# Research articles for the 2020-10-22

An assessment of European electricity arbitrage using storage systems
Fernando Núñez,David Canca,Ángel Arcos-Vargas
arXiv

This study analyses the current viability of this business based on a sample of European countries in the year 2019; countries where electricity prices (day-ahead market) and financial conditions show a certain degree of heterogeneity. We basically follow a sequence of three analyses in our study. Firstly, a Linear Mixed-Integrated Programming model has been developed to optimize the arbitrage strategy for each country in the sample. Secondly, using the cash-flows from the optimization model, we calculate two financial indicators (NPV and IRR) in order to select the optimal converter size for each country. Tax and discount rates specific to each country have been used with the calculation of this second rate following the methodology proposed by the Spanish regulator. Thirdly, a mixed linear regression model is proposed in order to investigate the importance of observed and unobserved heterogeneity (at country level) in explaining the business profitability.

China A-Shares: Strategic Allocation to Market and Factor Premiums
de Groot, Wilma,Swinkels, Laurens,Zhou, Weili
SSRN
We investigate the added value of strategically allocating to the Chinese A-shares equity market. Our results indicate a positive contribution to a portfolio which only considers traditional developed and emerging equity markets and bonds. We find that a diversified A-shares portfolio based on value, quality, and momentum factors exhibits a significantly better risk-adjusted performance than the passive A-shares market portfolio. Consequently, allocating to Chinese A-share factor premiums significantly improves the efficient frontier. The conclusions remain similar when incorporating conservative estimates of trading costs or when constructing value-weighted portfolios, which represent more realistic investor returns.

Conditional Systemic Risk Measures
Alessandro Doldi,Marco Frittelli
arXiv

We investigate to which extent the relevant features of (static) systemic risk measures can be extended to a conditional setting. After providing a general dual representation result, we analyze in greater detail Conditional Shortfall Systemic Risk Measures. In the particular case of exponential preferences, we provide explicit formulas that also allow us to show a time consistency property. Finally, we provide an interpretation of the allocations associated to Conditional Shortfall Systemic Risk Measures as suitably defined equilibria. Conceptually, the generalization from static to conditional systemic risk measures can be achieved in a natural way, even though the proofs become more technical than in the unconditional framework.

Corporate Social Responsibility and Credit Risk
Bannier, Christina E.,Bofinger, Yannik,Rock, BjÃ¶rn
SSRN
We study the effects of corporate social responsibility on credit risk for U.S. and European firms over the period 2003 to 2018. Differentiating between the various facets of corporate social responsibility shows that only environmental aspects reduce different measures of credit risk for U.S. firms, whereas both environmental and social aspects do so for European firms. Surprisingly, we find that credit ratings do not reflect these credit-risk reducing effects of corporate social responsibility. Our results are robust against different estimation methods.

Do Merges and Acquisitions Create Value for Shareholders?
S. Alkhalaf, Abdulmuhsen
SSRN
This paper investigates whether M&A create value for the firm and its shareholders. Using data on M&A announcement made by publicly listed firms in the United States in 2003, it conducts a standard event study by calculating the cumulative abnormal return around [-1, 0, +1], where -1 is the one trading day prior to the M&A announcement, 0 is the day of the announcement, and +1 is one trading-day after the announcement. It finds that M&A deals were, on average, value enhancing for firms over the short horizon. It also shows that firm size has a negative and statistically significant relationship with the cumulative abnormal return. Firms with small size are likely to have a positive cumulative abnormal return from M&A deals. In other words, small firms would benefit most on average from M&A deals.

Does it Pay Off to Learn a New Skill? Revealing the Economic Benefits of Cross-Skilling
Fabian Stephany
arXiv

This work examines the economic benefits of learning a new skill from a different domain: cross-skilling. To assess this, a network of skills from the job profiles of 4,810 online freelancers is constructed. Based on this skill network, relationships between 3,525 different skills are revealed and marginal effects of learning a new skill can be calculated via workers' wages. The results indicate that the added economic value of learning a new skill strongly depends on the already existing skill bundle but that acquiring a skill from a different domain is often beneficial. As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for mastering new technologies and designing individual training pathways. This can help to increase employability and reduce labour market shortages.

Duration of exposure to inheritance law in India: Examining the heterogeneous effects on empowerment
Shreya Biswas,Upasak Das,Prasenjit Sarkhel
arXiv

Higher duration of programs that involve legal protection may entail gradual positive changes in social norms that can be leveraged by potential beneficiaries in their favor. This paper examines the heterogeneous impact of the duration of exposure to gender-neutral reforms in the inheritance law in India on two latent domains of women empowerment: intrinsic, which pertains to expansion of agency and instrumental which relates to ability to make decisions. The time lag between the year of the amendment in the respective states and the year of marriage generate exogenous variation in reform exposure across women. The findings indicate a significant non-linear increase in the instrumental as well as intrinsic empowerment. Importantly, improvements in education along with increase in the age of marriage and changes in family structure are found to be the potential channels that signal gradual relaxation of social norms and explain the higher returns to exposure on empowerment.

Evaluate the Efficiency of Liquidity Management in Russian Banks
Abu-Alrop, Jalal Hafeth
SSRN
The purpose of this study is to measure the impact of liquidity on the performance of Russian banks (2008-17) to assess the efficiency of Russian banks in liquidity management to determine whether liquidity risk is reasonably priced. This study uses multiple regression analysis and DEA analysis to assess liquidity management efficiency. The study found that the effect of liquidity on the net interest margin (NIM) and the return on assets (ROA) is greater than the impact of liquidity on the return on equity (ROE), The study concluded that Medium banks were the most effective in liquidity managing, while small banks were more efficient than large banks. The study also further concluded that the Russian banks have a surplus of untapped liquidity and the efficiency of liquidity management in Russian banks is weak, Many banks could have achieved higher returns at the same liquidity levels or could have achieved the same returns at higher liquidity levels (Less liquidity risk).

Financial Constraints, Auditing, and External Financing
Becker, Mary J,Hoitash, Rani,Hoitash, Udi,Kurt, Ahmet C.
SSRN
This paper investigates whether financially constrained firms use costly audit signals to facilitate their access to external financing. We document that when facing higher financial constraints, firms pay higher audit fees and have their audit opinion reports completed sooner. The higher audit fees observed in this case do not seem to be driven by greater reporting risk of financially constrained firms because these firms are associated with lower restatement likelihood and fraud risk. We then test whether paying higher audit fees and having shorter audit lags enable financially constrained firms raise more financing in the equity and debt markets. We find that costlier and timelier audits facilitate equity-seeking constrained, but not debt-seeking constrained, firmsâ€™ access to financing. Our findings suggest that while financially constrained firms feel pressure to make cuts across various expenditure categories, negotiating lower audit fees in the face of higher financial constraints may not be a wise strategy.

Finding Corporate Credit Cycle for IFRS 9
Brown, Louis,Che, Xiaonan
SSRN
Under IFRS 9, the PD component of the ECL calculation has to be Point-in-Time, and the PiT PD can be considered to be a two-factor process, idiosyncratic and systematic factors, where the systematic factors are specific to the economy. The systematic factors can be observed in corporate default information and are measurable when default data is decomposed using a signal decomposition technique such as Empirical Mode Decomposition. An alternative and simple approach is proposed to incorporate the cyclicality effect to rating models, which can be adapted by other banks for PD or other components of the ECL calculation.

Fundamental Analysis Via Machine Learning
Cao, Kai,You, Haifeng
SSRN
We examine the efficacy of machine learning in one of the most important tasks in fundamental analysis, forecasting corporate earnings. Our analyses show that machine learning models, especially those that accommodate nonlinearities, generate significantly more accurate and informative forecasts than a host of state-of-the-art earnings prediction models in the extant literature. Further analysis suggests that machine learning models uncover economically sensible relationships between historical financial information and future earnings. We also find that the new information uncovered by machine learning models is of considerable economic significance to investors. The new information component of the machine learning-based forecasts is significantly associated with future stock returns. Stocks in the quintiles with the most favorable new information outperform those in the least favorable quintiles by approximately 70 bps per month, suggesting that the new information is not well understood by investors. Finally, insights from machine learning models are useful for improving the extant models.

Gender Diversity Goals, Supply Constraints, and the Market for Seasoned Female Directors: The U.S. Evidence
Boyallian, Patricia,Dasgupta, Sudipto,HomRoy, Swarnodeep
SSRN
We show that over the last decade, growing public pressure for board gender diversity and awareness of gender equality issues in the U.S. has manifested in â€œseasonedâ€ female board members accumulating multiple board appointments at a rate faster than seasoned male directors. The larger firms have been the most active in attracting seasoned female directors, at the expense of the smaller firms. This has likely contributed to the smaller firms lagging behind the larger firms in the pursuit of more gender balance. Our evidence is highly consistent with â€œsupply constraintsâ€, as reflected in high costs of recruiting first-time female directors, which the larger firms manage to avoid and the smaller firms find too costly to incur. Gender quota mandates are likely to expose the smaller firms even more to these costs; however, the absence of mandates may also not be optimal. Given growing public pressure, it may be necessary to mandate that larger firms maintain the ratio of first-time to seasoned female appointments above some level.

L\'evy-Ito Models in Finance
George Bouzianis,Lane P. Hughston,Sebastian Jaimungal,Leandro Sánchez-Betancourt
arXiv

We propose a class of financial models in which the prices of assets are L\'evy-Ito processes driven by Brownian motion and a dynamic Poisson random measure. Each such model consists of a pricing kernel, a money market account, and one or more risky assets. The Poisson random measure is associated with an $n$-dimensional L\'evy process. We show that the excess rate of return of a risky asset in a pure-jump model is given by an integral of the product of a term representing the riskiness of the asset and a term representing the level of market risk aversion. The integral is over the state space of the Poisson random measure and is taken with respect to the L\'evy measure associated with the $n$-dimensional L\'evy process. The resulting framework is applied to a variety of different asset classes, allowing one to construct new models as well as non-trivial generalizations of familiar models.

Learning Time Varying Risk Preferences from Investment Portfolios using Inverse Optimization with Applications on Mutual Funds
Shi Yu,Yuxin Chen,Chaosheng Dong
arXiv

The fundamental principle in Modern Portfolio Theory (MPT) is based on the quantification of the portfolio's risk related to performance. Although MPT has made huge impacts on the investment world and prompted the success and prevalence of passive investing, it still has shortcomings in real-world applications. One of the main challenges is that the level of risk an investor can endure, known as \emph{risk-preference}, is a subjective choice that is tightly related to psychology and behavioral science in decision making. This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization on the mean-variance portfolio allocation framework. Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings. Moreover, the quantified risk preference parameters are validated with two well-known risk measurements currently applied in the field. The proposed methods could lead to practical and fruitful innovations in automated/personalized portfolio management, such as Robo-advising, to augment financial advisors' decision intelligence in a long-term investment horizon.

Pitching Research â€¦ 100+ 'Rules of Engagement'
Faff, Robert W.
SSRN
The current paper offers an extensive array of advice â€" framed around so-called â€œrules of engagementâ€ â€" for budding researchers on how to optimize their experience in prospectively applying the â€œPitching Researchâ€ [Faff (2015, 2019)] framework to their research setting. This guidance amounts to a comprehensive collection of â€œhow toâ€ tips, that land within one of 3 key segments within the pitching template. Specifically, the â€œrulesâ€ are organized in relation to: (a) the 4-dimensional â€œBig Picture Framingâ€ part; (b) the 3-dimensional â€œBasic Building Blocksâ€ part; or (c) the â€œWhy this Study?â€ part (comprising the 2 â€œKey Questionsâ€, 1 â€œBottom-line and Other Considerations).

Pricing of Adverse Development Cover Using Option Pricing Methods
Dal Moro, Eric
SSRN
The market for Adverse Development Cover and Loss Portfolio Transfer has been growing in the past few years. Despite this growth, reinsurers are still struggling to define a standard method for pricing such covers. In this context, this article aims at providing an innovative method for pricing such contracts. The proposed method is based on the famous Mack model (see Mack 1993) and fits a Constant Elasticity of Variance (hereinafter â€œCEVâ€) model to the Mack results (expected value and standard deviation) on each future development year of each accident/underwriting year. Having fitted the CEV model, it is possible to estimate the value of the Adverse Development Cover for each accident/underwriting year using standard European option pricing techniques and to compare this valuation to usual General Insurance valuation techniques.

Quantifying Uncertainties in Estimates of Income and Wealth Inequality
Marta Boczon
arXiv

I measure the uncertainty affecting estimates of economic inequality in the US and investigate how accounting for properly estimated standard errors can affect the results of empirical and structural macroeconomic studies. In my analysis, I rely upon two data sets: the Survey of Consumer Finances (SCF), which is a triennial survey of household financial condition, and the Individual Tax Model Public Use File (PUF), an annual sample of individual income tax returns. While focusing on the six income and wealth shares of the top 10 to the top 0.01 percent between 1988 and 2018, my results suggest that ignoring uncertainties in estimated wealth and income shares can lead to erroneous conclusions about the current state of the economy and, therefore, lead to inaccurate predictions and ineffective policy recommendations. My analysis suggests that for the six top-decile income shares under consideration, the PUF estimates are considerably better than those constructed using the SCF; for wealth shares of the top 10 to the top 0.5 percent, the SCF estimates appear to be more reliable than the PUF estimates; finally, for the two most granular wealth shares, the top 0.1 and 0.01 percent, both data sets present non-trivial challenges that cannot be readily addressed.

SPAC IPO Waves
Blomkvist, Magnus,Vulanovic, Milos
SSRN
We examine the wave pattern of U.S. SPAC IPOs using a hand-collected data set of the entire SPAC population since their emergence in 2003. We find that both the SPAC volume and SPAC share of total IPOs are negatively related to market-wide uncertainty (VIX) and time-varying risk aversion (variance risk premium). We attribute our findings to risk-averse investors' reluctancy to invest in opaque securities. In response, the SPAC sponsor can credibly signal the issueâ€™s quality by increasing their â€œskin in the gameâ€ through the purchase of additional warrants.

Sequence Risk: Is It Really a Big Deal?
SSRN

Shareholder Protection in Close Corporations and the Curious Case of Japan: The Enigmatic Past and Present of Withdrawal in a Leading Economy
Koh, Alan K.
SSRN

Supervised Learning for the Prediction of Firm Dynamics
Bargagli Stoffi, Falco
SSRN
Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms' performance. In this contribution, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The stages we will focus on are (i) startup and innovation, (ii) growth and performance of companies, and (iii) firms exit from the market.First, we review SL implementations to predict successful startups and R\&D projects. Next, we describe how SL tools can be used to analyze company growth and performance. Finally, we review SL applications to better forecast financial distress and company failure. In the concluding Section, we extend the discussion of SL methods in the light of targeted policies, result interpretability, and causality.

The Health Consequence of Rising Housing Prices in China
Xu, Yuanwei,Wang, Feicheng
SSRN
China has experienced a rapid boom in real estate prices in the last few decades, leading to a substantial increase in living costs and heavy financial burdens on households. Using an instrumental variable approach, this paper exploits spatial and temporal variation in housing price appreciation linked to individual-level health data in China from 2000 to 2011. We find robust evidence that increases in housing prices significantly raise the probability of residents having chronic diseases. This negative health impact is more pronounced among individuals from low-income families, households that purchased rather than inherited or was allocated the home, and those who migrated from rural to urban areas. We also find evidence that marriage market competition exacerbates these negative health effects, particularly for males and parents with young adult sons. Further empirical results suggest that housing price appreciation induces negative health consequences through increased work intensity, higher mental stress, and reduced sleep time. This paper provides a novel explanation to the increased prevalence of chronic diseases in China.

The Role of Managerial Characteristics in FX Risk Management â€" Who Increases Risk?
Hecht, Andreas
SSRN
We examine the impact of managerial characteristics on the choice of risk-decreasing and risk-increasing/-constant strategies. Using unique data on firm-, year-, and currency-specific FX exposure before and after hedging with corresponding hedging instruments, we are able to measure how much a CEO has been involved in risk-increasing/-constant strategies over several years. We provide evidence that firms where the CEO has an MBA degree and is older are more likely to engage in risk-increasing/-constant strategies. In addition, we find that a CEOâ€™s affiliation to the ownerâ€™s family seems to reduce the amount of derivatives a firms uses, while hedging short tends to increase derivative volumes.

Using the Epps effect to detect discrete data generating processes
Patrick Chang,Etienne Pienaar,Tim Gebbie
arXiv

The Epps effect is key phenomenology relating to high frequency correlation dynamics in the financial markets. We argue that it can be used to determine whether trades at a tick-by-tick scale are best represented as samples from a Brownian diffusion, perhaps dressed with jumps; or as samples from truly discrete events represented as connected point processes. This can answer the question of whether correlations are better understood as an emergent time scale dependent property. In other words: Is the Epps effect a bias? To this end, we derive the Epps effect arising from asynchrony and provide a refined method to correct for the effect. The correction is compared against two existing methods correcting for asynchrony. We propose three experiments to discriminate between possible underlying representations: whether the data is best thought to be generated by discrete connected events (as proxied by a D-type Hawkes process), or if they can be approximated to arise from Brownian diffusions, with or without jumps. We then demonstrate how the Hawkes representation easily recovers the phenomenology reported in the literature; phenomenology that cannot be recovered using a Brownian representation, without additional ad-hoc model complexity, even with jumps. The experiments are applied to trade and quote data from the Johannesburg Stock Exchange. We find evidence suggesting that high frequency correlation dynamics are most faithfully recovered when tick-by-tick data is represented as a web of inter-connected discrete events rather than sampled or averaged from underlying continuous Brownian diffusions irrespective of whether or not they are dressed with jumps.

VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading
Prakhar Ganesh,Puneet Rakheja
arXiv

Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of financial trading. Financial markets have both long term and short term signals and thus a good predictive model in financial trading should be able to incorporate them together. One of the most sought after forms of electronic trading is high-frequency trading (HFT), typically known for microsecond sensitive changes, which results in a tremendous amount of data. LSTMs are one of the most capable variants of the RNN family that can handle long-term dependencies, but even they are not equipped to handle such long sequences of the order of thousands of data points like in HFT. We propose very-long short term memory networks, or VLSTMs, to deal with such extreme length sequences. We explore the importance of VLSTMs in the context of HFT. We compare our model on publicly available dataset and got a 3.14\% increase in F1-score over the existing state-of-the-art time-series forecasting models. We also show that our model has great parallelization potential, which is essential for practical purposes when trading on such markets.

Volterra Mortality Model: Actuarial Valuation and Risk Management with Long-Range Dependence
Wang, Ling,Chiu, Mei Choi,Wong, Hoi Ying
SSRN
While abundant empirical studies support the long-range dependence (LRD) of mortality rates, the corresponding impact on mortality securities are largely unknown due to the lack of appropriate tractable models for valuation and risk management purposes. We propose a novel class of Volterra mortality models that incorporate LRD into the actuarial valuation, retain tractability and are consistent with the existing continuous-time affine mortality models. We derive the survival probability in closed-form solution by taking into account of the historical health records. The flexibility and tractability of the models make them useful in valuing mortality-related products such as death benefit, annuity, longevity bond and many others as well as offering optimal mean-variance mortality hedging rules. Numerical studies are conducted to examine the impact of LRD within mortality rates on various insurance products and the hedging efficiency.