Research articles for the 2020-04-30

Artificial Market Timing in Mutual Funds
Busse, Jeffrey A.,Ding, Jing,Jiang, Lei,Tang, Yuehua
We document statistically significant market timing coefficients, both positive and perverse, when mutual fund managers follow commonly-used trading strategies that are unrelated to traditional notions of market timing. Our evidence of “artificial” market timing emerges when we estimate market timing regressions across time periods that span time variation in fund systematic risk levels, as is typical. Moreover, analyzing transaction-level data shows that artificial timing leads to higher transaction costs and lower fund performance. Overall, artificial timing provides an alternative explanation for two long-standing puzzles in the mutual fund market timing literature, including perverse market timing and the inverse relation between market timing and stock selectivity.

Banks to Basics! Why Banking Regulation Should Focus on Equity
Durand, Pierre,Le Quang, Gaëtan
Banking regulation faces multiple challenges that call for rethinking the way it is designed. Not only does it have to tackle the specific risks associated with banking activities, but it is now expected from it to help dealing with climate change. In this paper, we argue that regulators should focus on designing strong equity requirements instead of implementing several complex rules. Such a constraint in equity is however opposed by the banking industry because of its presumed adverse impact on banks' performance. Resorting to Random Forest regressions on a large dataset of banks balance sheet variables, we show that the ratio of equity over total assets has a clear positive effect on banks' performance, as measured by the return on assets. On the contrary, the impact of this ratio on the shareholder value, as measured by the return on equity, is most of the time weakly negative. Strong equity requirements do not therefore impede banks' performance, but do reduce the shareholder value. This may be the reason why the banking industry so fiercely opposes strong equity requirements.

Comments to the Joint Parliamentary Committee (JPC) on the Personal Data Protection Bill 2019 introduced in the Lok Sabha on 11 December 2019
Research, Dvara
Dvara Research is an independent Indian not-for-profit research institution guided by our mission of ensuring that every individual and every enterprise has complete access to financial services. Our work seeks to address challenges for policy and regulation in India given the waves of digital innovation sweeping financial services, focussing on the impact on lower income individuals in the country. The regulation and protection of consumer data has been a core area of our recent research.In this document, we present our comments on the Personal Data Protection Bill 2019 (the Bill), introduced in the Lok Sabha in December 2019, and referred to the JPC on the Bill. Our feedback on the Bill is presented in this document in two sections.Section I presents seven overarching concerns with the Bill, with detailed analysis and recommendations to address these concerns constructively. 1. User protections must be strengthened for the Bill to genuinely guarantee data privacy for Indians. 2. Changes to the institutional design of the DPA could limit its independence, accountability and effectiveness. 3. Immense powers and exemptions for the State will severely limit the effectiveness of the new regime. 4. Fair and reasonable processing should be an overarching obligation on data fiduciaries and data processors 5. “Harm” should not be condition on which rights and obligations depend in the Bill. 6. The Bill should not include provisions relating to the sharing of Non-Personal Data. 7. The Bill should contain transitional provisions to create certainty about its implementation.Section II presents a comprehensive Chapter-wise analysis of the provisions of the Bill against the previous draft Personal Data Protection Bill, 2018 (the previous Bill), flagging new issues arising from changes as well as persisting concerns.This response continues our engagement with the public consultation process on India’s new data protection regime since 2017.2 We are deeply concerned that aspects of the latest draft of the Bill could endanger users’ data protection and hamper the growth of a free and fair digital economy.We urge the JPC to engage with our recommendations to create an effective, consumer-friendly data protection framework for India’s unique context. We welcome any opportunity to present these views or respond to questions and comments on our research to the JPC.

Competition and Agency Problems Within Banks: Evidence From Insider Lending
Girotti, Mattia,Salvadè, Federica
This paper studies whether greater competition can mitigate agency problems within banks. We measure the intensity of the agency conflict within a bank by the volume of loans that the bank lends to its insiders (e.g., executives). We first check that these loans are a form of private benefit. By exploiting the relaxation of inter-state branching restrictions, we then show that banks react to greater competition by reducing insider lending, especially when the entry of new competitors may more strongly diminish bank profitability. We conclude that competitive pressure can attenuate incentive misalignments between insiders and providers of funds.

Consequences of Cov-Lite Loans
Demerjian, Peter R.,Horne, Eric,Moon, Keehea
Recent years have seen a new trend in commercial bank lendingâ€"loans with no financial covenants. These covenant light, or cov-lite, loans raise concerns about excessive risk to lenders due to lack of monitoring. In this study, we examine the consequences of cov-lite loans. Focusing on rated, institutional loans, we find that cov-lite loans are more likely to default than loans with financial covenants. Further, we find minimal evidence that investment riskiness is different for cov-lite borrowers but find evidence that cov-lite borrowers have worse future performance than other borrowers. The results collectively suggest a benefit to financial covenants to lenders which is lost when they issue cov-lite loans.

Der robuste Staat - Ein strategischer Rahmen zur Absicherung gegen Krisen und Katastrophen (The Robust State - A Strategic Framework for Protection against Crises and Disasters)
Gleißner, Werner
German Abstract: Die im Beitrag skizzierten Leitlinien für einen robusten Staat zeigen das Grundgerüst, das bei derDiskussion zur Verbesserung der Robustheit (oder Resilienz) eines Staates â€" auf dem Weg zum Angebot einer adäquaten Sicherheit für seine Bürger â€" zu durchdenken ist. Sie bieten auch einen Rahmen dafür, die aktuelle Robustheit eines Staates zu beurteilen und systematisch Handlungsbedarf aufzuzeigen.English Abstract: The guidelines for a robust state outlined in the article show the basic framework that is needed for discussion on how to improve the robustness (or resilience) of a state - on the way to offering adequate security to its citizens - should be considered. They also provide a framework for assessing the current robustness of a state and systematically highlighting the need for action.

Distress propagation on production networks: Coarse-graining and modularity of linkages
Ashish Kumar,Anindya S. Chakrabarti,Anirban Chakraborti,Tushar Nandi

Distress propagation occurs in connected networks, its rate and extent being dependent on network topology. To study this, we choose economic production networks as a paradigm. An economic network can be examined at many levels: linkages among individual agents (microscopic), among firms/sectors (mesoscopic) or among countries (macroscopic). New emergent dynamical properties appear at every level, so the granularity matters. For viral epidemics, even an individual node may act as an epicenter of distress and potentially affect the entire network. Economic networks, however, are known to be immune at the micro-levels and more prone to failure in the meso/macro-levels. We propose a dynamical interaction model to characterize the mechanism of distress propagation, across different modules of a network, initiated at different epicenters. Vulnerable modules often lead to large degrees of destabilization. We demonstrate our methodology using a unique empirical data-set of input-output linkages across 0.14 million firms in one administrative state of India, a developing economy. The network has multiple hub-and-spoke structures that exhibits moderate disassortativity, which varies with the level of coarse-graining. The novelty lies in characterizing the production network at different levels of granularity or modularity, and finding `too-big-to-fail' modules supersede `too-central-to-fail' modules in distress propagation.

Economic Analysis of Corporate Law
Gindis, David,Petrin, Martin
The economic analysis of corporate law applies the concepts and tools of microeconomics to the study of the legal rules, regulations and practices that govern the formation and operation of business corporations, most notably as regards the rights and duties of directors, officers, shareholders, and creditors. The literature has focused mostly on publicly-traded corporations, but the analytical framework extends to the simpler cases of close corporations and limited liability companies and the more complex case of corporate groups. This article outlines the foundations of the economic analysis of corporate law, contrasts the classic approach with more recent functional scholarship, and briefly discusses the law and economics of three specific issues: limited liability, managerial liability, and takeovers.

Effectiveness of Policy Measures to Promote Employee Share Ownership Programs in Banks
Tran, Henry
We investigate the effectiveness of the national supportive policy suggested by the European Commission to promote employee share ownership programs (ESOP) in European banks. We find that supportive measures are effective to promote ESOP in widely-held banks, independently of their degree of opacity and the level of shareholder protection. However, supportive measures are only effective to promote ESOP in closely-held banks if they are more transparent or located in countries with higher levels of shareholder protection. Our findings indicate that European countries not only need to implement supportive measures but also to enhance transparency and shareholder protection to promote ESOP. To identify the causal effect between the national supportive policy and ESOP adoption, we use the number of labor-support-parties in parliament as the instrumental variable for the supportive measures.

Expropriation Risk vs. Government Bailout: Implications for Minority Shareholders of State-Owned Banks
Achsanta, Aldy Fariz,Lepetit, Laetitia,Tarazi, Amine
We investigate the implications of government versus private ownership for bank minority shareholders. Specifically, we use unique data to examine whether the stock prices of government-owned and family-owned banks, equally engaged in related lending, differently react to loan announcements. Our empirical findings show that the expected negative market reaction due to minority shareholder expropriation driven by related lending (“grabbing hand” effect), is offset by shareholders’ expectations of future support from the government (“helping hand” effect). Positive announcement returns are also larger for new loans to state-owned firms than for those to private firms. Our findings support the view that in countries with weak shareholder protection, shareholders of state-owned banks rationally anticipate expropriation, but are willing to accept it in exchange for higher expectations of government support to state- owned banks and to state-owned firms.

Firm Prestige and the Cost of Bank Loans
Dai, Yunhao,Rau, P. Raghavendra,Tan, Weiqiang
Firm prestige reduces the cost of bank loans. Specifically, when borrowers are included in Fortune’s list of “America’s Most Admired Companies” (MAC), their loan costs decline by approximately 13 bps or US$5.122 million, on average. The effect appears causal. The negative relation between prestige and loan costs is more pronounced for borrowers in more competitive industries and with higher information uncertainty. Banks with low information gathering capacity offer favorable loan terms to the MAC ranked borrowers when they face a high degree of competition from other banks. The MAC ranking appears to be used by these banks as a summary statistic for loan quality in the face of competition.

Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
Humayra Shoshi,Indranil SenGupta

In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets. The refinement of the BN-S model is obtained with various machine and deep learning algorithms. The refinement leads to the extraction of a deterministic parameter from the empirical data set. The problem is transformed to an appropriate classification problem with a couple of different approaches: the volatility approach and the duration approach. The analysis is implemented to the Bakken crude oil data and the aforementioned deterministic parameter is obtained for a wide range of data sets. With the implementation of this parameter in the refined model, the resulting model performs much better than the classical BN-S model.

Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach
Pietro Murialdo,Linda Ponta,Anna Carbone

A perspective is taken on the intangible complexity of economic and social systems by investigating the underlying dynamical processes that produce, store and transmit information in financial time series in terms of the \textit{moving average cluster entropy}. An extensive analysis has evidenced market and horizon dependence of the \textit{moving average cluster entropy} in real world financial assets. The origin of the behavior is scrutinized by applying the \textit{moving average cluster entropy} approach to long-range correlated stochastic processes as the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Fractional Brownian motion (FBM). To that end, an extensive set of series is generated with a broad range of values of the Hurst exponent $H$ and of the autoregressive, differencing and moving average parameters $p,d,q$. A systematic relation between \textit{moving average cluster entropy}, \textit{Market Dynamic Index} and long-range correlation parameters $H$, $d$ is observed. This study shows that the characteristic behaviour exhibited by the horizon dependence of the cluster entropy is related to long-range positive correlation in financial markets. Specifically, long range positively correlated ARFIMA processes with differencing parameter $ d\simeq 0.05$, $d\simeq 0.15$ and $ d\simeq 0.25$ are consistent with \textit{moving average cluster entropy} results obtained in time series of DJIA, S\&P500 and NASDAQ.

Machine Learning for Zombie Hunting. Firms' Failures and Financial Constraints.
Bargagli Stoffi, Falco,Riccaboni, Massimo,Rungi, Armando
In this contribution, we exploit machine learning techniques to predict the risk of failure of firms. Then, we propose an empirical definition of zombie firms as firms that persist in a status of high risk, possibly misallocating financial and productive resources. We implement a Bayesian Additive Regression Tree with Missing Incorporated in Attributes (BART-MIA). The BART-MIA is specifically useful in our setting as we find that patterns of undisclosed financial accounts correlate with firms' failures. After we train our algorithm on 304,906 firms active in Italy in the period 2008-2017, we show how our technique outperforms proxy models, including the Z-scores and the Distance-to-Default, traditional econometric methods, and other widely used machine learning techniques. More in general, we argue that our application helps in the design of target-specific and evidence-based policies in the presence of market selection failures, for example in the design of optimal bankruptcy laws. We believe our framework can help to inform the design of support programs after the recent pandemic crisis for firms highly distressed due to rising financial constraints.

Mortgage Amortization and Wealth Accumulation
Bernstein, Asaf,Koudijs, Peter
The effect of mortgage amortization (debt repayment) on wealth accumulation is critical in understanding savings-debt repayment fungibility, macroprudential policies, and the importance of homeownership for household wealth building. Nevertheless, it is not well understood and difficult to cleanly identify. We provide the first empirical evidence on these effects using individual administrative data to examine plausibly exogenous variation in the timing of home purchases surrounding a January 2013 mortgage reform in the Netherlands which restricted the use of interest only mortgages for first time home buyers. For those who bought just after the regulation, we find little-to-no change in the accumulation of non-mortgage savings, even four years later, despite a significant increase in debt repayment. This surprisingly implies a near 1-for-1 rise in net worth - a response consistent with little savings-debt fungibility â€" financed via increased labor supply and reduced expenditures. Results hold using life-events, such as the birth of a child, as an instrument for the timing of home purchase, and appear unaffected by potential selection or confounded treatment concerns. Effects are unchanged focusing on buyers with substantial liquid savings and across the spectrum of ages, suggesting general applicability beyond just non-savers and the young. Our results are broadly consistent with savings decisions being almost entirely driven by short-term consumption smoothing and models of wealth accumulation driven by default settings, commitment devices, and mental accounting. Overall, our findings suggest that homeownership, when coupled with amortizing mortgages, are key drivers of wealth accumulation, and that the amortization-wealth elasticity is a crucial consideration for policy makers.

On Robust Inference for Consumption-based Asset Pricing
Kroencke, Tim Alexander
Kleibergen and Zhan (Robust Inference for Consumption-based Asset Pricing, Journal of Finance, 2020) propose a new approach to testing consumption-based asset pricing models. They find that recently proposed alternative consumption measures do not pass this test. I point out that their methodology also implies that the equity premium is insignificant and thus any asset pricing test is rendered useless (including their own). This counterfactual finding is due to the fact that their methodology is based on the false assumption that stock returns are normal-i.i.d. I show that reliable inference can be achieved without assuming that stock returns are normal-i.i.d.

On the Valuation and Analysis of Risky Debt: A Practical Approach Using Rating Migrations
Fischer, Edwin O.,Kampl, Lisa-Maria,Wöckl, Ines
This paper is concerned with the valuation and analysis of risky debt instruments with arbitrary interest and principal payments subject to default risk. For the valuation, we use a risk-neutral present value model with expected payments for risk-neutral investors and risk-free spot rates. The required risk-neutral default probabilities are derived from historically observable risk-averse migration matrices. Based on this debt valuation, we calculate various key figures for the analysis of risky debt from the point of view of risk-averse investors (e.g., promised and expected yields, yield spreads, Z-spreads, risk premia, risk-averse default probabilities, and risk-averse expected payments). Our approach is well-suited for practical applications, since the parameters required are easily available from observable data.

On the quantum behavior and clustering properties of correlated financial portfolios
Carlo Requião da Cunha,Roberto da Silva

We investigate 17 digital currencies making an analogy with quantum systems and develop the concept of eigenportfolios. We show that the density of states of the correlation matrix of these assets shows a behavior between that of the Wishart ensemble and one whose elements are Cauchy distributed. A metric for the participation matrix based on superposition of Gaussian functions is proposed and we show that small eigenvalues correspond to localized states. Nonetheless, some level of localization is also present for bigger eigenvalues probably caused by the fat tails of the distribution of returns of these assets. We also show through a clustering study that the digital currencies tend to stagger together. We conclude the paper showing that this correlation structure leads to an Epps effect.

On the uniqueness of solutions of stochastic Volterra equations
Alexandre Pannier,Antoine Jacquier

We prove strong existence and uniqueness, and H\"older regularity, of a large class of stochastic Volterra equations, with singular kernels and non-Lipschitz diffusion coefficient. Extending Yamada-Watanabe's theorem, our proof relies on an approximation of the process by a sequence of semimartingales with regularised kernels. We apply these results to the rough Heston model, with square-root diffusion coefficient, recently proposed in Mathematical Finance to model the volatility of asset prices.

Primer on Consumer Data Infrastructure
Raghavan, Malavika,Chugh, Beni,Singh, Anubhutie
The emergence of large-scale data infrastructures in India to support financial services delivery (such as the proposed Public Credit Registry, the GSTN system, and the Indiastack) raises foundational questions regarding the role of the State and of the regulation of these infrastructures. These data infrastructures include the databases that aggregate information and data utilities that allow for various databases to exchange information. They seek to re-imagine and improve the credit information markets in India with roles for private and public information-collecting entities. They also seek to bring the benefits of the aggregation of such data to improve financial inclusion and supervision of the financial sector.The impact for individuals and firms whose information will be aggregated and shared needs to be understood. Consumer data forms the backbone of the information that will be passed through these infrastructures. This requires an understanding of the issues that arise from the interplay between the regulation of data infrastructures and the regulation of personal data. The impact of these new projects for existing financial infrastructures and databases in the country also need to be understood.These developments raise new considerations for regulators. The approach to the regulation and supervision of these databases and infrastructures is by no means certain in the Indian context. The effect they will have on the financial sector and the wider market, and accordingly the optimal stance for regulation remains to be understood. Finally, there is a need for clarity on the liability framework for entities accessing these infrastructures and those in charge of running the infrastructures especially in cases where harms or losses are suffered due to their misuse, breach or failure.

Primer on Consumer Data Regulation
Research, Dvara,Singh, Anubhutie,Raghavan, Malavika,Chugh, Beni
The use of personal data by government and private service providers in interactions with individuals is becoming ubiquitous in India. These advances can greatly improve efficiency and reduce costs when delivering services to harder-to-reach users. However, the pervasive nature of data processing has raised concerns regarding privacy, exclusion due to digital service failure and related harms. These issues have special relevance for finance, given the increasing digitisation of the sector and the growing use of non-financial information to support the delivery of financial services.Without a robust framework to govern the regulation of consumer data, consumers and providers are exposed to significant risks and uncertainty. There has been significant policy movement in the past two years in reaction to some of these concerns, culminating in the release of a draft Personal Data Protection Bill that awaits introduction in the Indian Parliament. Within the financial sector, regulatory bodies have flagged relevant issues in the final report of the Inter-Regulatory Working Group on FinTech and Digital Banking, including those relating to consumer data protection, organisational data, shared utilities, big data analytics, data security and fraud prevention.In this background, some permanent concerns arise with respect to the regulation and use of consumer data in finance. Regulators and providers need to address the need to build trust for consumers on digital platforms given fears of fraud or financial loss. Providers have access to vast personal databases. In the absence of robust data protection regulations, provider considerations on the processing of data and the potential of system failures are crucial. Cost-efficient digital financial processes must not be created at the price of compromised consumer protection. A future regulation for data protection must be able to identify the unique risks Indian consumers might face while keeping in mind the potential of digital finance to promote financial inclusion.

Primer on Designing Optimal Regulation
Chugh, Beni,Raghavan, Malavika,Singh, Anubhutie
Technology is changing users’ experience of finance. The latest wave of financial technology goes beyond innovations in the delivery of financial services. It is expanding the choice set of financial services available to users and the ways in which they choose and consume from it.Several mechanisms enabled by technology are mediating these changes including the (i) disintermediation of traditional financial services; and (ii) the convergence of processes, platforms and financial infrastructure, bringing together services and products that were previously offered independently. Simultaneously, the increased availability and use of personal information in offering providers the opportunity to personalise financial products and services suitable for each consumer.As consumers begin transacting in this new environment, new considerations arise for the financial system and the existing regulatory rubric. The need arises to re-visit the regulatory posture assumed in a more analogue, less data-driven financial landscape on issues of micro-prudential risk, conduct regulation, consumer protection and systemic risk.Foundational questions that arise include where the regulatory perimeter should lie with respect to new non-financial sector entities in the chain of financial services provision, and how the tension between institution-based regulation and function-based regulation might be resolved in India. The robustness of existing frameworks for risk assessment, consumer protection, apportioning liability and the use of client data need to be reassessed in this new landscape. Designing the optimal regulation for data-driven finance will require the creation of a new, shared understanding of risks that emerge in this changing financial landscape, calibrating appropriate regulatory responses and new methods of coordination across regulators within and outside the financial sector.

SABR smiles for RFR caplets
Sander Willems

We present a natural extension of the SABR model to price both backward and forward-looking RFR caplets in a post-Libor world. Forward-looking RFR caplets can be priced using the market standard approximations of Hagan et al. (2002). We provide closed-form effective SABR parameters for pricing backward-looking RFR caplets. These results are useful for smile interpolation and for analyzing backward and forward-looking smiles in normalized units.

Soft Affirmative Action and Minority Recruitment
Daniel Fershtman,Alessandro Pavan

We study search, evaluation, and selection of candidates of unknown quality for a position. We examine the effects of "soft" affirmative action policies increasing the relative percentage of minority candidates in the candidate pool. We show that, while meant to encourage minority hiring, such policies may backfire if the evaluation of minority candidates is noisier than that of non-minorities. This may occur even if minorities are at least as qualified and as valuable as non-minorities. The results provide a possible explanation for why certain soft affirmative action policies have proved counterproductive, even in the absence of (implicit) biases.

The Impact of Product Innovation on Firm Value: Evidence from the Biopharmaceutical Industry
Niederreiter, Jan,Riccaboni, Massimo
In the current COVID-19 pandemic, we witness sharp fluctuations of biopharmaceutical equity prices as a response to innovation related news. On 23.04.2020, the stock price of Biotech firm Gilead Sciences fell by as much as 9% after rumors suggested that remdesivir, its experimental COVID-19 treatment, showed no benefit. One week later, official clinical trial results suggesting the opposite have led to a rebounce of 11% in equity value. Jumps in equity prices of this amplitude are frequent in the biopharmaceutical industry, yet we lack knowledge about the underlying forces that drive them. In this paper we investigate the impact of product innovation on firm equity value by linking venture specific characteristics to stock market reactions using a large data set of the biopharmaceutical industry. We find that market reactions increase in the portfolio importance of the product under development, measured by product specific net present value data, and diminish in its success probability, estimated by a combination of supervised learning methods. Our results aid corporations and investors to evaluate the financial consequences of innovation related news.

The Supply Channel of Uncertainty Shocks and the Cross-Section of Returns: Evidence From the COVID-19 Crisis
Bretscher, Lorenzo,Hsu, Alex,Tamoni, Andrea
The global spread of the COVID-19 virus has lead to a sudden surge in uncertainty and significantly negative equity returns. Combining geographical dispersion of firm headquarter location and the staggered spread of the virus throughout the U.S., we devise a natural experiment to examine the channel through which COVID-19 affects equity performance in the cross-section of firms. Using the first reported case of COVID-19 in a given county as the event day, firms headquartered in an affected county experience an average 27 bps lower return in the 10-day post-event window relative to returns before the event and compared with firms headquartered in non-COVID counties. We further document that this negative effect nearly doubles in magnitude for firms in counties with a higher infection rate (-50 bps). As our sample firms are large publicly traded companies with diverse sales networks, the local effect we document at the county-level suggests the presence of a supply channel through which uncertainty due to COVID-19 affect firm performance. Consistent with this interpretation we find that firms operating in labor intensive industries encounter even larger declines in returns after the first case is reported.

The convergence rate from discrete to continuous optimal investment stopping problem
Dingqian Sun

We study the optimal investment stopping problem in both continuous and discrete case, where the investor needs to choose the optimal trading strategy and optimal stopping time concurrently to maximize the expected utility of terminal wealth. Based on the work [9] with an additional stochastic payoff function, we characterize the value function for the continuous problem via the theory of quadratic reflected backward stochastic differential equation (BSDE for short) with unbounded terminal condition. In regard to discrete problem, we get the discretization form composed of piecewise quadratic BSDEs recursively under Markovian framework and the assumption of bounded obstacle, and provide some useful prior estimates about the solutions with the help of auxiliary forward-backward SDE system and Malliavin calculus. Finally, we obtain the uniform convergence and relevant rate from discretely to continuously quadratic reflected BSDE, which arise from corresponding optimal investment stopping problem through above characterization.

Time-frequency Forecast of the Equity Premium
Faria, Gonçalo,Verona, Fabio
Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting, this method significantly improves in both statistical and economic sense upon standard time series forecasting methods. This improvement is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.

What Drives Firms' Hiring Decisions? An Asset Pricing Perspective
Belo, Frederico,Donangelo, Andres,Lin, Xiaoji,Luo, Ding
In a neoclassical dynamic model of the firm with labor market frictions, optimal hiring is a forward-looking decision that depends on both discount rates and expected cash flows. Empirically, we show that: a) the aggregate hiring rate of publicly traded firms in the U.S. economy negatively predicts stock market excess returns and long-term cash flows both in-sample and out-of-sample, and positively predicts short-term cash flows; and b) through a variance decomposition, the time series variation in the aggregate hiring rate is mainly driven by changes in discount rates and short-term expected cash flows, each contributing roughly to 50% of the variation, with no contribution from variation in long-term expected cash flows. Through a structural estimation of the model, we show that labor adjustment costs and, to a lesser extent, time-variation in the price of aggregate productivity risk, are essential for the model to replicate the empirical patterns.