# Research articles for the 2020-06-03

A Mental Account-Based Portfolio Selection Model With an Application
Wong, Wing-Keung
SSRN
To obtain better optimal portfolios, academics develop the behavioral portfolio theory (BPT) with two mental accounts by minimizing the risk as well as maximizing the return. However, there are some limitations to the existing BPT. To circumvent the limitations, this paper proposes a new portfolio selection (PPMPSM) model with two mental accounts in which the lower-level problem of the model is used to avoid from getting big loss while the upper-level problem corresponds to the mental account is used to get good profit. We formulate our proposed PPMPSM model by replacing the probability terms with the expectation of indicator function and then design a sequential convex approximation algorithm to obtain the optimal solution of our proposed model. We prove that the optimal portfolio obtained by our proposed algorithm converges.We demonstrate the superiority and applicability of our proposed PPMPSM models, including both Maslow (0.10) and Maslow (0.11) models, over the traditional portfolio selection models, including the model generated by using the naive equal-weighted (EW) strategy, the model satisfying investors' safety needs (Safety model), and the model satisfying the self-actualization needs (SA model) by using trading data from the American stock market. We find that our proposed PPMPSM model can obtain the highest expected return and generate the highest final cumulative wealth such that the cumulative wealth for both of our proposed PPMPSM models, Maslow(0.10) and Maslow(0.11), uniformly outperform those for the Safety and EW strategies in any week and perform the best among all the strategies in the last 2 weeks, including the SA strategy.

A Model of Market Discipline
Ward, Colin,Ying, Chao
SSRN
We develop an equilibrium model of cash and costly refinancing policies in the presence of an agency conflict to quantify the market's influence on manager's ex ante behavior. Management's private quest for resource control separates interests, originating a disciplinary role for markets. We further derive a general formula that elucidates how agency and financing frictions shape payouts and compensation. Our benchmark calibration requires incremental payouts of 1.46 percent of assets to investors to hold agency-laden firms and compensating managers an additional 0.30 percent to operate cash-poor ones.

Accuracy of Deep Learning in Calibrating HJM Forward Curves
Fred Espen Benth,Nils Detering,Silvia Lavagnini
arXiv

We price European-style options written on forward contracts in a commodity market, which we model with a state-dependent infinite-dimensional Heath-Jarrow-Morton (HJM) approach. We introduce a new class of volatility operators which map the square integrable noise into the Filipovi\'{c} space of forward curves, and we specify a deterministic parametrized version of it. For calibration purposes, we train a neural network to approximate the option price as a function of the model parameters. We then use it to calibrate the HJM parameters starting from (simulated) option market data. Finally we introduce a new loss function that takes into account bid and ask prices and offers a solution to calibration in illiquid markets. A key issue discovered is that the trained neural network might be non-injective, which could potentially lead to poor accuracy in calibrating the forward curve parameters, even when showing a high degree of accuracy in recovering the prices. This reveals that the original meaning of the parameters gets somehow lost in the approximation.

An Adaptive Recursive Volatility Prediction Method
Nicklas Werge,Olivier Wintenberger
arXiv

The Quasi-Maximum Likelihood (QML) procedure is widely used for statistical inference due to its robustness against overdisper-sion. However, while there are extensive references on non-recursive QML estimation, recursive QML estimation has attracted little attention until recently. In this paper, we investigate the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model, extending the classical offline optimization routines to recursive approximation. We propose an adaptive recursive estimation routine for GARCH models using the technique of Variance Targeting Estimation (VTE) to alleviate the convergence difficulties encountered in the usual QML estimation. Finally, empirical results demonstrate a favorable trade-off between the ability to adapt to time-varying estimates and stability of the estimation routine.

An Essay on Minimum Disclosure Requirements for Cryptocurrency & Utility Token Issuers
Krapels, Nicholas J.,Liebau, Daniel
SSRN
Information asymmetries in financial markets are not a new phenomenon. In a recent poll conducted amongst 76 industry experts, a staggering 83% of participants stated they do not believe utility token issuers disclose enough information to their stakeholders. This essay provides actionable recommendations for disclosure that increase financial and non-financial transparency. These recommendations are valuable for utility token issuers, buyers, intermediaries, and regulators. Financial details include token issuer information, initial and current cash positions as well as token treasury information. Non-financial information includes contact information, project progress updates and open-source software elements. We highlight exemplary token issuers and conclude with the expectation that increasing the amount of information-rich project disclosures should assist in the expansion of the blockchain industry in general. Initial evidence suggests that minimum disclosure best practices also positively affect cryptocurrency and utility token prices.

Are Bank Merger Characteristics Important for Local Community Investment?
SSRN
How does bank consolidation affect local community investment? To address this question, we examine the impact of bank merger activity on small business lending at the county level from 1999 to 2016. We find that mergers involving small acquirers or in-state acquirers are positively associated with small- and medium-sized small business loan (SBL) originations in counties where the target bank has a presence; the results are stronger in counties with a larger number of small firms (opaque counties). In contrast, mergers involving out-of-state or large acquirers are associated with fewer small-sized SBL originations. Analyses of acquirer bank SBL origination activity post-merger corroborate our county-level results. Taken together, our findings underscore the importance of relationships in small business lending and suggest that certain types of mergers that adversely affect this relationship may be detrimental to community investment. The results have clear implications concerning bank mergers and their effect on community investment.

Banksâ€™ Sovereign Exposures: In Search of New Rules
Baglioni, Angelo S.,CefalÃ , Francesco
SSRN
We examine the reform of the prudential treatment of banksâ€™ sovereign exposures, with the purpose of introducing risk-sensitive capital charges and limiting the home bias. We consider six different options and measure their impact on the CET1 ratio of 82 banks from 10 euro-area countries, participating in the 2019 EBA EU-wide transparency exercise and subject to the ECB supervision. Our evidence shows that the BCBS (2017) proposal is the one with the most evenly distributed impact across countries, in terms of CET1 ratio decline. That proposal has the advantage of targeting two goals, risk sensitivity and diversification, with two independent instruments: rating-based risk weights and concentration add-ons. As a consequence, it is the only one introducing an incentive for banks located in all countries, low rated and high rated ones, to reduce their home bias. All the other proposals suffer severe drawbacks. Some focus on one objective only: either risk sensitivity or diversification. Other ones are strongly unbalanced: they introduce a heavy penalization for banks located in low rated countries, without addressing the home bias of banks located in high rated countries. Several options are prone to pro-cyclicality: we measure this effect by simulating the impact of a two-notch downgrading of high debt countries on the CET1 ratio of banks. Some relevant cross-country effects emerge from our analysis, due to the large cross-country exposures of a few intermediaries.

Garg, Ashish,Goulding, Christian L.,Harvey, Campbell R.,Mazzoleni, Michele
SSRN
We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard trend-following strategies across several assets and asset classes. The frequency of trend breaks has increased in recent years, which can help explain the lower performance of monthly trend following in the last decade. We illustrate how to repair trend-following strategies by exploiting the return forecasting properties of the different types of trend breaks: market corrections and rebounds. We construct dynamic multi-asset trend-following portfolios, which harvest more than double the average returns of standard trend-following investing strategies over the last decade.

Bridging the Gap between the Deposit Insurance Fund Target Level and the Current Fund Level
Kusaya, Charles,O'Keefe, John,Ufier, Alex
SSRN
We develop a model of the deposit insurerâ€™s choices for pricing deposit insurance, determining the target insurance fund, resolving bank failures and managing the investment portfolio (hereafter, key insurance operations). The academic literature treats these four areas as separate processes and deposit insurance laws address these processes separately. Deposit insurersâ€™ experience, however, shows that there are interactions and trade-offs between key insurance operations. We address the gap between deposit insurance laws and insurersâ€™ practices by developing a model that allows for the interactions of key insurance operations. Specifically, we use a risk aggregation model (copula) that includes income and expenses for key insurance operations in order to estimate the target insurance fund. Next we apply ruin theory to these same income and expense streams and target fund estimates to study their effect on insurer insolvency risk in a case study of the U.S. FDIC.

Business, Risk, & Chinaâ€™s MCF: Modest Tools of Financial Regulation for a Time of Great Power Competition
Kieff, F. Scott
SSRN
Doing business and investing both require mindfulness about risk, and market regulatory systems aim to ensure risk is appropriately disclosed. Recent discussions of todayâ€™s international system of Great Power Competition by U.S. Secretary of State Mike Pompeo mention risk from Chinaâ€™s Military-Civil Fusion (MCF). For U.S. and U.K individuals and business firms looking to invest in and do business with China, MCF awareness requires consideration of the ways in which all individuals and institutions in China are called to a duty that transcends their personal and civilian identities in their observable roles. As courts and regulators in China continue to demonstrate everimproving professional procedures for dispute resolutions and regulatory investigations, including fairness in avoiding bias towards either litigating party when both parties are ordinary commercial entities, connecting the dots to MCF reveals a distinct category of embedded risk. MCF imposes obligations flowing in multiple directions among personnel in Chinese courts and agencies, national leadership, national security apparatus, and state-owned or state-championed commercial firms. This risk fits well with familiar tool kits used by U.S. and U.K. investors, business firms, and related commercial parties to make their own best-informed business decisions, as well as the familiar toolkits used within the broader ecosystem of financial regulators and private parties who bring civil litigation, investigation, and whistleblower claims for material misstatements and the like.

COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model
Ian M. Trotter,Luís A. C. Schmidt,Bruno C. M. Pinto,Andrezza L. Batista,Jéssica Pellenz,Maritza Isidro,Aline Rodrigues,Attawan G. S. Suela,Loredany Rodrigues
arXiv

During the COVID-19 pandemic of 2019/2020, authorities have used temporary \textit{ad-hoc} policy measures, such as lockdowns and mass quarantines, to slow its transmission. However, the consequences of widespread use of these unprecedented measures are poorly understood. To contribute to the understanding of the economic and human consequences of such policy measures, we therefore construct a mathematical model of an economy under the impact of a pandemic, select parameter values to represent the global economy under the impact of COVID-19, and perform numerical experiments by simulating a large number of possible policy responses. By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate -- that is, the results suggest that the most dramatic measures should only be implemented when the disease is sufficiently spread. The degree of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship we assume between production shortfall and reduction in the infection rate. Our experiments further suggest that the intervention should last until after the peak determined by the reduced infection rate. The model and its implementation, along with the general insights from our policy experiments, may help policymakers design effective emergency policy responses in the face a serious pandemic, and contribute to our understanding of the relationship between the economic growth and the spread of infectious diseases.

Consistent Investment of Sophisticated Rank-Dependent Utility Agents in Continuous Time
Ying Hu,Hanqing Jin,Xun Yu Zhou
arXiv

We study portfolio selection in a complete continuous-time market where the preference is dictated by the rank-dependent utility. As such a model is inherently time inconsistent due to the underlying probability weighting, we study the investment behavior of sophisticated consistent planners who seek (subgame perfect) intra-personal equilibrium strategies. We provide sufficient conditions under which an equilibrium strategy is a replicating portfolio of a final wealth. We derive this final wealth profile explicitly, which turns out to be in the same form as in the classical Merton model with the market price of risk process properly scaled by a deterministic function in time. We present this scaling function explicitly through the solution to a highly nonlinear and singular ordinary differential equation, whose existence of solutions is established. Finally, we give a necessary and sufficient condition for the scaling function to be smaller than 1 corresponding to an effective reduction in risk premium due to probability weighting.

Could Loss Aversion Retain on the Market? Evidence from the Hong Kong Property Market
Li, Ling,Wan, Wayne Xinwei
SSRN
Loss aversion not only affects the list price of properties but can retain on actual transactions. Utilizing the data of over a million commercial and residential property transactions in Hong Kong from 1991 to 2015, we find that sellers facing nominal losses relative to their prior purchase prices attained higher selling prices than their counterparts. We suggest two market factors to account for the extent of the loss aversion effect on the market transaction prices. First, the loss aversion effect is only prominent when comparable transaction information is not readily accessible, such as in the less-transacted commercial property market. Second, our results suggest the relevance of loss aversion to the boom-bust property cycle in both the residential and commercial markets. The effect of loss aversion on transaction prices is relatively weak in the bust period between 1998 and 2003 when the Hong Kong property market lost almost two-thirds of its value, and it enlarges with the market recovering. The power of loss aversion is not attenuated at the aggregate market level but is associated with strong reductions in price declines in the bust period and in the commercial market. These results have implications for understanding the market adjustment of loss aversion in different property sectors and its association with the aggregate market dynamics in a boom-bust property cycle.

Crowdfunding and Demand Uncertainty
Scheuch, Christoph
SSRN
Reward-based crowdfunding allows entrepreneurs to sell claims on future products to finance investments and, at the same time, to generate demand information that benefits screening for viable projects. I characterize the profit-maximizing crowdfunding mechanism when the entrepreneur knows neither the number of consumers who positively value the product, nor their reservation prices. The entrepreneur can finance all viable projects by committing to prices that decrease in the number of pledgers, which grants consumers with high reservation prices information rents. However, if these information rents are large, then the entrepreneur prefers fixed high prices that lead to under-investment.

Debt De-Risking
Parise, Gianpaolo
SSRN
We examine the incentive of corporate bond fund managers to manipulate portfolio risk in response to competitive pressure. We find that bond funds engage in a reverse fund tournament in which laggard funds actively de-risk their portfolios, trading-off higher yields for more liquid and safer assets. De-risking is stronger for laggard funds that have a more concave sensitivity of flows-to-performance, in periods of market stress, and when bond yields are high. We provide evidence that debt de-risking also reduces ex post liquidation costs by mitigating the investors' incentive to run ex ante. We argue that, in the presence of de-risking behaviors, flexible NAVs (swing pricing) may be counter-productive and induce moral hazard.

Debt Rollover Risk, Credit Default Swap Spread and Stock Returns: Evidence from the COVID-19 Crisis
Liu, Ya,Qiu, Buhui,Wang, Teng
SSRN
This paper studies how the COVID-19 shock affects the CDS spread changes and abnormal stock returns of U.S. firms with different levels of debt rollover risk. We use the COVID-19 crisis as a quasi-natural experiment of adverse cash flow shock that increases the default risk of firms facing an immediate liquidity shortfall. We find that the COVID-19 shock significantly increased the CDS spread and decreased the shareholder value for firms facing higher debt rollover risk. The effect is stronger for non-financial firms, for firms that are financially constrained, and for firms that are highly volatile. The paper provides fresh insights into the role of firmsâ€™ debt rollover risk during the COVID-19 health crisis.

Deep xVA Solver â€" A Neural Network Based Counterparty Credit Risk Management Framework
Gnoatto, Alessandro,Reisinger, Christoph,Picarelli, Athena
SSRN
In this paper, we present a novel computational framework for portfolio-wide risk management problems where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective.The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.

Defining an Intrinsic â€˜Stickinessâ€™ Parameter of Stock Price Returns
Vitting Andersen, Jorgen
SSRN
We introduce a non-linear pricing model of individual stock returns that defines a â€stickinessâ€ parameter of the returns. The pricing model resembles the capital asset pricing model (CAPM) used in finance but has a non-linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding â€œstressâ€ to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the indexâ€™s price movements, can then be used to define their â€œstickinessâ€.

Do Time Delay and Investment Decisions: Evidence from an Experiment in Tanzania
Plamen Nikolov
arXiv

Attitudes toward risk underlie virtually every important economic decision an individual makes. In this experimental study, I examine how introducing a time delay into the execution of an investment plan influences individuals' risk preferences. The field experiment proceeded in three stages: a decision stage, an execution stage and a payout stage. At the outset, in the Decision Stage (Stage 1), each subject was asked to make an investment plan by splitting a monetary investment amount between a risky asset and a safe asset. Subjects were informed that the investment plans they made in the Decision Stage are binding and will be executed during the Execution Stage (Stage 2). The Payout Stage (Stage 3) was the payout date. The timing of the Decision Stage and Payout Stage was the same for each subject, but the timing of the Execution Stage varied experimentally. I find that individuals who were assigned to execute their investment plans later (i.e., for whom there was a greater delay prior to the Execution Stage) invested a greater amount in the risky asset during the Decision Stage.

Fintech: New Battle Lines in the Patent Wars?
La Belle, Megan M.,Schooner, Heidi Mandanis
SSRN
Historically, financial institutions have relied on trade secrets and first-mover advantages, rather than patents, to protect their inventions. For the few financial patents that were issued, conventional wisdom was that they werenâ€™t terribly interesting or important. In our 2014 study on financial patents, we showed that banks were breaking from past patterns and increasingly seeking patent protection. We explained that financial institutions were primarily building their patent portfolios as a defensive measure â€" i.e., to protect themselves from infringement suits. Indeed, the finance industry successfully lobbied Congress to include provisions in the America Invents Act of 2011 that made it easier to invalidate financial patents through administrative review. Yet, two significant developments call for a revisit of our 2014 study: first, the rise of fintech and, second, the recent $300 million verdict in the first bank-on-bank patent infringement suit â€" USAA v. Wells Fargo. This paper explores how the rise of fintech has changed the purpose of patenting among banks, and what a possible fintech patent war would mean for the future of both the financial and patent systems in this country. Firm-Level Exposure to Epidemic Diseases: COVID-19, SARS, and H1N1 Hassan, Tarek Alexander,Hollander, Stephan,van Lent, Laurence,Tahoun, Ahmed SSRN Using tools described in our earlier work (Hassan et al., 2019, 2020), we develop text-based measures of the costs, benefits, and risks listed firms in the US and over 80 other countries associate with the spread of Covid-19 and other epidemic diseases. We identify which firms expect to gain or lose from an epidemic disease and which are most affected by the associated uncertainty as a disease spreads in a region or around the world. As Covid-19 spreads globally in the first quarter of 2020, we find that firmsâ€™ primary concerns relate to the collapse of demand, increased uncertainty, and disruption in supply chains. Other important concerns relate to capacity reductions, closures, and employee welfare. By contrast, financing concerns are mentioned relatively rarely. We also identify some firms that foresee opportunities in new or disrupted markets due to the spread of the disease. Finally, we find some evidence that firms that have experience with SARS or H1N1 have more positive expectations about their ability to deal with the coronavirus outbreak. How Does Mobile Payment Technology Affect Consumer Payment Behavior? Zhang, Liang SSRN In recent years, mobile payment technology has been enjoying a rising popularity among consumers. While the innovation provides flexibility and convenience, it could also facilitate consumers' substitution of currency and checks by cards. Furthermore, the technology could affect consumers' credit card behavior. Understanding these possible effects of payment innovation is important for maintaining a healthy financial system and consumers' well-being. This project investigates the effect of mobile payment technology on consumer payment behavior in the context of consumers' choice of point-of-sale (POS) instruments and its implications for credit card behavior. Using a unique panel of U.S. consumers from 2015 to 2018, I find that mobile payment technology is positively associated with credit card use while being negatively associated with cash and check use. In addition to higher credit card usage, the adoption of mobile payment technology is associated with credit card revolving behavior. Applying the heteroskedasticity-based instrumental variable-generalized method of moments approach (IV-GMM), I find arguably causal evidence that mobile payment technology leads to a higher probability of using revolving credit as well as higher revolving balances. How Repeated Notifications and Notification Checking Mode Affect Investorsâ€™ Reactions to Managersâ€™ Strategic Title Emphasis? Chen, Wei,Tan, Hun-Tong,Wang, Elaine SSRN We conduct an experiment to examine how repeated exposure to earnings notifications and notification checking mode affect investorsâ€™ reactions to managersâ€™ strategic positive emphasis in the title of a disclosure when firm performance is mixed. We find that a title with (versus without) a positive emphasis leads to more positive performance judgments only when investors receive repeated notifications in a one-by-one mode, but not when they receive a notification for the first time or when they receive repeated notifications all at once. Overall, our results suggest that repeated one-by-one notifications render investors more vulnerable to strategic emphases in management disclosure titles, but checking repeated notifications all at once could protect investors from being misled by managementâ€™s strategic emphases in titles. Is the End of History Passive? Zaker, Sassan SSRN A powerful investor trend towards passive over active management seems to have become the new normal in the investment industry. The passive â€œend of historyâ€ thesis has a kernel of empirical truth. Passivation, however, is an imperfect match to wealth management clientsâ€™ individual demand for capital preservation (stability), let-alone performance enhancement. Active management needs a more precise specification to deserve a role in client portfolios. A possible justification could be tackling the distinction between fuzzy assets (subjective and exposed to instability) vs. eigen assets (objective and stable). J'Accuse! Antisemitism and Financial Markets in the Time of the Dreyfus Affair Do, Quoc-Anh,Galbiati, Roberto,Marx, Benjamin,Ortiz Serrano, Miguel Ãngel SSRN This paper explores the financial consequences of the "Dreyfus Affair", a major societal crisis that prompted an outburst of antisemitism in 19th century France. We show that the Dreyfus Affair had sizeable effects on the stock returns of firms with Jewish board members. First, these firms experienced abnormal returns in response to major episodes of the Affair---negative returns after the downfall of Dreyfus in 1895, and positive returns around his rehabilitation in 1899. Second, starting in 1898, the same firms experienced higher returns during a media campaign organized to rehabilitate Dreyfus. These higher returns more than compensated investors for the increase in stock volatility observed in the same period. We estimate these results using a difference-in-differences strategy comparing firms with and without Jewish connections before and after "J'Accuse!", a widely publicized pamphlet denouncing the conspiracy against Dreyfus. We do not observe corresponding changes in the underlying profitability of Jewish-connected firms as measured by dividends. Our preferred interpretation is that the Dreyfus rehabilitation campaign changed discriminatory beliefs among investors, allowing those who bet on Jewish-owned firms to capture excess returns. Overall, our findings provide novel evidence on the existence of rents from discrimination and the financial impacts of ethnic prejudice. Labor Unemployment Insurance and Pension Asset Allocations Liang, Yina,Kiosse, Paraskevi Vicky,Tarsalewska, Monika SSRN This paper examines the effect of unemployment risk on pension investment decisions of defined benefit (DB) pension plans. In particular, we examine whether unemployment insurance (UI) benefits result in increases in pension investment risk. Using fixed-effects and difference-in-difference analysis, we find evidence that firms take higher pension investment risk by investing more heavily in equities after unemployment insurance benefits increase. These results, which are robust to a number of sensitivity tests, are consistent with the notion that firms undertake more risk when the costs of unemployment decrease. In additional analyses, we find that changes in unemployment risk also affect other pension-related decisions such as earnings management using the expected rate of return on pension plan assets (ERR) and DB plan freezes. Malinvestment and Crisis-Emergent Asset Comovement: The Problem of Latent Correlation Chibane, Messaoud,Gabriel, Amadeus,GimÃ©nez Roche, Gabriel SSRN The common fall of asset prices during crises and recessions implies that asset correlation is strong during these events, while not necessarily showing up during the boom phase of the business cycle. Using insights from the malinvestment cycle theory, we show that this shift in correlation is not just triggered by a crash-related shock. It is also the result of risk build-up induced by money-boosted malinvestment taking place during the boom. We provide a model where the probability of a crash increases with bank credit expansion during the growth phase, which hints at a â€œlatentâ€ build-up of asset correlation. Credit expansion feeds asset prices, but also widens a gap between future-oriented cash inflows and present-oriented cash outflows. As new credit widens this gap, asset valuation becomes more funding-based rather than cash flow-based. Therefore, default risk, and hence the probability of a crash, increases with credit expansion. A change in credit expansion cuts the asset price rise short and reveals the malinvestments in the economy. This process implies a â€œlatentâ€ build-up of asset correlation during the boom phase that becomes â€œeffectiveâ€ with the crash. Practitioners and policy-makers would thus benefit from adopting the insights of the malinvestment cycle theory to complement their ad hoc empirical findings and estimations. Mean-Variance Portfolio Management with Functional Optimization Ka Wai Tsang,Zhaoyi He arXiv This paper introduces a new functional optimization approach to portfolio optimization problems by treating the unknown weight vector as a function of past values instead of treating them as fixed unknown coefficients in the majority of studies. We first show that the optimal solution, in general, is not a constant function. We give the optimal conditions for a vector function to be the solution, and hence give the conditions for a plug-in solution (replacing the unknown mean and variance by certain estimates based on past values) to be optimal. After showing that the plug-in solutions are sub-optimal in general, we propose gradient-ascent algorithms to solve the functional optimization for mean-variance portfolio management with theorems for convergence provided. Simulations and empirical studies show that our approach can perform significantly better than the plug-in approach. Monetary Policy with Opinionated Markets Caballero, Ricardo J.,Simsek, Alp SSRN Central banks (the Fed) and markets (the market) often disagree about the path of interest rates. We develop a model that explains this disagreement and study its implications for monetary policy and asset prices. We assume that the Fed and the market disagree about expected aggregate demand. Moreover, agents learn from data but not from each other---they are opinionated and information is fully symmetric. We then show that disagreements about future demand, together with learning, translate into disagreements about future interest rates. Moreover, these disagreements shape optimal monetary policy, especially when they are entrenched. The market perceives monetary policy "mistakes" and the Fed partially accommodates the market's view to mitigate the financial market fallout from perceived "mistakes." We also show that differences in the speed at which the Fed and the market react to the data---heterogeneous data sensitivity---matters for asset prices and interest rates. With heterogeneous data sensitivity, every macroeconomic shock has an embedded monetary policy "mistake" shock. When the Fed is more (less) data sensitive, the anticipation of these mistakes dampen (amplify) the impact of macroeconomic shocks on asset prices. More Robust Pricing of European Options Based on Fourier Cosine Series Expansions Fabien Le Floc'h arXiv We present an alternative formula to price European options through cosine series expansions, under models with a known characteristic function such as the Heston stochastic volatility model. It is more robust across strikes and as fast as the original COS method. Neural Networks and Value at Risk Arimond, Alexander,Borth, Damian,Hoepner, Andreas G. F.,Klawunn, Michael,Weisheit, Stefan SSRN Inspired by Gu, Kelly & Xiuâ€™s (GKX, 2020) advancement of the measurement of asset risk premia via the introduction of feed forward neural networks, we investigate, if machine learning can advance the process of â€˜estimating Value at Risk (VaR) thresholdsâ€™. For this purpose, we compare simple (GKXâ€™s feed forward) and advanced (convolutional, recurrent) neural networks with established approaches (Hidden Markov Model, Mean/Variance). Utilizing a generative regime switching framework, we perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network, (ii) its incentive function according to which it has been trained and (iii) the amount of data we feed. First, we compare neural networks with random seeding with networks that are initialized via estimations from the best-established model (i.e. the Hidden Markov). We find latter to outperform in terms of the frequency of VaR breaches (i.e. the realized return falling short of the estimated VaR threshold). Second, we balance the incentive structure of the loss function of our networks by adding a second objective to the training instructions so that the neural networks optimize for accuracy while also aiming to stay in empirically realistic regime distributions (i.e. bull vs. bear market frequencies). In particular this design feature enables the balanced incentive recurrent neural network (RNN) to outperform the single incentive RNN as well as any other neural network or established approach by statistically and economically significant levels. Third, we half our training data set of 2,000 days. We find our networks when fed with substantially less data (i.e. 1,000 days) to perform significantly worse which highlights a crucial weakness of neural networks in their dependence on very large data sets. Hence, we conclude that well designed neural networks, i.e. a recurrent neural network initialized with best current evidence and balanced incentives â€" can potentially advance the protection offered to institutional investors by VaR thresholds through a reduction in threshold breaches. However, such advancements rely on the availability of a long data history, which may not always be available in practice when estimating asset management VaR thresholds. Notes on Backward Stochastic Differential Equations for Computing XVA Jun Sekine,Akihiro Tanaka arXiv The X-valuation adjustment (XVA) problem, which is a recent topic in mathematical finance, is considered and analyzed. First, the basic properties of backward stochastic differential equations (BSDEs) with a random horizon in a progressively enlarged filtration are reviewed. Next, the pricing/hedging problem for defaultable over-the-counter (OTC) derivative securities is described using such BSDEs. An explicit sufficient condition is given to ensure the non-existence of an arbitrage opportunity for both the seller and buyer of the derivative securities. Furthermore, an explicit pricing formula is presented in which XVA is interpreted as approximated correction terms of the theoretical fair price. NÃ¢ng cao cháº¥t lÆ°á»£ng thu hÃºt FDI vÃ o Viá»‡t Nam trong thá»i gian tá»›i - nhÃ¬n tá»« gÃ³c Ä'á»™ thá»ƒ cháº¿ (Improving the Quality of Fdi Attraction in Vietnam in the Coming Years â€" Approaching From the Institutional Perspective) Nguyen, V.C. SSRN Vietnamese Abstract: Sau 32 nÄƒm ká»ƒ tá»« thá»i ká»³ Ä'á»•i má»›i nÄƒm 1986, Viá»‡t Nam Ä'Ã£ trá»Ÿ thÃ nh má»™t Ä'iá»ƒm Ä'áº¿n, thu hÃºt nhiá»u dá»± Ã¡n Ä'áº§u tÆ° trá»±c tiáº¿p nÆ°á»›c ngoÃ i (FDI). TÃ­nh lÅ©y káº¿ Ä'áº¿n cuá»'i nÄƒm nÄƒm 2018, cáº£ nÆ°á»›c cÃ³ hÆ¡n 27.353 dá»± Ã¡n cÃ²n hiá»‡u lá»±c vá»›i tá»•ng vá»'n Ä'Äƒng kÃ½ 340 tá»· USD. Vá»'n thá»±c hiá»‡n lÅ©y káº¿ cá»§a cÃ¡c dá»± Ã¡n Ä'áº§u tÆ° trá»±c tiáº¿p nÆ°á»›c ngoÃ i Æ°á»›c Ä'áº¡t khoáº£ng 191,4 tá»· USD, gÃ³p pháº§n ráº¥t quan trá»ng thÃºc Ä'áº©y phÃ¡t triá»ƒn vÃ chuyá»ƒn dá»‹ch cÆ¡ cáº¥u kinh táº¿. Äá»ƒ tiáº¿p tá»¥c thu hÃºt nhiá»u hÆ¡n ná»¯a dÃ²ng vá»'n nÃ y vÃ o Viá»‡t Nam trong thá»i gian tá»›i, cáº§n cÃ³ Ä'Ã¡nh giÃ¡ cá»¥ thá»ƒ vá» thá»ƒ cháº¿ vÃ tiáº¿p tá»¥c cÃ³ nhá»¯ng Ä'á»•i má»›i phÃ¹ há»£p vá»›i xu tháº¿.English Abstract: After more than 32 years of innovation since 1986 to the present, Vietnam has become a promising destination fo FDI projects. Accumulated figure by the end of 2018, there were 27.353 FDI projects active with total registered capital of 340 billion USD. The total implemented capital was 191.4 billion USD which contributed significantly to the development and transition of the economy. Optimal Reinsurance and Investment Strategies under Mean-Variance Criteria: Partial and Full Information Shihao Zhu,Jingtao Shi arXiv This paper is concerned with an optimal reinsurance and investment problem for an insurance firm under the criterion of mean-variance. The driving Brownian motion and the rate in return of the risky asset price dynamic equation cannot be directly observed. And the short-selling of stocks is prohibited. The problem is formulated as a stochastic linear-quadratic (LQ) optimal control problem where the control variables are constrained. Based on the separation principle and stochastic filtering theory, the partial information problem is solved. Efficient strategies and efficient frontier are presented in closed forms via solutions to two extended stochastic Riccati equations. As a comparison, the efficient strategies and efficient frontier are given by the viscosity solution for the Hamilton-Jacobi-Bellman (HJB) equation in the full information case. Some numerical illustrations are also provided. Passive and Unequal: The Karlsruhe Vision for the Eurozone Venizelos, Evangelos SSRN BVerfG's decision on the ECB's Quantitative Easing Program (PSPP) raises two fundamental constitutional theory questions: 1) Should a case of severe excess of EU competences (ultra vires) be unilaterally reviewed by any Member State and even by its respective courts or should there be an activation of the mechanism that is provided for in multilateral international conventions under International Law, in order to ensure the sovereignty and institutional equality of all Member States? 2) Did the CJEU, by exercising marginal judicial control over the ECB's decisions on the PSPP, actually violate a constitutional tradition common to the Member States, that of exercising intensive judicial control over the actions of independent central banks or there is, on the contrary, a common tradition for judicial self-restraint on complex technical matters such as financial engineering? However, the decision raises equally serious issues in terms of the interpretation of the TEU and the TFEU. Insisting on a strict distinction between economic and monetary policy, the BVerfG essentially says â€œnoâ€ to the PSPP â€" despite it not being monetary financing- because it forms a virtual secondary bond market, a market that is actually primary. Asking the Federal Government and the Federal Parliament to impose on the ECB the obligation to further justify its choices within a three-month transitional period and for the Bundesbank to prepare an exit plan from the PSPP, the BVerfG sends a financial message that it wants a Eurozone that is economically passive and internally unequal. In fact, it sends this message as the economic crisis due to the COVID-19 pandemic unfolds and the EU necessarily takes innovative and generous measures to support Member States. Personality Differences and Investment Decision-Making Jiang, Zhengyang,Peng, Cameron,Yan, Hongjun SSRN We administer a survey to thousands of affluent Americans about their personalities and investment decisions. We show that the Big Five personality traits can explain investments through three distinct channels: beliefs, risk preferences, and social interactions. Two personality traits â€" Neuroticism and Openness â€" stand out in their explanatory powers for equity investments. Both investors with high Neuroticism and those with low Openness tend to allocate less to equity. While the former is due to pessimistic beliefs about future stock returns, the latter is due to high risk aversion. We confirm these results out-of-sample using a representative panel of Australian households. Political Failures in Innovation Policy: A Cautionary Note KÃ¤rnÃ¤, Anders SSRN Within the field of innovation studies, researchers have identified several market failures that hamper investment in R&D, innovation and growth in a market economy. Several policies such as government subsidies, tax deductions, soft loans, and public venture capital provided to firms that pursue R&D have therefore been recommended by researchers, in addition to regulations to increase the quality and standards of goods and services. Less attention has been paid to government failures in cases where a policy fails to achieve its stated goal, often due to conflicts between the interests of special interest groups and the public. This paper discusses the concept of government failure within an innovation policy context and why this perspective is important for policy design since it is likely that policies that aim to reduce market failures could suffer from political failures. A text analysis of all papers published in 5 leading innovation journals between 2010 and 2019, a total of 5,526 papers, indicates a lack of research about government failures, which could lead to recommendations from researches to policymakers not being successful due to political failures. Public Futures Funds Irwin, Scott H.,Brorsen, B. Wade SSRN Futures funds fool investorâ€™s money to speculate in futures market. Due to favorable tax treatment, futures funds are usually organized as limited partnerships within the United States. Typically, an affiliate or subsidiary of a brokerage company acts as general partner. If limited partnership interests are sold in a public offering, the Securities and Exchange Commission (SEC) classifies a futures fund as public and requires its registration. Private futures funds must meet a series of tests to be exempted from SEC registration. As a rule-of-thumb, private funds have 35 or fewer investors. Relative Bound and Asymptotic Comparison of Expectile with Respect to Expected Shortfall Samuel Drapeau,Mekonnen Tadese arXiv Expectile bears some interesting properties in comparison to the industry wide expected shortfall in terms of assessment of tail risk. We study the relationship between expectile and expected shortfall using duality results and the link to optimized certainty equivalent. Lower and upper bounds of expectile are derived in terms of expected shortfall as well as a characterization of expectile in terms of expected shortfall. Further, we study the asymptotic behavior of expectile with respect to expected shortfall as the confidence level goes to$1$in terms of extreme value distributions. We use concentration inequalities to illustrate that the estimation of value at risk requires larger sample size than expected shortfall and expectile for heavy tail distributions when$\alpha$is close to$1$. Illustrating the formulation of expectile in terms of expected shortfall, we also provide explicit or semi-explicit expressions of expectile and some simulation results for some classical distributions. Scalar multivariate risk measures with a single eligible asset Zachary Feinstein,Birgit Rudloff arXiv In this paper we present results on scalar risk measures in markets with transaction costs. Such risk measures are defined as the minimal capital requirements in the cash asset. First, some results are provided on the dual representation of such risk measures, with particular emphasis given on the space of dual variables as (equivalent) martingale measures and prices consistent with the market model. Then, these dual representations are used to obtain the main results of this paper on time consistency for scalar risk measures in markets with frictions. It is well known from the superhedging risk measure in markets with transaction costs, as in Jouini and Kallal (1995), Roux and Zastawniak (2016), and Loehne and Rudloff (2014), that the usual scalar concept of time consistency is too strong and not satisfied. We will show that a weaker notion of time consistency can be defined, which corresponds to the usual scalar time consistency but under any fixed consistent pricing process. We will prove the equivalence of this weaker notion of time consistency and a certain type of backward recursion with respect to the underlying risk measure with a fixed consistent pricing process. Several examples are given, with special emphasis on the superhedging risk measure. Sig-SDEs model for quantitative finance Imanol Perez Arribas,Cristopher Salvi,Lukasz Szpruch arXiv Mathematical models, calibrated to data, have become ubiquitous to make key decision processes in modern quantitative finance. In this work, we propose a novel framework for data-driven model selection by integrating a classical quantitative setup with a generative modelling approach. Leveraging the properties of the signature, a well-known path-transform from stochastic analysis that recently emerged as leading machine learning technology for learning time-series data, we develop the Sig-SDE model. Sig-SDE provides a new perspective on neural SDEs and can be calibrated to exotic financial products that depend, in a non-linear way, on the whole trajectory of asset prices. Furthermore, we our approach enables to consistently calibrate under the pricing measure$\mathbb Q$and real-world measure$\mathbb P$. Finally, we demonstrate the ability of Sig-SDE to simulate future possible market scenarios needed for computing risk profiles or hedging strategies. Importantly, this new model is underpinned by rigorous mathematical analysis, that under appropriate conditions provides theoretical guarantees for convergence of the presented algorithms. Smart Beta Made Smart Johansson, Andreas,Sabbatucci, Riccardo,Tamoni, Andrea SSRN We construct synthetic, tradable risk factors (e.g., tradable HML and MOM) and individual factor legs (e.g., growth and value) using optimal combinations of large and liquid mutual funds and ETFs based on their holdings. We show that a large fraction of existing smart beta funds are simply market funds, and that both retail and institutional investors are not able to harvest the unconditional factor risk premia, with the exception of the value premium. We conclude that the investable set of strategies available to investors may be smaller than previously thought. We also show that smart beta funds' names might not be indicative of the actual fund strategy, although daily flows to smart beta strategies suggest that naive investors tend to get exposure to smart beta strategies based on funds' names. Our analysis has several important implications, including how we evaluate portfolio managers and cross-sectional returns' anomalies. Steadfast, Greedy, or Fearful? Strategies for Responding to Extreme Market Volatility White, James,Haghani, Victor SSRN March 2020 packed 2 Â½ years of normal U.S. stock market volatility into one month, making it the most volatile month on record. Daily variability clocked in at 6%, six times higher than the average over the past 90 years. How should an investor respond to such volatility? In this article we explore four possible approaches, two long-term and two short-term in nature. We give particular focus to Volatility Targeting and Momentum strategies, discussing the investor behavior that might make one or both of these approaches attractive as a driver of portfolio asset allocation. We discuss the similarities in the two strategies as applied to stock market scaling, and provide the results of long-term backtests. We conclude that all four approaches discussed, alone or in combination, have merits. Stocks for the Long Run? Evidence from a Broad Sample of Developed Markets Anarkulova, Aizhan,Cederburg, Scott,O'Doherty, Michael S. SSRN We characterize the distribution of long-term equity returns based on the historical record of stock market performance in a broad cross section of 39 developed countries over the period from 1841 to 2019. Our comprehensive sample mitigates concerns over survivorship and easy data biases that plague other work in this area. A bootstrap simulation analysis implies substantial uncertainty about long-horizon stock market outcomes, and we estimate a 12% chance that a diversified investor with a 30-year investment horizon will lose relative to inflation. The results contradict the conventional advice that stocks are safe investments over long holding periods. Strengthening science, technology, and innovation-based incubators to help achieve Sustainable Development Goals: Lessons from India Kavita Surana,Anuraag Singh,Ambuj D Sagar arXiv Policymakers in developing countries increasingly see science, technology, and innovation (STI) as an avenue for meeting sustainable development goals (SDGs), with STI-based startups as a key part of these efforts. Market failures call for government interventions in supporting STI for SDGs and publicly-funded incubators can potentially fulfil this role. Using the specific case of India, we examine how publicly-funded incubators could contribute to strengthening STI-based entrepreneurship. India's STI policy and its links to societal goals span multiple decades -- but since 2015 these goals became formally organized around the SDGs. We examine why STI-based incubators were created under different policy priorities before 2015, the role of public agencies in implementing these policies, and how some incubators were particularly effective in addressing the societal challenges that can now be mapped to SDGs. We find that effective incubation for supporting STI-based entrepreneurship to meet societal goals extended beyond traditional incubation activities. For STI-based incubators to be effective, policymakers must strengthen the 'incubation system'. This involves incorporating targeted SDGs in specific incubator goals, promoting coordination between existing incubator programs, developing a performance monitoring system, and finally, extending extensive capacity building at multiple levels including for incubator managers and for broader STI in the country. Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description Anuraag Singh,Giorgio Triulzi,Christopher L. Magee arXiv In this work, we attempt to provide a comprehensive granular account of the pace of technological change. More specifically, we survey estimated yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of technology domains. A technology domain is a body of patented inventions achieving the same technological function using the same knowledge and scientific principles. We obtain a set of 1757 domains using an extension of the previously defined classification overlap method (COM). These domains contain 97.14% of all patents within the entire US patent system. From the identified patent sets, we calculated the average centrality of the patents in each domain to estimate their improvement rates, following a methodology tested in prior work. The estimated improvement rates vary from a low of 1.9% per year for the Mechanical Skin treatment - Hair Removal and wrinkles domain to a high of 228.8% per year for the Network management - client-server applications domain. We developed a one-line descriptor identifying the technological function achieved and the underlying knowledge base for the largest 50, fastest 20 as well as slowest 20 of these domains, which cover more than forty percent of the patent system. In general, the rates of improvement were not a strong function of the patent set size and the fastest improving domains are predominantly software-based. We make available an online system that allows for automated searching for domains and improvement rates corresponding to any technology of interest to researchers, strategists and policy formulators. Terrorist Attacks, Managerial Sentiment, and Corporate Disclosures Chen, Wen,Wu, Haibin,Zhang, Liandong SSRN This study investigates the effect of managerial sentiment on corporate disclosure decisions. Using terrorist attacks in the United States as adverse shocks to managerial sentiment, we find that firms located in the metropolitan areas attacked issue more negatively biased earnings forecasts. The effect is stronger for firms with higher operating uncertainty and firms with younger, inexperienced, or less confident executives and it is weaker for firms located in states with increasing violent crime rates. A potential alternative explanation is that managers could strategically bias earnings forecasts downward and attribute the poor performance to terrorist attacks. To address this issue, we conduct a battery of additional analyses and the results are more consistent with managerial sentiment than strategic attribution. In addition, we show that our results are unlikely driven by any economic effects of terrorist attacks. Finally, firms in the attacked areas also exhibit a more pessimistic tone in 10-K/10-Q filings. The Covid-19 Shock and Equity Shortfall: Firm-Level Evidence from Italy Carletti, Elena,Oliviero, Tommaso,Pagano, Marco,Pelizzon, Loriana,Subrahmanyam, Marti G. SSRN This paper estimates the drop in profits and the equity shortfall triggered by the COVID-19 shock and the subsequent lockdown, using a representative sample of 80,972 Italian firms. We find that a 3-month lockdown entails an aggregate yearly drop in profits of e170 billion, with an implied equity erosion of e117 billion for the whole sample, and e31 billion for firms that became distressed, i.e., ended up with negative book value after the shock. As a consequence of these losses, about 17% of the sample firms, whose employees account for 8.8% of total employment in the sample (about 800 thousand employees), become distressed. Small and mediumsized enterprises (SMEs) are affected disproportionately, with 18.1% of small firms, and 14.3% of medium-sized ones becoming distressed, against 6.4% of large firms. The equity shortfall and the extent of distress are concentrated in the Manufacturing and Wholesale Trading sectors and in the North of Italy. Since many firms predicted to become distressed due to the shock had fragile balance sheets even prior to the COVID-19 shock, restoring their equity to their pre-crisis levels may not suffice to ensure their long-term solvency. The Effects of Cooperative Compliance on Firmsâ€™ Tax Risk, Tax Risk Management and Compliance Costs Eberhartinger, Eva,Zieser, Maximilian SSRN In cooperative compliance programs, firms and tax administrations agree on cooperation instead of confrontation. Firms provide full transparency and advanced tax control frameworks. Tax administrations, in turn, offer certainty as to the tax treatment of complex transactions. In this study, we test how firmsâ€™ perceptions of tax risk, the quality of tax risk management and compliance costs are related to cooperative compliance. To our knowledge, this is the first study that attempts to analyze both reasons for and consequences of participation in cooperative compliance programs. We examine the Austrian cooperative compliance pilot project known as horizontal monitoring that was aimed at large businesses and launched in 2011. We use survey data from representatives of firms participating in the pilot project and a sample of comparable firms under a traditional ex-post audit regime. We conduct mediation analyses to test differences between these groups, as well as more complex relationships between variables. Results show that firms in horizontal monitoring perceive a significantly higher increase in tax certainty, which is associated with significant relative decreases in tax risk and compliance costs. Furthermore, while the quality of tax risk management upon entering the pilot project appears significantly higher for firms in horizontal monitoring, they do not report greater improvement in tax risk management compared to the control group. These results are relevant for the development of cooperative compliance programs, as well as the decision to participate in them. The impact of Coronavirus (COVID-19) Outbreak on Faith-based Investments: An Original Analysis Sherif, Mohamed SSRN This paper examines the rapid spread of Coronavirus (COVID-19) and its short-term impact on the Shariah-compliant UK Dow Jones market index to capture the dynamic behavior of stock returns at economy and industry levels. Using daily data over the period January 20 to May 20 and ten UK industrial sector groupings, the findings suggest a strong and statistically significant relationship between the COVID-19 pandemic and the performance of the conventional stock market index. The findings also suggest that the disease interacts negatively but insignificantly with the Dow Jones faith-based ethical (Islamic) index compared to its UK counterpart. In addition, through an analysis of sector groupings, the paper shows that the stock returns of the information technology sector performed significantly better than the market, while stock returns of consumer discretionary sector, which includes transportation, beverages, tourism and leisure, consumer services performed significantly worse than the market during the COVID-19 outbreak. Other sector groupings fail to yield significantly plausible parameter values. Trade Liberalization, Economic Reforms and Foreign Direct Investment â€" A Critical Analysis of the Political Transformation in Vietnam Nguyen, V.C. SSRN The purpose of this study is to discuss the trends of integration into the global economy since political and economic reforms (so-called Doimoi) and its influence on every presence of foreign investment in Vietnam. Lasting 20-year-war period and ended in 1975, by the mid-1980s per capita income was stuck between$200 and \$300, Vietnamâ€™s government introduced Doimoi through a series of reforms, and steered the country to be a socialist-oriented market economy. Based on the analysis of reform process and integration, the results are concerned. Our results demonstrate that foreign direct investment performance has significantly embraced trade liberalization with gusto. Further, the open trade policy in relation to FTAs could significantly promote foreign investment and maximize its benefits on the economy.

Using Clustering Ensemble to Identify Banking Business Models
Marques, Bernardo P.,Alves, Carlos F.
SSRN
The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c-means (which allows us to handle fuzzy clustering), self-organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non-core banks), as well as banks with a stable business model over time (persistent banks) and others (non-persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.

Value by Design?
Kessler, Stephan,Scherer, Bernd,Harries, Jan Philipp
SSRN
Although academics and practitioners frequently refer in their work to equity value investing, no consensus exists as to what this style exactly encompasses. For a wide range of 3,168 alternative implementations (design choices) of what could all constitute value portfolios, the authors document the impact of parameter perturbations on risk-adjusted returns. The observed dispersion in Sharpe ratios allows the authors to identify the hierarchy of design choices and to better assess the degrees of freedom consumed within the strategy development process. The authors can therefore derive critical t-values that adjust for over-fitting. This will prove to be useful in research governance and strategy selection.

Zipf's Law for Atlas Models
Ricardo T. Fernholz,Robert Fernholz
arXiv

A set of data with positive values follows a Pareto distribution if the log-log plot of value versus rank is approximately a straight line. A Pareto distribution satisfies Zipf's law if the log-log plot has a slope of -1. Since many types of ranked data follow Zipf's law, it is considered a form of universality. We propose a mathematical explanation for this phenomenon based on Atlas models and first-order models, systems of positive continuous semimartingales with parameters that depend only on rank. We show that the stable distribution of an Atlas model will follow Zipf's law if and only if two natural conditions, conservation and completeness, are satisfied. Since Atlas models and first-order models can be constructed to approximate systems of time-dependent rank-based data, our results can explain the universality of Zipf's law for such systems. However, ranked data generated by other means may follow non-Zipfian Pareto distributions. Hence, our results explain why Zipf's law holds for word frequency, firm size, household wealth, and city size, while it does not hold for earthquake magnitude, cumulative book sales, the intensity of solar flares, and the intensity of wars, all of which follow non-Zipfian Pareto distributions.

Ã"rden(es) y disonancias de la reconciliaciÃ³n postdictatorial. Una comparaciÃ³n entre Chile y EspaÃ±a. (Order(s) and Dissonance of Reconciliation in Post-dictatorship Democracies. A Comparison between Chile and Spain)
Vera Gajardo, Sandra
SSRN