Research articles for the 2021-05-27

An Introduction To Regret Minimization In Algorithmic Trading: A Survey of Universal Portfolio Techniques
Thomas Orton

In financial investing, universal portfolios are a means of constructing portfolios which guarantee a certain level of performance relative to a baseline, while making no statistical assumptions about the future market data. They fall under the broad category of regret minimization algorithms. This document covers an introduction and survey to universal portfolio techniques, covering some of the basic concepts and proofs in the area. Topics include: Constant Rebalanced Portfolios, Cover's Algorithm, Incorporating Transaction Costs, Efficient Computation of Portfolios, Including Side Information, and Follow The Leader Algorithm.

Auctioning Annuities
Gaurab Aryal,Eduardo Fajnzylber,Maria F. Gabrielli,Manuel Willington

We propose and estimate a model of demand and supply of annuities to evaluate a privatized annuities market. To this end, we use rich data from Chile, where annuities are sold via a two-stage process: first-price auctions followed by bargaining. We model firms with private information about costs and retirees with different mortalities and preferences for bequests and firms' risk ratings. We find substantial costs and preference heterogeneity and that having many firms is crucial for good market outcomes. Counterfactuals show that simplifying the current mechanism with English auctions and "shutting down" risk ratings increase pensions, but only for high-savers.

Corruption Determinants, Geography, and Model Uncertainty
Sajad Rahimian

This paper aims to identify the robust determinants of corruption after integrating out the effects of spatial spillovers in corruption levels between countries. In other words, we want to specify which variables play the most critical role in determining the corruption levels after accounting for the effects that neighbouring countries have on each other. We collected the annual data of 115 countries over the 1985-2015 period and used the averaged values to conduct our empirical analysis. Among 39 predictors of corruption, our spatial BMA models identify Rule of Law as the most persistent determinant of corruption.

How to Value Private Companies using Multiples and Discounted Cash Flow Analysis
Burgess, Nicholas
In this paper we outline how to value private companies and work through a real case study. The corporate finance and valuation techniques on display can be used to value any private company or project that is illiquid with little or no market data. Together with this paper we provide the full background information for the case study, including consolidated balance sheet, cash flow and income statements and an Excel workbook with a full valuation breakdown . Two valuation approaches are presented. The first approach is called ‘multiples’ and requires we first identify sensible comparable companies with public data and can serve as a reasonable proxy for our underlying firm or project. Known proxy enterprise values are converted into multiples of EBITDA and extrapolated to value the private company or project of interest.Enterprise value can be measured as a multiple of sales, earnings, EBITDA, EBIAT and many income factors. Typically practitioners measure enterprise value as a multiple of EBITDA to exclude sales margin, capital structure, debt and leverage and other idiosyncratic biases. The second valuation method takes a discounted cash flow (DCF) approach. This approach is forward looking; it projects and values the future cash flows of a company. This method lends itself to richer analysis compared to the multiples approach, but relies heavily on assumptions surrounding cash flow projections, growth rates and the weighted average cost of capital (WACC). The WACC acts as a yield or discount factor and is required to value the cash flows of the firm or project (Burgess, 2020).Finally we compare the prices from several different approaches to arrive at a suitable price range. It is important to note that all approaches involve assumptions and approximations of some kind. Therefore practitioners seek to establish a reasonable price range rather than a single price. Transactions however must take place at a single price and law suits are a popular commonplace negation tactic to squeeze for additional value.

Impacts of Corporate Announcements on Stock Returns during the Global Pandemic: Evidence from the Indian Stock Market
Pandey, Dharen Kumar,Kumari, Vineeta
With a sample of ninety events (announcement and ex-date) using the event study methodology with the market model, we provide evidence for the impacts of the corporate announcements on stock returns during the pandemic stress. We find that all the corporate announcements do not impact the stock returns in a similar pattern. While the bonus announcement, ex-bonus and ex-split events led to positive significant abnormal returns on the event date, the rights issue and stock-split announcements failed to influence the stock returns. The findings suggest that before making such announcements, the corporates should wait until the market recovers because even the positively impacting events result in negative market response during pandemic stress. We conduct the first-ever study to examine the impacts of corporate announcements during a pandemic stress period that significantly contributes to the literature.

Interest Rate Risk in the Banking Book - Is the SSM's Regulatory Approach Tight Enough?
Wambold, Marc,Wieandt, Axel
The current low interest rate environment is an unprecedented situation for the European banking union’s single supervisory mechanism (SSM) in that it increases interest rate risk in the banking book (IRRBB) for euro area banks. Sudden upward movements in rates threaten the economic value of bank equity, and persistently high interest rates can lead to lower bank earnings. These risks point to the need for a comprehensive supervisory approach to regulating IRRBB. Given the extraordinary circumstances and high levels of IRRBB which banks are and will be exposed to, we evaluate whether the SSM’s regulatory approach is tight enough. Specifically, we assess the adequacy of the supervisory outlier tests by performing an empirical analysis on historical interest rate changes and discussing whether the earnings perspective should be included in the supervisory outlier tests. Furthermore, we consider the minimum capital requirements for IRRBB against the background of the current low interest rates.Overall, we conclude that the current SSM’s approach on IRRBB is not tight enough. While we confirm the adequacy of the existing supervisory outlier tests, we recommend complementing them with outlier tests regarding the net interest income of banks. We further recommend implementing a standardised approach for calculating minimum capital requirements to improve banks’ resilience against IRRBB.

Is Corporate Social Responsibility investing a free lunch? The relationship between ESG, tail risk, and upside potential of stocks before and during the COVID-19 crisis
Lööf, Hans,Sahamkhadam, Maziar,Stephan, Andreas
Did Corporate Social Responsibility investing benefit shareholders during the COVID-19 pandemic crisis? Distinguishing between downside tail risk and upside reward potential of stock returns, we provide evidence from 5,073 stocks listed on stock markets in ten countries. The findings suggests that better ESG ratings are associated with lower downside risk, but also with lower upside return potential. Thus, ESG ratings help investors to reduce their risk exposure to the market turmoil caused by the pandemic, while maintaining the fundamental trade-off between risk and reward.

Machine learning with kernels for portfolio valuation and risk management
Lotfi Boudabsa,Damir Filipovic

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.

Neural Options Pricing
Timothy DeLise

This research investigates pricing financial options based on the traditional martingale theory of arbitrage pricing applied to neural SDEs. We treat neural SDEs as universal It\^o process approximators. In this way we can lift all assumptions on the form of the underlying price process, and compute theoretical option prices numerically. We propose a variation of the SDE-GAN approach by implementing the Wasserstein distance metric as a loss function for training. Furthermore, it is conjectured that the error of the option price implied by the learnt model can be bounded by the very Wasserstein distance metric that was used to fit the empirical data.

Ordered Reference Dependent Choice
Xi Zhi "RC" Lim

We study how violations of structural assumptions like expected utility and exponential discounting can be connected to reference dependent preferences with set-dependent reference points, even if behavior conforms with these assumptions when the reference is fixed. An axiomatic framework jointly and systematically relaxes general rationality (WARP) and structural assumptions to capture reference dependence across domains. It gives rise to a linear order that determines references points, which in turn determines the preference parameters for a choice problem. This allows us to study risk, time, and social preferences collectively, where seemingly independent anomalies are interconnected through the lens of reference-dependent choice.

Proposal for a Common Categorisation of IT Incidents
Financial Authorities members of the G-7 Cyber Expert Group (CEG), Financial Authorities members of the G-7 Cyber Expert Group (CEG)
This paper presents the proposal for a common categorisation of malicious cyber incidents (cyberâ€'attacks) and other information technology (IT) incidents formulated by ten financial authorities that are members of the Gâ€'7 Cyber Expert Group (CEG) and that represent six of the Gâ€'7 jurisdictions. The aim of the proposal is to promote the harmonisation of the various incident reports that authorities require from financial institutions by defining common principles and developing a common taxonomy for incident reporting. The adoption of these common principles and taxonomy should make incident reporting more robust and effective by facilitating a common understanding of incidents, the sharing of information, and the joint management of IT crossâ€'border crises.

The Donut Effect of COVID-19 on Cities
Ramani, Arjun,Bloom, Nicholas
Using data from the US Postal Service and Zillow, we quantify the effect of Covid- 19 on migration patterns and real estate markets within and across US cities. We find two key results. First, within large US cities, households and businesses have moved from the dense central business districts (CBDs) towards lower density suburban zip-codes. We label this the “Donut Effect” reflecting the movement of activity out of city centers to the suburban ring. Second, while this observed reallocation occurs within cities, we do not see major reallocation across cities. That is, there is less evidence for large-scale movement of activity from large US cities to smaller regional cities or towns. We rationalize these findings by noting that working patterns post pandemic will be primarily hybrid, with workers commuting to their business premises typically 3 days per week. This level of commuting is less than pre-pandemic, making suburbs relatively more popular, but too frequent to allow employees to leave the cities containing their employer.

Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
Zhihan Zhou,Liqian Ma,Han Liu

In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize textual features (e.g., bag-of-words) and sentiments to directly make stock predictions, we consider corporate events as the driving force behind stock movements and aim to profit from the temporary stock mispricing that may occur when corporate events take place. The core of the proposed strategy is a bi-level event detection model. The low-level event detector identifies events' existences from each token, while the high-level event detector incorporates the entire article's representation and the low-level detected results to discover events at the article-level. We also develop an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. EDT includes 9721 news articles with token-level event labels as well as 303893 news articles with minute-level timestamps and comprehensive stock price labels. Experiments on EDT indicate that the proposed strategy outperforms all the baselines in winning rate, excess returns over the market, and the average return on each transaction.

Transformation from Microfinance to Bank: A Case Study of Bandhan
Tiwari, Rajesh,Anjum, Bimal
The Indian banking industry is dominated by public sector banks. The poor financial inclusion is a challenge for the banking industry. The private sector banks were given licenses by RBI. These banks have also focused on the urban areas and ignored financial inclusion. RBI recently awarded in principle approval to Bandhan and IDFC to set up banks. The selection of Bandhan is surprising and also points out to the priority of RBI to enhance financial inclusion rather than creating clones of existing banks. A microfinance institution getting a banking license is an opportunity to work for the poor and improve financial inclusion, at the same time presents a challenge to develop the infrastructure, deposit base, expertise and size to survive in a competitive industry dominated by big players.