Research articles for the 2021-01-24

A Contextualist Decision Theory
Saleh Afroogh

Decision theorists propose a normative theory of rational choice. Traditionally, they assume that they should provide some constant and invariant principles as criteria for rational decisions, and indirectly, for agents. They seek a decision theory that invaribably works for all agents all the time. They believe that a rational agent should follow a certain principle, perhaps the principle of maximizing expected utility everywhere, all the time. As a result of the given context, these principles are considered, in this sense, context-independent.

Furthermore, decision theorists usually assume that the relevant agents at work are ideal agents, and they believe that non-ideal agents should follow them so that their decisions qualify as rational. These principles are universal rules. I will refer to this context-independent and universal approach in traditional decision theory as Invariantism. This approach is, implicitly or explicitly, adopted by theories which are proposed on the basis of these two assumptions.

Adaptive Asset Allocation
Rowles, Shaun
Adaptive Asset Allocation builds on Harry Markowitz’s 1952 Modern Portfolio Theory by providing greater risk management to traditional static allocation models. By adjusting risk exposures within the portfolio in response to the macroeconomic environment, investors can reduce exposure to crisis market conditions and significantly improve long-run performance.

Banks and Negative Interest Rates
Heider, Florian,Saidi, Farzad,Schepens, Glenn
In this paper, we survey the nascent literature on the transmission of negative policy rates. We discuss the theory of how the transmission depends on bank balance sheets, and how this changes once policy rates become negative. We review the growing evidence that negative policy rates are special because the pass-through to banks' retail deposit rates is hindered by a zero lower bound. We summarize existing work on the impact of negative rates on banks' lending and securities portfolios, and the consequences for the real economy. Finally, we discuss the role of different ``initial'' conditions when the policy rate becomes negative, and potential interactions between negative policy rates and other unconventional monetary policies.

Better Housing: FHA Property Improvement Loans
Park, Kevin A.
The National Housing Act of 1934 is most well-known for creating the Federal Housing Administration (FHA) to insure home mortgages; however, the first section of the Act also covers insurance of financial institutions against losses on loans and advances of credit “for the purpose of financing alterations, repairs, and improvements upon real property.” Title I was at the heart of a New Deal initiative to modernize housing as both a means to improve the welfare of American households as well as employ surplus labor in home construction and repair. It also introduced commercial banks to consumer instalment lending, which revolutionized household finance. FHA endorsed more property improvement loans per year than home mortgages for its first 35 years. And the cumulative number of loans insured under Title I was greater than the more famous Section 203B program until the financial crisis of 2008. Yet the importance of Title I is often overlooked. “Historical scholars have argued that FHA programs were self-validating: by encouraging the neglect of older homes and neighborhoods, they made filtering inevitable, and with it the decline of older neighborhoods in central cities. There is some truth to this assessment of federal policy. However, and although the fact has been widely ignored, from the beginning the FHA also sought to improve existing homes and neighborhoods” (Harris 2009, p. 392). In part, this may be because the Title I program has not kept pace with changes in the financial industry, causing disbursements to tumble and the program to drift into obscurity. This paper briefly describes FHA’s Title I property improvement program, including its history and underwriting requirements. Using administrative data, the paper examines variation in interest rates across loan and borrower characteristics. In particular, I estimate the rate premium associated with unsecured lending during the Great Recession. I further estimate the likelihood of default and insurance claim amount given default. The results could be used to automate credit underwriting and facilitate modernization of property improvement financing.

Convertible Bond Arbitrage and the Term Structure of Volatility
Zeitsch, Peter,Hyatt, Matthew,Davis, Tom P. ,Liu, Xi
A mark-to-market approach for convertible bonds is proposed where the volatility from the bond optionality is implied from the traded credit spread and bond price. By linking the convertible bond implied volatility to the listed equity option implied volatility surface, the set of available tenors is significantly extended. The resulting volatility term structure classifies rich versus cheap bonds. Convertible bond arbitrage trades, where the trader buys the bond and hedges a combination of the underlying assets, are subsequently identified. Each bond’s relative cheapness translates to its arbitrage potential as a long volatility position, as shown through case studies.

Diversifying Diversification: Downside Risk Management with Portfolios of Insurance Securities
Bhansali, Vineer,Holdom, Jeremie
Investors are always in search of diversifying securities and strategies to assist in downside risk management. We consider six popular diversifying securities, i.e. Gold, Swiss Franc, Japanese Yen, Bond Futures, S&P 500 80% strike Put Options, and Trend Following strategies in this paper. Using fifty years of data, we demonstrate that a portfolio approach to diversification strategies results in more robust outcomes when combined with a portfolio which has large equity exposure. While each of the individual securities can be more or less beneficial in specific periods and environments, we conclude that a simple portfolio approach to diversification, whether optimized or not, allows investors to robustly manage risk while not being overly concentrated.

Do Credit Supply Shocks Affect Fertility Choices?
Kim, Jeong Ho (John),Lee, Heebum,Lee, Sung Kwan
We empirically investigate the role of credit supply in fertility decisions. Using the U.S. banking deregulation in the 1980s and the 2007â€"2009 Great Recession as two different laboratories for credit supply shocks, we find that an increase in credit supply consistently implies higher fertility rates, as well as higher probability of giving birth. This relation, which is economically and statistically significant, differs across individuals: it is more pronounced for young women and for families with unemployed husbands. Finally, we provide suggestive evidence that increased credit access leads to more optimistic expectations about personal prospects, and in turn, higher fertility rates.

Extensive networks would eliminate the demand for pricing formulas
Jaegi Jeon,Kyunghyun Park,Jeonggyu Huh

In this study, we generate a large number of implied volatilities for the Stochastic Alpha Beta Rho (SABR) model using a graphics processing unit (GPU) based simulation and enable an extensive neural network to learn them. This model does not have any exact pricing formulas for vanilla options, and neural networks have an outstanding ability to approximate various functions. Surprisingly, the network reduces the simulation noises by itself, thereby achieving as much accuracy as the Monte-Carlo simulation. Extremely high accuracy cannot be attained via existing approximate formulas. Moreover, the network is as efficient as the approaches based on the formulas. When evaluating based on high accuracy and efficiency, extensive networks can eliminate the necessity of the pricing formulas for the SABR model. Another significant contribution is that a novel method is proposed to examine the errors based on nonlinear regression. This approach is easily extendable to other pricing models for which it is hard to induce analytic formulas.

Generalized BN-S Model Application: Analysis of Stock Index Option Price Volatility Based on Machine Learning and Fuzzy Parameters
Xianfei Hui,Baiqing Sun,Indranil SenGupta,Hui Jiang

We use the superposition of the Levy processes to optimize the classic BN-S model. Considering the frequent fluctuations of price parameters difficult to accurately estimate in the model, we preprocess the price data based on fuzzy theory. The price of S&P500 stock index options in the past ten years are analyzed, and the deterministic fluctuations are captured by machine learning methods. The results show that the new model in a fuzzy environment solves the long-term dependence problem of the classic model with fewer parameter changes, and effectively analyzes the random dynamic characteristics of stock index option price time series.

Graphical Models for Financial Time Series and Portfolio Selection
Ni Zhan,Yijia Sun,Aman Jakhar,He Liu

We examine a variety of graphical models to construct optimal portfolios. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture the time varying patterns in the covariance matrix and allow the creation of an optimal and robust portfolio. We compared the resulting portfolios from the different models with baseline methods. In many cases our graphical strategies generated steadily increasing returns with low risk and outgrew the S&P 500 index. This work suggests that graphical models can effectively learn the temporal dependencies in time series data and are proved useful in asset management.

High-dimensional Statistical Learning Techniques for Time-varying Limit Order Book Networks
Chen, Shi,Härdle, Wolfgang K.,Schienle, Melanie
This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit order books (LOB) and quantify their impacts on the size and structure of price interdependencies across stocks. For correct empirical network determination of such dynamic liquidity price effects even in small portfolios, we require high-dimensional statistical learning methods with an integrated general bootstrap procedure. We document the importance of LOB liquidity network spillovers even for a small blue-chip NASDAQ portfolio.

How Consumers Budget
Zhang, C. Yiwei,Sussman, Abigail B.,Wang-Ly, Nathan,Lyu, Jennifer
Although budgeting is widely considered a common method of managing household finances, surprisingly little is known about the budgeting process. Using a nationally-representative survey (N=3,826) of US adults, we examine budgeting behaviors and beliefs, including who budgets and why, how individuals categorize consumption, and how they adjust their behavior after over- or under-spending. We identify five facts that illuminate key features of budgeting and supplement findings with administrative data (N=194,678) from a large financial institution in Australia. Understanding systematic patterns in how individuals and households budget may serve a critical role in informing economic models of consumption-savings behavior.

How does COVID-19 change insurance and vaccine demand? Evidence from short-panel data in Japan
Eiji Yamamura,Yoshiro Tsutsui

In this study, we explored how the coronavirus disease (COVID-19) affected the demand for insurance and vaccines in Japan from mid-March to mid-April 2020. Through independent internet surveys, respondents were asked hypothetical questions concerning the demand for insurance and vaccines for protection against COVID-19. Using the collected short-panel data, after controlling for individual characteristics using the fixed effects model, the key findings, within the context of the pandemic, were as follows: (1) Contrary to extant studies, the demand for insurance by females was smaller than that by their male counterparts; (2) The gap in demand for insurance between genders increased as the pandemic prevailed; (3) The demand for a vaccine by females was higher than that for males; and (4) As COVID-19 spread throughout Japan, demand for insurance decreased, whereas the demand for a vaccine increased.

Information Efficiency or Hold-up with Universal Banks? Some Evidence from Lending after Equity Underwriting
Pinto, Gerard,Zhang, Donghang
We find that spreads on loans originated by lenders affiliated to the IPO underwriters (informed lenders) are 12 basis points (bps) higher than loans originated by unaffiliated (uninformed) lenders. The price of these affiliated post-IPO loans is 59 bps higher than that of those unaffiliated loans when they start trading on the secondary market, suggesting too high a spread for affiliated loans. Loans around the SEO window also have higher spreads when they are originated by SEO underwriters. Our results for both IPOs and SEOs support the hold-up hypothesis of informed lenders exploiting private information to earn economic rents.

International Portfolio Investments with Trade Networks
Chau, Vu
What determines the composition of international portfolio investments remains an open question in international finance. In this paper, I propose a theory of international portfolio choice where trade networks play a key role. I solve in closed form for the optimal equity and bond portfolio investments in a multi-country model with arbitrary global input - output linkages and taste differences. I show that a measure of international demand exposure, called the “International Domar Weights” (IDWs), is key in determining international equity portfolios. The IDWs extend the closed-economy “Domar weights” to the international setting and capture countries' interdependence through both direct and indirect trade linkages. Using data from the World Input - Output Database (WIOD) and Coordinated Portfolio Investment Survey (CPIS), I apply the framework to a network of 43 major developed and emerging economies and obtain four main results. First, the theoretical network portfolio is a significant predictor and explains almost half of the variation in international bilateral portfolio investments. The significance of the network portfolio is robust to controlling for gravity factors (market capitalization, distance, EU membership, etc.). Second, including the network-based portfolio in a gravity model for assets resolves the puzzle of why distance matters for asset trade at all. Third, indirect trade linkages matter for portfolio determination, highlighting the need to explicitly account for trade in intermediate inputs. Finally, the model predicts both the levels and the changes in equity home bias that have occurred since 2000.

Markowitz portfolio selection for multivariate affine and quadratic Volterra models
Eduardo Abi Jaber,Enzo Miller,Huyên Pham

This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and non-semimartingale market framework with unbounded random coefficients, the optimal portfolio strategy is expressed by means of a Riccati backward stochastic differential equation (BSDE). In the case of affine Volterra models, we derive explicit solutions to this BSDE in terms of multi-dimensional Riccati-Volterra equations. This framework includes multivariate rough Heston models and extends the results of \cite{han2019mean}. In the quadratic case, we obtain new analytic formulae for the the Riccati BSDE and we establish their link with infinite dimensional Riccati equations. This covers rough Stein-Stein and Wishart type covariance models. Numerical results on a two dimensional rough Stein-Stein model illustrate the impact of rough volatilities and stochastic correlations on the optimal Markowitz strategy. In particular for positively correlated assets, we find that the optimal strategy in our model is a `buy rough sell smooth' one.

Measuring an adaptive change in human decision-making from AI: Application to evaluate changes after AlphaGo
Minkyu Shin,Minkyung Kim,Jin Kim

Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision-making abilities. But how can we quantify such human adaptation to AI? We develop a simple measure of human adaptation to AI and test its usefulness in two case studies. In Case Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe AI's reasoning processes rather than mere actions of AI. In Case Study 2, we test whether our measure is sensitive enough to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players. We discuss our measure's applications in domains other than Go, especially in domains in which AI's decision-making ability will likely surpass that of human experts.

Mortgage Payments and Equity Premium Puzzle
Sing, Tien Foo,Zou, Yiheng
The equity premium puzzle argues that equity risk alone is insufficient to justify observed equity premiums with a reasonable value of risk aversion. Mortgages account for a substantial part of household debt, it is thus necessary to take the mortgage payment obligations into consideration when addressing the puzzle. This paper examines how the mortgage payment obligations affect the stochastic discount factor in the pricing kernel and influence the equity premium. The proposed asset pricing model incorporating the mortgage payment factor explains the observed equity premium with an acceptable risk aversion value of $1.2$. Mortgage payments affect the utility function in two dimensions. First, the payments hurt individuals by decreasing their overall utility level. Second, the countercyclical effect of the payments benefits individuals by decreasing the volatility of the utility stream. This paper contributes to the literature by highlighting the importance of mortgages in predicting the equity premium.

Nine Challenges in Modern Algorithmic Trading and Controls
Jackie Shen

This editorial article partially informs the algorithmic trading community about launching of the new journal "Algorithmic Trading and Controls" (ATC). ATC is an online open-access journal that publishes novel works on algorithmic trading and its control methodologies. In this inaugural article, we discuss nine major challenges that contemporary Algo trading faces. There is nothing superstitiously magical about the number "nine," but so is any other one. Several of these challenges are at the strategy level, including for example, trading of illiquid securities or optimal portfolio execution. Others are more at the level of risk management and controls, such as on how to develop automated controls, testing and simulations. The editorial views could be inevitably personal and biased, but have been explored with the most innocent intention of contributing to this important field in modern financial services and technologies.

Origins of Mutual Fund Skill: Market versus Accounting Based Asset Pricing Anomalies
Christiansen, Charlotte,Xing, Ran,Xu, Yue
We investigate the information source of active U.S. equity mutual funds’ value added using 234 public asset pricing anomalies. On average, mutual funds add value through their positive exposures to anomalies based on market information (e.g., momentum and liquidity risk) and lose value through their negative exposures to anomalies based on accounting information of firm fundamentals (e.g., investment and profitability), corroborating that both the semi-strong and weak forms of the efficient market hypothesis do not hold. We also find weak evidence that mutual funds profit from their private information, supporting the rejection of the strong form efficient market hypothesis.

Predicting Earnings Management from Qualitative Disclosures
Jaspersen, Johannes Gerd,Richter, Andreas,Zoller, Sandra
While analysts, customers, and lenders rely on financial disclosures to make decisions regarding a company, executives often manage the disclosed earnings. Detecting these practices is thus a concern for company stakeholders and regulators. We use supervised machine learning models to predict earnings management by property and casualty insurers from the Management's Discussion and Analysis filings. We show that these filings can be used to predict earnings management, revealing that executives are unable to remove all subliminal messages from them. For this, we analyze a new algorithm that interprets textual data conditional on the reported financial situation of the company.

Probabilistic Framework For Loss Distribution Of Smart Contract Risk
Petar Jevtic,Nicolas Lanchier

Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. Up to our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a {random} star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples.

Reporting Rules in Bank Runs
Zhang, Gaoqing,Zheng, Ronghuo
We study the role of reporting rules in the context of bank runs. In our model, a financial institution receives an early but imprecise estimate of the performance of its investment and issues a report subject to a reporting rule. We find that, from a financial-stability standpoint, the optimal reporting rule requires full disclosure when the financial institution's early estimate is sufficiently unfavorable, but no disclosure otherwise. Importantly, the threshold below which the financial institution reports should be tailored to the financial institution's exposure to bank-run risk. In particular, the optimal reporting threshold is non-monotonic and U-shaped in the bank-run risk. We also relate our results to current accounting standards for asset impairments.

Sentiment and Covariance Characteristics
Tran, Vu Le
We propose a bridging model that connects risk-based factor models to sentiment models by using characteristics. Investors use stock characteristics as information to form their biased view and hence creating mispricing. Characteristics also serve as the proxy for the covariance risk to a latent factor. The α from our factor model of mispricing ranges from 0.70% to 1.38% monthly after controlling for other common factors and mispricing measures. Well-known anomalies are only represented in either underpriced or overpriced stocks but not in all the cross-section.

Short-Term Debt Catering
Lugo, Stefano
Focusing on investments by US money market mutual funds (MMFs) in nonfinancial commercial paper, this study shows that the demand for corporate short-term securities by preferred-habitat investors is positively associated with the use of short-term debt by firms. Consistent results are found when using a longitudinal dataset with a monthly frequency and a firm-quarter panel dataset; instrumenting the demand by MMFs; measuring the demand by MMFs at the level of individual issuers; and exploiting an exogenous change in regulation for identification. These findings support the idea that corporations cater to investors' preferences in choosing their debt maturity structure.

Strategic Debt Restructuring and Asset Substitution
Giamouridis, Daniel,Prassa, Chara
This paper examines whether debt renegotiation mitigates the agency costs of asset substitution. Inspired by the studies of Mella-Barral and Perraudin (1997) and Leland (1998), we have developed an analytical continuous time model of a firm that has the option to switch to a higher risk activity and renegotiate the terms of the debt. Our model creates a trade-off between increasing firm volatility and decreasing growth rate which characterizes the potential for asset substitution. Our findings indicate that, in most cases, debt renegotiation substantially reduces agency costs of asset substitution, whereas non-renegotiable debt is optimal when equity holders have a weak bargaining position and the opportunity cost of their risk-taking incentives is high. Several testable empirical implications regarding the design of debt contracts are developed.

Tapping Home Equity: Income and Spending Trends Around Cash-Out Refinances and HELOCs
Farrell, Diana,Greig, Fiona,Zhao, Chen
Approximately two thirds of American families own a home and for most homeowners, their house is also their most important source of wealth. Homeowners are currently sitting on historically high levels of home equity and the potential withdrawal of this home equity has important implications for consumption at the macroeconomic and household levels. In this report, we examine the extent to which liquidating home equity boosts consumption, as well as how income dynamics around equity extraction may play a role in influencing households’ decision to draw from this source of wealth. Using loan-level servicing data from Chase mortgage customers combined with corresponding Chase deposit account data from 2012 to 2018, we create a sample of more than 50,000 homeowners who either obtained a cash-out refinance or drew on a home equity line of credit (HELOC). We find that for homeowners who cash-out refinanced, most refinanced into a lower interest rate but a higher monthly payment because of a larger loan balance. Also, after controlling for secular trends, homeowners who obtained a cash-out refinance had no change in income whereas homeowners who extracted equity via a HELOC experienced declining income. For both groups, consumption spiked considerably as soon as the liquidated equity flowed into the bank account but quickly settled to steady state-levels at a higher level, 5 percent and 7 percent above baseline for HELOCs and cash-out refinances, respectively. After one year, cash-out refinance homeowners spent 33 percent of their total equity extracted while those with a HELOC spent 47 percent overall. For both sample groups, these marginal propensities to consume (MPCs) were highest for younger homeowners and those with higher loan-to-values (LTVs). Taken together, these findings have important implications for macroeconomic and housing policies.

The Differential Impact of Leverage on Firm Investment: Evidence From Advanced and Developing Countries
Segara, Reuben,Yang, Jin Young
This paper examines the differential impact of leverage on investment across firms with varying levels of growth opportunities in both advanced and developing countries. Contradictory research findings on the strength of this relationship for firms with low or high growth opportunities are reported; where even divergent results in previous studies using the same country for analysis are found. By using a multi-country framework, we confirm a negative impact of leverage on firm investment, where a stronger negative impact is consistently found for firms with low growth opportunities in both advanced and developing countries.

The Effect of Monetary Policy Shocks on Mutual Fund Investing
Banegas, Ayelen,Montes-Rojas, Gabriel,Siga, Lucas
We study the links between unexpected changes in the stance of monetary policy and mutual fund performance and flows using data over the 2000â€"17 period. We find that monetary policy shocks, through their direct effect on asset prices, can have an impact on mutual fund returns and flow dynamics, and that the effect of these shocks differs by investment strategy. Using monetary policy measures capturing both target and path shocks during periods of conventional and unconventional monetary policy, results show that an unexpected tightening of the stance of policy is associated with negative performance and outflows from bond funds, in particular those investing in investment grade, government, and international bonds. However, these effects appear to be transitory, persisting only for a few months, and providing little support to the hypothesis that unanticipated monetary policy changes could trigger large and destabilizing effects on the returns and flows of mutual funds.

The Effects of Monetary Policy Shocks on Mutual Fund Investing
Banegas, Ayelen,Montes-Rojas, Gabriel,Siga, Lucas
We study the effects of unexpected changes in the stance of monetary policy on mutual fund performance and allocation decisions over the recent period of unconventional U.S. monetary policy. Taking an agnostic approach on the transmission mechanism of monetary policy on mutual fund investing, we find that monetary policy shocks have a direct effect on fund performance and flow dynamics, and that the effect of these surprises differs by investment strategy. The results show that an unexpected tightening of the stance of policy is associated with negative performance and outflows from bond funds, in particular those investing in government, investment grade, and international bonds. Results also point to a negative relationship between equity fund performance and policy tightening. Moreover, Federal Reserve’s balance sheet policy (i.e. large-scale asset purchases) have a strong positive effect on equity fund returns, especially on those investing in emerging markets.

The Impact of CEO Overconfidence on Profitability and Stock Return: A Firm’s Life Cycle Perspective
Demirkan, Sebahattin,Mishra, Birendra K.,Toksoz, Tuba
We examine how CEO overconfidence impacts profitability and stock return for firms at different stages in their life cycle. Extant research has shown that an overconfident personality could affect the CEO's decision-making on investment, financial reporting, and the firm's choice of policies. It is well known that firms have lifecycle stages as they evolve with time. The firm's evolution is triggered by changes in internal factors like an investment and managerial decisions and external factors such as the evolution of industry and economy. Prior literature posits that life cycle stages have an impact on firms' decision making and profitability. We find that firms with overconfident CEOs perform differently and have abnormal returns depending on the firm's life cycle. Especially they perform better and have positive abnormal returns in growth, mature and shake-out stages if the CEOs are overconfident. We find no significant result for firms in the introduction and decline phases.

The Intra-Regional Spillover Effect of Bond Defaults: Evidence from China's Credit Bond Market
Wang, Wenlong,Huang, Yuqin,Watson, John,Yang, Bowen
Taking the first bond defaults in each province in China as credit events, we adopt a difference-in-difference model and find that credit spreads of other corporate bonds in the same province increase by 6 basis points on the first default event day, suggesting a spillover effect. The spillover effect is stronger for local state-owned-enterprise bond defaults, and the magnitude of the spillover effect is negatively related to firm-government connections. Meanwhile, defaults related to investor protection clauses have the largest spillover effect. We also find that provinces with higher GDP growth rates and higher general public budget revenue are less affected by the first bond defaults. Overall, our paper provides new evidence of an intra-regional spillover effect in bond defaults.

The Missing Home Buyers: Regional Heterogeneity and Credit Contractions
Mabille, Pierre
This paper demonstrates that the protracted decrease in young homeownership since the Great Recession was driven by high-house price regions, despite credit standards changing mostly nationally. Using a panel of U.S. metro areas, I calibrate a multi-region dynamic equilibrium model with overlapping generations of mobile households. Aggregate and regional dynamics are explained by the heterogeneous impacts of an aggregate credit contraction rather than by local shocks. Preexisting differences between regions and cohorts amplify differences in busts. The effect of subsidies to first-time buyers is dampened, because they fail to stimulate regions that suffer larger busts. Place-based subsidies achieve larger gains.

The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of their Determinants
Fischer, Henning,Stolper, Oscar Anselm
This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit riskâ€"rather than interest rate movementsâ€" contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.

The Volatility Risk Premium: An Empirical Study on the S&P 500 Index
Guo, Ivan,Loeper, Gregoire
We perform an empirical analysis of trading strategies based on the systematic selling of delta hedged options, aiming at capturing the so-called volatility risk premium. We compare the performance across different strikes and maturities, and perform a breakdown of the drivers of performance. We also examine how such strategies can be combined to extract other premia related to the profile of the volatility surface, e.g. the skew and the term structure. In this first paper we focus on the S&P 500 index over the period 2010â€"2018.

Time-varying neural network for stock return prediction
Steven Y. K. Wong,Jennifer Chan,Lamiae Azizi,Richard Y. D. Xu

We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time-varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly U.S. stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators, exhibit time varying stock return predictiveness. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.

Viability and Arbitrage under Knightian Uncertainty
Matteo Burzoni,Frank Riedel,H. Mete Soner

We reconsider the microeconomic foundations of financial economics. Motivated by the importance of Knightian Uncertainty in markets, we present a model that does not carry any probabilistic structure ex ante, yet is based on a common order. We derive the fundamental equivalence of economic viability of asset prices and absence of arbitrage. We also obtain a modified version of the Fundamental Theorem of Asset Pricing using the notion of sublinear pricing measures. Different versions of the Efficient Market Hypothesis are related to the assumptions one is willing to impose on the common order.

Volatility and Insider Sales before Earnings Announcement
Hu, Yue
Mandatory earnings announcement reduces the information advantage of corporate insiders. Prior studies suggest that insiders opportunistically sell their stocks before firms announce bad news and that it is difficult to identify these opportunistic sales as insiders also sell for benevolent reasons. I examine whether volatility is useful in detecting these opportunistic insider sales. I argue that insider sales when volatility is low are suspicious. Because when volatility is low, there is limited price information that can stimulate insiders to sell, and there is limited space for insiders to speculate. Using insider sales before the quarterly earnings announcement in China's A-share market from 2008 to 2018. I find that insider sales that occur when volatility is low are more likely to be followed by ROE decline and that these insider sales cause a larger price drop at the day that sales occur. These findings suggest that pre-sale volatility reveals to what probability that insider sales are driven by to-be-announced bad news, thereby relevant to regulators and investors.

What’s in a Rating Report? Parsing the Content of Moody’s Credit Rating Reports from 1998-2016
Kiesel, Florian,Kisgen, Darren J.
We examine the readability, length, numerical content, uncertainty, and uniqueness of Moody’s rating reports and analyze how regulatory events have influenced these measures between 1998-2016. We find that information in rating reports significantly dropped after the Credit Rating Reform Act in 2006, but readability and the length of rating reports significantly improved after the Dodd-Frank regulation in 2010. We also find that greater readability leads to lower announcement returns after downgrades. Reports that are more similar to previous reports are associated with less negative announcement returns, providing evidence that the content of rating reports plays a significant role for investors.

Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits
Ni Zhan

This paper examines deposits of individuals ("retail") and large companies ("wholesale") in the U.S. banking industry, and how these deposit types are impacted by macroeconomic factors, such as quantitative easing (QE). Actual data for deposits by holder are unavailable. We use a dataset on banks' financial information and probabilistic generative model to predict industry retail-wholesale deposit split from 2000 to 2020. Our model assumes account balances arise from separate retail and wholesale lognormal distributions and fit parameters of distributions by minimizing error between actual bank metrics and simulated metrics using the model's generative process. We use time-series regression to forward predict retail-wholesale deposits as function of loans, retail loans, and reserve balances at Fed banks. We find increase in reserves (representing QE) increases wholesale but not retail deposits, and increase in loans increase both wholesale and retail deposits evenly. The result shows that QE following the 2008 financial crisis benefited large companies more than average individuals, a relevant finding for economic decision making. In addition, this work benefits bank management strategy by providing forecasting capability for retail-wholesale deposits.