Research articles for the 2019-07-29

A Model of Presidential Debates
Doron Klunover,John Morgan

Presidential debates are thought to provide an important public good by revealing information on candidates to voters. However, this may not always be the case. We consider an endogenous model of presidential debates in which an incumbent and a contender (who is privately informed about her own quality) publicly announce whether they are willing to participate in a public debate, after taking into account that a voter's choice of candidate depends on her beliefs regarding the candidates' qualities and on the state of nature. Surprisingly, it is found that in equilibrium a debate occurs or does not occur independently of the contender's quality or the sequence of the candidates' announcements to participate and therefore the announcements are uninformative.

A hybrid neural network model based on improved PSO and SA for bankruptcy prediction
Fatima Zahra Azayite,Said Achchab

Predicting firm's failure is one of the most interesting subjects for investors and decision makers. In this paper, a bankruptcy prediction model is proposed based on Artificial Neural networks (ANN). Taking into consideration that the choice of variables to discriminate between bankrupt and non-bankrupt firms influences significantly the model's accuracy and considering the problem of local minima, we propose a hybrid ANN based on variables selection techniques. Moreover, we evolve the convergence of Particle Swarm Optimization (PSO) by proposing a training algorithm based on an improved PSO and Simulated Annealing. A comparative performance study is reported, and the proposed hybrid model shows a high performance and convergence in the context of missing data.

Algorithmic market making: the case of equity derivatives
Bastien Baldacci,Philippe Bergault,Olivier Guéant

In this article, we tackle the problem of a market maker in charge of a book of equity derivatives on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an equity option market maker is in fact tractable. More precisely, the problem faced by an equity option market maker is characterized by a two-dimensional functional equation that can be solved numerically using interpolation techniques and classical Euler schemes, even for large portfolios. Numerical examples are provided for a large book of equity options.

Calibrating rough volatility models: a convolutional neural network approach
Henry Stone

In this paper we use convolutional neural networks to find the H\"older exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We contextualise this as a calibration problem, thereby providing a very practical and useful application.

Cities and space: Common power laws and spatial fractal structures
Tomoya Mori,Tony E. Smith,Wen-Tai Hsu

City size distributions are known to be well approximated by power laws across a wide range of countries. But such distributions are also meaningful at other spatial scales, such as within certain regions of a country. Using data from China, France, Germany, India, Japan, and the US, we first document that large cities are significantly more spaced out than would be expected by chance alone. We next construct spatial hierarchies for countries by first partitioning geographic space using a given number of their largest cities as cell centers, and then continuing this partitioning procedure within each cell recursively. We find that city size distributions in different parts of these spatial hierarchies exhibit power laws that are again far more similar than would be expected by chance alone -- suggesting the existence of a spatial fractal structure.

Corporate Cash Holdings: Stock Liquidity and the Repurchase Motive
Nyborg, Kjell G.,Wang, Zexi
We document that enhanced stock liquidity increases a firm's propensity to hold cash. Endogeneity is addressed using a difference-in-differences approach based on tick-size decimalization. Our finding is surprising in light of the view that improved stock liquidity reduces financial constraints. We propose that firms hold cash also to buy back shares and higher stock liquidity strengthens this incentive. Tests are supportive. Endogeneity is controlled for using the introduction of repurchase safe harbor rules. We conclude that with respect to the effect of stock liquidity on cash holdings, the repurchase motive dominates the real investments motive.

Disentangling Adverse Selection and Moral Hazard: Evidence from China’s Automobile Insurance Market
Zhang, Juan,Zhou, Hua
Adverse selection and moral hazard are different information asymmetry problems in the automobile insurance market, but their empirical evidence cannot be separated using the traditional positive risk-coverage correlation test. This paper uses an experience-rating based method to disentangle the two problems by testing the correlation between past and current claim numbers. We expect that the serial correlation of claim numbers to be negative under moral hazard but positive under adverse selection. Since an experience rating system incentivizes drivers to exert more effort to avoid further premium increase after they have an accident, drivers with moral hazard should be less likely to have an accident in the future. However, high-risk drivers should have a consistently high number of claims over time because their risk type does not change in a short period. We use the panel data of a Chinese large domestic insurer during 2010-2013. We find a negative correlation between past and current claim numbers, and the relationship is more significant to drivers who have better rating discounts. The result indicates that moral hazard plays a more critical role than adverse selection in explaining the information asymmetry in the Chinese automobile insurance market. Moral hazard problem remains significant possibly because of the pricing scheme -- the price of full insurance is not high enough so that over 90 percent of drivers have comprehensive coverage.

Dissecting Momentum: We Need to Go Deeper
Borisenko, Dmitry
Cross-sectional predictability of returns by past prices, or momentum, is a lasting market anomaly. Previous research reports numerous ways to measure momentum and establishes a multitude of factors predicting its performance. The emerging machine learning asset pricing literature further identifies price-based firm characteristics as major predictors of returns. I investigate predictive power of a broad set of price-based variables over various time horizons in a deep learning framework and document rich non-linear structure in impact of these variables on expected returns in the US equity market. The magnitude and sign of the impact exhibit substantial time variation and are modulated by interaction effects among the variables. The degree of non-linearity in expected returns varies over time and is highest in distressed markets. Incorporating insights from the literature on time-varying, market state-dependent momentum risks and momentum crashes helps to improve out-of-sample performance of neural network portfolios, especially with respect to the downside risk -- investment strategies built on predictions of the deep learning model actively exploit the non-linearities and interaction effects, generating high and statistically significant returns with a robust risk profile and their performance virtually uncorrelated with the established risk factors including momentum. Lastly, I make a case for adoption of automated hyperparameter optimization techniques as an important component of disciplined research in financial machine learning.

Do Hedge Fund Managers Work Harder Under Pressure? A Unique View From Hedge Fund Flow-Related Trading
Cui, Xinyu,Kolokolova, Olga
Analyzing trading of hedge funds facing substantial outflows, we find that hedge funds that "trade-against-the-flow'' display significant stock picking skills. Stocks purchased by hedge funds facing large outflows deliver positive ex-post abnormal returns, which are larger than those of stocks purchased upon inflows. Such "revealed under pressure'' stock-picking skills are associated with hedge funds that are more dependent on management fee income and more prone to sudden outflows due to less stringent share restrictions.

Downside Uncertainty Shocks in the Oil and Gold Markets
Xu, Yahua,Cho, Ro,Roh, Tai-Yong
We construct downside variance risk premiums from the crude oil and gold option data and use them as proxies for market downside uncertainty risks. We find that these downside variance risk premiums contain commodity market-specific pricing information. Further- more, the gold market's exposure to downside uncertainty shocks is cross-sectionally priced in several sectors of the stock market while its crude oil market counterpart is not. This implies that the downside uncertainty for the gold market may be a key state variable rep- resenting investment opportunity sets under the Intertemporal Capital Asset Pricing Model (ICAPM).

Introducing shrinkage in heavy-tailed state space models to predict equity excess returns
Florian Huber,Gregor Kastner,Michael Pfarrhofer

We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space model. The shrinkage priors are complemented by heavy tailed state innovations that cater for potential large breaks in the latent states. Moreover, we allow for leptokurtic stochastic volatility in the observation equation. The empirical findings indicate that several variants of the proposed approach outperform typical competitors frequently used in the literature, both in terms of point and density forecasts.

Investigating the effect of competitiveness power in estimating the average weighted price in electricity market
Naser Rostamni,Tarik A. Rashid

This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price forecasting model based on multi-layer perception via back propagation with the Levenberg-Marquardt mechanism is used. The Residual Supply Index (RSI) and other variables that affect prices are used as inputs to the model to evaluate the market competitiveness. The results show that using market power indices as inputs helps to increase forecasting accuracy. Thus, the competitiveness extent of the market power in different daily time periods is a notable variable in price formation. Moreover, market players cannot ignore the explanatory power of market power in price forecasting. In this research, the real data of the electricity market from 2013 is used and the main source of data is the Grid Management Company in Iran.

Killer Technologies: the destructive creation in the technical change
Mario Coccia

Killer technology is a radical innovation, based on new products and/or processes, that with high technical and/or economic performance destroys the usage value of established techniques previously sold and used. Killer technology is a new concept in economics of innovation that may be useful for bringing a new perspective to explain and generalize the behavior and characteristics of innovations that generate a destructive creation for sustaining technical change. To explore the behavior of killer technologies, a simple model is proposed to analyze and predict how killer technologies destroy and substitute established technologies. Empirical evidence of this theoretical framework is based on historical data on the evolution of some example technologies. Theoretical framework and empirical evidence hint at general properties of the behavior of killer technologies to explain corporate, industrial, economic and social change and to support best practices for technology management of firms and innovation policy of nations. Overall, then, the proposed theoretical framework can lay a foundation for the development of more sophisticated concepts to explain the behavior of vital technologies that generate technological and industrial change in society.

Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
Haoran Wang,Xun Yu Zhou

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaussian feedback policy, with time-decaying variance, in trading off exploration and exploitation. Based on a provable policy improvement theorem, we devise a scalable and data-efficient RL algorithm and conduct large scale empirical tests using data from the S&P 500 stocks. We found that our method consistently achieves over 10% annualized returns and it outperforms econometric methods and the deep RL method by large margins, for both long and medium terms of investment with monthly and daily trading.

Liquidity, Information Production, and Debt-Equity Choice
Cheung, William M.,Im, Hyun Joong,Noe, Thomas H. ,Zhang, Bohui
We examine the effect of stock liquidity on the choice between debt and equity financing. Using three quasi-natural experiments, we show that a firm with more liquid stocks has a higher propensity to raise debt capital. In addition, a firm with more liquid stocks tends to have a significantly larger gap between the costs of debt and equity. Taken together, these results suggest that high stock liquidity, by facilitating security market information production, reduces insider and market uncertainty about future firm cash flows and thereby increases the attractiveness of debt financing.

Marked Hawkes process modeling of price dynamics and volatility estimation
Kyungsub Lee,Byoung Ki Seo

A simple Hawkes model have been developed for the price tick structure dynamics incorporating market microstructure noise and trade clustering. In this paper, the model is extended with random mark to deal with more realistic price tick structures of equities. We examine the impact of jump in price dynamics to the future movements and dependency between the jump sizes and ground intensities. We also derive the volatility formula based on stochastic and statistical methods and compare with realized volatility in simulation and empirical studies. The marked Hawkes model is useful to estimate the intraday volatility similarly in the case of simple Hawkes model.

Market and Long Term Accounting Operational Performance
M. S. S. Rosa,P. R. B. Lustosa

Following the value relevance literature, this study verifies whether the marketplace differentiates companies of high, medium, and low long-term operational performance, measured by accounting information on profitability, sales variation and indebtedness. The data comprises the Corporate Financial Statements disclosed during the period from 1996 to 2009 and stock prices of companies listed on the Sao Paulo Stock Exchange and Commodities and Futures Exchange - BM&FBOVESPA. The final sample is composed of 142 non-financial companies. Five year mobile windows were used, which resulted in ten five-year periods. After checking each company indices, the accounting variables were unified in an Index Performance Summary to synthesize the final performance for each five-year period, which allowed segregation in operational performance levels. Multiple regressions were performed using panel data techniques, fixed effects model and dummies variables, and then hypothesis tests were made. Regarding the explanatory power of each individual variable, the results show that not all behaviors are according to the research hypothesis and that the Brazilian stock market differentiates companies of high and low long-term operational performance. This distinction is not fully perceived between companies of high and medium operational performance.

Regulatory Interventions in Consumer Financial Markets: The Case of Credit Cards
Galenianos, Manolis,Gavazza, Alessandro
We build a framework to understand the effects of regulatory interventions in credit markets, such as caps on interest rates and higher compliance costs for lenders. We focus on the credit card market, in which we observe U.S. consumers borrowing at high and very dispersed interest rates, despite receiving many credit card offers. Our framework includes two main features that may explain these patterns: endogenous effort of examining offers and product differentiation. Our calibration suggests that these patterns occur because borrowers do not examine most of the offers that they receive. The calibrated model implies that interest rate caps reduce credit supply modestly and curb lenders' market power significantly, leading to large gains in consumer surplus, whereas higher compliance costs unambiguously decrease consumer surplus.

Resolving New Keynesian Anomalies with Wealth in the Utility Function
Pascal Michaillat,Emmanuel Saez

At the zero lower bound, the New Keynesian model predicts that output and inflation collapse to implausibly low levels, and that government spending and forward guidance have implausibly large effects. To resolve these anomalies, we introduce wealth into the utility function; the justification is that wealth is a marker of social status, and people value social status. Since people save not only for future consumption but also to accrue social status, the Euler equation is modified. As a result, when the marginal utility of wealth is sufficiently large, the dynamical system representing the equilibrium at the zero lower bound transforms from a saddle to a source---which resolves all the anomalies.

SlideVaR: a risk measure with variable risk attitudes
Wentao Hu

To find a trade-off between profitability and prudence, financial practitioners need to choose appropriate risk measures. Two key points are: Firstly, investors' risk attitudes under uncertainty conditions should be an important reference for risk measures. Secondly, risk attitudes are not absolute. For different market performance, investors have different risk attitudes. We proposed a new risk measure named SlideVaR which sufficiently reflects the different subjective attitudes of investors and the impact of market changes on investors' attitudes. We proposed the concept of risk-tail region and risk-tail sub-additivity and proved that SlideVaR satisfies several important mathematical properties. Moreover, SlideVaR has a simple and intuitive form of expression for practical application. Several simulate and empirical computations show that SlideVaR has obvious advantages in markets where the state changes frequently.

Spectral backtests of forecast distributions with application to risk management
Michael B. Gordy,Alexander J. McNeil

We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and further propose novel variants which are easily implemented, well-sized and have good power. In an empirical application, we backtest forecast distributions for the overnight P&L of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.

Taxable Stock Trading with Deep Reinforcement Learning
Shan Huang

In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategies with and without considering taxes by reinforcement learning. The result shows that tax ignorance could induce more than 62\% loss on the average portfolio returns, implying that taxes should be embedded in the environment of continuous stock trading on AI platforms.

The Effect of Option-implied Skewness on Delta- and Vega-Hedged Option Returns
Borochin, Paul,Zhao, Yanhui,Wu, Zekun
We study the relation between option-implied skewness (IS) and the cross section of option returns under daily hedging. Creating portfolios of delta-hedged (D-hedged) and delta-vega-hedged (DV-hedged) options with daily rebalancing, we find that IS is negatively related to both D-hedged and DV-hedged call option returns, but has no significant relation to hedged put option returns. The negative relationship observed between IS and hedged call option returns is stronger when the underlying stock has a larger market beta and when the firm is more opaque. Combined with the finding that this relationship is stronger in periods of high investor sentiment, our results suggest that the relation between IS and call option portfolio returns is driven primarily by overvaluation of positive-skew assets. Investors not only have skewness preference, but also this preference is stronger when mistakes in the valuation of skew-sensitive assets are more likely due to information quality and sentiment variation.

The Effects of Oil Price Shocks on the Prices of EU Emission Trading System and European Stock Returns
Krokida, Styliani Iris,Lambertides, Neophytos,Savva, Christos S.,Tsouknidis, Dimitris A.
This paper examines whether oil price shocks of different origin affect the price of carbon emission allowance traded under the European Union's Emissions Trading System (EU-ETS); leading to changes in aggregate and sector specific European equity returns. The results show that an unexpected oil supply disruption has an imminent but weak positive effect on carbon emission price, while a positive aggregate demand shock has a strong positive effect on carbon emission price. By contrast, a positive oil-specific (precautionary) demand shock has a negative but weak effect on carbon emission price. These findings are economically important as positive shocks on the CO2 emission allowance price trigger a decrease on the aggregate stock return of the European equity market, albeit they trigger a large and persistent increase on European equity returns of oil-related industries with the exception of the Energy sector.

The Evolution of Ownership Structures: Privatization, Business Groups, and Pyramids
Aldunate, Felipe,González, Felipe,Prem, Mounu,Urzúa I., Francisco
What is the contribution of privatization to the formation of business groups and pyramids? We use new data to study how Pinochet’s privatizations in Chile (1973-1990) affected the evolution of ownership structures. Using non-privatized firms in the same industry as comparison, and accounting for pre-privatization characteristics, we find that privatized firms were more likely to become part of business groups, began to act as providers of credit within groups, and pyramidal ownership structures were built on top of them. As most privatized firms became part of new (instead of traditional) business groups we argue that this privatization reform facilitated the renovation of elites and contributed to the formation of contemporaneous business groups.

Time consistency for scalar multivariate risk measures
Zachary Feinstein,Birgit Rudloff

In this paper we present results on dynamic multivariate scalar risk measures, which arise in markets with transaction costs and systemic risk. Dual representations of such risk measures are presented. These are then used to obtain the main results of this paper on time consistency; namely, an equivalent recursive formulation of multivariate scalar risk measures to multiportfolio time consistency. We are motivated to study time consistency of multivariate scalar risk measures as the superhedging risk measure in markets with transaction costs (with a single eligible asset) (Jouini and Kallal (1995), Roux and Zastawniak (2016), Loehne and Rudloff (2014)) does not satisfy the usual scalar concept of time consistency. In fact, as demonstrated in (Feinstein and Rudloff (2018)), scalar risk measures with the same scalarization weight at all times would not be time consistent in general. The deduced recursive relation for the scalarizations of multiportfolio time consistent set-valued risk measures provided in this paper requires consideration of the entire family of scalarizations. In this way we develop a direct notion of a "moving scalarization" for scalar time consistency that corroborates recent research on scalarizations of dynamic multi-objective problems (Karnam, Ma, and Zhang (2017), Kovacova and Rudloff (2018)).