Research articles for the 2019-09-24

A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series
Constandina Koki,Stefanos Leonardos,Georgios Piliouras

Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified approach, we apply a recently developed Non-Homogeneous Hidden Markov (NHHM) model with an extended set of financial and blockchain specific covariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on the observable series, the NHHM model offers a novel perspective on the underlying microstructure of the cryptocurrency market and provides insight on unobservable parameters such as the behavior of investors, traders and miners. The algorithm identifies two alternating periods (hidden states) of inherently different activity -- fundamental versus uninformed or noise traders -- in the Bitcoin ecosystem and unveils differences in both the short/long run dynamics and in the financial characteristics of the two states, such as significant explanatory variables, extreme events and varying series autocorrelation. In a somewhat unexpected result, the Bitcoin and Ether markets are found to be influenced by markedly distinct indicators despite their perceived correlation. The current approach backs earlier findings that cryptocurrencies are unlike any conventional financial asset and makes a first step towards understanding cryptocurrency markets via a more comprehensive lens.

Analysing the Dynamic Influence of US Macroeconomic News Releases on Turkish Stock Markets
Ekinci, Cumhur,Akyildirim, Erdinc,Corbet, Shaen
We investigate the effects of macroeconomic announcements made in the United States on trading activity of stocks listed in Borsa Istanbul. The influence of these releases on the selected variables are an important source of information for market participants. Results show a clear negative impact on weighted bid, ask and mid-prices in the five-minute period post-release. Available liquidity measured by pending orders in limit order book decreases with the news arrival. These results present implications for market dynamics and signal that liquidity consumption (through market orders) largely dominates liquidity provision (through limit orders) in the five-minute period following the release.

Approximate hedging with proportional transaction costs in stochastic volatility models with jumps
Thai Huu Nguyen,Serguei Pergamenschchikov

We study the problem of option replication under constant proportional transaction costs in models where stochastic volatility and jumps are combined to capture the market's important features. Assuming some mild condition on the jump size distribution we show that transaction costs can be approximately compensated by applying the Leland adjusting volatility principle and the asymptotic property of the hedging error due to discrete readjustments is characterized. In particular, the jump risk can be approximately eliminated and the results established in continuous diffusion models are recovered. The study also confirms that for the case of constant trading cost rate, the approximate results established by Kabanov and Safarian (1997)and by Pergamenschikov (2003) are still valid in jump-diffusion models with deterministic volatility using the classical Leland parameter in Leland (1986).

Benchmarking Mutual Fund Returns
Cheng, Tingting,Yan, Cheng,Yan, Yayi
Fund benchmark can be either a linear factor model or a passive investable index. Theoretically, these two choices are equivalent if the latter can be fully explained by the former. Empirically, this is not the case as a positive and significant ‘alpha’ emerges when we regress popular passive indices on risk factors. We further doubt the appropriateness of using time-invariant indices as benchmarks and propose a regime-switching methodology to identify the time-varying de facto benchmarks from a pool of the market-based indices, with or without a risk-free asset. We highlight the benchmark mismatch phenomenon and the role of risk-free rate as an additional benchmark, reflecting the importance of fund cash holdings. Our de facto benchmark better capture fund styles than other benchmark choices and substantially improves the identification of significant fund alphas. Importantly, we identify a larger portion of statistically significant mutual fund alphas with a smaller magnitude on average.

Calibration of Local-Stochastic Volatility Models by Optimal Transport
Ivan Guo,Gregoire Loeper,Shiyi Wang

In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number of European options. We formulate the problem as a convex optimisation problem, for which we provide a dual formulation. Then we solve numerically the dual problem, which involves a fully non-linear Hamilton-Jacobi-Bellman equation. The method is tested by calibrating a Heston-like LSV model with simulated data and foreign exchange market data.

Close Cooperation within the SRM: Centralised Decision-Making, Decentralised Implementation â€" Shared Responsibilities
Binder, Jens-Hinrich
At first sight, the very concept as well as the modus operandi of close cooperation between the SRM and non-euro Member States are closely realigned with the application of the concept within the SSM. In fact, pursuant to Art. 4(1) SRMR, the establishment of close cooperation cannot be agreed separately, but follows automatically from the decision of non-euro Member States to join the SSM. Likewise, by virtue of Art. 4(2) SRMR, suspension or termination of close cooperation within the SRM is triggered by the termi-nation of the arrangements under Art. 7 SSMR, but cannot be agreed independently from the decision whether or not to end close cooperation within the SSM. In some respects, especially in terms of institutional features (including the representation of national interests within the relevant institutions) and within the context of recovery and resolution planning, close cooperation is likely to encounter challenges and problems similar to those to be expected within the SSM. At the same time, however, given residual differ-ences in terms of governance structure as well as the substantive and procedural framework for the opera-tion of the respective regimes, the implementation of the concept within the SRM will meet fundamentally different problems. Only one of these problems, i.e. the recoupment of contributions to the SRF following the termination of close cooperation, has been addressed explicitly in the SRMR (Art. 4(3) SRMR). Against this backdrop, the paper examines potential challenges to the establishment and operation of close coop-eration in the SRM in the light of fundamental structural differences between the SSM and the SRM.

Collateral Reuse, Collateral Mismatch, and Financial Crises
Park, Hyejin
This paper builds a model that links collateral reuse, collateral allocation, and aggregate output. The model shows that collateral reuse affects aggregate output through two channels. First, collateral reuse allows multiple investment projects to be financed by economizing on scarce collateral, thereby increasing aggregate output. Second, collateral reuse may lead collateral to be remained with less efficient hands in case that intermediaries who reused the collateral go bankrupt. These two effects combined with decisions of initial borrowers whether to permit collateral reuse or not affect collateral allocation in the economy, and initiate fluctuations in aggregate output. We show that if a negative shock that increases the default risk of the intermediaries causes the borrower to prohibit collateral reuse, the downturn becomes more severe, while decreased collateral reuse can lead to faster recovery by preventing potential collateral mismatch. We also consider a case with stochastic shocks and show that a long period of boom with high frequency of collateral reuse can be followed by a sharp recession, consistent with observed patterns in the financial crisis.

Collusive vs. Coercive Corporate Corruption: Evidence from Demand-Side Shocks and Supply-Side Disclosures
Lee, Edward,Tang, Xiaojian,Zhang, Junsheng
We examine whether and how collusive and coercive corporate corruption have different effects on firm value. Our identification strategy combines two unique settings in China. To capture exogenous shocks on bribery demand, we draw on news of anti-corruption prosecutions against high-ranking regional government officials in the firms’ provinces. To determine cross-sectional variations in bribery supply, we estimate the alleged bribery expenditure from the entertainment and travel costs mandatorily disclosed in firms’ financial statements. Among state-owned (privately-owned) enterprises, where corruption is more likely to be perceived as rent sharing collusion (rent extraction coercion), we observe a negative (positive) association between alleged bribery expenditure and the share price responses to the news of anti-corruption prosecutions. These findings are stronger in regions (industries) with greater government intervention (business competition), and are corroborated by a subsequent decrease (increase) in firms’ net profit margins. Our evidence implies downside risks (upside gains) for the supply-side of collusive (coercive) corporate corruptions when negative demand-side shocks occurs.

Contagion of Crypto-Assets: Spillovers Across Asset Classes
Buchwalter, Bastien
This paper investigates the impact crypto-assets have on traditional asset classes (stocks, bonds, foreign exchange (FX), and commodities). While returns of cryptoasset and tra- ditional asset are uncorrelated, we show that cryptoassets are linked to the traditional assets via two channels. We use a variance format error decom- position to capture linkages across asset classes. The wealth channel captures captures how shocks occurring to crypto-asset reverberate back to other asset classes via debt. That is, when crypto-investors or crypto-trading platforms are in financial distress, their lenders face financial restriction which reverberate to other asset allocation decisions. Further, the remittance chan- nel captures how uncertainty in the FX (Crypto) market pushes user to the crypto (FX) market in order to send their remittances.

Do Credit Booms Predict U.S. Recessions?
Mihai, Marius
This paper investigates the role of bank credit in predicting U.S. recessions since the 1960s in the context of a bivariate probit model. A set of results emerge. First, credit booms are shown to have strong positive effects in predicting declines in the business cycle at horizons ranging from six to nine months. Second, I propose to isolate the effect of credit booms by identifying the contribution of excess bank liquidity alongside a housing factor in the downturn of each cycle. Third, the out-of-sample performance of the model is tested on the most recent credit-driven recession, the Great Recession of 2008. The model performs better than a more parsimonious version where we restrict the effect of credit booms on the business cycle in the system to be zero.

Does Board Independence Reduce Informed Short Selling Prior to Earnings Announcements? Evidence From Quasi-Natural Experiment
Mishra, Suchi,Rahman, Anisur,Upadhyay, Arun
Independent boards improve financial disclosure quality and reduce the opacity of information between insiders and outsiders, which lowers the incentives of informed traders that benefit from the information gap. We test this conjecture by estimating short sellers’ predictions of the direction of unexpected quarterly earnings. We find that short sellers’ predictions are less accurate in firms with independent boards relative to firms with non-independent boards. Furthermore, short-selling excess returns in firms with independent boards are insignificant. A quasi-natural experiment using the exogenous shock to board independence represented by the Sarbanes-Oxley Act of 2002 provides further support for these findings. We also show that a decrease in both information asymmetry and information leakage in firms with independent boards are potential reasons for a decrease in the accuracy of short sellers’ predictions. Further analyses show that the effects of independent boards are stronger in firms with larger information asymmetry and also in firms that have poor governance mechanisms. Sub-sample analyses suggest that these results are not driven by the idiosyncrasies of unusual periods such as the 2007â€"2009 global financial crisis.

Does Board Network Centrality Increase Corporate Social Responsibility?
Vo, Lai Van,Le, Huong T. T.,Kim, Youngbin
The previous business literature mainly investigates the effects of corporate social responsibility (CSR) on both the financial and non-financial performance of firms. In this paper, we contribute to the literature by examining its determinant. We find that firms with central or well-connected boards of directors invest more in CSR. We further show that the positive effect of the network centrality of a board on CSR is more pronounced for firms that need high commitment of stakeholders to success, such as firms with larger investments in research and development (R&D) or those in more competitive industries. These findings are consistent with the view that well-connected boards are positively associated with better monitoring and advising.

Does the Cost of Private Debt Respond to Monetary Policy? Heteroskedasticity‐Based Identification in a Model with Regimes
Guidolin, Massimo ,Pedio, Manuela,Massagli, Valentina
We investigate the effects of a conventional monetary expansion, the quantitative easing, and maturity extension programs on the yields of corporate bonds. We adopt a multiple-regime VAR identification based on heteroskedasticity. An impulse response function analysis shows that a traditional, rate based expansionary policy leads to an increase in yields. The response to quantitative easing is instead a general and persistent decrease, in particular for long-term bonds. The responses generated by the maturity extension program are significant and of larger magnitude. A decomposition shows that the unconventional programs reduce the cost private debt primarily through a reduction in risk premia.

Does the leverage effect affect the return distribution?
Dangxing Chen

The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointed out a puzzle: the return distributions of many assets do not appear to be affected by the leverage effect. We analyze the determinants of the return distribution and find that the impact of the leverage effect comes primarily from an interaction between the leverage effect and the mean-reversion effect. When the leverage effect is large and the mean-reversion effect is small, then the interaction exerts a strong effect on the return distribution. However, if the mean-reversion effect is large, even a large leverage effect has little effect on the return distribution. To better understand the impact of the interaction effect, we propose an indirect method to measure it. We apply our methodology to empirical data and find that the S&P 500 data exhibits a weak interaction effect, and consequently its returns distribution is little impacted by the leverage effect. Furthermore, the interaction effect is closely related to the size factor: small firms tend to have a strong interaction effect and large firms tend to have a weak interaction effect.

Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis
Daiki Matsunaga,Toyotaro Suzumura,Toshihiro Takahashi

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. The main goal of this work is to test the validity of this approach across different markets and longer time horizons for backtesting using rolling window analysis.In this work, we concentrate on the prediction of individual stock prices in the Japanese Nikkei 225 market over a period of roughly 20 years. For the knowledge graph, we use the Nikkei Value Search data, which is a rich dataset showing mainly supplier relations among Japanese and foreign companies. Our preliminary results show a 29.5% increase and a 2.2-fold increase in the return ratio and Sharpe ratio, respectively, when compared to the market benchmark, as well as a 6.32% increase and 1.3-fold increase, respectively, compared to the baseline LSTM model.

Gender board diversity and the cost of bank loans
Karavitis, Panagiotis,Kokas, Sotirios,Tsoukas, Serafeim
We examine the relationship between female board representation and the cost of lending, using a dataset that contains 13,714 loans originated by 386 banks matched with 2,432 non-financial firms over the period 1999 to 2013. We find that firms with female directors command lower loan spreads. In addition, female independent directors have a stronger impact on lowering spreads compared to female directors' other attributes. However, as firms build relationships with their lenders this effect becomes less potent. Finally, when we introduce firm-level heterogeneity we document that changes in gender diversity exert a stronger impact on the cost of lending in the case of financially constrained firms, especially for relationship borrowers.

How Smart Is the Real Estate Smart Beta? Evidence from Optimal Style Factor Strategies for REITs
Guidolin, Massimo ,Pedio, Manuela
This paper has a twofold objective. First, we contribute to the stream of literature that investigates whether traditional asset pricing factors show any predictive power for the cross-section of Real Estate Investment Trust (REIT) returns. In particular, we investigate the existence of a premium associated to the Value, Size, Momentum, Investment, and Profitability factors over the period 1993-2018. We find support for all the pricing factors but for the Profitability one. Second, we investigate whether a set of smart beta strategies, based on the combination of the identified factors, may outperform similar allocation techniques that do not exploit factors. We find that all the proposed factor-based strategies display a higher risk-adjusted out-of-sample performance than a simple buy-and-hold investment in the real estate market (proxied by the FTSE NAREIT All REITs Index). In addition, we find that when factor-based strategies are implemented, REIT-only portfolios display risk-adjusted performances comparable to those of diversified portfolios that include equity, bond, and commodities.

Implied volatility surface predictability: the case of commodity markets
Fearghal Kearney,Han Lin Shang,Lisa Sheenan

Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in the implied volatility surfaces of the most actively traded commodity options between 2006 and 2016. Adopting a rolling out-of-sample forecasting framework that addresses the common multiple comparisons problem, we establish that, for energy and precious metals options, explicitly modeling the term structure of implied volatility using the Nelson-Siegel factors produces the most accurate forecasts.

Inertia and Pass-Through in Retail Deposit Markets
Deuflhard, Florian
This paper investigates heterogeneous pass-through of monetary policy rates to variable-rate savings accounts using monthly account-level panel data from a Dutch comparison website. I find incomplete and delayed pass-through that varies widely across banks but even account products offered by the same bank. Bank-specific factors explain less than half of the variation in pass-through rates. Within banks, internet-managed and newer accounts capturing market segments with more flexible consumers exhibit substantially higher pass-through than regular and older accounts. This suggests an important role of inertia for incomplete monetary transmission.

Investigation of Optimal Capital Structure: A Panel Threshold Regression Analysis Over Egyptian Non-Financial Firms
William, Ramy,Iatridis, George Emmanuel
The purpose of this quantitative research is to investigate whether non-linear effects of capital structure choice on firm value are present for the Egyptian non-financial firms, and if yes, investigate the existence of an optimal capital structure that maximizes firm value. The authors employ the advanced panel threshold regression developed by Hansen (1999) to investigate the existence of thresholds effect of firm leverage on firm value. This estimation technique is superior over the traditional non-linear regressions and has been extensively used to estimate threshold effect in different financial applications. This research is intended to fill literature gap where there is lack of empirical studies investigating the existence of optimal capital structure in Egypt. Too, inclusion of political uncertainty among controlling variables falls outside the conventional use of firm-specific variables; the action that best suits the Egyptian market that was subject to political changes during the past years. Outcome of this study shall contribute to better understanding of implications of the choice of capital structure as one of the important and complex decisions in finance. Research results revealed robust, linear and negative effect of firm leverage on firm value in the presence of four controlling variables (firm size, assets growth, sales growth and political uncertainty). Firm value is found to be affected by firm size, assets growth and political uncertainty.

Moment constrained optimal dividends: precommitment \& consistent planning
Sören Christensen,Kristoffer Lindensjö

A moment constraint that limits the number of dividends in the optimal dividend problem is suggested. This leads to a new type of time-inconsistent stochastic impulse control problem. First, the optimal solution in the precommitment sense is derived. Second, the problem is formulated as an intrapersonal sequential dynamic game in line with Strotz' consistent planning. In particular, the notions of pure dividend strategies and a (strong) subgame perfect Nash equilibrium are adapted. An equilibrium is derived using a smooth fit condition. The equilibrium is shown to be strong. The uncontrolled state process is a fairly general diffusion.

Network Centrality, Connections, and Information: Evidence from CEO Insider Trading Gains
El‐Khatib, Rwan,Jandik, Dobrina Georgieva,Jandik, Tomas
CEO’s insider trading gains are affected by the position of the CEO within the hierarchy of all business executives, as assessed by the CEO’s network centrality. CEOs with high centrality are associated with significantly more positive abnormal returns following purchases of their company’s stocks, compared to the CEOs with low centrality. These results hold even after considering potential endogeneity, and CEO personal characteristics and firm determinants related to network centrality. High-centrality CEOs earn higher abnormal returns following their share purchases primarily in firms that are riskier, have weak corporate governance, or are managed by a CEO with no career background in finance. High centrality CEOs also generate more significant personal gains by selling their shares prior to bad news event experienced by their firm. Finally, trading gains are further positively affected by CEO having past connections to the current CFO. Our findings suggest high network centrality, as well as bilateral connections to people with financial knowledge, allow CEOs to more efficiently gather information about the value of their company.

On Belief Singularity and the Optimality of Deductible Indemnity Schedules
Ghossoub, Mario
In Ghossoub (2019), we studied a problem of optimal insurance design under belief heterogeneity. We examined the effect of the divergence in beliefs, and the singularity between the beliefs, on optimal indemnities; and we provided a closed-form characterization of optimal indemnity schedules. In this paper, we build upon the work of Ghossoub (2019) and introduce a belief singularity metric to examine the effect of the level of singularity between the beliefs on the shape of optimal indemnity schedules. Our main result shows that for any level x of belief singularity with respect to the insurer's belief Q, there exists a probability measure P exhibiting a level x of belief singularity with respect to Q, such that an optimal indemnity for a decision maker with belief P is a linear deductible with Q-probability 1, and full insurance with Q-probability 0. In this sense, we show that from the perspective of the insurer, linear deductible indemnity schedules are robust to belief singularity, which gives a possible explanation of the popularity of these contracts in practice.

Optimizing Execution Cost Using Stochastic Control
Akshay Bansal,Diganta Mukherjee

We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumption of discretized geometric movement of the stock prices. We consider two different cost functions where the first function involves just the fiscal cost while the cost function of the second kind incorporates the risks of non-strategic constrained investments along with fiscal costs. Precisely, the strategic development of constrained execution of K stocks within a stipulated time frame of T units is established mathematically using a well-defined stochastic behaviour of stock prices and the same is compared with some of the commonly-used execution strategies using the historical stock price data.

PAGAN: Portfolio Analysis with Generative Adversarial Networks
Giovanni Mariani,Yada Zhu,Jianbo Li,Florian Scheidegger,Roxana Istrate,Costas Bekas,A. Cristiano I. Malossi

Since decades, the data science community tries to propose prediction models of financial time series. Yet, driven by the rapid development of information technology and machine intelligence, the velocity of today's information leads to high market efficiency. Sound financial theories demonstrate that in an efficient marketplace all information available today, including expectations on future events, are represented in today prices whereas future price trend is driven by the uncertainty. This jeopardizes the efforts put in designing prediction models. To deal with the unpredictability of financial systems, today's portfolio management is largely based on the Markowitz framework which puts more emphasis in the analysis of the market uncertainty and less in the price prediction. The limitation of the Markowitz framework stands in taking very strong ideal assumptions about future returns probability distribution.

To address this situation we propose PAGAN, a pioneering methodology based on deep generative models. The goal is modeling the market uncertainty that ultimately is the main factor driving future trends. The generative model learns the joint probability distribution of price trends for a set of financial assets to match the probability distribution of the real market. Once the model is trained, a portfolio is optimized by deciding the best diversification to minimize the risk and maximize the expected returns observed over the execution of several simulations. Applying the model for analyzing possible futures, is as simple as executing a Monte Carlo simulation, a technique very familiar to finance experts. The experimental results on different portfolios representing different geopolitical areas and industrial segments constructed using real-world public data sets demonstrate promising results.

Personalized Robo-Advising: Enhancing Investment through Client Interactions
Capponi, Agostino,Olafsson, Sveinn,Zariphopoulou, Thaleia
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional human advisors. Their viability crucially depends on the frequency of interactions with the clients they serve. We develop a novel client/robo-advisor framework, in which the robo-advisor solves a multi-period mean-variance optimization problem where the risk-return trade-off adapts to the risk profile communicated by the client. In determining the communication schedule, the client faces a trade-off between a more personalized portfolio strategy and an increased level of delegation to the robo-advisor. Our model predicts that a client that values having a more personalized portfolio prefers the robo-advisor over a human-advisor, unless the latter is highly sophisticated or the client's risk profile changes frequently. A client that places more emphasis on delegation favors the human-advisor, consistently with empirical findings.

Quantifying the Correlation of Media Coverage and Stock Price Crash Risk: A Panel Study From China
Zhao, Ruwei
In this paper, we explore the correlation between media coverage and stock price crash risk of all the listed stocks in China stock market. Particularly, we utilize the news report frequencies, sourcing from traditional media (TMC) and Internet media (IMC), as proxies for media coverages and investigate their correlations with stock price crash risk under panel regression models. We find that TMC is positively related to stock price crash risk one year after, indicating that prior rise of TMC would be a red alert for future price drop. While, no significant coefficients are detected with IMC, showing that IMC are of no influence with future stock price crash risk. We also perform the robustness check with other stock price crash risk measurement proxy, and the results are in line with those of the original study.

Sell-Side Analyst Heterogeneity and Insider Trading
Contreras, Harold,Marcet, Francisco
This study explores insider trading patterns after earnings announcements under different surprises. We show that insiders sell more aggressively depending on the heterogeneity of analysts whose EPS forecasts are met or beaten in order to camouflage their trades. Specifically, insiders sell more shares of their companies' stocks and sooner after the publications of earnings when top analysts forecasts are met or beaten. Consistent with the informed trading literature, insiders strategically select these moments because stock price impact is low and legal scrutiny of their trades is minimal. In line with the camouflage incentives, by selling after top analysts' forecasts are met or beaten stock prices become less efficient as they adjust slowly to insider trades. Finally, we show that the incentives of insiders to hide their trades is mainly concentrated on members of the top management team who are more likely to bear the costs of selling shares after positive news.

Stability properties of Haezendonck-Goovaerts premium principles
Niushan Gao,Cosimo Munari,Foivos Xanthos

We investigate a variety of stability properties of Haezendonck-Goovaerts premium principles on their natural domain, namely Orlicz spaces. We show that such principles always satisfy the Fatou property. This allows to establish a tractable dual representation without imposing any condition on the reference Orlicz function. In addition, we show that Haezendonck-Goovaerts principles satisfy the stronger Lebesgue property if and only if the reference Orlicz function fulfills the so-called $\Delta_2$ condition.

Stock Market Volatility and Mathematical Expectations
James, Robert G.
It is generally believed that excessive stock market volatility reflects non-mathematical market expectations that are driven by “irrational exuberance” or “animal spirits”. As shown in this paper, there is an alternative explanation. If ex-ante and ex-post expectations are calculated in different stochastic processes, uncertainty can cause mathematical market expectations to be more volatile than their fundamentals.

The Global Credit Spread Puzzle
Nozawa, Yoshio,Shi, Zhan,Huang, Jing-Zhi
Using security-level credit spread data in eight developed economies, we document a large cross-country difference in credit spreads conditional on credit ratings and other default risk measures. Regardless of the calibration methods used, the structural model of Black and Cox (1976) does not explain this cross-country variation in credit spreads. Since this cross-country variation is positively related to illiquidity measures, we calibrate an extended structural model that incorporates endogenous liquidity in the secondary market, and find that this model largely explains credit spreads in cross sections and over time. Therefore, default risk itself unlikely explains corporate credit spreads.

The Great Divorce Between Investment and Profitability
Kilic, Mete,Yang, Louis,Zhang, Miao Ben
We study the cross-sectional relation between investment, profitability, and equity returns over the last century. We document that high-profit firms invest more than low-profit firms before the late 1970s but invest less than low-profit firms afterwards. This reversal coincides with the emergence of the investment and profitability asset pricing factors and a corresponding reversal in the two factors' correlation. We ascribe these changes to decreased long-term discount rates, which we document in the data. We develop a model where firms invest in short- and long-term projects. Responding to low discount rates, firms invest more in long-term projects, leading to high investment from low-profit firms and high average factor returns, as we observe in recent decades. The influx of long-term focused firms following the rise of venture capital in the late 1970s may explain the divorce between investment and profitability.

The Industry Supply Function and the Long-Run Competitive Equilibrium with Heterogeneous Firms
Ignacio Esponda,Demian Pouzo

In developing the theory of long-run competitive equilibrium (LRCE), Marshall (1890) used the notion of a representative firm. The identity of this firm, however, remained unclear. Subsequent theory either focused on the case where all firms are identical or else incorporated heterogeneity but disregarded the notion of a representative firm. Using Hopenhayn's (1992) model of competitive industry dynamics, we extend the theory of LRCE to account for heterogeneous firms and show that the long-run supply function can indeed be characterized as the solution to the minimization of a representative average cost function.

The Information Content of The Implied Volatility Surface: Can Option Prices Predict Jumps?
Han, Yufeng ,Liu, Fang
We ask whether option prices contain information on the likelihood and direction of jumps in the underlying stock prices. Applying the partial least squares (PLS) approach to the entire surface of the implied volatilities (IV), we show that option prices can successfully predict downward jumps in stock prices, but not upward jumps. The PLS estimated downward jump factor can predict stock returns with a spread of 1.53% per month between stocks predicted to have the lowest probability of downward jumps and stocks predicted to have the highest probability of downward jumps. Both put and call option prices, and options of both short and long maturity contribute to the predictability. Furthermore, the predictability of the downward jump is robust to many firm characteristics as well as options related variables. Consistent with the notion that informed investors trade in the options markets to profit from negative information in order to circumvent the short-sale constraint, we find that stronger predictability is associated with tighter short-sale constraints in the equity market, and in periods when the market has poor performance.

Unveiling the relation between herding and liquidity with trader lead-lag networks
Carlo Campajola,Fabrizio Lillo,Daniele Tantari

We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative ``opinion" to all agents about whether the traded asset price will go up or down. This opinion is reflected by their trading behavior, but whenever the trader is not active in a given time window, a missing value will arise. Using a recently developed inference algorithm, we are able to reconstruct a lead-lag network and to estimate the unobserved opinions, giving a clearer picture about the state of supply and demand in the market at all times.

We apply our method to a dataset of clients of a major dealer in the Foreign Exchange market at the 5 minutes time scale. We identify leading players in the market and define a herding measure based on the observed and inferred opinions. We show the causal link between herding and liquidity in the inter-dealer market used by dealers to rebalance their inventories.

WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Michael Poli,Jinkyoo Park,Ilija Ilievski

Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwards (NDF), a derivatives contract used in foreign exchange (FX) trading, presents additional difficulty in the form of long-term planning required for an effective selection of start and end date of the contract. In this work, we focus on tackling the problem of NDF tenor selection by leveraging high-dimensional sequential data consisting of spot rates, technical indicators and expert tenor patterns. To this end, we construct a dataset from the Depository Trust & Clearing Corporation (DTCC) NDF data that includes a comprehensive list of NDF volumes and daily spot rates for 64 FX pairs. We introduce WaveATTentionNet (WATTNet), a novel temporal convolution (TCN) model for spatio-temporal modeling of highly multivariate time series, and validate it across NDF markets with varying degrees of dissimilarity between the training and test periods in terms of volatility and general market regimes. The proposed method achieves a significant positive return on investment (ROI) in all NDF markets under analysis, outperforming recurrent and classical baselines by a wide margin. Finally, we propose two orthogonal interpretability approaches to verify noise stability and detect the driving factors of the learned tenor selection strategy.