Research articles for the 2019-04-09

(Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual Algorithm
Pierre Henry-Labord`ere

In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. As some additional applications, we consider anomaly detection and automatic generation of financial data.

A new median-based formula for the Black-Scholes-Merton Theory
Takuya Okabe,Jin Yoshimura

The Black-Scholes-Merton (BSM) theory for price variation has been well established in mathematical financial engineering. However, it has been recognized that long-term outcomes in practice may divert from the Black-Scholes formula, which is the expected value of the stochastic process of price changes. While the expected value is expected for the long-run average of infinite realizations of the same stochastic process, it may give an erroneous picture of nearly every realization when the probability distribution is skewed, as is the case for prices. Here we propose a new formula of the BSM theory, which is based on the median of the stochastic process. This formula makes a more realistic prediction for the long-term outcomes than the current Black-Scholes formula.

Active Investing and the Efficiency of Security Markets
Wermers, Russ
This paper surveys the vast body of literature on the relationship between active investment management and the efficiency of public security markets in the United States, considering both peer-reviewed academic studies and commentary from investment practitioners. The literature broadly indicates that active investment management simultaneously generates value-added for investors â€" through the bundle of services offered â€" and makes public security markets more efficient, thereby aligning the incentives of investors in actively-managed funds with the positive externalities that they create for all investors, including investors in passively-managed funds and both index and rules-based ETFs.Importantly, active managers counteract many of the “misbehaviors” (biases) of other investors. Further, the benefits of active management are amplified in small- and mid-capitalization U.S. stocks, relative to large-capitalization stocks â€" since active managers, in aggregate, overweight smaller-capitalization issues relative to their representation in capitalization-weighted market benchmarks. In turn, the improved market efficiency afforded by active management especially enhances the ability of small- and mid-sized companies to raise capital for investments in the real economy. And, the improved efficiency serves to appropriately discipline capital expenditures by all corporations through a more efficient stock price and its resulting impact on the cost-of-capital for corporate investments.Other services provided by active managers to public security markets, beyond providing improved market efficiency (such as the provision of liquidity to other investors), are also discussed. Finally, recent trends in active investment management are presented, followed by some conjectures about the future of active management.

Bank Shocks, Financial Constraints, and Targeting Behavior: Evidence from Credit Supply Fluctuations
Shikimi, Masayo
This study investigates whether fluctuations in credit supply in a macroeconomy and a relational bank’s financial condition affect the capital structure adjustment of firms. Using data for Japanese listed firms from 1988 to 2014, we find that firms adjust their capital structure slower during credit contraction periods than during other periods, and that the effects of credit market tightness are more evident for small firms. Examining firm-bank matched data, we also find that credit supply shocks have heterogeneous effects on the rebalancing behavior of firms. Firms that are associated with banks that have limited capacity to supply loans or those associated with failed banks show a slower adjustment, thereby suggesting that a bank’s financial weakness places the leverage of relationship firms into a suboptimal level for a long period of time. These findings suggest that bank supply shocks play a significant role in the targeting behavior of firms.

Banking & Economic Implications of Farm Loan Waivers
Manda, Vijaya Kittu,Yamijala, Satya
Governments of developing and agrarian-intensive economies such as India depend on banking channels to extend farming and agriculture credit. Agricultural credit is a priority sector lending in such countries and banks have to invariably have a part of their loan book pie allocated it`. Statistical trends show that some farmers who take these benefits are turning "smart" and intentionally delaying loan repayments with the expectation of a loan waiver, particularly few months ahead of the elections. They feel that their loan will get waived once the political party for which they voted comes to power. Overtime, it gets revealed that a very small number of farmers actually get the loan waiver benefit as promised. By then, the loan account balloons because of interest and penalties - too big for him to ever repay. The helpless farmer shows his inability to repay while the deficit budget-driven Governments could not reimburse. This puts banks into a difficult situation and have to mark the loans as Non-Performing Asset (NPA)s and to eventually write-offs.This paper examines the various dimensions surrounding the agriculture loan waiver. The problem is examined from a bank-specific perspective. Loan waivers can turn into epidemic and spread to other segments causing a systemic risk to the economy. We study the economic implications with a specific focus on the impact on the banking system. The findings of this study will be of importance to policy makers, the banking regulator and banks.

BigTech and the Changing Structure of Financial Intermediation
Frost, Jon,Gambacorta, Leonardo,Huang, Yi,Shin, Hyun Song,Zbinden, Pablo
We consider the drivers and implications of the growth of "BigTech" in finance - ie the financial services offerings of technology companies with established presence in the market for digital services. BigTech firms often start with payments. Thereafter, some expand into the provision of credit, insurance, and savings and investment products, either directly or in cooperation with financial institution partners. Focusing on credit, we show that BigTech firms lend more in countries with less competitive banking sectors and less stringent regulation. Analysing the case of Argentina, we find support for the hypothesis that BigTech lenders have an information advantage in credit assessment relative to a traditional credit bureau. For borrowers in both Argentina and China, we find that firms that accessed credit expanded their product offerings more than those that did not. It is too early to judge the extent of BigTech's eventual advance into the provision of financial services. However, the early evidence allows us to pose pertinent questions that bear on their impact on financial stability and overall economic welfare.

Catch Me If You Can: Improving the Scope and Accuracy of Fraud Prediction
Chakrabarty, Bidisha,Moulton, Pamela C.,Pugachev, Leonid,Wang, Xu (Frank)
We propose a parsimonious metric â€" the Adjusted Benford score (AB-score) â€" to improve the detection of financial misstatements. Based on Benford’s Law, which predicts the leading-digit distribution of naturally occurring numbers, the AB-score estimates a firm-year’s likelihood of financial statement manipulation, compared to its peers and controlling for time-series trends. The AB-score requires less data than the leading accounting-based misstatement metric (the F-score) and can be computed for many more firm-years, including for financial firms. For firm-years with all data available, combining the AB-score and F-score variables into one model yields higher accuracy in predicting misstatements in- and out-of-sample.

China’s Visible Hand: An Analysis of the Chinese Government’s Intervention in its Economy During 2015â€"17
Liu, Kerry
While the Washington Consensus supports limited government involvement in economic life, the new structural economics holds that government can play a facilitating role. This paper contributes to this debate by examining examples of the Chinese government’s intervention in its economy during 2015â€"17. The Chinese government’s intervention has been generally successful, including significant reduction in overcapacity in the steel and coal industry, reduced liquidity of Chinese real estate as an asset class, and change in investment style through direct intervention in stock markets. Under President Xi Jinping the governance model of China Inc. resembles family ownership and family management; thus, with Xi Jinping as an ownerâ€"manager, it is expected that the current government would outperform its predecessors, under presidents who were mainly managers. This study is the first of its kind to examine the performance of Chinese government intervention from the perspective of financial economics.

Chinese Acquisitions Abroad: Are They Different?
Fuest, Clemens,Hugger, Felix,Sultan, Samina,Xing, Jing
In recent years Chinese acquisitions abroad have increased significantly. This paper uses a large dataset on cross-border M&A deals to investigate whether Chinese foreign acquisitions differ from acquisitions coming from other countries. We find that Chinese acquirers buy targets with lower profitability, larger size, higher debt levels, and more patents. However, private and state-owned Chinese investors differ in preferences for location in offshore financial centers, industry diversification, natural resources and technology. Chinese state-owned acquirers are similar to government-led acquirers from other countries in pursuing target firms in the resource extraction industry. Policy initiatives like the Belt and Road Initiative and Made in China 2025 influence investment patterns of Chinese state-owned acquirers but not those of private investors. Surprisingly, for acquisition prices, we find that Chinese investors pay less for firms with similar observable characteristics than investors from other countries.

Consolidation in Light of Solvency II â€" The Impact of Mergers and Acquisitions on Insurers’ Default Risk
Lange, Marc
As a result of Solvency II, academics and practitioners anticipate further consolidation in the insurance industry as the new regulatory framework rewards well-diversified insurers with lower capital requirements and challenges smaller insurers to meet the (operational) regulatory requirements. Therefore, this study examines the implications of 478 M&A between 1984 and 2015 on acquiring insurers’ default risk. By employing Merton’s distance to default, we show that mergers increase risk on average. This effect is particularly pronounced for pre-merger low-risk insurers and reinsurers. As Solvency II generally aims to enhance the soundness of the insurance sector and its firms, policymakers should be aware of this merger-related contrary risk effect. By contrast, the analysis also documents that mergers announced during the recent financial crisis were risk reducing. Consolidation can thus have a stabilizing effect on the insurance sector during market turmoil. Finally, the study reveals that merger-related default risk changes are mostly driven by acquirer characteristics and to a limited extent by deal characteristics. In this regard, we find that diversifying M&A strategies provide default risk-reducing diversification benefits. However, most identified determinants differ significantly for life and non-life insurers.

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang,Stefan Zohren,Stephen Roberts

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.

ESG Preference and Market Efficiency: Evidence from Mispricing and Institutional Trading
Cao, Jie,Titman, Sheridan ,Zhan, Xintong,Zhang, Weiming Elaine
We explore how the trend towards socially responsible investing affects the informational efficiency of stock prices. The return predictability of mispricing signals is much stronger among firms held by more socially responsible institutions (SR_Is). The results are driven by the divergence of trading implications from ESG performance and mispricing signals. SR_Is are less likely to buy underpriced stocks with bad ESG performance or sell overpriced stocks with good ESG performance. We rule out alternatives, such as known limits to arbitrage. The inefficiency only emerges in recent years with the rise of ESG investing, and is not fully offset by ESG-neutral arbitrageurs due to funding liquidity constraints.

Efficient Exponential Tilting for Portfolio Credit Risk
Cheng-Der Fuh,Chuan-Ju Wang

This paper considers the problem of measuring the credit risk in portfolios of loans, bonds, and other instruments subject to possible default under multi-factor models. Due to the amount of the portfolio, the heterogeneous effect of obligors, and the phenomena that default events are rare and mutually dependent, it is difficult to calculate portfolio credit risk either by means of direct analysis or crude Monte Carlo under such models. To capture the extreme dependence among obligors, we provide an efficient simulation method for multi-factor models with a normal mixture copula that allows the multivariate defaults to have an asymmetric distribution, while most of the literature focuses on simulating one-dimensional cases. To this end, we first propose a general account of an importance sampling algorithm based on an unconventional exponential embedding, which is related to the classical sufficient statistic. Note that this innovative tilting device is more suitable for the multivariate normal mixture model than traditional one-parameter tilting methods and is of independent interest. Next, by utilizing a fast computational method for how the rare event occurs and the proposed importance sampling method, we provide an efficient simulation algorithm to estimate the probability that the portfolio incurs large losses under the normal mixture copula. Here the proposed simulation device is based on importance sampling for a joint probability other than the conditional probability used in previous studies. Theoretical investigations and simulation studies, which include an empirical example, are given to illustrate the method.

Essays in Minimum Relative Entropy Implementations for Views Processing
Colasante, Marcello
In this thesis we investigate aspects of the theory of minimum relative entropy models (MRE in the sequel) within the class of exponential-family distributions. We use this technique for an application in portfolio management to compute Bayesian-like statistical features that incorporate fully general views on multivariate markets.

Financial Deglobalisation in Banking?
McCauley, Robert N.,Bénétrix, Agustín S.,McGuire, Patrick,von Peter, Goetz
This paper argues that the decline in cross-border banking since 2007 does not amount to a broad-based retreat in international lending (“financial deglobalisation”). We show that BIS international banking data organised by the nationality of reporting banks provide a clearer picture of international financial integration than the traditional “residence”, or balance-of-payments, view. They show that what appears to be a global shrinkage of bank positions is actually driven by European banks. These banks uniquely responded to credit losses after 2007 by shedding assets abroad to restore capital ratios. Other banking systems’ global footprints, notably those of Japanese, Canadian and even US banks, have expanded since 2007. Using a global dataset of banks’ affiliates (branches and subsidiaries), we demonstrate that the who (i.e., bank nationality) accounts for more of the peak-to-trough shrinkage in foreign claims than does the where (i.e., locational factors). We relate bank nationality in turn to EU membership, which may reflect asset shrinkage required by the EU competition authorities in response to state aid, bank profitability and credit losses.

From (Martingale) Schrodinger bridges to a new class of Stochastic Volatility Models
Pierre Henry-Labordere

Following closely the construction of the Schrodinger bridge, we build a new class of Stochastic Volatility Models exactly calibrated to market instruments such as for example Vanillas, options on realized variance or VIX options. These models differ strongly from the well-known local stochastic volatility models, in particular the instantaneous volatility-of-volatility of the associated naked SVMs is not modified, once calibrated to market instruments. They can be interpreted as a martingale version of the Schrodinger bridge. The numerical calibration is performed using a dynamic-like version of the Sinkhorn algorithm. We finally highlight a striking relation with Dyson non-colliding Brownian motions.

Heterogenous Banks and Macroprudential Regulations
Bhowal, Subhendu
This paper studies how financial intermediation varies across banks. Bank size is a first-order determinant of banks’ capital structure in the cross-section. Largest banks have the lowest capital-to-asset ratio and the lowest ratio of Tier-1 capital against risk-weighted assets. These large banks earn a larger interest income per dollar invested in their loan portfolio than small banks, and they maintain the highest net interest margins among all banks. A cash flow sensitivity analysis shows that the largest banks are the most tightly constrained by minimum capital requirement, while all other banks maintain capital in excess of minimum capital requirement regulation. Empirically, banks do not adjust their lending portfolio dollar for dollar as their net profits increase or lever up immediately by issuing more deposits. Further, we find that the financial accelerator amplifies productivity shock in aggregate data. The impulse response to total productivity shock shows that the loan volume of the capital-constrained largest banks does not respond positively to positive productivity shocks. This is in contrast to smaller banks that increase loans when productivity improves in the economy.

Machine Learning Risk Models
Zura Kakushadze,Willie Yu

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.

Martingale Optimal Transport Duality
Patrick Cheridito,Matti Kiiski,David J. Promel,H. Mete Soner

We obtain a dual representation of the Kantorovich functional defined for functions on the Skorokhod space using quotient sets. Our representation takes the form of a Choquet capacity generated by martingale measures satisfying additional constraints to ensure compatibility with the quotient sets. These sets contain stochastic integrals defined pathwise and two such definitions starting with simple integrands are given. Another important ingredient of our analysis is a regularized version of Jakubowski's $S$-topology on the Skorokhod space.

Model and Estimation Risk in Credit Risk Stress Tests
Grundke, Peter,Pliszka, Kamil,Tuchscherer, Michael
This paper deals with stress tests for credit risk and shows how exploiting the discretion when setting up and implementing a model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model using US data ranging from 2004 to 2016. We show that seemingly only slightly differing specifications can lead to entirely different stress test results - in relative and absolute terms. That said, our findings reveal that the conversion of a shock (i.e., stress event) increases the (non-stress) default probability by 20% to 80% - depending on the stress test model selected. Interestingly, forecasts for non-stress default probabilities are less exposed to model and estimation risk. In addition, the risk horizon over which the stress default probabilities are forecasted and whether we consider mean stress default probabilities or quantiles seem to play only a minor role for the dispersion between the results of the different model specifications. Our findings emphasize the importance of extensive robustness checks for model-based credit risk stress tests.

Price Discovery in Bitcoin Futures: Evidence From BitMEX
Choi, Jaehyuk,Park, Heungju,Sohn, Sungbin
This study examines the lead-lag relationship between Bitcoin spot and futures markets. We document that BitMEX, an unregulated cryptocurrency exchange, dominates the Bitcoin/US Dollar futures trades and that Bitcoin futures in BitMEX play a dominant role in price discovery, contrary to recent findings of futures in the Chicago Mercantile Exchange and the Chicago Board Options Exchange. We show that relative trading volume and volatility are important determinants of price discovery. We argue that given the substantial role of BitMEX, regulators should investigate the legitimacy of both futures and spot exchanges in whether to consider Bitcoin as a mainstream investment asset.

Procyclical Leverage in Europe and its Role in Asset Pricing
Baltzer, Markus,Koehl, Alexandra,Reitz, Stefan
Broker-dealer leverage has recently proven to be strongly procyclical, exhibiting impressive explanatory power for a large cross-section of asset returns in the US. In this paper we add empirical evidence to this finding, showing that European and German broker-dealers actively manage their balance sheets. Moreover, by applying standard Fama-MacBeth regressions as well as dynamic asset pricing models (Adrian, Crump, and Moench, 2015), we confirm the importance of brokerdealer balance-sheet indicators for asset pricing. In particular, leverage shows a procyclical behavior with a positive price of risk. Moreover, high leverage coincides with high asset prices, thereby forecasting lower future returns.

Revising SA-CCR
Mourad Berrahoui,Othmane Islah,Chris Kenyon

From SA-CCR to RSA-CCR: making SA-CCR self-consistent and appropriately risk-sensitive by cashflow decomposition in a 3-Factor Gaussian Market Model

Risk Minimization, Regret Minimization and Progressive Hedging Algorithms
Jie Sun,Xinmin Yang,Qiang Yao,Min Zhang

Optimization models based on coherent and averse risk measures are of essential importance in financial management and business operations. This paper begins with a study on the dual representations of risk and regret measures and their impact on modeling multistage decision making under uncertainty. The relationship between risk envelopes and regret envelopes is established by using the Lagrangian duality theory. It is then pointed out that such a relationship opens a door to a decomposition scheme, called progressive hedging, for solving multistage risk minimization and regret minimization problems. In particular, the classical progressive hedging algorithm is modified in order to handle a new class of constraints that arises from a reformulation of risk and regret minimization problems. Numerical results are provided to show the efficiency of the progressive hedging algorithms.

Robust Pricing and Hedging around the Globe
Sebastian Herrmann,Florian Stebegg

We consider the martingale optimal transport duality for c\`adl\`ag processes with given initial and terminal laws. Strong duality and existence of dual optimizers (robust semi-static superhedging strategies) are proved for a class of payoffs that includes American, Asian, Bermudan, and European options with intermediate maturity. We exhibit an optimal superhedging strategy for which the static part solves an auxiliary problem and the dynamic part is given explicitly in terms of the static part.

Semimartingale Theory of Monotone Mean-Variance Portfolio Allocation
Černý, Aleš
We study dynamic optimal portfolio allocation for monotone mean-variance preferences in a general semimartingale model. Armed with new results in this area we revisit the work of Cui, Li, Wang and Zhu (2012, MAFI) and fully characterize the circumstances under which one can set aside a non-negative cash flow while simultaneously improving the mean-variance efficiency of the left-over wealth. The paper analyzes, for the first time, the monotone hull of the Sharpe ratio and highlights its relevance to the problem at hand.

The Banking Regulatory Bubble and How to Get Out of It
Ferri, Giovanni,Neuberger, Doris
We claim that we currently live in a banking regulatory bubble. We review how: i) banking intermediation theory hinges on dealing with borrower-lender asymmetry of information; ii) instead, the presence of complete information is the keystone of the finance theory. Next, we document how finance theory prevailed over banking intermediation theory in shaping banking regulation: This appalling contradiction is the true culprit behind lower credit standards, mounting systemic risk in banking, and macroeconomic debt overhang. Consequently, we discuss actions that, by restoring the consistency of banking regulation with the theory of banking intermediation, would make banking sounder.

The Effect of Tltro-Ii on Bank Lending
Laine, Olli-Matti
This study applies a difference-in-differences approach to estimate the effect of the European Central Bank’s second series of targeted longer-term refinancing operations (TLTRO-II) on bank lending. Effects on corporate loans, loans for house purchase and loans for consumption are analysed separately. The results indicate that TLTRO-II increased lending to non-financial corporations. The cumulative effect of TLTRO-II on participating banks’ corporate lending is estimated to be about 30 per cent. The estimated effects for house purchase and consumption loans are positive, but statistically insignificant.

The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of Their Determinants
Fischer, Henning,Stolper, Oscar Anselm
This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit risk - rather than interest rate movements - contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.

Till Death (Or Divorce) Do us Part: Early-Life Family Disruption and Fund Manager Behavior
Betzer, André,Limbach, Peter,Rau, P. Raghavendra,Schürmann, Henrik
We show that early-life family disruption (death or divorce of a parent) causes fund managers to be more risk averse when they manage their own funds. Treated managers take lower systematic, idiosyncratic, and downside risk than non-treated managers. This effect is most pronounced for managers who experienced family disruption during their formative years or who had little social support. Treated managers invest less in lottery-like stocks, make smaller tracking errors, and bet less on factors during recessions, but do not perform worse than their untreated cohorts. Our evidence indicates that familial background affects economic decisions later in life even for finance professionals.

What Constrains Liquidity Provision? Evidence from Hedge Fund Trades
Franzoni, Francesco A.,Plazzi, Alberto,Cotelioglu, Efe
The paper investigates the determinants of limits of arbitrage for liquidity providers. Using data on institutional transactions, we find that hedge funds' liquidity provision is more exposed to financial conditions than that of other institutions, notably mutual funds. We identify leverage, age, asset illiquidity, and reputational capital as a relevant set of characteristics that explain the exposure of hedge funds' liquidity supply to funding conditions. Stocks with more exposure to constrained liquidity providing hedge funds suffered more during the financial crisis. Finally, we find that the trades of financially constrained hedge funds underperform for at least one quarter following negative funding shocks.