Research articles for the 2019-06-09

A comparison principle between rough and non-rough Heston models - with applications to the volatility surface
Martin Keller-Ressel,Assad Majid

We present a number of related comparison results, which allow to compare moment explosion times, moment generating functions and critical moments between rough and non-rough Heston models of stochastic volatility. All results are based on a comparison principle for certain non-linear Volterra integral equations. Our upper bound for the moment explosion time is different from the bound introduced by Gerhold, Gerstenecker and Pinter (2018) and tighter for typical parameter values. The results can be directly transferred to a comparison principle for the asymptotic slope of implied volatility between rough and non-rough Heston models. This principle shows that the ratio of implied volatility slopes in the rough vs. the non-rough Heston model increases at least with power-law behavior for small maturities.

Are Options Redundant? The Benefits of Synthetic Diversification
Liu, Yifan,Piccotti, Louis R.
This paper examines an alternative avenue through which trading in options can expand investors' opportunity sets, unrelated to private information, differing opinions, endowments, or trading restrictions in the stock market. Investors can synthetically replicate the return profile of optionable stocks using options for a fraction of the cost of holding the underlying securities, which makes diversification more cost-efficient. We find that the option to stock volume ratio increases when stock price, idiosyncratic risk, stock illiquidity, borrowing cost, and market risk aversion are high. In addition, institutional holdings and option trading have a U-shaped relation.

Assessment of Business Angel Activity in Northern European Countries
Prohorovs, Anatolijs,Fainglozs, Levs
Business angels are one of the main sources of financing for innovative start-up companies. In this regard, it has been discussed in the existing literature that policy-makers and stakeholders are in great need for a tool to measure the level and scale of the development of the business angel market. It has been also mentioned that business angel activity in different countries is highly heterogeneous. However, so far in the existing literature the comparison of the level of business angel activity in the countries of Northern Europe has not been made. Therefore, the aim of our research is to compare the level of the visible business angel market activity in Northern European countries. To conduct a comparative assessment of the business angels' activity, we used the Business Angel Activity Index calculation methodology developed by us. In this methodology, we justified the set of indicators and the weight of indicators for the calculation of the composite index. We have discovered that in 2016 â€" 2017 Estonia and Finland demonstrated the highest level of business angel activity among the countries of Northern Europe. We have also established that population size, the size of economy and GDP per capita rate in the Northern European countries are not the main factors that have a considerable impact on the level of business angel activity. Moreover, we confirm that business angel activity in the countries of Northern Europe is highly heterogeneous. We have also found that high scores of the ratio of the number of business angels to the working population in particular countries do not guarantee high level of business angel investment activity in these countries. The paper also discusses the number of factors ensuring high business angel activity.

Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?
Michael Allan Ribers,Hannes Ullrich

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict diagnostic test outcomes for urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and policy implementation when patient distributions vary over time. The proposed policies delay antibiotic prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, this result is likely to be a lower bound of what can be achieved elsewhere.

Could Regulation of the ABS Secondary Market Improve Social Welfare?
Losada, Ramiro
This paper examines a model of market making in the ABS market with heterogeneous investors and a lack of price transparency. In this set-up, market makers enjoy market power due to the diversity of assets that back ABS bonds with the same rating: mortgages, credit cards, loans, corporate bonds, etc. It is shown that in a world with no price transparency, allowing free entry of market makers might not be social optimum. Social welfare would be improved by a regulation to restrict the number of market makers in the ABS market to the extent that price competition is guaranteed providing they are forced to buy and sell all possible types of ABS bonds: RMBS, CMBS, CDO, CLO, etc.

Decomposing the Accrual Premium: The Evidence From Two Markets
Chichernea, Doina,Holder, Anthony Dewayne,Petkevich, Alex
We decompose the accrual premium and study its components in the debt and equity markets. We show that the importance of each accrual component depends on the sample and the type of market considered. The short-term accruals component is primarily observed in equity markets, among small and young companies, which is consistent with mispricing arguments. The long-term accruals premium is consistently positive and significant in different samples and markets. This component reflects growth in capital expenditures, and it is counter-cyclical and predictable, which is in line with investment-based explanations. Finally, the financial accruals component does not generate predictability.

Evidence of Non-Linear Relationship between Non-Interest Income and Profitability of Commercial Banks in Pakistan
Noor, Iraj,Siddiqui, Danish Ahmed
Pakistan's commercial banks are lately facing hindrance in earning substantial profits due to low-interest rates and low-interest margins on Government Securities which is evidently reflected in the low Earning per Share and low share prices of commercial banks. To confronting this, the banks are forced to diversify their income. The past studies show the mixed inferences about the reliance on non-interest income can be profitable for commercial banks in Pakistan's case. This research fills the gap for the existence of a non-linear relationship between the non-interest income and profitability of banks in Pakistan. Threshold Regression Model is applied on a panel data of 13 commercial for the period 2007-2017. The results have shown that optimal diversification benefit can be attained by reaching to a certain level of non-interest income proportion. The findings of the study are: (1) there exist a single threshold, confirming the non-linear relationship between the Non-Interest Income ratio (NIR) and profitability (ROE). (2) The NIR impacts positively on profitability (ROE) when NIR (≤61.1%) and beyond this value i.e. NIR (>61.12%) the relationship is negative. The study can help the Pakistani banks in exploiting their maximum level of diversification and in earning large profits in unfavorable times.

Excess Price Volatility: Is There a Puzzle?
Sy, Oumar
The excess volatility puzzle, which asserts that prices are far more volatile than what is justified by the changes in the fundamentals, is a serious challenge to the efficient market hypothesis. This paper argues that this puzzle is explained by the omission of the temporary deviations of prices from values (the price noise). If one considers the standard definition of market efficiency, which does not overlook the price noise, the classical bound on price volatility changes in a way that cracks the puzzle. The anomaly is also solved when one accounts for the endogeneity of the price forecasts based on dividends.

Law, Endowment and Inequality in Access to Finance
Armitage, Seth,Hou, Wenxuan,Liu, Xianda,Cong, Wang
Theoretical work suggests an ambiguous relationship between the strength of institutions and the distribution of access to finance. Using a sample of listed firms from 70 countries, this study constructs country-level measures of inequality in access to external finance and explores its relation to institutions. We show that inequality of access is positively related to financial development as well as inequality in the distribution of firm size, firm revenue, and residents’ incomes. Countries with stronger investor protection for equity and debt have higher inequality in equity and debt financing respectively, presumably as a result of higher efficiency in fund allocation. Finally, we find that the historical determinants of institutions, including the civil law tradition and the disease environment encountered by colonizers, are negative related to inequality in access to external finance. The results support both law and endowment theories.

Learning from Others in the Financial Market
Matthias Feiler,Thibaut Ajdler

Prediction problems in finance go beyond estimating the unknown parameters of a model (e.g of expected returns). This is because such a model would have to include knowledge about the market participants' propensity to change their opinions on the validity of that model. This leads to a circular situation characteristic of markets, where participants collectively create the target variables they wish to estimate. In this paper, we introduce a framework for generating expectation models and study the conditions under which they are adopted by a majority of market participants.

Marginal Tax Rates and Corporate Investment
Eskandari, Ruhollah,Zamanian, Morteza
Constructing new narrative measures of exogenous variations in corporate marginal tax rates for postwar legislated tax changes in the US, this study estimates the response of small versus large manufacturing firms to marginal rate cuts. Our estimates show that a decline in the marginal tax rate leads to an increase in investment of large firms nearly two times more than small firms at all horizons. Moreover, while large firms use a combination of debt and cash to increase investment, small firms rely heavily on debt. The different behavior of firms is consistent with their relative degree of credit constraint. The effects are strongly significant, highly robust, and not driven by a small number of very large firms. Heterogeneous responses to changes in the marginal tax rates are much different from those using broader measures of exogenous tax changes.

Market Implementation of Multiple-Arrival Multiple-Deadline Differentiated Energy Services
Yanfang Mo,Wei Chen,Li Qiu,Pravin Varaiya

An increasing concern in power systems is on how to elicit flexibilities in demands for better supply/demand balance. To this end, several differentiated energy services have been put forward, wherein demands are discriminated by their different flexibility levels. Motivated by the duration-differentiated energy services, we have proposed energy services differentiated by durations, arrival times, and deadlines. The purpose of this paper is to study the market implementation of such multiple-arrival multiple-deadline differentiated energy services. To verify the economic feasibility, we establish that in a forward market, there exists an efficient competitive equilibrium which attains the maximum social welfare. In addition, we show that future information will influence current decisions on power delivery by studying a special kind of causal allocation policy. Finally, we propose two tractable integer programs, namely the optimal arbitrage and the minimum-cost allocation problems, which can be embedded in a two-level hierarchical real-time implementation of differentiated energy services.

On the Role of Transparency in the ABS Secondary Market
Losada, Ramiro
As a consequence of the subprime crisis, a debate about the convenience of transparency enhancement in financial markets has been put forward by the securities regulators. In this paper, it is shown that under transparency, on the one hand, market makers’ bid-ask spreads are lower and depth is higher in the ABS secondary market. On the other hand, the lower bid-ask spreads enjoyed by market makers reduces the number of market makers. However, introducing transparency in this market is social-welfare improving, providing the costs of implementing it are not excessively high (it could be necessary to create an information platform to spread information) and the remaining market makers buy and sell all types of ABS bonds: RMBS, CMBS, CDO, etc. This last condition guarantees that competition among market makers is kept at the same level as under an opaque ABS secondary market.

Tensor Processing Units for Financial Monte Carlo
Francois Belletti,Davis King,Kun Yang,Roland Nelet,Yusef Shafi,Yi-Fan Chen,John Anderson

Monte Carlo methods are core to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional state spaces where they are still a method of choice in the financial industry. Recently, Tensor Processing Units (TPUs) have provided considerable speedups and decreased the cost of running Stochastic Gradient Descent (SGD) in Deep Learning. After having highlighted computational similarities between training neural networks with SGD and stochastic process simulation, we ask in the present paper whether TPUs are accurate, fast and simple enough to use for financial Monte Carlo. Through a theoretical reminder of the key properties of such methods and thorough empirical experiments we examine the fitness of TPUs for option pricing, hedging and risk metrics computation. We show in the following that Tensor Processing Units (TPUs) in the cloud help accelerate Monte Carlo routines compared to Graphics Processing Units (GPUs) which in turn decreases the cost associated with running such simulations while leveraging the flexibility of the cloud. In particular we demonstrate that, in spite of the use of mixed precision, TPUs still provide accurate estimators which are fast to compute. We also show that the Tensorflow programming model for TPUs is elegant, expressive and simplifies automated differentiation.

The Silent Weapon in the Rise of Passive: Shaping the Corporate Landscape
Cocoma, Paula,Zhang, Jinyuan
We present a new channel that rationalizes the recent rise in the share of passive investment in the asset management industry. By including the effect that firm shareholders have on corporate decisions, we establish that the preferred firm strategy of different shareholders varies conditional on their portfolio allocations. Passive investors who hold a market portfolio influence firms to pursue strategies that reduce the value of holding any other portfolio. In a rational information model, endogenous strategic complementarities arise where passive investors decrease the expected profits from information acquisition. This effect leads to passive investors giving rise to more passive investors as the equilibrium outcome. Furthermore, we offer various empirical predictions to guide future research.

The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage
Clement Gastaud,Theophile Carniel,Jean-Michel Dalle

We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as well as the position of investors in the investment network, pioneering the use of Graph Neural Networks (GNN), a recent deep learning technique that enables the handling of graphs as such and as a whole. We show that the different stages of fundraising, early- and growth-stage, are associated with different success factors. Our results suggest a marked relevance of startup competition for early stage while growth-stage fundraising is influenced by network features. Both of these factors tend to average out in global models, which could lead to the false impression that startup success in fundraising would mostly if not only be influenced by its intrinsic characteristics, notably those of their founders.

Towards Improved Generalization in Financial Markets with Synthetic Data Generation
Brandon Da Silva,Sylvie Shang Shi

Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ratio. High dimensionality can be dealt with using robust feature selection or dimensionality reduction, but limited observations often result in a model that overfits due to the large parameter space of most deep neural networks. We propose a generative model for financial time series, which allows us to train deep learning models on millions of simulated paths. We show that our generative model is able to create realistic paths that embed the underlying structure of the markets in a way stochastic processes cannot.

Transaction Cost Analytics for Corporate Bonds
Xin Guo,Charles-Albert Lehalle,Renyuan Xu

With the rise of the electronic trading, corporate bond traders have access to data information of past trades. As a first step to automation, they have to start monitoring their own trades, and using past data to build a benchmark for the expected transaction costs with given bond characteristics and market conditions. Given the limited liquidity of corporate bonds which are traded few times daily, a statistical model is the only way to benchmark effective costs. It brings focused attention of the dealing desk of an institutional investor on the most costly trades, and enables identifying and improving business practices such as the market timing for selection counterparties.

Unlike existing literature which focuses on general measurements using OLS, this paper takes the viewpoint of a given investor, and provides an analytical approach to establish a benchmark for transaction cost analysis in corporate bond tradings. Regularized methods are used to improve the selection of explanatory variables, as fewer variables provide easier analytics from a business perspective. This benchmark is constructed in two steps. The first step is the regression analysis with cross validation to identify abnormal trades. Three regression approaches, OLS, two-step Lasso and Elastic Net, are adopted to identify key features for the bid-ask spread of corporate bonds. The second step is to use the non-parametric approach to estimate the amplitude and decay pattern of price impact. A key discovery is the price impact asymmetry between customer-buy orders and consumer-sell orders. This benchmark can aid decision makings for retail investors when requesting quotes on the electronic platform.