# Research articles for the 2019-09-17

Attention to the Tail(s): Global Financial Conditions and Exchange Rate Risks
Eguren Martin, Fernando,Sokol, Andrej
SSRN
We document how the entire distribution of exchange rate returns responds to changes in global financial conditions. We measure global financial conditions as the common component of country-specific financial condition indices, computed consistently across a large panel of developed and emerging economies. Based on quantile regression results, we provide a characterisation and ranking of the tail behaviour of a large sample of currencies in response to a tightening of global financial conditions, corroborating some of the prevailing narratives about safe haven and risky currencies. We then carry out a portfolio sorting exercise to identify the macroeconomic fundamentals associated with such different tail behaviour, and find that currency portfolios sorted on the basis of relative interest rates, current account balances and levels of international reserves display a higher likelihood of large losses in response to a tightening of global financial conditions.

Can Inclusion in Religious Index Membership Mitigate Earnings Management?
SSRN
This paper investigates whether religious-based index membership is important in mitigating earnings management. Using a large sample of firms domiciled across 12 European countries, our empirical results show that firms included in the Shariah-compliant index, as a proxy for religious index, are more likely to engage in accruals manipulation vis-a-vis non-Shariah-compliant firms. Our results are robust using the Heckman two-stage treatment effect model, weighted least squares model, alternative earnings quality metrics and after controlling for the potential effects of home-country characteristics. Furthermore, our empirical results indicate that corporate governance of Shariah-compliant firms does not constrain managerial opportunistic behaviour in misreporting earnings, and firms that with low scores of board functions, shareholder rights and vision and strategy are more likely to engage in earnings management. Further, Shariah-compliant firms domiciled in Coordinated Market Economies are more likely to manipulate earnings than those in Liberal Market Economies. Taken together, our findings suggest that the Shariah index membership does not indicate good corporate governance that can mitigate earnings management, and it may serve as a legitimacy mechanism to conform to stakeholdersâ€™ expectations. Our findings support arguments that the religious-based index membership is plausibly used as a â€˜labelâ€™ and an impression management tool to attract investment.

Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective
Shenhao Wang,Qingyi Wang,Nate Bailey,Jinhua Zhao
arXiv

While researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain as obstacles in theory and practice. By using statistical learning theory, this study presents a framework to examine the tradeoff between estimation and approximation errors, and between prediction and interpretation losses. It operationalizes the DNN interpretability in the choice analysis by formulating the metrics of interpretation loss as the difference between true and estimated choice probability functions. This study also uses the statistical learning theory to upper bound the estimation error of both prediction and interpretation losses in DNN, shedding light on why DNN does not have the overfitting issue. Three scenarios are then simulated to compare DNN to binary logit model (BNL). We found that DNN outperforms BNL in terms of both prediction and interpretation for most of the scenarios, and larger sample size unleashes the predictive power of DNN but not BNL. DNN is also used to analyze the choice of trip purposes and travel modes based on the National Household Travel Survey 2017 (NHTS2017) dataset. These experiments indicate that DNN can be used for choice analysis beyond the current practice of demand forecasting because it has the inherent utility interpretation, the flexibility of accommodating various information formats, and the power of automatically learning utility specification. DNN is both more predictive and interpretable than BNL unless the modelers have complete knowledge about the choice task, and the sample size is small. Overall, statistical learning theory can be a foundation for future studies in the non-asymptotic data regime or using high-dimensional statistical models in choice analysis, and the experiments show the feasibility and effectiveness of DNN for its wide applications to policy and behavioral analysis.

Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Shenhao Wang,Jinhua Zhao
arXiv

Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability and predictive power, and to identify effective regularization methods for specific tasks. This study designs a particular DNN architecture with alternative-specific utility functions (ASU-DNN) by using prior behavioral knowledge. Unlike a fully connected DNN (F-DNN), which computes the utility value of an alternative k by using the attributes of all the alternatives, ASU-DNN computes it by using only k's own attributes. Theoretically, ASU-DNN can dramatically reduce the estimation error of F-DNN because of its lighter architecture and sparser connectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy than F-DNN over the whole hyperparameter space in a private dataset that we collected in Singapore and a public dataset in R mlogit package. The alternative-specific connectivity constraint, as a domain-knowledge-based regularization method, is more effective than the most popular generic-purpose explicit and implicit regularization methods and architectural hyperparameters. ASU-DNN is also more interpretable because it provides a more regular substitution pattern of travel mode choices than F-DNN does. The comparison between ASU-DNN and F-DNN can also aid in testing the behavioral knowledge. Our results reveal that individuals are more likely to compute utility by using an alternative's own attributes, supporting the long-standing practice in choice modeling. Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.

Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation
Shenhao Wang,Qingyi Wang,Jinhua Zhao
arXiv

While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives, elasticities, marginal rates of substitution (MRS), and heterogeneous values of time (VOT). Unlike DCMs, DNNs can automatically learn the utility function and reveal behavioral patterns that are not prespecified by domain experts. However, the economic information obtained from DNNs can be unreliable because of the three challenges associated with the automatic learning capacity: high sensitivity to hyperparameters, model non-identification, and local irregularity. To demonstrate the strength and challenges of DNNs, we estimated the DNNs using a stated preference survey, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs. We found that the economic information either aggregated over trainings or population is more reliable than the disaggregate information of the individual observations or trainings, and that even simple hyperparameter searching can significantly improve the reliability of the economic information extracted from the DNNs. Future studies should investigate other regularizations and DNN architectures, better optimization algorithms, and robust DNN training methods to address DNNs' three challenges, to provide more reliable economic information from DNN-based choice models.

Determinants of Bank-Firm Lending Relationship: Human Capital Transfer Channel
Chernykh, Lucy ,Mityakov, Sergey
SSRN
We study the impact of human capital transfer from banks into non-financial firms on firmsâ€™ ability to borrow from banks. Using unique, employee-employer matched dataset from Russia, we find that hiring an ex-employee of a bank increases the firmâ€™s likelihood to secure a loan. We establish that this relation is causal by exploiting exogenous local labor market shocks to the supply of bank ex-employees. Notably, former bank managers possess more general human capital compared to ordinary employees: while ordinary bank ex-employees help the firm to borrow from their bank only, bank ex-managers facilitate borrowing from a broader set of banks.

Diversification and Efficiency in the Indonesian Banking Industry
, Fredio O. Tarore,Prasetyo, Muhammad Budi
SSRN
The global financial crisis in 2008 caused banks to look for business models that can increase efficiency levels. Several previous studies of developed markets suggest that diversification has a positive effect on efficiency. This study aims to analyze the effect of diversification on Indonesian banking efficiency as one of the emerging markets. We used stochastic frontier analysis (SFA) to measure efficiency; the results showed that the majority of Indonesian banks operate at relatively low efficiency. Using the panel data, this study found the same result; diversification can improve Indonesian bank efficiency. Diversification can optimize the output without additional input costs that cause an increase in Indonesian bank efficiency. Other factors such as the level of bank capital also have an impact on increased efficiency. In addition, the influence of bank size and the global financial crisis is not statistically significant.

Financial Distress Risk and Stock Price Crashes
Andreou, Christoforos,Andreou, Panayiotis C.,Lambertides, Neophytos
SSRN
Using 331,917 monthly observations of U.S. public firms, this study documents a strong positive relationship between short-term changes in financial distress risk and future stock price crashes. The results show that a one-unit increase in distress risk increases the probability of a future stock price crash by 15.42 percent. The distress-crash risk relationship is more pronounced when firmsâ€™ information asymmetry is higher, as captured by firmsâ€™ accounting opacity and stock illiquidity. Interestingly, the results also support that the impact of distress risk on future stock price crashes is stronger during investor sentiment-waning phases, and periods with heightened market-wide distress risk. The findings of this study are of interest to investors who wish to take long-run positions in the stock market because stock price crash risk cannot be easily diversified away. In this vein, investors should be cautious of a firmâ€™s distress risk, as short-term increases could be an early warning sign of forthcoming crash risk problems.

For Whom the Bell (Curve) Tolls: A to F, Trade Your Grade Based on the Net Present Value of Friendships with Financial Incentives
Ravi Kashyap
arXiv

We discuss a possible solution to an unintended consequence of having grades, certificates, rankings and other diversions in the act of transferring knowledge; and zoom in specifically to the topic of having grades, on a curve. We conduct a thought experiment, taking a chapter (and some more?) from the financial markets, (where we trade pollution and what not?), to create a marketplace, where we can trade our grade, similar in structure to the interest rate swap. We connect this to broader problems that are creeping up, unintentionally, due to artificial labels we are attaching, to ourselves. The policy and philosophical implications of our arguments are to suggest that all trophies that we collect (including certificates, grades, medals etc.) should be viewed as personal equity or private equity (borrowing another widely used term in finance) and we should not use them to determine the outcomes in any selection criteria except have a cutoff point: either for jobs, higher studies, or, financial scholarships, other than for entertainment or spectator sports. We suggest alternate methods for grading and performance assessment and put forth tests for teaching and learning similar to the Turing Test for intelligence.

Forecasting the Volatility of Bitcoin: The Importance of Jumps and Structural Breaks
Shen, Dehua,Urquhart, Andrew,Wang, Pengfei
SSRN
This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. Using high-frequency data, we perform a model-free decomposition of realized variance into its continuous and discontinuous components, positive and negative semivariances, signed jumps and leverage components. We show the importance of this decomposition in the in-sample regressions using eighteen competing heterogeneous autoregressive (HAR) models. In the out-of-sample setting, we find that the HARQ-F-J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that the HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method.

Hong Kong -- Shanghai Connect / Hong Kong -- Beijing Disconnect (?): Scaling the Great Wall of Chinese Securities Trading Costs
Ravi Kashyap
arXiv

Law and Finance in Britain c.1900
Coyle, Christopher,Musacchio, Aldo,Turner, John D.
SSRN
In this paper, using new estimates of the size of the UKâ€™s capital market, we examine financial development and investor protection laws in Britain c.1900 to test the influential law and finance hypothesis. Our evidence suggests that there was not a close correlation between financial development and investor protection laws c.1900 and that the size of the UKâ€™s share market is a puzzle given the paucity of statutory investor protection. To illustrate that Britain was not unique in its approach to investor protection in this era, we examine investor protection laws across legal families c.1900.

Chatterjee, Somnath,Jobst, Andreas (Andy)
SSRN
This paper presents a forward-looking approach to measure systemic solvency risk using contingent claims analysis (CCA) as a theoretical foundation for determining an institutionâ€™s default risk based on the uncertainty in its asset value relative to promised debt payments over time. Default risk can be quantified as market-implied expected losses calculated from integrating equity market and balance sheet information in a structural default risk model. The expected losses of multiple banks and their non-parametric dependence structure define a multivariate distribution that generates portfolio-based estimates of the joint default risk using the aggregation technique of the Systemic CCA framework (Jobst and Gray, 2013). This market-implied valuation approach (â€˜shadow capital adequacyâ€™) endogenises bank solvency as a probabilistic concept based on the perceived default risk (in contrast to accounting-based prudential measures of capital adequacy). The presented model adds to the literature of analytical tools estimating market-implied systemic risk by augmenting the CCA approach with a jump diffusion process of asset changes to inform a more comprehensive and flexible assessment of common vulnerabilities to tail risks of the four largest UK commercial banks.

Multi-agent reinforcement learning for market microstructure statistical inference
Johann Lussange,Sacha Bourgeois-Gironde,Stefano Palminteri,Boris Gutkin
arXiv

Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents, which trade via a centralised order book to emulate complex and diverse market phenomena. Nevertheless, the issue of agent learning in MAS, which is crucial to price formation and hence to all market activity, has not yet fully benefited from the recent progress of artificial intelligence, and namely reinforcement learning. In order to address this, we present here a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. We calibrate it to real market data from the London Stock Exchange over the years 2007 to 2018, and use it to highlight the beneficial impact of agent suboptimal learning on market stability.

No-Arbitrage Commodity Option Pricing with Market Manipulation
René Aïd,Giorgia Callegaro,Luciano Campi
arXiv

We design three continuous-time models in finite horizon of a commodity price, whose dynamics can be affected by the actions of a representative risk--neutral producer and a representative risk-neutral trader. Depending on the model, the producer can control the drift and/or the volatility of the price whereas the trader can at most affect the volatility. The producer can affect the volatility in two ways: either by randomizing her production rate or, as the trader, using other means such as spreading false information. Moreover, the producer contracts at time zero a fixed position in a European convex derivative with the trader. The trader can be price-taker, as in the first two models, or she can also affect the volatility of the commodity price, as in the third model. We solve all three models semi-explicitly and give closed-form expressions of the derivative price over a small time horizon, preventing arbitrage opportunities to arise. We find that when the trader is price-taker, the producer can always compensate the loss in expected production profit generated by an increase of volatility by a gain in the derivative position by driving the price at maturity to a suitable level. Finally, in case the trader is active, the model takes the form of a nonzero-sum linear-quadratic stochastic differential game and we find that when the production rate is already at its optimal stationary level, there is an amount of derivative position that makes both players better off when entering the game.

Optimal Investment for a Retirement Plan with Deferred Annuities
SSRN
We construct an optimal investment portfolio model with deferred annuities for an individual investor saving for retirement. The objective function consists of power utility in terms of secured retirement income from the deferred annuity purchases, as well as bequest from remaining wealth invested in equity, bond, and cash funds. The asset universe is governed by a vector autoregressive model incorporating the Nelson-Siegel term structure and equity returns. We use multi-stage stochastic programming to solve the optimization problem numerically. Our numerical results show that deferred annuity purchases are made continuously over the working lifetime of the investor, increasing particularly in the years before retirement. The investment strategy hedges price changes in deferred annuities, and bond holding and deferred annuity purchases increase when interest rates are high. Optimal investment and deferred annuity choices depend on realised and expected values of state variables. The optimal strategy is also compared with typical retirement plan strategies such as glide paths. Our results provide novel support for deferred annuities as a major source of retirement income.

STO vs ICO: A Theory of Token Issues Under Moral Hazard and Demand Uncertainty
Miglo, Anton
SSRN
This paper considers a financing problem for an innovative firm that is considering launching a web-based platform. Our model is the first one that analyzes an entrepreneur's choice between security tokens (via a security token offering (STO)) and utility tokens (via initial coin offering (ICO)). The entrepreneur on one hand faces a large degree of demand uncertainty on his product and on the other hand has to deal with incentive problems of professional blockchain participants who contribute to the development and sales of the product. We argue that utility tokens with profit rights are a better option for the firm compared to straight utility tokens or security tokens because they help the firm better deal with both the moral hazard problems (via profit sharing incentives) and demand uncertainty (they help the firm learn the product demand). This finding is consistent with some recent evidence. The paper also generates new predictions that have not been tested so far.

Secret Scouting
Li, Xuelin,Yu, Fangyuan
SSRN
VCs prefer secrecy when searching for targets. As a result, only the investments in viable startups are disclosed but the failed ones are discarded silently. We extend the standard preemption game to explain the efficiency loss and the individual rationale of doing so. We show that secrecy creates pessimism. Compared to the fully disclosing case, VCs will stop hunting for startups too early in an initially promising industry. This could happen even if no technology failures are observed in realization. However, hiding failures becomes a dominant strategy when the return of the VC industry is right-skewed. VCs use secret scouting to make the competitors believe that the industry is a dead end and reduce the preemption threats.

Solving the Equity Risk Premium Puzzle and Inching Towards a Theory of Everything
Ravi Kashyap
arXiv

The equity risk premium puzzle is that the return on equities has far exceeded the average return on short-term risk-free debt and cannot be explained by conventional representative-agent consumption based equilibrium models. We review a few attempts done over the years to explain this anomaly: 1. Inclusion of highly unlikely events with low probability (Ugly state along with Good and Bad), or market crashes / Black Swans. 2. Slow moving habit, or time-varying subsistence level, added to the basic power utility function. 3. A separation of the inter-temporal elasticity of substitution and risk aversion, combined with long run risks which captures time varying economic uncertainty. We explore whether a fusion of the above approaches supplemented with better methods to handle the below reservations would provide a more realistic and yet tractable framework to tackle the various conundrums in the social sciences: 1. Unlimited ability of individuals to invest as compared to their ability to consume. 2. Lack of an objective measuring stick of value 3. Unintended consequences due to the dynamic nature of social systems 4. Relaxation of the transversality condition to avoid the formation of asset price bubbles 5. How durable is durable? Accounting for durable goods since nothing lasts forever The world we live in produces fascinating phenomenon despite (or perhaps, due to) being a hotchpotch of varying doses of the above elements. The rationale for a unified theory is that beauty can emerge from chaos since the best test for a stew is its taste. Many long standing puzzles seem to have been resolved using different techniques. The various explanations need to stand the test of time before acceptance; but then unexpected outcomes set in and new puzzles emerge. As real analysis and limits tell us: We are getting Closer and Closer; Yet it seems we are still Far Far Away...

The Asymmetric Prospects of Maximal and Minimal Extremes: Evidence from Capital Raising in the U.S. Private Equity Market
Assenova, Valentina,Huang, Laura
SSRN
Past performance is considered one of the most salient, reliable indicators of future performance. For this reason, when considering making investments, investors typically focus on opportunities that have, on average, shown high net returns in the past. In many markets where there is extreme uncertainty about future returns, investors may direct their attention not at straightforward financial metrics such as past net returns, but rather at performance extremes. We posit that in markets with uncertainty about future performance and with reward structures for which payoffs increase with volatility, investors may direct their attention to maximum extremes, where potential gains loom larger than losses. In these cases, maximum extremes enable firms to achieve positive differentiation and attract capital for future funds. We test our theory using data from the universe of private equity firms raising capital for new venture capital (VC) and leveraged buyout (LBO) funds in the United States from 1999-2017, and demonstrate that advantages in fundraising among firms arose from asymmetric effects of maximum and minimum extremes, whereby peak performance (i.e., gains) improved a firmâ€™s likelihood of raising capital by more than mediocre performance (i.e., losses) hurt these prospects. Consistent with our theory, we find that these effects were more salient for firms raising funds with more considerable uncertainty (i.e., VC vs. LBO funds) and investors embracing higher risk (i.e., high-beta (beta>1) vs. low-beta strategies). We discuss the implications of our findings for decision making under uncertainty and advantages in capital raising.

The value of knowing the market price of risk
Katia Colaneri,stefano Herzel,Marco Nicolosi
arXiv

This paper presents an optimal allocation problem in a financial market with one risk-free and one risky asset, when the market is driven by a stochastic market price of risk. We solve the problem in continuous time, for an investor with a Constant Relative Risk Aversion (CRRA) utility, under two scenarios: when the market price of risk is observable (the {\em full information case}), and when it is not (the {\em partial information case}). The corresponding market models are complete in the partial information case and incomplete in the other case, hence the two scenarios exhibit rather different features. We study how the access to more accurate information on the market price of risk affects the optimal strategies and we determine the maximal price that the investor would be willing to pay to get such information. In particular, we examine two cases of additional information, when an exact observation of the market price of risk is available either at time $0$ only (the {\em initial information case}), or during the whole investment period (the {\em dynamic information case}).

What About the Future of European Banks? Board Characteristics and ESG Impact