# Research articles for the 2019-12-02

A Machine Learning Approach to Adaptive Robust Utility Maximization and Hedging
Tao Chen,Michael Ludkovski
arXiv

We investigate the adaptive robust control framework for portfolio optimization and loss-based hedging under drift and volatility uncertainty. Adaptive robust problems offer many advantages but require handling a double optimization problem (infimum over market measures, supremum over the control) at each instance. Moreover, the underlying Bellman equations are intrinsically multi-dimensional. We propose a novel machine learning approach that solves for the local saddle-point at a chosen set of inputs and then uses a nonparametric (Gaussian process) regression to obtain a functional representation of the value function. Our algorithm resembles control randomization and regression Monte Carlo techniques but also brings multiple innovations, including adaptive experimental design, separate surrogates for optimal control and the local worst-case measure, and computational speed-ups for the sup-inf optimization. Thanks to the new scheme we are able to consider settings that have been previously computationally intractable and provide several new financial insights about learning and optimal trading under unknown market parameters. In particular, we demonstrate the financial advantages of adaptive robust framework compared to adaptive and static robust alternatives.

A Mixed Frequency Approach for Stock Returns and Valuation Ratios
Dergiades, Theologos,Milas, Costas,Panagiotidis, Theodore
SSRN
We employ a Mixed-Frequency VAR to study the effect of four valuation ratios (the price-dividend ratio, the price-earnings ratio, the Cyclically Adjusted Price Earnings Ratio and the Total Return Cyclically Adjusted Price Earnings Ratio) on the US stock market. We quantify the interaction between high and low frequency data. We show that all valuation ratios (observed at a monthly frequency) significantly affect stock market returns (observed at a daily frequency) at both long and short horizons.

A Price Dynamic Equilibrium Model with Trading Volume Weights Based on a Price-Volume Probability Wave Differential Equation
Shi, Leilei,Wang, Bing-Hong,Guo, Xinshuai,Li, Honggang
SSRN
In view of liquidity constrains and cognitive limits in an economy, we construct an individual trading constraint utility function to study intraday dynamic equilibrium in stock market. We select an intraday cumulative trading volume distribution over a price range for individual mental representation and determine a price equilibrium point by the maximum volume price. We hypothesize that a stock price can deviate away from the price equilibrium point in momentum and restore to it in reversal, and the trading volume distribution embodies market dynamic equilibrium. Based on a price-volume probability wave differential equation, we develop a set of explicit price dynamic equilibrium models with trading volume weights in a friction market and have its stability conditions. In addition, we measure momentum trading, reversal trading, and interactive trading. Then, we examine the market dynamic equilibrium hypothesis by the models using tick by tick high frequency data in Chinese A shares stock market in 2019. It holds true. We can infer that the theory is applied for a broader scope because it embraces major components in expected utility theory, prospect theory, and reflexivity theory. It can help us build a better market asset pricing model and risk management model in behavioral finance.

Anti-Corruption Reforms and Microfinancing: Evidence from Households' Fintech Borrowing
Zhang, Yifei
SSRN
Despite a surging literature in investigating different impacts of corruption and/or anti-corruption from firmsâ€™ perspective, it is still unclear whether and how corruption and/or anti-corruption affect householdsâ€™ borrowing behaviour. In this paper, we focus on a Chinese online peer-to-peer lending market and analyse the impact of the recent Chinaâ€™s anti-corruption campaign on householdsâ€™ borrowing costs. We employ a Difference-in-Differences (DID) estimation strategy and investigate three exogenous shocks regarding the movement: 1) the 2012 Eight Point Policy announcement; 2) multiple rounds of the Central Inspection Team Campaigns during 2013 and 2014; 3) and the anti-corruption rules for military-related personnel in early 2015. Our results show that equilibrium interest rates of borrowers pertaining to Non-SOEs dropped significantly comparing to that of SOEs and/or government agencies in the wake of the first two events. Borrowers affiliating with military-related institutions were also worsened after the military specific anti-corruption campaign. Finally, we examine the two possible economic channels. Suggestive evidences show that both a rise of interest risk premium for SOEs borrowers and a better outlook of Non-SOEs after the anti-corruption reform could account for the observed favour of the borrowing costs towards Non-SOEs borrowers. These findings are consistent with previous studies regarding the effects of anti-corruptions from firmsâ€™ aspects such as Lin et al., (2016) and Griffin et al., (2016). This study also complements the P2P literature by demonstrating the importance of online borrowersâ€™ occupations / job affiliations.

Artificial boundary method for the solution of pricing European options under the Heston model
Hongshan Li,Zhongyi Huang
arXiv

This paper considers the valuation of a European call option under the Heston stochastic volatility model. We present the asymptotic solution to the option pricing problem in powers of the volatility of variance. Then we introduce the artificial boundary method for solving the problem on a truncated domain, and derive several artificial boundary conditions (ABCs) on the artificial boundary of the bounded computational domain. A typical finite difference scheme and quadrature rule are used for the numerical solution of the reduced problem. Numerical experiments show that the proposed ABCs are able to improve the accuracy of the results and have a significant advantage over the widely-used boundary conditions by Heston in the original paper (Heston, 1993).

Asset Price Bubbles in market models with proportional transaction costs
Francesca Biagini,Thomas Reitsam
arXiv

We study asset price bubbles in market models with proportional transaction costs $\lambda\in (0,1)$ and finite time horizon $T$ in the setting of [49]. By following [28], we define the fundamental value $F$ of a risky asset $S$ as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash. We then obtain a dual representation for the fundamental value by using the super-replication theorem of [50]. We say that an asset price has a bubble if its fundamental value differs from the ask-price $(1+\lambda)S$. We investigate the impact of transaction costs on asset price bubbles and show that our model intrinsically includes the birth of a bubble.

CEO Connectedness and the Cost of Equity Capital
Lee, Min Suk,Low, Angie,Teoh, Siew Hong
SSRN
A larger CEO network can reduce the cost of equity by reducing information asymmetry between the firm and outsiders, and by increasing trust between the firm and stakeholders. Alternatively, a larger CEO network can increase the cost of equity because higher CEO connectedness encourages greater agency problems and more risk-taking by reducing the costs borne by the CEO from a termination. We find a positive relation between a CEOâ€™s connectedness and the firmâ€™s cost of equity, suggesting that, on average, the costs of CEO connectedness outweigh the benefits. The positive relation between a CEOâ€™s connections and the firmâ€™s cost of equity is attenuated for firms with high information asymmetry. This additional cross-sectional result is consistent with the higher benefits of improved information flow from connectedness in firms with high information asymmetry that can help mitigate some of the adverse effects of agency costs and risk-taking. We use multiple methods to address endogeneity and reverse causality problems, and our results remain generally robust.

Collateralized Debt Networks with Lender Default
Chang, Jin-Wook
SSRN
The Lehman Brothers' 2008 bankruptcy spread losses to its counterparties even when Lehman was a lender of cash, because collateral for that lending was tied up in the bankruptcy process. I study the implications of such lender default using a general equilibrium network model featuring endogenous leverage, endogenous asset prices, and endogenous network formation. The multiplex graph model has two channels of contagion: a counterparty channel of contagion and a price channel of contagion through endogenous collateral price. Borrowers diversify their lenders because of the counterparty risk, but they have to deal with lenders who lend at a higher margin. This diversification generates positive externalities by reducing systemic risk, but any decentralized equilibrium is constrained inefficient due to under-diversification. The key externalities here, arising from the tradeoff between counterparty risk and leverage (margin), are absent in models with exogenous leverage or exogenous networks. I use this framework to analyze the introduction of a central counterparty (CCP). I show that the loss coverage by the CCP reduces diversification incentives and exacerbates the externality problem which can rather increase systemic risk.

Common Ownership Theories, Governance â€˜Mechanismsâ€™ & Policy (Presentation Slides)
Schmalz, Martin C.
SSRN
These slides summarize the theoretical literature on horizontal common ownership concentration and its impact on competition, as presented at the FTC's hearings on common ownership and competition in December 2018. They are primarily based on this literature review: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3046829

Comparative Companies' Stock Valuation through Financial Metrics
Jack Colleran,Harris K. Kim,Benny C. Pickle,John Sun
arXiv

Out of the companies, Dolby is the company with the best overall financial and operation health. According to the table that accounted its financial statements for the past three years, Dolby has stable profit margins that generates a revenue in the billions, the only company in ten figures.

Controlling Family and Corporate Governance
Fan, Joseph P. H.,Yu, Xin
SSRN
Building on the theory of Burkart et al. (2003) that founding family ownership and control of firms mitigate the twin conflicts between owners and managers and between majority and minority owners, we suggest that the partition of firm ownership rights and associated governance within the controlling family helps mitigate conflicts among family members, a third type of agency problem. We examine the degrees of controlling family members participating as owners and/or managers of more than 1,200 emerging Chinese publicly traded private sector firms, and their effects on suspicious related party transactions that concern both family and outside shareholders. Consistent with the twin agency theory, firms with more family participation engage in fewer problematic related-party transactions, and the effects are stronger in firms subject to weaker market discipline. Furthermore, the effects are stronger in firms whose controlling families are more influenced by collectivistic culture and when more senior family members or more distant relatives participate in the firms. These findings are in contrast with the conventional view of controlling family expropriation but consistent with positive spillover effects of controlling family governance.

Corporate Governance and Ethical Culture: Do Boards of Directors and Ownership Structure Mattter?
Di Miceli da Silveira, Alexandre
SSRN
A poor ethical culture has been considered one of the reasons for the emergence of many corporate governance scandals. In this paper, I investigate the link between two corporate governance mechanisms â€" the composition of the board of directors and ownership structure â€" and ethical culture for a sample of Brazilian companies. My measure of ethical culture is based on a text analysis of around 50,000 employee reviews posted at Glassdoor for around 1,400 terms associated with five ethical dimensions: organizational trust, ethical leadership, benevolent orientation, empathy, and speaking out & efficacy. I find partial support, though far from conclusive, for the hypotheses that a higher ratio of independent directors or of women on boards is associated with better ethical culture. My clearest results refer to a corporate governance feature little discussed in the literature: the percentage of directors appointed by minority shareholders. In this case, all specifications show a strong negative relationship between the percentage of such directors and ethical culture. As minority directors are usually appointed by institutional investors, one conjecture is that the short-term horizon of some institutional investors could lead these directors to prioritize short-term profits instead of focusing on building an ethical culture whose benefits would be mostly reaped over the longer term. Other variables related to the board of directors and ownership structure, such as ownership concentration and the identity of the shareholder of reference, were not significant in explaining ethical culture. To my knowledge, this is the first paper to document a link between ethical culture and corporate governance mechanisms.

Do Firms Care About Investment Opportunities? Evidence From China
Ding, Sai,Kim, Minjoo,Zhang, Xiao
SSRN
What drives a firmâ€™s investment decisions in China? While most literature focuses on the role of financial factors (such as cash flow), we explore this most important question in corporate finance from the perspective of economic fundamentals. Empirical results show that private firms cherish investment opportunities more in China. State-owned enterprises respond more to the investment opportunities from the supply side, but much less so to demand-side shocks. These findings provide interesting policy implications for Chinaâ€™s ongoing ownership and financial sector reforms.

Dynamic Trading, Imperfect Competition and Forward Contracts
Dastarac, Hugues
SSRN
I provide a model in which forward-like contracts are traded in equilibrium because of imperfect competition. Risk averse traders have successive opportunities to trade a risky asset: with imperfect competition sellers slice their order in small pieces to realize a higher average selling price, and similarly for buyers who want a low average purchase price. If exogenous customers are expected to trade later an unknown quantity independent from the asset value, this creates uncertainty for future transaction prices: buyers and sellers are sensitive to this risk in opposite ways, which creates gains from trading this risk. Abstract contracts indexed on the customer's quantity traded, resembling forwards or futures because equilibrium is linear in this quantity, are traded in imperfectly competitive equilibrium. Sellers keep the asset for longer, and sell forwards, implying a large balance sheet.

Endogenous Liquidity Crises
Antoine Fosset,Jean-Philippe Bouchaud,Michael Benzaquen
arXiv

Empirical data reveals that the liquidity flow into the order book (depositions, cancellations andmarket orders) is influenced by past price changes. In particular, we show that liquidity tends todecrease with the amplitude of past volatility and price trends. Such a feedback mechanism inturn increases the volatility, possibly leading to a liquidity crisis. Accounting for such effects withina stylized order book model, we demonstrate numerically that there exists a second order phasetransition between a stable regime for weak feedback to an unstable regime for strong feedback,in which liquidity crises arise with probability one. We characterize the critical exponents, whichappear to belong to a new universality class. We then propose a simpler model for spread dynamicsthat maps onto a linear Hawkes process which also exhibits liquidity crises. If relevant for thereal markets, such a phase transition scenario requires the system to sit below, but very close tothe instability threshold (self-organised criticality), or else that the feedback intensity is itself timedependent and occasionally visits the unstable region. An alternative scenario is provided by a classof non-linear Hawkes process that show occasional "activated" liquidity crises, without having to bepoised at the edge of instability.

Estimation and simulation of the transaction arrival process in intraday electricity markets
Michał Narajewski,Florian Ziel
arXiv

We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error), RMSE (root mean squared error) and CRPS (continuous ranked probability score) for the simulated count processes. This paper fills the gap in the literature regarding the intensity estimation of transaction arrivals and is a major contribution to the topic, yet leaves much of the field for further development. The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market. For the German market, a specific generalized gamma distribution setup explains the overall behaviour significantly best, especially as the tail behaviour of the process is well covered.

Financial Market Directional Forecasting With Stacked Denoising Autoencoder
Shaogao Lv,Yongchao Hou,Hongwei Zhou
arXiv

Forecasting stock market direction is always an amazing but challenging problem in finance. Although many popular shallow computational methods (such as Backpropagation Network and Support Vector Machine) have extensively been proposed, most algorithms have not yet attained a desirable level of applicability. In this paper, we present a deep learning model with strong ability to generate high level feature representations for accurate financial prediction. Precisely, a stacked denoising autoencoder (SDAE) from deep learning is applied to predict the daily CSI 300 index, from Shanghai and Shenzhen Stock Exchanges in China. We use six evaluation criteria to evaluate its performance compared with the back propagation network, support vector machine. The experiment shows that the underlying financial model with deep machine technology has a significant advantage for the prediction of the CSI 300 index.

Firm-Level Employment, Labour Market Reforms, and Bank Distress
Stieglitz, Moritz,Setzer, Ralph
SSRN
We explore the interaction between labour market reforms and financial frictions. Our study combines a new cross-country reform database on labour market reforms with matched firm-bank data for nine euro area countries over the period 1999 to 2013. While we find that labour market reforms are overall effective in increasing employment, restricted access to bank credit can undo up to half of long-term employment gains at the firm-level. Entrepreneurs without sufficient access to credit cannot reap the full benefits of more flexible employment regulation.

Flight to Quality - Gold Mining Shares versus Gold Bullion
Baur, Dirk G.,Prange, Philipp,Schweikert, Karsten
SSRN
This paper uses the co-movement of gold mining shares with the price of gold to assess the strength of flight to quality and the severity of financial shocks by distinguishing between flight to physical gold and flight to gold mining companies. The analysis of a global sample of gold mining companies reveals that flights to quality are very different across financial shocks with the bankruptcy of Lehman Brothers and the Brexit vote being the most extreme at opposite ends of the spectrum. We also find evidence that a flight from gold mining shares leads to a stronger price reaction and thus safe haven effect of gold bullion. This study demonstrates that gold mining companies can enrich our understanding of the flight to quality phenomenon.

Fund Performance and Social Responsibility: New Evidence using Social Active Share and Social Tracking Error
El Ghoul, Sadok,Karoui, Aymen
SSRN
We examine the effects of socially responsible investing (SRI) on mutual fund performance. We use two proxies of deviation from SRI: social active share (SAS) and social tracking error (STE) which, respectively, capture the differences in holdings and returns between a fund and a socially responsible index, namely the MSCI KLD 400. Using a sample of 2516 U.S. mutual funds over the period 2010-2017, our univariate analysis shows that less socially responsible funds do not outperform more socially responsible funds. The multivariate analysis shows, however, some evidence that more socially responsible funds display higher risk-adjusted performance than their less socially responsible peers. Our results are consistent with the hypothesis that SRI does not significantly damage fund performance.

Gender Competitiveness and Predictability, and Prize Money in Grand Slam Tennis Tournaments
Feldman, David,Gross, Shulamith,Long, Yang
SSRN
Tournament competitiveness and predictability of outcomes are inversely related. We introduce a new measure of competitiveness or predictability using discrepancies between tennis playersâ€™ seed values and attained ranks in all rounds of the four Grand Slam Tennis Tournaments (GST). Using data from the Association of Tennis Professionals (ATP) and Womenâ€™s Tennis Association (WTA), we study gender-differentiated competitiveness or predictability and its dependence on prize money. Menâ€™s GST are less predictable and more competitive than womenâ€™s GST. While competitiveness increases with prize money, the increase for women is significantly larger than for men.

Global Cooling Through Blockchain to Avoid Catastrophic Climate Changes by 2050
Taskinsoy, John
SSRN
Innumerable factors contributing to global warming is nothing of new, but increased human-induced greenhouse gas emissions resulting from the burning of vast amounts of fossil fuels have pushed the Earthâ€™s natural systems (balanced carbon cycle) out of balance causing extreme climate changes in the new millennium. Since 1900, the global mean surface temperature has warmed up approximately +1.0 Â°C (1.8 Â°F) above the pre-industrial temperature, nearly half of which has occurred over the past three decades. If no actions are taken immediately in the next decade (by 2030), the global warming may accelerate to the critical +1.5 Â°C (2.8 Â°F) by 2050; at that time, extreme weather events may lead to irreversible disruptions to the economy, human health, and the ecosystems. Despite the fact that blockchain became a household name with the launch of Bitcoin in January 2009, its immense unique opportunities are well beyond Bitcoin and altcoins. Blockchain can contribute positively to the efforts of reducing human-induced greenhouse gas emissions therefore global warming by removing trusted third parties (distributed ledger technology), enabling purely peer-to-peer transactions, bringing network participants to transact and interact with each other on the same platform, creating crowd sourced funding, making governance transparent, increasing accessibility to data, making traceability easier, decentralizing the internet, automating various systems remotely located, increasing financial inclusion, finally making identity and private data management highly secure.

Heuristic Strategies in Uncertain Approval Voting Environments
Jaelle Scheuerman,Jason L. Harman,Nicholas Mattei,K. Brent Venable
arXiv

In many collective decision making situations, agents vote to choose an alternative that best represents the preferences of the group. Agents may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility. In such situations, it is often assumed that voters will vote truthfully rather than expending the effort to strategize. However, being truthful is just one possible heuristic that may be used. In this paper, we examine the effectiveness of heuristics in single winner and multi-winner approval voting scenarios with missing votes. In particular, we look at heuristics where a voter ignores information about other voting profiles and makes their decisions based solely on how much they like each candidate. In a behavioral experiment, we show that people vote truthfully in some situations and prioritize high utility candidates in others. We examine when these behaviors maximize expected utility and show how the structure of the voting environment affects both how well each heuristic performs and how humans employ these heuristics.

Innovationskraft in Deutschland verbessern: Ã–kosystem fÃ¼r Wachstumsfinanzierung stÃ¤rken (Enhancing Innovation in Germany by Strengthening the Growth Finance Ecosystem)
Achleitner, Annâ€Kristin,Braun, Reiner,Behrens, Jan Henning,Lange, Thomas
SSRN
German Abstract: Fehlendes Kapital fÃ¼r Hightech-Wachstumsunternehmen ist eine der zentralen SchwÃ¤chen des deutschen Innovationssystems. Gerade im Zuge der digitalen Transformation, in der radikale technologische Innovationen und neue GeschÃ¤ftsmodelle sowie schnelles Wachstum gefragt sind, wird diese SchwÃ¤che zu einem entscheidenden Wettbewerbsnachteil.acatech hat in einem gemeinsamen Projekt mit der KfW und der Deutschen BÃ¶rse eine Vielzahl von Akteuren aus dem Finanzbereich mit Hightech-Wachstumsunternehmen sowie Vertreterinnen und Vertretern der Industrie und der Wissenschaft zusammengebracht, um eine differenzierte Bestandsaufnahme vorzunehmen und Handlungsoptionen fÃ¼r Politik, Wirtschaft und Wissenschaft zu erarbeiten.Die Handlungsoptionen der vorliegenden Studie beziehen sich nicht nur auf die Mobilisierung klassischen Wagniskapitals. Es werden auch andere Finanzierungsangebote beleuchtet und vor allem die Schnittstellen von Hightech-Wachstumsunternehmen, etablierten Unternehmen und wissenschaftlichen Einrichtungen adressiert. Diese Schnittstellen sind ein SchlÃ¼ssel fÃ¼r eine aussichtsreiche Position Deutschlands im Wettbewerb um die Digitalisierung der Industrie.English Abstract: Insufficient access to capital for high-tech growth companies is one of the key weaknesses of Germanyâ€™s innovation system. This weakness is becoming a serious competitive disadvantage, especially in the context of the radical technological innovation, new business models and rapid growth demanded by the digital transformation.In a joint project with KfW and Deutsche BÃ¶rse, acatech brought together a range of actors from the financial sector with high-tech growth companies and representatives of academia and industry in order to carry out a detailed analysis of the status quo and formulate recommendations for government, academia and industry.The studyâ€™s recommendations are not confined to the mobilisation of traditional venture capital. They also highlight alternative forms of financing and in particular the interfaces between high-tech growth companies, established businesses and academic institutions. These interfaces are one important key to build a strong competitive position in Germany with regard to industrial digitalisation.

Making Good on LSTMs' Unfulfilled Promise
Daniel Philps,Artur d'Avila Garcez,Tillman Weyde
arXiv

LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We examine an alternative called Continual Learning (CL), a memory-augmented approach, which can provide transparent explanations, i.e. which memory did what and when. This work has implications for many financial applications including credit, time-varying fairness in decision making and more. We make three important new observations. Firstly, as well as being more explainable, time-series CL approaches outperform LSTMs as well as a simple sliding window learner using feed-forward neural networks (FFNN). Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM). Thirdly, we examine how real-world, time-series noise impacts several similarity approaches used in CL memory addressing. We provide these insights using an approach called Continual Learning Augmentation (CLA) tested on a complex real-world problem, emerging market equities investment decision making. CLA provides a test-bed as it can be based on different types of time-series learners, allowing testing of LSTM and FFNN learners side by side. CLA is also used to test several distance approaches used in a memory recall-gate: Euclidean distance (ED), dynamic time warping (DTW), auto-encoders (AE) and a novel hybrid approach, warp-AE. We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task.

Media Ownership and Media Reporting
Guo, Shijun,Yu, Xin,Faff, Robert W.
SSRN
This paper investigates whether ownership in media affects a corporate media coverage. Using a sample of publicly listed firms in China, we find that firms with media affiliations do receive favorable slant coverage from the financial media. Two possible explanations are explored, one is firm management hypothesis in which firms actively manage their relationship with media outlets through media affiliations, and the other is journalist attention hypothesis in which media affiliations attract the attention of news outlets. We find support for the firm management hypothesis, but not the journalist attention hypothesis.

Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach
Roberto Baviera,Giulia Bianchi
arXiv

In this paper we consider the worst-case model risk approach described in Glasserman and Xu (2014). Portfolio selection with model risk can be a challenging operational research problem. In particular, it presents an additional optimisation compared to the classical one. We find the analytical solution for the optimal mean-variance portfolio selection in the worst-case scenario approach. In the minimum-variance case, we prove that the analytical solution is significantly different from the one found numerically by Glasserman and Xu (2014) and that model risk reduces to an estimation risk. A detailed numerical example is provided.

On Extensions of the Barone-Adesi & Whaley Method to Price American-Type Options
Ludovic Mathys
arXiv

The present article provides an efficient and accurate hybrid method to price American standard options in certain jump-diffusion models as well as American barrier-type options under the Black & Scholes framework. Our method generalizes the quadratic approximation scheme of Barone-Adesi & Whaley (1987) and several of its extensions. Using perturbative arguments, we decompose the early exercise pricing problem into sub-problems of different orders and solve these sub-problems successively. The obtained solutions are combined to recover approximations to the original pricing problem of multiple orders, with the 0-th order version matching the general Barone-Adesi & Whaley ansatz. We test the accuracy and efficiency of the approximations via numerical simulations. The results show a clear dominance of higher order approximations over their respective 0-th order version and reveal that significantly more pricing accuracy can be obtained by relying on approximations of the first few orders. Additionally, they suggest that increasing the order of any approximation by one generally refines the pricing precision, however that this happens at the expense of greater computational costs.

Optimal forest rotation under carbon pricing and forest damage risk
Tommi Ekholm
arXiv

Forests will have two notable economic roles in the future: providing renewable raw material and storing carbon to mitigate climate change. The pricing of forest carbon leads to longer rotation times and consequently larger carbon stocks, but also exposes landowners to a greater risk of forest damage. This paper investigates optimal forest rotation under carbon pricing and forest damage risk. I provide the optimality conditions for this problem and illustrate the setting with numerical calculations representing boreal forests under a range of carbon prices and damage probabilities. The relation between damage probability and carbon price towards the optimal rotation length is nearly linear, with carbon pricing having far greater impact. As such, increasing forest carbon stocks by lengthening rotations is an economically attractive method for climate change mitigation, despite the forest damage risk. Carbon pricing also increases land expectation value and reduces the economic risks of the landowner. The production possibility frontier under optimal rotation suggests that significantly larger forests carbon stocks are achievable, but imply lower harvests. However, forests' societally optimal role between these two activities is not yet clear-cut; but rests on the future development of relative prices between timber, carbon and other commodities dependent on land-use.

Pay for Performanceâ€¦ But Not Too Much Pay: The American Public's View of CEO Pay
Larcker, David F.,Tayan, Brian
SSRN
Among the controversies in corporate governance, perhaps none is more heated or widely debated across society than that of CEO pay. The views that American citizens have on CEO pay is centrally important because public opinion influences political decisions that shape tax, economic, and regulatory policy, and ultimately determine the standard of living of average Americans. This Closer Look reviews survey data of the American public to understand their views on compensation. We ask:â€¢ How can societyâ€™s understanding of pay and value creation be improved and the controversy over CEO pay resolved?â€¢ How should the level of CEO pay rise with complexity and profitability, particularly among Americaâ€™s largest corporations?â€¢ Should pay be reformed in the boardroom, or should high pay be addressed solely through the tax code?â€¢ Are negative views of CEO pay driven by broad skepticism and lack of esteem for CEOs? Or do high pay levels themselves contribute to low regard for CEOs?

Political Connections and Media Slant
Guo, Shijun,Yu, Xin,Faff, Robert W.
SSRN
We examine the influence of political connections on media slant for a sample of Chinese listed firms. We find that firms with stronger political connections obtain more positive reports from eight major Chinese business newspapers. The association is more pronounced when firms are located in provinces with more government intervention and when the media are state-controlled (compared with market-oriented media). We further examine two explanations for the positive association between political connections and media slant. On the demand side, the firm management channel sees firms using their political connections to manage the relationship with the media for favorable reporting. On the supply side, the media intention channel has media outlets providing favorable reporting to connected firms to enhance their own relationship with the government. We find evidence supporting the firm management channel.

Range-Based Expectations
Zhu, Natalie
SSRN
I propose and document empirically that investors form â€œrange-basedâ€ expectations â€" expectations that are influenced by an assetâ€™s past trading range â€" and that these beliefs affect trading behavior and asset prices. I find that, if an assetâ€™s price is high (low) relative to its 52- week trading range, investors erroneously believe that the assetâ€™s future return distribution is negatively (positively) skewed. Consistent with these beliefs, less sophisticated investors trade options in a way that decreases their exposure to underlying stocks that have a high price relative to their 52-week range; moreover, individual investors are more likely to sell and not buy such stocks. Also consistent with these beliefs, stocks with a high (low) price relative to their past trading range earn high (low) subsequent returns, on average.

Reports of Valueâ€™s Death May Be Greatly Exaggerated
Arnott, Robert D.,Harvey, Campbell R.,Kalesnik, Vitali,Linnainmaa, Juhani T.
SSRN
Value investing has underperformed growth investing for over 12 years with a -39.1% drawdown from peak to trough, using the classic Fama-French definition of the value factor. The second longest duration of underperformance during the tech bubble, while deeper than the recent drawdown, was less than four years. As a result, many now argue that this investment style is no longer viable, based on a variety of narratives. We examine some of these narratives, and find them wanting. Using a bootstrapping analysis, we show how likely (given the historical data) it is to observe such a drawdown. We then decompose the value-growth performance into three components: a valuation spread, the migration of securities, and a profit differential. Our analysis of the pre- and post-2007 data reveal the following. There is no significant difference between the migration of stocks (value to neutral or growth and growth to neutral to value) in the two periods nor is there a difference in profitability. About two-thirds of the variance in value factor returns is driven by changes in the relative valuation spread between growth and value, and well over 100% of the underperformance since 2007 is due to value becoming considerably cheaper relative to growth. Our analysis shows that, even with a small mean reversion in relative valuations between growth and value, from 97th to the 95th percentile, value should outperform growth by 9% over the next year.

Risk-shifting, Regulation and Government Assistance
Sharma, Padma
SSRN
This paper examines an episode when policy response to a financial crisis effectively incentivized financial institutions to reallocate their portfolios toward safe assets. Following a shift to a regime of enhanced regulation and scaled-down public assistance during the Savings and Loans (S&L) crisis in 1989, this paper finds that S&Ls with a high probability of failure increased their composition of safe assets relative to S&Ls with a low probability of failure. The findings also show a shift to safe assets among stock S&Ls relative to mutual S&Ls, thereby providing evidence of risk-shifting from equity-holders to debt-holders of stock S&Ls prior to the regulatory reforms. These findings show that credible signals to shareholders around government assistance will be crucial for the policies aimed at reducing moral hazard (such as the Orderly Liquidation Authority under Title II of the Dodd-Frank Act) to succeed. This paper identifies the effect of the policy change by developing a new Bayesian estimation method for causal studies.

Sovereign Risk after Sovereign Restructuring. Private and Official Defaults
Marchesi, Silvia,Masi, Tania
SSRN
This paper studies the relationship between sovereign debt default and sovereign credit risk by taking into account the depth of a debt restructuring and by distinguishing between commercial and official debt. We take different proxies for credit risk measures, such as rating agencies and institutional investors' ratings as well as bond yield spreads (EMBIG). By controlling for both the occurrence and the magnitude of debt defaults, we find that commercial and official defaults are associated with different outcomes. Private defaults seem to involve some reputational costs up to seven years since the last agreement, while official defaulters are not affected (or may even benefit) by the restructuring episodes. Using the Synthetic Control Method, we find further evidence for the heterogeneity of the economic impact of debt restructurings, confirming that official and private defaults may have different costs and then induce selective defaults.

Speed of Rational Social Learning in Networks with Gaussian Information
Krishna Dasaratha,Kevin He
arXiv

We consider a sequential social-learning environment with rational agents and Gaussian private signals, focusing on how the observation network affects the speed of learning. Agents learn about a binary state and take turns choosing actions based on own signals and network neighbors' behavior. The observation network generally presents an obstruction to the efficient rate of signal aggregation, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers. We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation. Adding links to the observation network can harm agents even without introducing new confounds. We then consider a network structure where agents move in generations and observe all members of the previous generation. The additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals, even when generations are arbitrarily large.

Strategic Liquidity Mismatch and Financial Sector Stability
Silva, AndrÃ© F.
SSRN
This paper examines whether banks strategically incorporate their competitorsÃ¢â‚¬â„¢ liquidity mismatch policies when determining their own and how these collective decisions impact financial sector stability. Using a novel identification strategy exploiting the presence of partially overlapping peer groups, I show that banksÃ¢â‚¬â„¢ liquidity transformation activity is driven by that of their peers. These correlated decisions are concentrated on the asset side of riskier banks and are asymmetric, with mimicking occurring only when competitors are taking more risk. Accordingly, this strategic behavior increases banksÃ¢â‚¬â„¢ default risk and overall systemic risk, highlighting the importance of regulating liquidity risk from a macroprudential perspective.

The Dynamics of Corporate Debt Structure
Halling, Michael,Yu, Jin,Zechner, Josef
SSRN
We find that US public firms spread out their debt more across different sources in recession quarters, making measures of debt concentration move pro-cyclically. There is substantial cross-sectional variation in these dynamics. Firms with less leverage and higher debt concentration further decrease leverage and increase debt concentration in recessions. The opposite is true for firms with higher leverage and lower debt concentration. The latter (former) group consists of firms that are larger (smaller), less risky (riskier), have fewer (more) growth options and lower (higher) cash levels. While the fraction of total assets funded by bank debt increases in the recession by approximately 18% of its average non-recession level, the equivalent measure for market debt drops by approximately 7%. Bank debt (in particular, term loans) appears to become more attractive during recession quarters, especially for borrowers characterized by high profitability and having a rating. Only firm size, in contrast, has a positive effect on the use of market debt in recessions.

The Effects of Ownership Structure, Sub-Optimal Cash Holdings and Investment Inefficiency on Dividend Policy: Evidence From Indonesia
Moin, Abdul,Guney, Yilmaz,El Kalak, Izidin
SSRN
We investigate how a firmâ€™s decision to hold excessive cash or to over-invest could influence its dividend payout policy in Indonesia. Additionally, we examine the association between corporate ownership structure and cash dividends. Using a data set of Indonesian listed firms for the period from 1995 to 2014, we find that excessive cash holding (over-investment) positively (negatively) affects a firmâ€™s likelihood of paying dividends. Also, we find that family, foreign, state and institutional ownership have significantly negative links with dividends, which suggests the signals of expropriation of firmsâ€™ wealth by major shareholders. These findings strongly support the expropriation hypothesis that commonly applies to firms with higher level of concentration or to firms in a weak legal environment by which the rights of minority interests are put at risk by large shareholders.

Valuing Tradeability in Exponential L\'evy Models
Ludovic Mathys
arXiv

The present article provides a novel theoretical way to evaluate tradeability in markets of ordinary exponential L\'evy type. We consider non-tradeability as a particular type of market illiquidity and investigate its impact on the price of the assets. Starting from an adaption of the continuous-time optional asset replacement problem initiated by McDonald and Siegel (1986), we derive tradeability premiums and subsequently characterize them in terms of free-boundary problems. This provides a simple way to compute non-tradeability values, e.g. by means of standard numerical techniques, and, in particular, to express the price of a non-tradeable asset as a percentage of the price of a tradeable equivalent. Our approach is illustrated via numerical examples where we discuss various properties of the tradeability premiums.