targeting and supporting illiquid banks with low core stable funding sources through the crisis.

A Finite Agent Equilibrium in an Incomplete Market and its Strong Convergence to the Mean-Field Limit
Masaaki Fujii,Akihiko Takahashi
arXiv

We investigate the problem of equilibrium price formation in an incomplete securities market. Each financial firm (agent) tries to minimize its cost via continuous-time trading with a securities exchange while facing the systemic and idiosyncratic noises as well as the stochastic order-flows from its over-the-counter clients. We have shown, in the accompanying paper (Fujii-Takahashi (2020)), that the solution to a certain forward backward stochastic differential equation of conditional McKean-Vlasov type gives a good approximate of the equilibrium price which clears the market in the large population limit. In this work, we prove the existence of a unique market clearing equilibrium among the heterogeneous agents of finite population size. We show the strong convergence to the corresponding mean-field limit under suitable conditions. In particular, we provide the stability relation between the market clearing price for the heterogeneous agents and that for the homogeneous mean-field limit.

A review of two decades of correlations, hierarchies, networks and clustering in financial markets
Gautier Marti,Frank Nielsen,Mikołaj Bińkowski,Philippe Donnat
arXiv

We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of this review is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econometrics, behavioral finance. We hope it will help researchers to use more effectively this alternative modeling of the financial time series. Decision makers and quantitative researchers may also be able to leverage its insights. Finally, we also hope that this review will form the basis of an open toolbox to study correlations, hierarchies, networks and clustering in financial markets.

Additive normal tempered stable processes for equity derivatives and power law scaling
Michele Azzone,Roberto Baviera
arXiv

We introduce a simple model for equity index derivatives. The model generalizes well known L\evy Normal Tempered Stable processes (e.g. NIG and VG) with time dependent parameters. It accurately fits Equity index implied volatility surfaces in the whole time range of quoted instruments, including small time horizon (few days) and long time horizon options (years). We prove that the model is an Additive process that is constructed using an Additive subordinator. This allows us to use classical L\evy-type pricing techniques. We discuss the calibration issues in detail and we show that, in terms of mean squared error, calibration is on average two orders of magnitude better than both L\evy processes and Self-similar alternatives. We show that even if the model loses the classical stationarity property of L\evy processes, it presents interesting scaling properties for the calibrated parameters.

Ambiguous Text
Tham, Eric
SSRN
Text is inherently ambiguous. I use textual analysis to extract beliefs from financial news and social media sources to construct a Big Data ambiguity index. The index uses mixture of distributions that regards disagreement between sources and their individual uncertainties. Empirical results show the ambiguity index as an omitted variable bias in the fundamental relation between risks and returns. An ambiguity averse agent is shown to love ambiguity in unfavorable markets and dislike ambiguity in favorable markets. The paper finds that news media have a bigger influence on asset prices than social media except during the recession from Jun 2009 to Nov 2016.

Analysis of the impact of maker-taker fees on the stock market using agent-based simulation
Isao Yagi,Mahiro Hoshino,Takanobu Mizuta
arXiv

Recently, most stock exchanges in the U.S. employ maker-taker fees, in which an exchange pays rebates to traders placing orders in the order book and charges fees to traders taking orders from the order book. Maker-taker fees encourage traders to place many orders that provide market liquidity to the exchange. However, it is not clear how maker-taker fees affect the total cost of a taking order, including all the charged fees and the market impact. In this study, we investigated the effect of maker-taker fees on the total cost of a taking order with our artificial market model, which is an agent-based model for financial markets. We found that maker-taker fees encourage market efficiency but increase the total costs of taking orders.

Asset Price Forecasting using Recurrent Neural Networks
Hamed Vaheb
arXiv

This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices of two other stocks that lie in rather close correlation with GS. Other models such as ARIMA were used as benchmark. Empirical results manifest the practical challenges when using LSTM for forecasting stocks. One of the main upheavals was a recurring lag which we called "forecasting lag".

The second purpose is to develop a more general and objective perspective on the task of time series forecasting so that it could be applied to assist in an arbitrary that of forecasting by ANNs. Thus, attempts are made for distinguishing previous works by certain criteria (introduced by a review paper written by Ahmed Tealab) so as to summarise those including effective information. The summarised information is then unified and expressed through a common terminology that can be applied to different steps of a time series forecasting task.

The last but not least purpose of this thesis is to elaborate on a mathematical framework on which ANNs are based. We are going to use the framework introduced in the book "Neural Networks in Mathematical Framework" by Anthony L. Caterini in which the structure of a generic neural network is introduced and the gradient descent algorithm (which incorporates backpropagation) is introduced in terms of their described framework. In the end, we use this framework for a specific architecture, which is recurrent neural networks on which we concentrated and our implementations are based. The book proves its theorems mostly for classification case. Instead, we proved theorems for regression case, which is the case of our problem.

Bridging the gap between Markowitz planning and deep reinforcement learning
arXiv

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment, (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.

Central Bank Communication with a Financial Stability Objective
Arseneau, David M.
SSRN
An endogenous financial crisis is introduced into the canonical model used to study central bank transparency. The central bank is endowed with private information about the real economy and credit conditions which jointly determine financial vulnerabilities. An optimal choice is made regarding whether to communicate this information to the public. A key finding is that the optimal communication strategy depends on the state of the credit cycle and the\composition of shocks driving the cycle. From a policy perspective, this raises the possibility that central bank communication in the presence of a financial stability objective faces a time inconsistency problem.

Consumption and Voluntary Savings Response to Service Tax
Seal, Jayanta Kumar
SSRN
In this paper, we estimate the effect of a tax policy change on households' consumption and retirement savings behavior, by using a unique panel data of voluntary retirement savings (PPF) and debit card spending transactions. On average, we find that households reduce their consumption by 14% and increase their PPF savings by 16% in response to an increase in the service tax policy. Individuals close to their retirement age (55 years of age and above) and without any withdrawal restrictions from their PPF account tend to reduce their expenditure more and save more. Individuals with financial constraints and withdrawal restrictions do not reduce their expenditure significantly.

Corporate Social Irresponsibility and Portfolio Performance: A Cross-National Study
Harjoto, Maretno A.,Hoepner, Andreas G. F.,Li, Qian
SSRN
This study examines the impact of reputational risk, measured by corporate social irresponsibility (CSI) ratings, on shareholder abnormal returns. Based on 7,368 non-financial companies from 42 countries during 2007-2017, we find that long-short portfolios (buying no reputation risk and selling high reputation risk portfolios) earn significantly positive abnormal returns. The cross-national results indicate that the long-short portfolio returns are more pronounced (i) in the emerging market segment than in the developed market segment, (ii) in civil law jurisdictions than in their common law peers, (iii) within nations with higher confidence in corporations and, (iv) within nations with higher institutional trust.

Credit Growth and the Financial Crisis: A New Narrative
Albanesi, Stefania,De Giorgi, Giacomo,Nosal, Jaromir
SSRN
This paper studies the evolution of mortgage debt and defaults between 2001 and 2013 using a large, nationally representative panel of credit reports. Our analysis suggests that the 2007-2009 spike in mortgage defaults was concentrated among borrowers with mid to high credit scores. Among those borrowers, we find that real estate investors played a critical role in the rise of delinquencies and foreclosures. We also examine the determinants of geographical variation in the evolution of mortgage debt and defaults. We find that areas with a concentration of low credit score borrowers tend to be urban and displayed strong real estate investment activity by high credit score borrowers, though demographic factors such as age distribution and educational attainment may account for their greater exposure to the 2007-2009 recession. Our findings suggest that, borrower characteristics, such as the credit score, may be less important than behavior, such as investment activity, in driving mortgage defaults, thus providing an alternative narrative that challenges the large role of subprime credit for the crisis.

Cross-Border Lending and the International Transmisson of Banking Crises
Dieckelmann, Daniel
SSRN
This paper introduces a new transmission channel of banking crises where sizable cross- border bank claims on foreign countries with high domestic crisis risk enable contagion to the home economy. This asset-side channel opposes traditional views that see bank- ing crises originating from either domestic credit booms or from cross-border borrowing. I propose a combined model that predicts banking crises using both domestic and for- eign factors. For developed economies, the channel is predictive of crises irrespective of other types of capital ows, while it is entirely inactive for emerging economies. I show that policy makers can significantly enhance current early warning models by incorporating exposure-based risk from cross-border lending.

Cross-validated covariance estimators for high-dimensional minimum-variance portfolios
Sven Husmann,Antoniya Shivarova,Rick Steinert
arXiv

The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix of asset returns, estimating the optimal solution remains a challenge. In the presence of high-dimensionality in the data, the sample covariance estimator becomes ill-conditioned and leads to suboptimal portfolios out-of-sample. To address this issue, we review recently proposed efficient estimation methods for the covariance matrix and extend the literature by suggesting a multi-fold cross-validation technique for selecting the necessary tuning parameters within each method. Conducting an extensive empirical analysis with four datasets based on the S&P 500, we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio. In addition, we identify estimators that are strongly influenced by the choice of the tuning parameter and detect a clear relationship between the selection criterion within the cross-validation and the evaluated performance measure.

Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk
Tetsuo Kurosaki,Young Shin Kim
arXiv

We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.

Cultural Value Orientation and Stock Trading Behavior: A Study of Individual Investors from India
Chatterjee, Devlina
SSRN
Cross-cultural studies in finance have often used country-wise data for cultural values. Our objective was to understand how individual differences in cultural values affect an investor's stock trading behavior in an emerging market. Based on the theory of planned behavior, we conceptualized a model for intention to participate in the stock market. A structured questionnaire was designed and used to collect primary data for 259 respondents from India. Partial least square structural equation modeling was used to estimate path coefficients.Perceived financial efficacy and risk tolerance are the key factors that positively affected the intention to participate. Among cultural variables, uncertainty avoidance positively affected long term orientation, which in turn positively affected financial efficacy. Thus their effect on participation was indirect but positive. Power distance and social influence had direct positive effects. Males, younger adults, high-income individuals, and those living outside India were more likely to participate in the stock market. We estimated a second model to see how cultural values affect stock market engagement. Unlike the participation model, uncertainty avoidance and long term orientation negatively affected engagement. Collectivism had a positive effect when mediated through perceived financial efficacy and a negative effect when mediated through risk tolerance. Our final research question was to understand how cultural values affect the motives for investing. Individuals with long term orientation invested for high returns, while those high on uncertainty avoidance looked for portfolio diversification. Individuals who scored high on power distance or masculinity invested for thrill-seeking and social motives. Our results provide interesting and new insights into how intra-cultural variations affect participation and engagement in the stock market of an emerging economy.

Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England
Roberto Baviera,Giuseppe Messuti
arXiv

Probabilistic forecasting of power consumption in a middle-term horizon (months to a year) is a main challenge in the energy sector. It plays a key role in planning future generation plants and transmission grid. We propose a new model that incorporates trend and seasonality features as in traditional time-series analysis and weather conditions as explicative variables in a parsimonious machine learning approach, known as Gaussian Process. Applying to a daily power consumption dataset in North East England provided by one of the largest energy suppliers, we obtain promising results in Out-of-Sample density forecasts up to one year, even using a small dataset, with only a two-year In-Sample data. In order to verify the quality of the achieved power consumption probabilistic forecast we consider measures that are common in the energy sector as pinball loss and Winkler score and backtesting conditional and unconditional tests, standard in the banking sector after the introduction of Basel II Accords.

Deep Learning modeling of Limit Order Book: a comparative perspective
Antonio Briola,Jeremy Turiel,Tomaso Aste
arXiv

The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space and dataset, and then clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. We observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.

Deep Reinforcement Learning for Asset Allocation in US Equities
Noguer i Alonso, Miquel,Srivastava, Sonam
SSRN
Reinforcement learning is a machine learning approach concerned with solving dynamic optimization problems in an almost model-free way by maximizing a reward function in state and action spaces. This property makes it an exciting area of research for financial problems. Asset allocation, where the goal is to obtain the weights of the assets that maximize the rewards in a given state of the market considering risk and transaction costs, is a problem easily framed using a reinforcement learning framework. So it is first a prediction problem for the vector of expected returns and covariance matrix and then an optimization problem for returns, risk, and market impact, usually a quadratic programming one. Investors and financial researchers have been working with approaches like mean-variance optimization, minimum variance, risk parity, and equally weighted and several methods to make expected returns and covariance matrices' predictions more robust and after use mean-variance like the Black Litterman model. This paper demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem, learning to solve the problem using time series and deep neural networks. We demonstrate this on daily data for the top 24 stocks in the US equities universe with daily rebalancing. We use a deep reinforcement model on US stocks using different deep learning architectures. We use Long Short Term Memory networks, Convolutional Neural Networks, and Recurrent Neural Networks and compare them with more traditional portfolio management approaches like mean-variance, minimum variance, risk parity, and equally weighted. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks. In Finance, no training to test error generalization results come guaranteed. We can say that the modeling framework can deal with time series prediction and asset allocation, including transaction costs.

Differentiation of subjects of the Russian Federation according to the main parameters of socio-economic development
Natalia A. Sadovnikova,Leysan A. Davletshina,Olga A. Zolotareva,Olga O. Lebedinskaya
arXiv

This article presents the results of a cluster analysis of the regions of the Russian Federation in terms of the main parameters of socio-economic development according to the data presented in the official data sources of the Federal State Statistics Service (Rosstat). Studied and analyzed the domestic and foreign (Eurostat) methodology for assessing the socio-economic development of territories. The aim of the study is to determine the main parameters of territorial differentiation and to identify key indicators that affect the socio-economic development of Russian regions. The authors have carried out a classification of the constituent entities of the Russian Federation not in terms of territorial location and geographical features, but in terms of the specifics and key parameters of the socio-economic situation.

Do Firms Experiencing More Wildfires Disclose More Wildfire Information in Their 10-Ks?
Griffin, Paul A.,Jiang, Yijing,Sun, Estelle
SSRN
Wildfires pose a significant risk that threatens firm value. That risk may have risen significantly in recent years in light of record-breaking wildfires in the United States and elsewhere. It is an open question, however, whether such increasing wildfire risk affects firmsâ€™ disclosures of wildfire-related information. We match the location of wildfires in the United States to firms with headquarters in the same county as the wildfire. We find that firms exposed to more wildfire days on average provide more textual 10-K disclosure of wildfire-related information in their filings compared to firms exposed to fewer wildfire days. Our results are also significant economically. For example, when firms experience three versus zero wildfire days this increases 10-K wildfire word count by 50 percent. We attribute this positive relation to increasing trends in climate change risks and disclosure requirements related to heightened wildfire concerns. These concerns emanate mainly from wildfire-exposed firms in the western states, in the utility and banking industries, those exhibiting a high level of tangible assets, and those having experienced wildfire impacting their past operations. Overall, the findings deepen our knowledge of a heretofore unstudied risk factor important for firm value.

ESG Preferences, Risk and Return
SSRN
There are two primary factors that can affect expected returns for ESG companies with high ratings â€" investor preferences and risk. While it is true that investor preferences for highly rated ESG companies can lower the cost of capital and, thereby, increase the value of those companies, the flip side of the coin is lower expected returns for investors. With respect to risk, the jury remains out on whether there is an ESG related risk factor. However, early research indicates that if there is an ESG related risk factor, it also points toward lower expected returns for investments in highly rated ESG companies because those companies provide a hedge against ESG related risk. The conclusion is that while ESG investing may have social benefits, higher expected returns for investors is not among them.

Equilibrium price in intraday electricity markets
René Aid,Andrea Cosso,Huyên Pham
arXiv

We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity with decreasing volatility error. Forecasts are prone to idiosyncratic noise as well as common noise (weather). Agents production capacities are subject to independent random outages, which are each modelled by a Markov chain. The equilibrium price is defined as the price that minimises trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following. (i) When there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price follows Almgren and Chriss's model and we identify the fundamental part as well as the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for the Samuelson's effect to hold. (ii) When there is production uncertainty, the price volatility becomes stochastic but converges to the case without production uncertainty when the number of agents increases to infinity. Further, on a two-agent case, we show that the potential outages of a low marginal cost producer reduces her sales position.

Evolutionary dynamics in financial markets with heterogeneities in strategies and risk tolerance
Wen-Juan Xu,Chen-Yang Zhong,Fei Ren,Tian Qiu,Rong-Da Chen,Yun-Xin He,Li-Xin Zhong
arXiv

In nature and human societies, the effects of homogeneous and heterogeneous characteristics on the evolution of collective behaviors are quite different from each other. It is of great importance to understand the underlying mechanisms of the occurrence of such differences. By incorporating pair pattern strategies and reference point strategies into an agent-based model, we have investigated the coupled effects of heterogeneous investment strategies and heterogeneous risk tolerance on price fluctuations. In the market flooded with the investors with homogeneous investment strategies or homogeneous risk tolerance, large price fluctuations are easy to occur. In the market flooded with the investors with heterogeneous investment strategies or heterogeneous risk tolerance, the price fluctuations are suppressed. For a heterogeneous population, the coexistence of investors with pair pattern strategies and reference point strategies causes the price to have a slow fluctuation around a typical equilibrium point and both a large price fluctuation and a no-trading state are avoided, in which the pair pattern strategies push the system far away from the equilibrium while the reference point strategies pull the system back to the equilibrium. A theoretical analysis indicates that the evolutionary dynamics in the present model is governed by the competition between different strategies. The strategy that causes large price fluctuations loses more while the strategy that pulls the system back to the equilibrium gains more. Overfrequent trading does harm to one's pursuit for more wealth.

Firm Leverage and Wealth Inequality
Bakota, Ivo
SSRN
English Abstract:This paper studies the effects of a change in firm leverage on wealth inequality and macroeconomic aggregates. The question is studied in a general equilibrium model with a continuum of heterogeneous agents, life-cycle, incomplete markets, and idiosyncratic and aggregate risk. The analysis focuses on the particular change in firm leverage that occurred in the U.S. during the 1980s, when firm leverage increased significantly, and subsequently has been dropping since the early 1990s. In the benchmark model, an increase in firm leverage of the size that occurred during the 1980s increases capital accumulation by 5.38 %, decreases wealth inequality by 1.07 Gini points and decreases government revenues by 0.11 % of output. Consequently, the model implies that the increase in firm leverage did not contribute to rising inequality in the U.S. in the 1980s, but rather the opposite; that the reduction in leverage from the early 1990s to 2008 has contributed to rising wealth inequality. Furthermore, I show that if the model abstracts from beneficial tax treatment of corporate debt, the change in leverage has only minor effects on macro aggregates and inequality, despite having significant implications for asset prices. This is consistent with the previous result in the literature showing that the Modigliani-Miller theorem approximately holds in the heterogeneous agents model with imperfect markets.German Abstract:Dieses Papier untersucht die Auswirkungen einer VerÃ¤nderung der Kapitalstruktur von Unternehmen auf VermÃ¶gensungleichheit und makroÃ¶konomische Aggregate. Die Frage wird in einem allgemeinen Gleichgewichtsmodell mit einem Kontinuum von heterogenen Agenten, Lebenszyklus, unvollstÃ¤ndigen MÃ¤rkten und idiosynkratischem und aggregiertem Risiko untersucht. Die Analyse konzentriert sich auf die besondere VerÃ¤nderung der Kapitalstruktur, die in den USA wÃ¤hrend der 1980er Jahre stattfand, als das VerhÃ¤ltnis von Fremd- zu Eigenkapital deutlich zunahm und seit Anfang der 1990er Jahre wieder abnahm. Im Referenzmodell erhÃ¶ht eine Zunahme des Verschuldungsgrades in der GrÃ¶ÃŸenordnung der 1980er Jahre die Kapitalakkumulation um 5,38 %, verringert die VermÃ¶gensungleichheit um 1,07 Gini-Punkte und verringert die Staatseinnahmen um 0,11 % der Produktion. Folglich impliziert das Modell, dass der erhÃ¶hte Fremdkapitalanteil von Unternehmen in den 1980er Jahren nicht zu einer zunehmenden Ungleichheit in den USA beigetragen hat, sondern eher das Gegenteil: dass die Verringerung des des Verschuldungsgrades von Anfang der 1990er Jahre bis 2008 zu einer zunehmenden VermÃ¶gensungleichheit beigetragen hat. DarÃ¼ber hinaus zeige ich, dass, wenn das Modell von der vorteilhaften steuerlichen Behandlung von Unternehmensschulden abstrahiert, die VerÃ¤nderung der Kapitalstruktur nur geringe Auswirkungen auf Makroaggregate und Ungleichheit hat, obwohl sie erhebliche Auswirkungen auf die VermÃ¶genspreise hat. Das stimmt mit dem frÃ¼heren Ergebnis in der Literatur Ã¼berein, das zeigt, dass das Modigliani-Miller-Theorem im Modell mit heterogenen Agenten in unvollkommenen MÃ¤rkten annÃ¤hernd zutrifft.

How Does Debt Composition Influence Credit Risk?
Kaya, Mehmet Caglar
SSRN
How do different types of debt influence firm credit risk? This paper sheds new light on this issue by decomposing the leverage ratio into market debt, bank debt, and trade credit leverage ratios by balance sheet account type classification; and short-term debt and long-term debt leverage ratios by debt maturity classification. The pecking order theory (Myers & Majluf, 1984) suggests that these debt types differ in terms of the information asymmetry. Therefore, their effects on credit risk might be distinct. We find that debt to financial markets (commercial papers, bonds, etc.) is more positively correlated with the next period's CDS spread than other debt types. CDS spread also reacts positively to bank debt leverage and responds positively to trade credit leverage only for firms in competitive sectors. With regard to maturity classification, the CDS market attributes more credit risk to long-term debt than short-term debt. We also document that the CDS market tracks firmsâ€™ accounts receivable. Our findings are also robust when we control for the credit quality of firms via credit ratings.

Impact of Government Bailout on Banks' Cost of Equity: Additional Evidence from the Financial Bailout of 2008-2009
Wang, Daphne,Ngo, Thanh,Houmes, Robert,Esqueda, Omar A.
SSRN
This study evaluates the effect of the Capital Purchase Program during the 2008-2009 financial crisis on the cost of equity of 170 publicly listed banks in the United States that received funding. We document robust evidence that the liquidity provided by the government bailout reduced the cost of equity for recipient banks, especially for those banks that repaid their bailout funds in full. The decrease in the cost of equity is particularly significant for banks with high market-to-book ratios, low concentrations of institutional ownership, and those with at least one large blockholder. Our findings have important implications for the assessment of government bailout programs and future regulation of financial institutions.

Indonesiaâ€™s Financial Markets and Monetary Policy Dynamics Amid the COVID-19 Pandemic
Sugandi, Eric Alexander
SSRN
This paper is based on a study that examines the impacts of the COVID-19 pandemic on Indonesiaâ€™s financial markets and monetary policy dynamics. The study explores five types of Indonesiaâ€™s financial markets: (1) Indonesian Rupiah (IDR) interbank money market; (2) US Dollar (USD) interbank money market; (3) government conventional bond (SUN) markets; (4) stock market; and (5) USD/IDR spot market. It examines Bank Indonesia's (BI)â€™s three types of monetary policy instruments: (1) BI 7-day Reverse Repo Rate; (2) minimum reserve requirement ratios; and (3) BIâ€™s monetary operations. The study finds that the COVID-19 pandemic causes different impacts of particular monetary policy instruments on Indonesiaâ€™s financial markets during the pandemic compared to those in the non-pandemic period.

Influencing Competition Through Shelf Design
Francisco Cisternas,Wee Chaimanowong,Alan Montgomery
arXiv

Extensive research has established a strong influence of product display on demand. In this domain, managers have many tools at their disposal to influence the demand through the product display, like assortment,shelf-space or allocating profitable products to highly attractive shelf locations. In this research, we focus on the influence of the product arrangement on competition among products within the display. Intuitively,products located next to each other are compared more often than when they are placed far apart. We introduce a model that allows product competition effects mediated by their relative position in the display. This model naturally produces an increased competition on products located closer together, inducing demand correlations based on products proximity with their competitors in the shelf, and not only their relative characteristics. We fit this model to experimental data from physical retail stores and in online product displays.The proposed model shows this effect to be significant; moreover, this model outperforms traditional models in fit and prediction power, and shows that ignoring the shelf-implied competition generates a bias in the price sensitivity, affecting price and promotion strategies. The proposed model was used to evaluate different shelf displays, and to evaluate and select displays with higher profitability, by exploiting the influence on competition among products to shift demand to higher profitability products. Finally, from the model,we generate recommendations for retail managers to construct better shelf designs;testing these suggestions on our fitted model on retail store data, we achieve a 3% increase in profits over current shelf designs.

Information Asymmetry, Price Informativeness and Cost of Capital
Zheng, Weinan
SSRN
This paper examines the relationship between two important financial variables (price informativeness, and cost of capital) and information asymmetry, controlling for the total amount of information in the market. In the model, each investor has a private signal. We measure information asymmetry by the dispersion of the precision of the private signals. By doing so, we can isolate the effect of the total amount of information and focus on the influence of information asymmetry. We show that without the non-learnable component in the asset payoff (residual uncertainty) or transaction cost, information asymmetry will not affect the cost of capital and price informativeness, which is consistent with Lambert and Verrecchia (2015). In contrast, with residual uncertainty or transaction cost, an increase in information asymmetry will decrease price informativeness and increase the cost of capital, even in a fully competitive market. Our results highlight the importance for regulators of alleviating information asymmetry to improve market efficiency.

Information Coefficient as a Performance Measure of Stock Selection Models
Feng Zhang,Ruite Guo,Honggao Cao
arXiv

Information coefficient (IC) is a widely used metric for measuring investment managers' skills in selecting stocks. However, its adequacy and effectiveness for evaluating stock selection models has not been clearly understood, as IC from a realistic stock selection model can hardly be materially different from zero and is often accompanies with high volatility. In this paper, we investigate the behavior of IC as a performance measure of stick selection models. Through simulation and simple statistical modeling, we examine the IC behavior both statically and dynamically. The examination helps us propose two practical procedures that one may use for IC-based ongoing performance monitoring of stock selection models.

Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance
Dan Wang,Tianrui Wang,Ionuţ Florescu
arXiv

In 2012, SEC mandated all corporate filings for any company doing business in US be entered into the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. In this work we are investigating ways to analyze the data available through EDGAR database. This may serve portfolio managers (pension funds, mutual funds, insurance, hedge funds) to get automated insights into companies they invest in, to better manage their portfolios. The analysis is based on Artificial Neural Networks applied to the data.} In particular, one of the most popular machine learning methods, the Convolutional Neural Network (CNN) architecture, originally developed to interpret and classify images, is now being used to interpret financial data. This work investigates the best way to input data collected from the SEC filings into a CNN architecture. We incorporate accounting principles and mathematical methods into the design of three image encoding methods. Specifically, two methods are derived from accounting principles (Sequential Arrangement, Category Chunk Arrangement) and one is using a purely mathematical technique (Hilbert Vector Arrangement). In this work we analyze fundamental financial data as well as financial ratio data and study companies from the financial, healthcare and IT sectors in the United States. We find that using imaging techniques to input data for CNN works better for financial ratio data but is not significantly better than simply using the 1D input directly for fundamental data. We do not find the Hilbert Vector Arrangement technique to be significantly better than other imaging techniques.

Learning in a Small/Big World
Benson Tsz Kin Leung
arXiv

Savage (1972) lays down the foundation of Bayesian decision theory, but asserts that it is not applicable in big worlds where the environment is complex. Using the theory of finite automaton to model belief formation, this paper studies the characteristics of optimal learning behavior in small and big worlds, where the complexity of the environment is low and high, respectively, relative to the cognitive ability of the decision maker. Confirming Savage's claim, optimal learning behavior is closed to Bayesian in small worlds but significantly different in big worlds. In addition, I show that in big worlds, the optimal learning behavior could exhibit a wide range of well-documented non-Bayesian learning behavior, including the use of heuristic, correlation neglect, persistent over-confidence, inattentive learning, and other behaviors of model simplification or misspecification. These results establish a clear and testable relationship between the prominence of non-Bayesian learning behavior, complexity and cognitive ability.

Lending When Relationships Are Scarce: The Role of Information Spread via Bank Networks
Alperovych, Yan,Divakaruni, Anantha,Manigart, Sophie
SSRN
We investigate how bank networks facilitate syndicate formation and lending in the leveraged buyout (LBO) market, where ties between banks and borrowers are scarce and borrower opacity is high. Using novel centrality measures that capture how information might flow through the network, we show that networks disseminate borrower-relevant information that influences which banks join the syndicate, the share the lead bank holds, and LBO lending terms. Further, banksâ€™ knowledge of their network is highly localized, unrelated to their reputation, and a useful information source when ties to the borrower are lacking. Our findings emphasize the importance of information diffusion via networks for resolving asymmetric information problems during loan syndication.

Liquidations: DeFi on a Knife-edge
Daniel Perez,Sam M. Werner,Jiahua Xu,Benjamin Livshits
arXiv

The trustless nature of permissionless blockchains renders overcollateralization a key safety component relied upon by decentralized finance (DeFi) protocols. Nonetheless, factors such as price volatility may undermine this mechanism. In order to protect protocols from suffering losses, undercollateralized positions can be \textit{liquidated}. In this paper, we present the first in-depth empirical analysis of liquidations on protocols for loanable funds (PLFs). We examine Compound, one of the most widely used PLFs, for a period starting from its conception to September 2020. We analyze participants' behavior and risk-appetite in particular, to elucidate recent developments in the dynamics of the protocol. Furthermore, we assess how this has changed with a modification in Compound's incentive structure and show that variations of only 3% in an asset's price can result in over 10m USD becoming liquidable. To further understand the implications of this, we investigate the efficiency of liquidators. We find that liquidators' efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated. Lastly, we provide a discussion on how a false sense of security fostered by a misconception of the stability of non-custodial stablecoins, increases the overall liquidation risk faced by Compound participants.

Macroeconomics, Firm Dynamics and IPOs
GonzÃ¡lez, Beatriz
SSRN

Mental Accounting, Loss Aversion, and Tax Evasion: Theory and Evidence
SSRN
The evidence shows source-dependent entitlement to income sources and individuals are reluctant to part with income they feel more entitled to, e.g., earned labor income. Taxpayers may also be more reluctant to part with tax payments (evade more) from income sources they feel more entitled to- a form of mental accounting. We embed two main hypotheses within a rigorous theoretical model based on prospect theory. From incomes sources they feel more entitled to, taxpayers experience (i) greater loss aversion from paying taxes, and (ii) lower moral costs of evasion. We confirm the predictions of our model through MTurk experiments. Evasion is increasing in the tax rate and decreasing in the audit penalty. Moral costs influence taxpayers decisions. Loss aversion, measured â€œdirectlyâ€ for the first time for each individual in an evasion experiment, reduces evasion, as predicted by our theory. Loss aversion, risk aversion, and their interaction, are critical determinants of evasion.

MiFID II Unbundling: Was it Successful?
Lourie, Ben,Shanthikumar, Devin M.,Yoo, Il Sun
SSRN
The revised Markets in Financial Instruments Directive, known as MiFID II, requires the unbundling of research payments from trading execution. Using a difference-in-differences research design, we examine whether this regulation achieved its intended objective. We find that MiFID II weakened the link between the brokerage trading share and analyst research. Forecast frequency, optimism, and accuracy are less likely to be associated with the brokerage trading share after MiFID II. Analysts appear to respond to these changes. Forecast frequency and forecast optimism both decrease after MiFID II among brokers who earned the highest brokerage trading share due to these behaviors before MiFID II. Overall, our evidence suggests that MiFID II is at least partially successful in unbundling research from execution.

Objective Measures of Market Efficiency; Applications to Securities Class Actions and Valuations
Bhattacharya, Rajeev R.
SSRN
For every U.S.-listed security for every year between 2001-2017, I run four different event studies to calculate four separate objective measures of the efficiency of the market for that security for that year, which provide an objective characterization of the market for that security in that year to be sufficiently efficient or otherwise. I apply these methodologies to Petrobras's ADR (PBR on NYSE) in 2001-2017, and conclude that the Petrobras Court got it partially right and partially wrong when it certified a 2010-2015 class period, because the market for PBR was sufficiently efficient in 2010, 2011, and 2014 and not sufficiently efficient in 2012, 2013, and 2015. I apply these methodologies to the valuation of each U.S.-listed firm in 2001-2017; here are three examples: a) the market for GS (Goldman Sachs Group, Inc., common equity) was sufficiently efficient in each year in 2001-2017, and therefore, market prices represented value for Goldman Sachs over 2001-2017, b) the market for MSFT (Microsoft Corporation, common equity) was sufficiently efficient in 2001 and 2003-2017, and therefore, market prices represented value for Microsoft Corporation in 2001-2017 except 2002, and c) the market for AAME (Atlantic American Corporation, common equity) was not sufficiently efficient in any year in 2001-2017, and therefore, market prices did not represent value for Atlantic American Corporation in 2001-2017.

Offshore Currency Markets: Non-Deliverable Forwards (NDFs) in Asia
Schmittmann, Jochen M,Chua, Han Teng
SSRN
Non-deliverable forward (NDF) markets in many Asian emerging market currencies are large, rapidly growing, and often exceed onshore markets in transaction volume. NDFs tend to price significant depreciation during market stress episodes including COVID-19. Spillovers from NDFs to onshore markets are a policymaker concern. Our analysis shows that influences tend to run both ways after controlling for differences in timezones between markets. For the COVID-19 pandemic there is some evidence of NDFs leading onshore markets for a few currencies. Policy approaches to NDFs vary widely across Asia from close integration with onshore markets to severe restrictions on NDF trading.

Open-Ended Real Estate Funds: From Flows to Property
Baranyai, Eszter
SSRN
The purpose of this paper is to uncover the relationship between flows and real estate investmentat open-ended real estate funds (OEREFs). The study employs fixed-effects panel regressions, relying on data from the Hungarian fund managersâ€™ trade association. First, the effect of lagged flows on allocation to real estate is assessed. Second, the paper studies how this relationship changes as the cyclical position of CRE market advances using two proxies. Flows are found to affect fundsâ€™ real estate holdings if they occurred 12â€"18 months earlier. Inflows (outflows) in the preceding six months demonstrably lower (increase) fundsâ€™ real estate holdings ratio. Beyondthis relationship, findings do not suggest that less funds are channelled to real estate as â€œCRE heatâ€ intensifies. In an environment marked by strong cash inflows, the investment lag cantranslate into a significant drop in fundsâ€™ exposure to real estate. The share of real estate at Hungarian funds in the sample, for example, fell from 79 to 50 per cent on average over the period of 2011â€"2017. Measures designed to limit inflows are in the interest of those existing investors who wish to avoid a dilution of the core investment strategy. The paper adds to the literature on OEREFs which has been particularly scarce on liquidity transformation during non-crisis times and on non-German funds.

Optimal Retirement Problem Under Partial Information
Chen, Kexin,Jeon, Junkee,Wong, Hoi Ying
SSRN
The optimal retirement decision is essentially an optimal stopping problem when retirement is irreversible. We investigate the optimal consumption, investment and retirement problem when the growth rate is unobservable and is estimated by filtering from historical stock prices. To ensure both consumption rate and wealth of the representative agent be nonnegative, the analysis is based on a duality approach. We offer conditions for the filtering scheme for the unobserved growth rate so that the dual problem can be characterized by another optimal stopping problem with stochastic volatility. We further link the dual problem to an American option with stochastic volatility, and prove for the close of duality gap. The theory is then applied to a hidden Markov model for regime-switching growth rate with the Woham filter. We fully characterize the existence and uniqueness of the variational inequality in the dual, and the free boundary. Asymptotic closed-form solution is derived for the optimal retirement by a small-scale perturbation. We discuss the potential use of the result for other types of partial information settings.

Privacy-Preserving Network Analytics
Hastings, Marcella,Hemenway Falk, Brett,Tsoukalas, Gerry
SSRN
Using financial networks as a backdrop, we develop a new framework for privacy-preserving network analytics. Adopting the debt and equity models of Eisenberg and Noe (2001) and Elliott et al. (2014) as proof of concept, we show how aggregate-level statistics required for stress testing and stability assessment can be derived on real network data, without any individual node revealing its private information to any third party, be it other nodes in the network, or even a central agent. Our work helps bridge the gap between the theoretical models of financial networks that assume agents have full information, and the real world, where information sharing is hindered by privacy and security concerns.

Private Firm and Shareholder Response to Dividend Taxation: Evidence from the Taxation of Corporate Minority Shareholders
Hillmann, Lisa
SSRN
This paper examines the response of private firms and their shareholders to a dividend tax increase, which affects only a small group of shareholders. Using an exogenous shock in Germany, my results suggest that firms do not adjust their payout policy but corporate minority shareholders, the only ones affected by the increase in shareholder taxes, reduce their minority stakes in private firms after the dividend tax reform. Additional cross-sectional tests indicate a higher shareholder response, if corporate minority shareholders are financially distressed, own a minority stake in a firm with a high dividend payout and a majority shareholder, and do not belong to the same group as the firm in which they own a minority stake. My findings add to the very limited literature on the effects of dividend taxes on payout decisions of private firms and reactions of their shareholders.

Recent Developments Concerning Corporate Takeover Rules in Japan (Current Topics of Japanese Law)
Watanabe, Hiroyuki
SSRN
For a Japanese model of takeover rules, the following regulatory approaches may be considered:1) imposition of restrictions to unlimited share trading by means of defensive measures and/or by providing relevant provisions in the Companies Act, but with minimum restriction possible to the tender offer rules (basic features of the American model)2) development of an environment, which allows the shareholders of a target company to make an autonomous judgment in determining whether or not to apply for a tender offer (i.e. elimination of coerciveness) and on the basis of satisfactory information disclosure (basic features of the European model)In reviewing the Japanese model, it is essential to acknowledge the background to and the significance of the regulatory context of takeover rules by returning to its basic structure and choose an option which best suits the realities of Japan.

Scenario-decomposition Solution Framework for Nonseparable Stochastic Control Problems
Xin Huang,Duan Li,Daniel Zhuoyu Long
arXiv

When stochastic control problems do not possess separability and/or monotonicity, the dynamic programming pioneered by Bellman in 1950s fails to work as a time-decomposition solution method. Such cases have posted a great challenge to the control society in both theoretical foundation and solution methodologies for many years. With the help of the progressive hedging algorithm proposed by Rockafellar and Wets in 1991, we develop a novel scenario-decomposition solution framework for stochastic control problems which could be nonseparable and/or non-monotonic, thus extending the reach of stochastic optimal control. We discuss then some of its promising applications, including online quadratic programming problems and dynamic portfolio selection problems with smoothing properties.

Sentimental Habits
Tham, Eric
SSRN
Habits and sentiment are key psychological behaviors in asset pricing. This paper studies the interactive impacts of sentiment and habits on asset pricing using the Campbell and Cochrane 1999 habit model as a framework model. A positive sentiment shock emanating from firms is modeled in the drift of the consumption and the habits sensitivity. It has a lagged effect on inter-temporal consumption and increases the risk-free rate by an increased habit sensitivity and the precautionary savings motive. The increased habit sensitivity also increases the risk-taking activity of the agents. The model further offers a behavioral explanation of the value premium puzzle in the context of habit models due to relatively lower price-consumption ratios in a negative sentiment environment.

Single Stock Futures and Their Impact on Market Quality: Be Careful What You Wish For
Curran, Edward,Mollica, Vito
SSRN
The impact of derivatives is almost invariably measured by the liquidity outcomes on the underlying. We explore the relationship between efficiency, fairness and derivatives with respect to the underlying. We provide evidence that the presence of a derivative improves liquidity in the underlying but decreases the degree of fairness - proxied by manipulation likelihood. Our study highlights that a leveraged derivative entices manipulation in the underlying and that typical inhibitors to manipulation, namely high visible execution costs, are in fact desirable.

Sparse Signals in Market Anomalies
Guo, Li
SSRN
With a machine learning method, this study optimally combines the market anomalies to forecast cross sectional stock returns. A long-short portfolio achieves 5.20% (equal weight) Fama-french five-factor adjusted monthly return. The trading strategy outperforms all the individual anomalies, as well as beating the equal weight of anomaly combination strategy. The results are robust to the inclusion of transaction cost, representative anomaly predictors, small and large stocks, across recession and expansion periods, pre- and post- anomaly publication periods. It is noteworthy that the strategy is mainly driven by the long leg instead of short leg and performs consistently well regardless of high and low sentiment periods. Overall, the findings of this study suggest that machine learning method can efficiently combine anomaly information, and its return predictability is primarily driven by investors' un-recognition of undervalued stocks, thereby showing important economic importance to the investors.

Stakeholder Orientation and Operations Outcomes: Evidence from Constituency Statutes
Aral, Karca,Giambona, Erasmo,Van Wassenhove, Luk N.
SSRN
We study the effect of stakeholder orientation on operations outcomes. Our work contributes to the sustainable OM literature by identifying the functional separation between shareholders and managers as a fundamental determinant of responsible operations. For identification, we exploit a quasi-natural experiment provided by the staggered adoption of constituency statutes across U.S. states, allowing executives of affected companies to explicitly account for the interest of non-equity stakeholders into their corporate decisions. We find that stakeholder orientation, as measured by the KLD social responsibility index, increased by 21% for firms incorporated in constituency states (treated firms) relative to firms not incorporated in constituency states (control firms) in the post adoption period. We also find that number of suppliers, payables, and inventory increased by 21.3%, 6.3%, and 4.3%, respectively, for treated firms relative to control firms post adoption, indicating that operations outcomes shifted with the emphasis on stakeholder interests induced by the reform. Further, we find an improvement in production efficiency, in terms of lower cost of goods sold and operating expenses, which ultimately benefited shareholders by increasing firm value by nearly 6%. In line with our model, these findings suggest that allowing executives to incorporate the interests of non-equity stakeholders can be beneficial not only to these stakeholders but also to shareholders. Interestingly, while constituency statutes were intended to protect executives adopting stakeholder-oriented policies against the actions of shareholders, therefore limiting shareholdersâ€™ ability to sanction executives, our findings suggest that constituency statutes ultimately benefited not only executives and non-equity stakeholders, but also shareholders. Altogether, our results indicate that executives and policymakers need to assess the implications of regulatory changes beyond their target constituencies.

Synchronization of Prefectural Business Cycles in Japan 1978-2018
Makoto Muto,Tamotsu Onozaki,Yoshitaka Saiki
arXiv

Two decades of studies have found significant regional differences in the timing of transitions in national business cycles and their durations. Earlier studies detect regional synchronization during business cycle expansions and contractions in Europe (Grayer, 2007), the U.S. (Hamilton and Owyang, 2012; Chung, 2016), and Japan (Wall, 2007). We investigate those findings more comprehensively for Japan. We draw upon business cycle data spanning 1978-2018 for all 47 Japanese prefectures and measure synchronization between them using a method prominent in nonlinear sciences but infrequently applied in business cycle studies. Our findings confirm that synchronization in Japan's prefectural business cycles increased during contractions and decreased during expansions throughout the period studied.

The Cross-Section of Non-Professional Analyst Skill
Farrell, Michael,Jame, Russell,Qiu, Tian
SSRN
We examine the cross-section of skill among non-professional analysts (NPAs) on Seeking Alpha, a prominent crowd-sourced investment research platform. We estimate that 60% of NPAs are skilled, and we document substantial dispersion in skill. Even after accounting for bid-ask spreads and allowing for a three-day investment delay, following NPAs in the top quintile of past skill earns annualized abnormal returns of 10%. In contrast, an unconditional strategy that follows all NPAs earns insignificant returns. An examination of retail and institutional order imbalances following NPA recommendations suggests that neither group recognizes the size-able differences in ability across NPAs.

The Dynamics of Sovereign Yields Over Swap Rates in the Eurozone Market
David-Pur, Lior,Galil, Koresh,Rosenboim, Mosi
SSRN
We explore the dynamics of the adjusted swap spread (calculated as the difference between the swap rate and sovereign yields over the credit default swap premium) in the Eurozone market by studying three markets simultaneously: 1) sovereign bonds, 2) credit default swaps (CDS), and 3) swap rates. We find a strong relationship between the markets. Specifically, based on the no-arbitrage argument, we show that the difference between the Euribor and Repo rates is a key driver of the adjusted swap spread. However, illiquidity premiums and systemic risk also play an essential role in times of economic stress and for less creditworthy countries. The findings also shed light on the recent negative swap spreads puzzle in the United States.

The English Patient: Evaluating Local Lockdowns Using Real-Time COVID-19 & Consumption Data
John Gathergood,Benedict Guttman-Kenney
arXiv

We find UK 'local lockdowns' of cities and small regions, focused on limiting how many people a household can interact with and in what settings, are effective in turning the tide on rising positive COVID-19 cases. Yet, by focusing on household mixing within the home, these local lockdowns have not inflicted the large declines in consumption accompanying the March 2020 national lockdown, which limited all social contact. Our study harnesses a new source of real-time, transaction-level consumption data that we show to be highly correlated with official statistics. The effectiveness of local lockdowns are evaluated applying a difference-in-difference approach which exploits nearby localities not subject to local lockdowns as comparison groups. Our findings indicate that policymakers may be able to contain virus outbreaks $\textit{without}$ killing local economies. However, the ultimate effectiveness of local lockdowns is expected to be highly dependent on co-ordination between regions and an effective system of testing.

The Impact of Foreign Direct Investment (FDI) on Stock Market Development in GCC Countries
SSRN
Despite the huge number of studies in relation to the FDI, studies on the nexus between FDI and stock market development in GCC are still limited. This paper investigates the impact of FDI on stock market development in Gulf Cooperation Council countries that have become an important economic trading bloc after inclusion of Saudi Arabia in the G-20, leading to a big increase in stock prices and FDI in recent years. This research utilised data from 2002 to 2015 for all the six GCC countries i.e. Bahrain, Kuwait, Qatar, Saudi Arabia, the United Arab Emirates and Oman. Using four control variables, economic growth, economic size, openness and domestic credit to private sector and utilising the panel unit-root test, panel co-integration analysis and panel error-correction model, the research concludes that foreign direct investment has played a long-term significant role in stock market development in GCC countries. Moreover, the research results on short-term impact concludes that FDI affects stock market development positively but not significantly. From a policy perspective, the research evidence convincingly supports the increasingly growing initiative of GCC governments to attract flow of FDI towards non-oil based sectors to diversify their economies and develop stock markets.

The Oligarch Vanishes: Defensive Ownership, Property Rights, and Political Connections
Earle, John S.,Gehlbach, Scott,Shirikov, Anton,Shpak, Solomiya
SSRN
We examine the use of proxies, shell companies, and offshore firms to defend property against seizure by private and state actors. Our theoretical framework emphasizes the role of political connections in defensive ownership. Linking information from investigative journalists on the key holdings of numerous Ukrainian oligarchs with firm-level administrative data on formal ownership ties, we observe some form of defensive ownership among more than two-thirds of oligarch-controlled firms, but such conduct is much less common for those connected to the incumbent regime. Further exploiting the abrupt shock to political connections that accompanied the Orange Revolution, we find a sharp rise in defensive ownership among previously connected oligarchs.

The Role of Uncertainty in Controlling Climate Change
Yongyang Cai
arXiv

Integrated Assessment Models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their geophysical sector determines the mean surface temperature increase over the preindustrial level, which in turn determines the damage function. Most of the existing IAMs are perfect-foresight forward-looking models, assuming that we know all of the future information. However, there are significant uncertainties in the climate and economic system, including parameter uncertainty, model uncertainty, climate tipping risks, economic risks, and ambiguity. For example, climate damages are uncertain: some researchers assume that climate damages are proportional to instantaneous output, while others assume that climate damages have a more persistent impact on economic growth. Climate tipping risks represent (nearly) irreversible climate events that may lead to significant changes in the climate system, such as the Greenland ice sheet collapse, while the conditions, probability of tipping, duration, and associated damage are also uncertain. Technological progress in carbon capture and storage, adaptation, renewable energy, and energy efficiency are uncertain too. In the face of these uncertainties, policymakers have to provide a decision that considers important factors such as risk aversion, inequality aversion, and sustainability of the economy and ecosystem. Solving this problem may require richer and more realistic models than standard IAMs, and advanced computational methods. The recent literature has shown that these uncertainties can be incorporated into IAMs and may change optimal climate policies significantly.

The Student Managed Investment Program: A Guide for Small, Not-for-profit, Private Schools
Wang, Daphne,Houmes, Robert
SSRN
The student managed investment programs accord students the unique opportunity to gain â€œreal worldâ€ investment experience, and their growth in numbers and size provides evidence of their educational efficacy. This study provides a qualitative analysis of the operations and performance of a small, not-for-profit, private universityâ€™s (the University) student managed investment fund. Our analysis begins with a discussion of applied â€œbest practicesâ€ from both start-up and ongoing improvement perspectives. Evaluative emphasis is given to relevant topics that include undergraduate â€" graduate course curriculum, studentâ€"board engagement, funding needs, and sources as well as the importance of implementing and maintaining effective internal controls. In addition, recent events have highlighted the increasing financial challenges faced by smaller not-for-profit, tuition-driven schools. In juxtaposition with these practices, we further examine important issues related to the successful and sustainable funding of the programâ€™s operations.

The World Is at a Dangerous Crossroads on 'China Virus' and US 'Political Virus'
SSRN
As of 17 October 2020, the novel coronavirus without vaccination has resulted in 1,109,654 deaths; the United States is leader in total cases of 8,288,278 and 223,644 deaths with the mortality rate of 2.7%. While the virus is in its 2nd wave of resurgence, this is a crucial time for unity to fight against the invisible coronavirus, and this is the only way to defeat it. Leaders of nations across the world (President Trump in particular) should be extra careful at a time like this and avoid using xenophobic language that may create stigma. Attaching a disease caused by a virus to a nation and calling it â€œChina virusâ€, â€œWuhan virusâ€, â€œChinese virusâ€, or â€œAsian virusâ€ is an ugly case of US â€˜bullyingâ€™ and US â€˜political virusâ€™. Coronavirus (also known as COVID-19, and in virology defined as SARS-CoV-2) recognizes no borders, ethnicities, or skin colors; moreover, the virus spares no people and nations based on levels of wealth and economic status. Political leaders, government officials, and medical experts commit an unlawful act by describing the novel coronavirus as China virus, trying to influence others with words without accuracy and empathy will only result in a dangerous spike in abhorrence, racism, aggression, stigma, and xenophobic attacks. Although unilateral economic sanctions have become the centerpiece of U.S. foreign policy under the Trump administration, this low cost/low risk combatant has been ineffective in exerting the â€œmaximum economic pressureâ€ on rogue nations to deter their malign activities. Abundance of research on the topic shows that unwarranted sanctions without multilateral support do not work well as a security tool. Iran and North Korea are two examples that the increased US abuse of sanctions and its use of the dollar as a weapon of mass economic destruction have produced trivial results, not mentioning that all parties involved have been adversely affected.

The sub-fractional CEV model
Axel A. Araneda,Nils Bertschinger
arXiv

The sub-fractional Brownian motion is a stochastic process, characterized by non-stationarity in their increments and long-range dependency, considered as an intermediate step between the standard Brownian motion (Bm) and the fractional Brownian motion (fBm). The mixed process, a linear combination between a Bm and an independent sfBm, called mixed sub-fractional Brownian motion (msfBm), keeps the features of the sfBm adding the semi-martingale property for H>3/4, is a suitable candidate to use in price fluctuation modeling, in particular for option pricing. In this note, we arrive at the European Call price under the consider the Constant Elasticity of Variance (CEV) model driven by a mixed sub-fractional Brownian motion.

The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool
Ioannis P. Antoniades,Giuseppe Brandi,M. H. Hanias,T. Di Matteo
arXiv

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), $H_q$, for various values of the parameter $q$. Using $H_q$, we introduce a new visual methodology to algorithmically detect critical changes in the scaling of the underlying complex time-series. The methodology involves the degree of multiscaling at a particular time instance, the multiscaling trend which is calculated by the Change-Point Analysis method, and a rigorous evaluation of the statistical significance of the results. Using this algorithm, we have identified particular patterns in the temporal co-evolution of the different $H_q$ time-series. These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A). We apply the visual methodology to time-series comprising of daily close prices of four stock market indices: two major ones (S\&P~500 and NIKKEI) and two peripheral ones (Athens Stock Exchange general Index and Bombay-SENSEX). Results show that multiscaling varies greatly with time: time periods of strong multiscaling behavior and time periods of uniscaling behavior are interchanged while transitions from uniscaling to multiscaling behavior occur before critical market events, such as stock market bubbles. Moreover, particular asymmetric multiscaling patterns appear during critical stock market eras and provide useful information about market conditions. In particular, they can be used as 'fingerprints' of a turbulent market period as well as provide warning signals for an upcoming stock market 'bubble'. The applied visual methodology also appears to distinguish between exogenous and endogenous stock market crises, based on the observed patterns before the actual events.

To Delegate or Not To Delegate? On the Quality of Voluntary Corporate Financial Disclosure and Its Market Impact
Qiao, Yankuo
SSRN
This study investigates the impact of delegation structure of the top management team upon the quality of corporate voluntary disclosure on financial outcomes. The paper develops two competing hypotheses pertaining to the functional relationship between the degree of delegation and the management forecast accuracy. On the one hand, as indicated by the literature on internal governance, the efficacy of the top management team is optimized when neither CEO nor subordinate managers are dominant. On the other hand, literature has extensively documented the importance and centrality of the CEO as well as the relevancy of the subordinate managers to the voluntary disclosure activities. The empirical findings are in support of an inverted hump shape relationship between the degree of delegation and the quality of voluntary information provision, suggesting that an internal optimality of responsibility sharing between CEO and her immediate subordinates does not exist for internal information production and external information dissemination. Partial delegation and mixed executive duties lead to deteriorating quality of voluntary disclosure. In particular, the paper analyzes several aspects of managerial earnings forecasts (MFs), including bias, error, accuracy and optimism. The uncovered functional shapes are generally persistent across multiple quality metrics for MFs. Consistent with the literature on executive horizon and risk propensity, the inverted hump shape is more significant when the top management team is led by an older CEO. The paper employs an identification strategy of structural equations, controlling for selection bias and reverse causality. The empirical results are more significant in the analysis of structural equations. To theoretically underpin the arguments and empirical findings, a model of internal information production is developed in the framework of Baysian Nash Equilibrium. The paper further documents that when the delegation structure is clear, namely either the CEO or subordinates are in charge, the liquidity of companyâ€™s shares improves. The empirical evidence also indicates that the variation of liquidity driven by delegation structure is not actively incorporated in stock prices.

Valuing Companies by Cash Flow Discounting: Only APV Does Not Require Iteration
Fernandez, Pablo
SSRN
The most used methods for valuing companies by Cash Flow Discounting are equity cash flow, free cash flow, capital cash flow and APV (Adjusted Present Value). Only APV does not require iteration All four methods, if properly applied, always give the same value. This result is logical, as all the methods analyze the same reality under the same hypotheses; they differ only in the cash flows or parameters taken as the starting point for the valuation. Many valuations are incorrect because the authors do not iterate and, therefore, the four methods do not provide the same value.

What is ESG, and is it Right for You?
DeGennaro, Ramon P.
SSRN
Some people want to make investing a source of social change. Is that possible? If so, then whatâ€™s the best way to do that? Different ways to approach such investing exist. They have different goals, different costs, and benefits. This article discusses some of the more common reasons for ESG investing, and highlights potential pitfalls.

When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders
Elior Nehemya,Yael Mathov,Asaf Shabtai,Yuval Elovici
arXiv

In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize learning models to predict the market's behavior and execute an investment strategy accordingly. However, learning models have been shown to be susceptible to input manipulations called adversarial examples. Yet, the trading domain remains largely unexplored in the context of adversarial learning. This is mainly because of the rapid changes in the market which impair the attacker's ability to create a real-time attack. In this study, we present a realistic scenario in which an attacker gains control of an algorithmic trading bots by manipulating the input data stream in real-time. The attacker creates an universal perturbation that is agnostic to the target model and time of use, while also remaining imperceptible. We evaluate our attack on a real-world market data stream and target three different trading architectures. We show that our perturbation can fool the model at future unseen data points, in both white-box and black-box settings. We believe these findings should serve as an alert to the finance community about the threats in this area and prompt further research on the risks associated with using automated learning models in the finance domain.