# Research articles for the 2020-01-28

arXiv

We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. We investigate whether all information provided to investors in the offering circular prior to a new issuance is equally important in predicting its spread. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. The random forest shows an impressive predictive power on unseen primary catastrophe bond data explaining 93% of the total variability. For comparison, linear regression, our benchmark model, has inferior predictive performance explaining only 47% of the total variability. All details provided in the offering circular are predictive of spread but in a varying degree. The stability of the results is studied. The usage of random forest can speed up investment decisions in the catastrophe bond industry.

arXiv

We study combinations of risk measures under no restrictive assumption on the set of alternatives. The main result is the representation for resulting risk measures from the properties of both alternative functionals and combination functions. To that, we develop a representation for arbitrary mixture of convex risk measures. In this case, we obtain a penalty that recall the notion of inf-convolution under theoretical measure integration. As an application, we address the context of probability-based risk measurements for functionals on the set of distribution functions. We develop results related to this specific context. We also explore features of individual interest generated by our framework, such as the preservation of continuity properties, the representation of worst-case risk measures, stochastic dominance and elicitability.

SSRN

During the post-1925 era, institutional, evolutionary and financial economics have been predominant within the natural and constructed environment. For instance, from 1st to 22nd July 1944, experts representing forty-four nations convened at Bretton Woods, New Hampshire for the United Nations Monetary and Financial Conference. Among its work there, the Conference drew up a plan for what became known as the International Monetary Fund (IMF). 74 years henceforth speaking at the 8th Kissinger Lecture Series at the US Library of Congress on 4th December 2018, the IMF managing director broached the idea for institutional recognition of evolutionary phenomena in financial economics.Encouraged by the adoption of UN Resolution 72/277, â€œTowards a Global Pact for the Environmentâ€ on 10th May 2018 and in alignment with UN SDG #17 which was adopted by all UN member states in 2015, this invited paper explores the viability of Accountability Asset Recovery: A Leadership and Sustainability Initiative in seeking to facilitate institutional change, mindful of the gap that heretofore frustrated the promises enshrined in international conventions.

SSRN

Using the random assignment of judges to corporate criminal cases, we document that, on average, judges appointed by a Democrat president impose larger monetary damages for crimes that Democrats are more likely to view as important (i.e., violations of environmental and labor regulations) while Republican-appointed judges impose larger fines for crimes that Republicans are more likely to view as important (i.e., the hiring of illegal immigrants). These differences are amplified during time periods of greater political partisanship and are robust to controlling for other judicial characteristics (e.g., age, race, and gender). There is no evidence, however, that judgesâ€™ political affiliations are associated with decisions on guilt. The findings suggest that shifts in judicial political affiliations and increased political polarization have the potential to affect firmsâ€™ investment and hiring decisions.

SSRN

Terminal Value (aka Horizon Value) is the value of a firm when the firm is expected to grow at a constant rate forever. The method to calculate value a constant-growth firm is sometimes called Capitalization. Currently, and for decades, the primary method, if not the only method, to calculate Terminal Value is a formula commonly known as the Gordon Growth Model (GGM). In addition to the constant growth assumption, Gordon Growth Model assumes â€œconstant capital structureâ€ which results into â€œConstant WACCâ€ (Weighted Average Cost of Capital) as a discount rate. This paper will show following: 1) The â€œconstant WACCâ€ assumption of the Gordon Growth Model implies that the capital markets will accept â€œDividend Firstâ€ financing terms. However, the capital markets function with â€œDebt Firstâ€ financing terms. When a firm operates with â€œDebt Firstâ€ financing terms, but the value is based on â€œDividend Firstâ€ financing terms, the equity IRR is less than expected. This means GGM overvalues a firm. And, such over valuation is material, 10 to 50% and sometimes even more. 2) Introduce Advance Growth Model (AGM) to value a constant-growth firm assuming capital markets acceptable â€œDebt Firstâ€ financing terms. AGM formula is a generalized formula for Terminal Value; AGM value is equal to GGM value if GGM assumptions are plugged into the AGM formula.GGM formula is very elegant; AGM formula is very complex even though it has only 3 more input variables. A free spreadsheet with GGM and AGM formulas is available in the main article.

arXiv

As the Securities and Exchange Commission(SEC) has implemented a new regulation on short-sellings, short-sellers are required to repurchase stocks once the clearing risk rises to a certain level. Avellaneda and Lipkin proposed a fully coupled SDE system to describe the mechanism which is referred as Hard-To-Borrow(HTB) models. Guiyuan Ma obtained the PDE system for both American and European options. There is a technical error in Guiyuan Ma where two correlated Brownian motion should be converted before change of measure. In this paper, I will provide supplement conditions.

SSRN

The issue of consumer disengagement has troubled regulators and policy makers for years, since it undermines the functioning of sound competitive markets by allowing incumbents to enjoy economic supracompetive returns to the detriment of innovation and consumer welfare. The market investigation into the banking industry launched by the UK Competition and Market Authority (CMA) represents an original attempt to tackle the problem through antitrust enforcement. By building on the access-to-account rule enshrined within the European regulatory framework of the revised Payment Service Directive and the most recent developments of FinTech innovation, the CMA has designed a set of measures aimed at addressing some of the structural features causing adverse effects on competition in the retail markets, ultimately paving the way towards Open Banking. The paper highlights the rationales, benefits and potential drawbacks of the UK Open Banking plan, investigating if this regulatory intervention can act as a blueprint for harnessing the competitive potential of data-driven innovation of the financial industry and the digital economy as a whole.

SSRN

We provide evidence that automated asset management affects wealth inequality by reducing a fixed cost of household investment in risky asset markets: account minimums. Using data from a large U.S. robo advisor, we show how an unexpected, 90% reduction in account minimum shifts the wealth distribution of robo investors leftward to become more representative of the U.S. population (i.e. more â€œdemocraticâ€). The reduction increases stock market participation among households from the middle quintiles of the U.S. wealth distribution, raising their risky share by 27 percentage points and their total return on liquid assets by 2 percentage points, relative to wealthier households. However, the reduction has no effect on households in the bottom quintile of the U.S. wealth distribution, suggesting that automation has an ambiguous effect on wealth inequality by favoring middle class households over both the lower and upper classes.

SSRN

Corporate executives receive a considerable portion of their compensation in the form of equity and, from time to time, sell a portion of their holdings in the open market. Executives nearly always have access to nonpublic information about the company, and routinely have an information advantage over public shareholders. Federal securities laws prohibit executives from trading on material nonpublic information about their company, and companies develop an Insider Trading Policy (ITP) to ensure executives comply with applicable rules. In this Closer Look we examine the potential shortcomings of existing governance practices as illustrated by four examples that suggest significant room for improvement.We ask: â€¢ Should an ITP go beyond legal requirements to minimize the risk of negative public perception from trades that might otherwise appear suspicious?â€¢ Why donâ€™t all companies make the terms of their ITP public?â€¢ Why donâ€™t more companies require the strictest standards, such as pre-approval by the general counsel and mandatory use of 10b5-1 plans?â€¢ Does the board review trades by insiders on a regular basis? What conversation, if any, takes place between executives and the board around large, single-event sales?

arXiv

In recent years, hyperparameter optimization (HPO) has become an increasingly important issue in the field of machine learning for the development of more accurate forecasting models. In this study, we explore the potential of HPO in modeling stock returns using a deep neural network (DNN). The potential of this approach was evaluated using technical indicators and fundamentals examined based on the effect the regularization of dropouts and batch normalization for all input data. We found that the model using technical indicators and dropout regularization significantly outperforms three other models, showing a positive predictability of 0.53% in-sample and 1.11% out-of-sample, thereby indicating the possibility of beating the historical average. We also demonstrate the stability of the model in terms of the changes in its feature importance over time.

arXiv

This paper assesses the role of financial performance in explaining firms' investment dynamics in the wine industry from the three European Union (EU) largest producers. The wine sector deserves special attention to investigate firms' investment behavior given the high competition imposed by the latecomers. More precisely, we investigate how the capitalization, liquidity and profitability influence the investment dynamics using firm-level data from the wine industry from France (331 firms), Italy (335) firms and Spain (442) firms. We use data from 2007 to 2014, drawing a comparison between these countries, and relying on difference-and system-GMM estimators. Specifically, the impact of profitability is positive and significant, while the capitalization has a significant and negative impact on the investment dynamics only in France and Spain. The influence of the liquidity ratio is negative and significant only in the case of Spain. Therefore, we notice different investment strategies for wine companies located in the largest producer countries. It appears that these findings are in general robust to different specifications of liquidity and profitability ratios, and to the different estimators we use.

SSRN

The purpose of this paper is to compare the cyclical behavior of various credit impairment accounting regimes, namely IAS 39, IFRS 9 and US GAAP. We model the impact of credit impairments on the Prot and Loss (P&L) account under all three regimes. Our results suggest that although IFRS 9 is less procyclical than the previous regulation (IAS 39), it is more procyclical than US GAAP because it merely requests to provision the expected loss of one year under Stage 1 (initial category). Instead, since US GAAP prescribes that lifetime expected losses are fully provisioned at inception, the amount of new loans originated is negatively correlated with realized losses. This leads to relatively higher (lower) provisions during the upswing (downswing) phase of the financial cycle. Nevertheless, the lower procyclicality of US GAAP seems to come at cost of a large increase in provisions.

SSRN

Using a sample of around 340 banks from 48 countries, we examine whether and how corporate governance moderates the relationship between monetary policy interest rates and bank stability. Our results show that low interest rates reduce bank stability and have an adverse effect on risk-adjusted returns, and risk-adjusted capitalization; however, this effect can be mitigated by a bankâ€™s commitment and effectiveness towards following corporate governance principles. The results also show that high values of corporate governance can completely offset the low ratesâ€™ adverse effects. Our findings are robust to the use of bank fixed effects and numerous variables that control for bank-specific and country-specific characteristics.

SSRN

Optimal asset allocation and consumption policies have been important issues in finance in the past decades. We study these issues under constant relative risk aversion (CRRA) utility functions in a general setting: stochastic volatility, incomplete markets and finite investment horizons. So far, numerical computation has been the main method for obtaining solutions in this general setting. We present a closed-form approximate solution for this dynamic optimization problem. We show that our theoretical predictions are in good agreement with numerical results and our approximation error is even smaller than the parameter-estimation errors in underlying dynamics.

arXiv

The aim of this thesis is to analyze and renovate few main-stream models on inflation derivatives. In the first chapter of the thesis, concepts of financial instruments and fundamental terms are introduced, such as coupon bond, inflation-indexed bond, swap.

In the second chapter of the thesis, classic models along the history of developing quantified interest rate models are introduced and analyzed. Moreover, the classification of interest rate models is introduced to help audiences understand the intrinsic ideology behind each type of models.

In the third chapter of the thesis, the related mathematical knowledge is introduced. This part has the contribution on understanding the terms and relation among terms in each model introduced previously.

In the fourth part of the thesis, the renovation of HJM frame work is introduced and analysis has been initiated.

arXiv

Smart cities that make broad use of digital technologies have been touted as possible solutions for the population pressures faced by many cities in developing countries and may help meet the rising demand for services and infrastructure. Nevertheless, the high financial cost involved in infrastructure maintenance, the substantial size of the informal economies, and various governance challenges are curtailing government idealism regarding smart cities. This review examines the state of smart city development in developing countries, which includes understanding the conceptualisations, motivations, and unique drivers behind (and barriers to) smarty city development. A total of 56 studies were identified from a systematic literature review from an initial pool of 3928 social sciences literature identified from two academic databases. Data were analysed using thematic synthesis and thematic analysis. The review found that technology-enabled smart cities in developing countries can only be realised when concurrent socioeconomic, human, legal, and regulatory reforms are instituted. Governments need to step up their efforts to fulfil the basic infrastructure needs of citizens, raise more revenue, construct clear regulatory frameworks to mitigate the technological risks involved, develop human capital, ensure digital inclusivity, and promote environmental sustainability. A supportive ecosystem that encourages citizen participation, nurtures start-ups, and promotes public-private partnerships needs to be created to realise their smart city vision.

arXiv

The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible.

Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress". Traditional asymptotics deal mainly with either n=1 or $n=\infty$, and the real world is in between, under of the "laws of the medium numbers" --which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence.

A few examples:

+ The sample mean is rarely in line with the population mean, with effect on "naive empiricism", but can be sometimes be estimated via parametric methods.

+ The "empirical distribution" is rarely empirical.

+ Parameter uncertainty has compounding effects on statistical metrics.

+ Dimension reduction (principal components) fails.

+ Inequality estimators (GINI or quantile contributions) are not additive and produce wrong results.

+ Many "biases" found in psychology become entirely rational under more sophisticated probability distributions

+ Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions.

This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

arXiv

Optimized certainty equivalents (OCEs) is a family of risk measures widely used by both practitioners and academics. This is mostly due to its tractability and the fact that it encompasses important examples, including entropic risk measures and average value at risk.

In this work we consider stochastic optimal control problems where the objective criterion is given by an OCE risk measure, or put in other words, a risk minimization problem for controlled diffusions. A major difficulty arises since OCEs are often time inconsistent. Nevertheless, via an enlargement of state space we achieve a substitute of sorts for time consistency in fair generality. This allows us to derive a dynamic programming principle and thus recover central results of (risk-neutral) stochastic control theory. In particular, we show that the value of our risk minimization problem can be characterized via the viscosity solution of a Hamilton--Jacobi--Bellman--Issacs equation. We further establish the uniqueness of the latter under suitable technical conditions.

arXiv

Monte Carlo methods are critical 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 highlighting computational similarities between training neural networks with SGD and simulating stochastic processes, 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. In particular we demonstrate that, in spite of the use of mixed precision, TPUs still provide accurate estimators which are fast to compute when compared to GPUs. We also show that the Tensorflow programming model for TPUs is elegant, expressive and simplifies automated differentiation.

SSRN

Recent findings that suggest a robust negative association between changes in the cross-currency basis and the broad dollar have taken center stage in the international finance literature. In this article, we revisit this issue, from a purely empirical, data-driven perspective, using G10 and 10 emerging market currencies, and employing dynamic correlations, rather than static correlations, at different rolling windows. Overall, results obtained do not support a consistently negative dynamic relation between changes in the basis and the dollar, even in the post-crisis era, especially at short rolling windows. At the same time, as evidenced by the negative correlations in some historical periods, a negative comovement between changes in the basis and the dollar cannot be fully ruled out, particularly at longer rolling windows. Hence the nature of the relation is dynamic, varying in direction from negative to positive or vice-versa. As such, like nearly everything else in the financial markets, the comovement between changes in the basis and the dollar is anything but directionally static. This result has broader implications for optimal positioning in the cross-currency basis swap markets.

SSRN

Regulators and commentators around the world are increasingly demanding that institutional investors engage in stewardship with respect to their portfolio companies. Further, the demand for stewardship has broadened from an expectation that investors engage to reduce agency costs and promote economic value to a call for investors to demand that companies serve a broader range of societal interests and objectives. This chapter considers calls for stewardship in the context of the U.S. capital markets specifically as applied to index funds. It argues that, irrespective of the merits of institutional stewardship generally, the structure of index funds and the business environment in which they operate limits their ability to engage in effective stewardship. Although index fund sponsors have had a powerful influence on their portfolio companies, well-intentioned calls for them to play a more significant role and, in particular, claims that they should incorporate non-economic objectives more broadly into their engagement strategy, are in tension with the valuable role that index funds serve in the U.S. markets by providing a low-cost diversified investment option for an increasing segment of ordinary citizens. The chapter concludes by considering the possibility of using pass-through voting to enhance the stewardship potential of index funds.

SSRN

I discuss an intellectual revolution, social economics and finance: the study of the social processes that shape economic thinking and behavior. This emerging field recognizes that people observe and talk to each other. A key, under-exploited building block of social economics and finance is social transmission bias: a systematic directional shift in signals or ideas in social transactions. I use five â€œfablesâ€ (models) to illustrate the novelty and scope of the transmission bias approach, and offer several emergent themes. For example, social transmission bias compounds recursively, which can help explain booms, bubbles, return anomalies, and swings in economic sentiment.Presentation Slides available at: https://ssrn.com/abstract=3513210