# Research articles for the 2020-02-24

A Conceptual Framework for Research on Investment Determinants among Non-Institutional Investors
Jakhar, Mukesh Kumar ,Motwani, Dr. R. K.
SSRN
In modern economic world income plays a very vital role in every oneâ€™s daily life. Investment has been one of the major concerns for the Non-institutional investors as their todayâ€™s small savings would be meeting with the expenses of tomorrow. The risk and returns proportion from each of these investment options varies from one to another. Investorâ€™s behavior plays an important role in investment decision making, which is influenced by many a factors during the process of investment decision making. Today, investors have many avenues of investment with different features to cater their present and future needs. The focus of this paper (non-institutional investors), unlike institutional investor, suffers from various sort of perception while deploying their funds due to their low investible funds, risk taking capacity, low investment education and their exposure to evaluate the available information. This situation of the non-institutional investors motivates to study as these investors are the finest source of small savings in investment set-up of the country. This paper presents a conceptual framework for research on investment determinants among noninstitutional investors to be carried out further by the researchers.

A New Pricing Theory That Solves the St. Petersburg Paradox
Dahang Li
arXiv

The St. Petersburg Paradox was proposed two centuries ago. In the paper, we propose a new pricing theory with several rules to solve the paradox and state that the fair pricing should be judged by the buyer and the seller independently. The seller is concerned about costs, and the buyer is concerned about the realistic prospect of returns. This pricing theory can be applied to financial market to solve the confusion with fat tail distribution.

A Put-Call Transformation of the Exchange Option Problem under Stochastic Volatility and Jump Diffusion Dynamics
Len Patrick Dominic M. Garces,Gerald H. L. Cheang
arXiv

We price European and American exchange options where the underlying asset prices are modelled using a Merton (1976) jump-diffusion with a common Heston (1993) stochastic volatility process. Pricing is performed under an equivalent martingale measure obtained by setting the second asset yield process as the numeraire asset, as suggested by Bjerskund and Stensland (1993). Such a choice for the numeraire reduces the exchange option pricing problem, a two-dimensional problem, to pricing a call option written on the ratio of the yield processes of the two assets, a one-dimensional problem. The joint transition density function of the asset yield ratio process and the instantaneous variance process is then determined from the corresponding Kolmogorov backward equation via integral transforms. We then determine integral representations for the European exchange option price and the early exercise premium and state a linked system of integral equations that characterizes the American exchange option price and the associated early exercise boundary. Properties of the early exercise boundary near maturity are also discussed.

A new hybrid approach for crude oil price forecasting: Evidence from multi-scale data
Yang Yifan,Guo Ju'e,Sun Shaolong,Li Yixin
arXiv

Faced with the growing research towards crude oil price fluctuations influential factors following the accelerated development of Internet technology, accessible data such as Google search volume index are increasingly quantified and incorporated into forecasting approaches. In this paper, we apply multi-scale data that including both GSVI data and traditional economic data related to crude oil price as independent variables and propose a new hybrid approach for monthly crude oil price forecasting. This hybrid approach, based on divide and conquer strategy, consists of K-means method, kernel principal component analysis and kernel extreme learning machine , where K-means method is adopted to divide input data into certain clusters, KPCA is applied to reduce dimension, and KELM is employed for final crude oil price forecasting. The empirical result can be analyzed from data and method levels. At the data level, GSVI data perform better than economic data in level forecasting accuracy but with opposite performance in directional forecasting accuracy because of Herd Behavior, while hybrid data combined their advantages and obtain best forecasting performance in both level and directional accuracy. At the method level, the approaches with K-means perform better than those without K-means, which demonstrates that divide and conquer strategy can effectively improve the forecasting performance.

An $\alpha$-Stable Approach to Modelling Highly Speculative Assets and Cryptocurrencies
Taurai Muvunza
arXiv

We investigate the behaviour of cryptocurrencies' return data. Using return data for bitcoin, ethereum and ripple which account for over 70% of the cyrptocurrency market, we demonstrate that $\alpha$-stable distribution models highly speculative cryptocurrencies more robustly compared to other heavy tailed distributions that are used in financial econometrics. We find that the Maximum Likelihood Method proposed by DuMouchel (1971) produces estimates that fit the cryptocurrency return data much better than the quantile based approach of McCulloch (1986) and sample characteristic method by Koutrouvelis (1980). The empirical results show that the leptokurtic feature presented in cryptocurrencies' return data can be captured by an ${\alpha}$-stable distribution. This papers covers predominant literature in cryptocurrencies and stable distributions.

Approximating intractable short ratemodel distribution with neural network
Anna Knezevic,Nikolai Dokuchaev
arXiv

We propose an algorithm which predicts each subsequent time step relative to the previous timestep of intractable short rate model (when adjusted for drift and overall distribution of previous percentile result) and show that the method achieves superior outcomes to the unbiased estimate both on the trained dataset and different validation data.

Consumer Debt and Default: A Macro Perspective
Exler, Florian,Tertilt, MichÃ¨le
SSRN
In this survey, we review the quantitative macroeconomic literature analyzing consumer debt and default. We start by providing an overview of consumer bankruptcy law in the US and document the relevant institutional changes over time. We proceed with a comprehensive empirical section, describing key facts about consumer debt, defaults and delinquencies, as well as charge-off and interest rates for the United States. In addition to the evolution of these variables over time, we construct life-cycle profiles using data from the Survey of Consumer Finances and show that debt and defaults display a clear hump-shaped profile by age. Third, we show how credit card debt has evolved along the income distribution. Finally, we document a large amount of heterogeneity in credit card interest rates across consumers. In the second part of the survey, we describe what has by now become the workhorse model of consumer credit and default. We discuss a quantitative version of the model and use it to decompose the main reasons for default. We also use the model to illustrate how the details of default costs matter. The remainder of the survey then discusses the literature centered around two questions. First, what are the welfare implications of various bankruptcy laws? And second, what caused the rise in filings over time? We end with a discussion of open questions and fruitful avenues for future research

Data Anonymisation, Outlier Detection and Fighting Overfitting with Restricted Boltzmann Machines
Kondratyev, Alexei,Schwarz, Christian,Horvath, Blanka
SSRN
We propose a novel approach to the anonymisation of datasets through non-parametric learning of the underlying multivariate distribution of dataset features and generation of the new synthetic samples from the learned distribution. The main objective is to ensure equal (or better) performance of the classifiers and regressors trained on synthetic datasets in comparison with the same classifiers and regressors trained on the original data. The ability to generate unlimited number of synthetic data samples from the learned distribution can be a remedy in fighting overtting when dealing with small original datasets. When the synthetic data generator is trained as an autoencoder with the bottleneck information compression structure we can also expect to see a reduced number of outliers in the generated datasets, thus further improving the generalization capabilities of the classifiers trained on synthetic data. We achieve these objectives with the help of the Restricted Boltzmann Machine, a special type of generative neural network that possesses all the required properties of a powerful data anonymiser.

Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia
Grammig, Joachim,Hanenberg, Constantin,Schlag, Christian,Sönksen, Jantje
SSRN
We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging in the computer-intensive hyper-parameter tuning of statistical models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine learning methodology (random forest) performs substantially better. We also consider ways to combine the opposing modeling philosophies, and identify the use of random forests to account for the approximation residuals of the theory-based approach as a promising hybrid strategy. It combines the advantages of the two diverging paths in the finance world.

Do Analysts Learn from Each Other? A New Approach to an Unresolved Question
Cen, Ling,Chang, Yuk Ying (Candie),Dasgupta, Sudipto
SSRN
Existing empirical evidence calls into question the informativeness of analystsâ€™ earnings forecasts, pointing to career-concern related incentives for uninformed herding or conflict of interest. We develop a new methodology to show that analysts assign weights to their peerâ€™s forecasts, and these weights apply not only to common information, but also to idiosyncratic information. Moreover, the weights vary based on measures of the precision of the analystâ€™s signal and those of the peers in ways that are consistent with the objective of making efficient weighted-average forecasts. Overall, we find strong evidence of analyst learning.

Dual-Class Share Structure on the Dividend Payout Policy: Evidence From China
Chen, Zonghao,Zhu, Mi,Yang, JingJing
SSRN
This study investigates the impact of dual-class share structure on the dividend payout policy of Chinese Concepts Stocks listed on US stock exchanges. We find that dual-class share structure is negatively associated with both the propensity to pay dividends and dividend payout ratios. Among firms with a dual-class share structure, the divergence in voting and cash-flow rights is negatively associated with the propensity to pay dividends and dividend payout ratios. We further find that dual-class firms experience more tunneling to controlling shareholders. Our findings highlight the potential cost of adopting dual-class share structures in China.

Embedding an NPV Analysis into a Binomial Tree with a Real Options Application
SSRN
Financial statements and an accompanying NPV calculation are embedded into a binomial tree. This generalization of traditional static NPV analysis allows the financial statements to both evolve through time and, at any given time, to vary with states of the world (similar to a Monte Carlo analysis). Modelling the component cash flows in a tree reveals dynamic detailed structure, leading to a more useful NPV analysis than if only the final cash flow value was modelled in a tree or if component cash flows were modelled without a tree. This dynamic detail provides credible cash flow forecasts that can improve hedging of adverse events and allow for leveraging of beneficial circumstances. The financial statements take the form of pro forma after-tax operating cash flows in this treatment. However, any cash flow model driven by the random variable in the tree and allowing for separate treatment of fixed costs, can be used. The benefits of this technique are illustrated via a real options example.

Enforcement Waves and Spillovers
Choi, Hae Mi,Karpoff, Jonathan M.,Lou, Xiaoxia,Martin, Gerald S.
SSRN
We document that regulatory enforcement actions for financial misrepresentation cluster in industry-specific waves and that wave-related enforcement has information spillovers on industry peer firms. Waves and spillovers have significant effects on share prices. Early-wave target firms have the largest short-run losses in share values and the largest information spillovers on industry peer firms. Late-wave targetsâ€™ short-run losses are smaller, but not because they involve less costly instances of misconduct. Rather, late-wave targets are subject to more information spillovers from earlier in the wave. These results indicate that prices efficiently incorporate changes in the likelihood a firm will face enforcement action for financial misconduct. As a result, short-window losses are biased measures of the total share price impact, particularly for firms whose financial misrepresentation is revealed late in an enforcement wave.

Estrutura a Termo Baseada em TÃ­tulos com Pagamentos IntermediÃ¡rios (Term Structure Fitting with Coupon Bonds)
Varga, Gyorgy
SSRN
Portuguese Abstract: A estrutura a termo das taxas de juro (ET) apresenta as taxas conhecidas como spot ou zero coupon, que nÃ£o carregam risco de reinvestimento. Neste artigo, mostra-se como obter tais taxas com base em tÃ­tulos com pagamentos intermediÃ¡rios e como a utilizaÃ§Ã£o da Taxa Interna de Retorno (TIR) versus duration leva ao cÃ¡lculo incorreto de preÃ§os de tÃ­tulos e Ã s possibilidades de arbitragem.English Abstract: We show the effect of coupon payments on the Internal Rate of Return and several distorted cases.

Evolution and Current Status of the Competitive Environment in the Serbian Banking Sector: Concentration Indices Analysis
Bukvic, Rajko
SSRN
This paper analyzes the degree of concentration and competition in the Serbian banking sector during the 2010-2017 period and in its current state, by considering the financial statements of banks for the years 2016 and 2017. For this purpose, both traditional concentration indicators (concentration ratio CRn and the Herfindahl-Hirschman index), and the relatively rarely used Linda indices have been used. The degree of concentration has been calculated based on five variables: total assets, deposits, capital, operating income of banks, and loans. The degree to which these indicators are compliant with the basic antitrust regulations has been illustrated. It has been demonstrated that in the current case of a relatively large number of banks operating in Serbia, the existing degree of concentration is relatively low. This provides suitable conditions for the development of healthy competition among them. However, the approximation of the indices to moderate concentration within the period analyzed warns of the appearance of oligopoly.

Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques
Manav Kaushik,A K Giri
arXiv

In todays global economy, accuracy in predicting macro-economic parameters such as the foreign the exchange rate or at least estimating the trend correctly is of key importance for any future investment. In recent times, the use of computational intelligence-based techniques for forecasting macroeconomic variables has been proven highly successful. This paper tries to come up with a multivariate time series approach to forecast the exchange rate (USD/INR) while parallelly comparing the performance of three multivariate prediction modelling techniques: Vector Auto Regression (a Traditional Econometric Technique), Support Vector Machine (a Contemporary Machine Learning Technique), and Recurrent Neural Networks (a Contemporary Deep Learning Technique). We have used monthly historical data for several macroeconomic variables from April 1994 to December 2018 for USA and India to predict USD-INR Foreign Exchange Rate. The results clearly depict that contemporary techniques of SVM and RNN (Long Short-Term Memory) outperform the widely used traditional method of Auto Regression. The RNN model with Long Short-Term Memory (LSTM) provides the maximum accuracy (97.83%) followed by SVM Model (97.17%) and VAR Model (96.31%). At last, we present a brief analysis of the correlation and interdependencies of the variables used for forecasting.

Geometric Step Options with Jumps. Parity Relations, PIDEs, and Semi-Analytical Pricing
Walter Farkas,Ludovic Mathys
arXiv

The present article studies geometric step options in exponential L\'evy markets. Our contribution is manifold and extends several aspects of the geometric step option pricing literature. First, we provide symmetry and parity relations and derive various characterizations for both European-type and American-type geometric double barrier step options. In particular, we are able to obtain a jump-diffusion disentanglement for the early exercise premium of American-type geometric double barrier step contracts and its maturity-randomized equivalent as well as to characterize the diffusion and jump contributions to these early exercise premiums separately by means of partial integro-differential equations and ordinary integro-differential equations. As an application of our characterizations, we derive semi-analytical pricing results for (regular) European-type and American-type geometric down-and-out step call options under hyper-exponential jump-diffusion models. Lastly, we use the latter results to discuss the early exercise structure of geometric step options once jumps are added and to subsequently provide an analysis of the impact of jumps on the price and hedging parameters of (European-type and American-type) geometric step contracts.

How Director Remuneration Impacts Firm Performance: An Empirical Analysis of Executive Director Remuneration in Pakistan
SSRN
This study empirically investigates the interrelationship between pay and performance of CEOs/board of directors in an emerging market, Pakistan. The study uses GMM approach to account for the problem of potential endogeneity and unobserved heterogeneity that arises due to the potential reverse causality (pay and performance) for a sample of non-financial firms listed in the KSE over the period of 2009e2016. This study provides evidence that the pay-performance framework supports the agency theory whereby CEOs/board of directors are compensated for their prior level of market-based performance. In addition, it weakly supports the notion of the steward/tournament theory. Thus, CEOs/board director's remuneration is highly persistent and takes time to adjust to long-run equilibrium.

How Do Private Equity Fees Vary Across Public Pensions?
Begenau, Juliane,Siriwardane, Emil
SSRN
We document large variation in net-of-fee performance across public pension funds investing in the same private equity fund. In aggregate, these differences imply that the pensions in our sample would have earned $45 billion more â€" equivalent to$8.50 more per $100 invested â€" had they each received the best observed terms in their respective funds. There are also large pension-effects in the sense that some pensions systematically pay more fees than others when investing in the same fund. With better terms, the 95th percentile pension would have earned$14.91 more per $100 invested compared to$1.12 for the 5th percentile pension. Attributes like size, relationships, and governance account for a modest amount of the pension effects, meaning similar pensions consistently pay different fees.

Hybrid, adaptive, and positivity preserving numerical methods for the Cox-Ingersoll-Ross model
Cónall Kelly,Gabriel Lord,Heru Maulana
arXiv

We introduce an adaptive Euler method for the approximate solution of the Cox-Ingersoll-Ross short rate model. An explicit discretisation is applied over an adaptive mesh to the stochastic differential equation (SDE) governing the square root of the solution, relying upon a class of path-bounded timestepping strategies which work by reducing the stepsize as solutions approach a neighbourhood of zero. The method is hybrid in the sense that a backstop method is invoked if the timestep becomes too small, or to prevent solutions from overshooting zero and becoming negative. Under parameter constraints that imply Feller's condition, we prove that such a scheme is strongly convergent, of order at least 1/2. Under Feller's condition we also prove that the probability of ever needing the backstop method to prevent a negative value can be made arbitrarily small. Numerically, we compare this adaptive method to fixed step schemes extant in the literature, both implicit and explicit, and a novel semi-implicit adaptive variant. We observe that the adaptive approach leads to methods that are competitive over the entire domain of Feller's condition.

Implementing the Personal Data Protection Bill: Mapping Points of Action for Central Government and the future Data Protection Authority in India
SSRN
The Central Government and the future Data Protection Authority (DPA) will face the complex task of notifying several rules and regulations in order to bring Indiaâ€™s Personal Data Protection Bill (the Bill) into full effect. In the absence of such regulations, even if the Bill is enacted it could have limited impact and effect. There is a pressing need for a clear blueprint of how Central Government and the DPA will work together to systematically release regulation to bring to life the provisions of the Bill. A systematic approach could prevent the ad-hoc passage of rules which could create severe disruptions in the data economy and gaps in consumer protection.In this policy brief, we set out the actions required from Central Government and the future DPA following enactment of the Bill. These actions are sequenced in order of priority based on our analysis of the inter-linkages of sections within the Bill and the practical requirements of any data protection regime. The sequencing is aimed at ensuring that the main elements of the law come into effect without compromising consumer protections and inducing business uncertainty. This initial blueprint aims to drive forward the conversation on effective implementation, capacity and enforcement for Indiaâ€™s future data protection regime taking into account our unique context.

Brett R Gordon,Kinshuk Jerath,Zsolt Katona,Sridhar Narayanan,Jiwoong Shin,Kenneth C Wilbur
arXiv

Digital advertising markets are growing and attracting increased scrutiny. This paper explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking and ad fraud. These topics are not unique to digital advertising, but each manifests in new ways in markets for digital ads. We identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research.

Inflated Credit Ratings, Regulatory Arbitrage and Capital Requirements: Do Investors Strategically Allocate Bond Portfolios?
Adriaan Boermans, Martijn,van der Kroft, Bram
SSRN
This study investigates whether banks and insurance corporations perform regulatory arbitrage by buying bonds with inflated credit ratings. We argue that flaws in minimum capital requirements incentivize risk-taking behavior by financial institutions, diminishing financial stability. We estimate the probability of a bond having an inflated credit rating using conditional credit default swap spread distributions. We merge this data with a unique bond-level portfolio holdings dataset. The results show that banks and insurance corporations invest more in bonds with inflated credit ratings, while this effect is absent for investors who do not face capital requirements based on credit ratings. Consequently, the regulatory capital buffers of banks and insurance corporations are effectively reduced by respectively 13 and 28 percent.

KVA is Incomplete
Arnsdorf, Matthias
SSRN
In this article we derive a capital valuation adjustment for derivatives transactions due to market incompleteness. This is motivated by the fact that a return on equity (RoE) in excess of the riskless rate is the result of undiversifiable portfolio risk. The valuation adjustment represents the value of the excess return over the portfolio lifetime. More specifically, we show that the value of a derivative can be written as the sum of the risk-neutral value and an adjustment which is the sum of a KVA and a FVA term. The KVA is proportional to the equity level held as well as the RoE. We derive the appropriate level of the RoE as function of the market price of the unhedgeable risk. A key consequence is that in our model the RoE is a function of the firms leverage and not an externally fixed hurdle rate. This means that the KVA defined here is insensitive to changes in the firms capital level as required by the Modigliani-Miller theorem. The valuation adjustment is fundamentally risk based and we show that it can be written in terms of the economic capital for which we provide the appropriate definition. This allows for a rigorous framework for allocating capital to sub-portfolios and individual trades.

Learning to Deal With Repeated Shocks Under Strategic Complementarity: An Experiment
SSRN
Experimental evidence shows that the rational expectations hypothesis fails to characterize the path to equilibrium after an exogenous shock when actions are strategic complements. Under identical shocks, however, repetition allows adaptive learning, so that inertia in adjustment should fade away with experience. If this finding proves to be robust, inertia in adjustment may be irrelevant among experienced agents. The conjecture in the literature is that inertia would still persist, perhaps indefinitely, in the presence of real-world complications such as nonidentical shocks. Herein, we empirically test the conjecture that the inertia in adjustment is more persistent if the shocks are nonidentical. For both identical and nonidentical shocks, we find persistent inertia and similar patterns of adjustment that can be explained by backward-looking expectation rules. A reformulation of naÃ¯ve expectations with similarity-based learning approach is found to have a higher predictive power than rational and trend-following rules.

Market Crashes and Merger Completions
Heath, Davidson,Mitchell, Mark L.
SSRN
A primary concern in mergers and acquisitions is the risk that the deal may be cancelled before completion. We document that this "interim risk" varies asymmetrically with the aggregate stock market: When the market falls sharply, cash deals are more than twice as likely to be cancelled. For stock deals and for cash deals with a definitive agreement in place there is no effect, consistent with costly renegotiation as a mechanism. Interim risk also alters the terms of merger deals that are announced and completed.

Modelling volatility with v-transforms
Alexander J. McNeil
arXiv

An approach to the modelling of financial return series using a class of uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the return distribution and quantiles of the distribution of a predictable volatility proxy variable constructed as a function of the return. V-transforms can be represented as copulas and permit the construction and estimation of models that combine arbitrary marginal distributions with linear or non-linear time series models for the dynamics of the volatility proxy. The idea is illustrated using a transformed Gaussian ARMA process for volatility, yielding the class of VT-ARMA copula models. These can replicate many of the stylized facts of financial return series and facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk. Estimation of models is carried out by adapting the exact maximum likelihood approach to the estimation of ARMA processes.

Movimentos da Estrutura a Termo Brasileira e ImunizaÃ§Ã£o (Term Structure Movements and Imunization)
Varga, Gyorgy,Valli, Marcos
SSRN
Portuguese Abstract: Este artigo utiliza o analise de componentes principais para avaliar os movimentos da Estrutura a Termo brasileira. Com os fatores obtidos aplicam-se os procedimentos de imunizaÃ§Ã£o de carteira de renda fixa local e compara-se o resultado a uma estratÃ©gia de hedge simples por duration.English Abstract: This article uses a PCA factor model to evaluate the Brazilian local yield curve movements. With the factors obtained we apply a immunization procedure for a local fixed income portfolio and compare this result to a more simple hedging procedure (duration hedge), which provides protection for parallel yield curve movements. This application was held with data collected during the Asia crisis.

Novel Insights in the Levy-Levy-Solomon Agent-Based Economic Market Model
Maximilian Beikirch,Torsten Trimborn
arXiv

The Levy-Levy-Solomon model (A microscopic model of the stock market: cycles, booms, and crashes, Economic Letters 45 (1))is one of the most influential agent-based economic market models. In several publications this model has been discussed and analyzed. Especially Lux and Zschischang (Some new results on the Levy, Levy and Solomon microscopic stock market model, Physica A, 291(1-4)) have shown that the model exhibits finite-size effects. In this study we extend existing work in several directions. First, we show simulations which reveal finite-size effects of the model. Secondly, we shed light on the origin of these finite-size effects. Furthermore, we demonstrate the sensitivity of the Levy-Levy-Solomon model with respect to random numbers. Especially, we can conclude that a low-quality pseudo random number generator has a huge impact on the simulation results. Finally, we study the impact of the stopping criteria in the market clearance mechanism of the Levy-Levy-Solomon model.

Optimal Dividend Strategy for an Insurance Group with Contagious Default Risk
Zhuo Jin,Huafu Liao,Yue Yang,Xiang Yu
arXiv

This paper studies the optimal dividend for a multi-line insurance group, in which each subsidiary runs a product line and is exposed to some external credit default risk. The credit default contagion is considered in the sense that one credit default event can affect the default probabilities of all surviving subsidiaries. The total dividend problem is formulated for the insurance group and we reveal for the first time that the optimal singular dividend strategy is still of the barrier type. Furthermore, we show that the optimal barrier for each subsidiary is modulated by the current default state, namely how many and which subsidiaries have defaulted will determine the dividend threshold of each surviving subsidiary. These interesting conclusions are based on the analysis of the associated recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs). The existence of the classical solution is established and the proof of the verification theorem is provided. For the case of two subsidiaries, the value function and optimal barriers of dividend are given in analytical forms. Numerical examples are also presented to illustrate some economic insights.

Eyal Neuman,Moritz Voß
arXiv

We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The stochastic control problem is solved by following a probabilistic and convex analytic approach. We show that the optimal trading strategy is given by a system of four coupled forward-backward SDEs, which can be solved explicitly. Our results reveal how the induced transient price distortion provides together with the predictive signal an additional predictor about future price changes. As a consequence, the optimal signal-adaptive trading rate trades off exploiting the predictive signal against incurring the transient displacement of the execution price from its unaffected level. This answers an open question from Lehalle and Neuman [27] as we show how to derive the unique optimal signal-adaptive liquidation strategy when price impact is not only temporary but also transient.

Political Polarization in Financial News
Goldman, Eitan,Gupta, Nandini,Israelsen, Ryan D.
SSRN
Political news is known to be polarized, but standard explanations for polarization do not apply to financial news. Nevertheless, we find strong evidence of political polarization in the tone and coverage of corporate financial news. In particular, we find that the tone of corporate financial news coverage is more positive, and the likelihood that good (bad) news is reported is higher (lower), if the firm is politically aligned with the news source. Such polarization implies that different market participants may be exposed to differing news about the same firm on the same day. Consistent with this argument, we find that disagreement between news sources increases trading volume, and these effects are larger for firms at the political extremes. Our paper highlights a novel source of bias in financial news that can be important for investors.

Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
Ben Moews,Gbenga Ibikunle
arXiv

Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.

PreÃ§o e EstratÃ©gias para Contrato Futuro de DI (Price and Strategies for Brazilian (DI) Interest Rate Futures Contract)
Varga, Gyorgy
SSRN
Portuguese Abstract: Atualmente, todos os estudos do mercado de renda fixa demandam algum conhecimento dos futuros de taxa de juro. No caso brasileiro, o futuro de DI e o FRA negociados na BM&F sÃ£o especialmente importantes, uma vez que neles ocorre a maior parte da transferÃªncia de risco de taxa de juro. SÃ£o, portanto, referÃªncia fundamental para a obtenÃ§Ã£o da taxa efetiva de juro nos vÃ¡rios prazos. Neste artigo, mostra-se a mecÃ¢nica de funcionamento dos futuros de DI e cupom cambial e do FRA, bem como alguns exemplos com dados extraÃ­dos do mercado.English Abstract: We show the very particular mechanics of DI future contract traded in Brazil.

Representation of Exchange Option Prices under Stochastic Volatility Jump-Diffusion Dynamics
Gerald H. L. Cheang,Len Patrick Dominic M. Garces
arXiv

In this article, we provide representations of European and American exchange option prices under stochastic volatility jump-diffusion (SVJD) dynamics following models by Merton (1976), Heston (1993), and Bates (1996). A Radon-Nikodym derivative process is also introduced to facilitate the shift from the objective market measure to other equivalent probability measures, including the equivalent martingale measure. Under the equivalent martingale measure, we derive the integro-partial differential equation that characterizes the exchange option prices. We also derive representations of the European exchange option price using the change-of-numeraire technique proposed by Geman et al. (1995) and the Fourier inversion formula derived by Caldana and Fusai (2013), and show that these two representations are comparable. Lastly, we show that the American exchange option price can be decomposed into the price of the European exchange option and an early exercise premium.

Returns Volatility in Stock Market and Performance of Banks: Evidence from Pakistan
ul Rahman, Saif,Rashid, Dr. Abdul ,Ilyas, Muhammad
SSRN
Irregular behavior of stock market affects all sectors of economy however, financial sector is the most vulnerable sector. The study attempts to examine the impact of stock market returns volatility on performance of banking sector in Pakistan. Two main hypotheses are constructed to achieve the objectives of study: i.e., (1) There exists a significant relationship between the returns volatility in stock market and the banking performance, and (2) Bank size has a significant role in establishing the volatility-performance relationship. Two step GMM system estimator is used to test these hypotheses. The results reveal that stock market volatility has a significant negative impact on return, equity, and the assets of banks; and, the bank-size has a significant negative impact on volatility-performance relationship. Specifically, the results suggest that during the time of high volatility, banksâ€™ profitability starts to decline but this profitability decline is not same, for all size of banks. The negative impact of volatility for larger banks is high.

Robust XVA
Maxim Bichuch,Agostino Capponi,Stephan Sturm
arXiv

We introduce an arbitrage-free framework for robust valuation adjustments. An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in defaultable bonds. The investor does not know the return rate of her counterparty's bond, but is confident that it lies within an uncertainty interval. We derive both upper and lower bounds for the XVA process of the portfolio, and show that these bounds may be recovered as solutions of nonlinear ordinary differential equations. The presence of collateralization and closeout payoffs leads to important differences with respect to classical credit risk valuation. The value of the super-replicating portfolio cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction. Our comparative statics analysis indicates that credit contagion has a nonlinear effect on the replication strategies and on the XVA.

Scoring Functions for Multivariate Distributions and Level Sets
Xiaochun Meng,James W. Taylor,Souhaib Ben Taieb,Siran Li
arXiv

Interest in predicting multivariate probability distributions is growing due to the increasing availability of rich datasets and computational developments. Scoring functions enable the comparison of forecast accuracy, and can potentially be used for estimation. A scoring function for multivariate distributions that has gained some popularity is the energy score. This is a generalization of the continuous ranked probability score (CRPS), which is widely used for univariate distributions. A little-known, alternative generalization is the multivariate CRPS (MCRPS). We propose a theoretical framework for scoring functions for multivariate distributions, which encompasses the energy score and MCRPS, as well as the quadratic score, which has also received little attention. We demonstrate how this framework can be used to generate new scores. For univariate distributions, it is well-established that the CRPS can be expressed as the integral over a quantile score. We show that, in a similar way, scoring functions for multivariate distributions can be "disintegrated" to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level set, including those for densities and cumulative distributions. To compute the scoring functions, we propose a simple numerical algorithm. We illustrate our proposals using simulated and stock returns data.

Stock Liquidity and the Value of Innovation: Evidence from U.S. Patent Data
Im, Hyun Joong,Selvam, Srinivasan,Tan, Kelvin Jui Keng
SSRN
Does stock liquidity increase the value of innovation? Exploiting a quasi-random experiment, we show that stock liquidity increases the economic value of innovation. These results are mainly driven by firms in innovation-oriented sectors, such as those that are science-based or software-related or involve specialized suppliers. We show that stock liquidity increases concentrated institutional ownership; monitoring can potentially make innovation valuable. The literature shows that stock liquidity reduces the quantity of innovation, but the U.S. as a nation continues to be highly innovative despite having one of the most liquid stock markets. Our findings help solve this puzzle.

Teste de Modelos EstatÃ­sticos para a Estrutura a Termo no Brasil (Testing Term Structure Fitting Models)
Varga, Gyorgy
SSRN
Portuguese Abstract: Apresentamos os modelos Spline polinomial, Flat Forward e Nelson-Siegel para a interpolaÃ§Ã£o da estrutura a termo da taxa de juro. SÃ£o modelos adotados internacionalmente por praticantes e autoridades monetÃ¡rias, mas pouco conhecidos na literatura local. Testamos os modelos com uma larga base de dados e todos eles indicaram problemas de especificaÃ§Ã£o. Esse Ã© um resultado semelhante ao obtido por Bliss (1997) para o mercado norte americano. A anÃ¡lise realizada tambÃ©m mostrou os limites na aplicaÃ§Ã£o de diversos modelos de interpolaÃ§Ã£o para construÃ§Ã£o da ET.English Abstract: We apply and test term structure fitting models like polynomial splines, flat forward and Nelson-Siegel to the Brazilian local term structure. They are models used all over the world by authorities and financial markets practitioners but less known locally. These models were tested with a large database with all of then presenting some specification problems. These results are similar to Bliss (1997) for US term structure and showed several limitations to the use of these models in the term structure fitting.

Uncertainty and Downside Risk in International Stock Returns
Aslanidis, Nektarios,Christiansen, Charlotte,Kouretas, Georgios P.
SSRN
We conduct an international analysis of the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. We consider the stock markets in five regions separately. Internationally, uncertainty has negative risk premiums which is similar to previous findings for the US. This implies that investors get lower returns for assets with high uncertainty betas. We further contribute with an analysis of downside uncertainty risk. Here, the downside uncertainty risk factor is high uncertainty which has additional risk premiums. We measure uncertainty by the logs of the local and US economic policy uncertainty indices.

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.