# Research articles for the 2021-06-17

A Note on Tweeting and Equity Markets before and during the COVID-19 Pandemic
Chatterjee, Ujjal,French, Joseph J.
SSRN
We investigate the differential effects of a new index of Twitter-based market uncertainty (TMU) and variables for the US equity market before and during the COVID-19 pandemic. We find that markets are significantly more sensitive to the uncertainty contained in tweets during the pandemic, the TMU is a leading indicator of returns only during the pandemic, and the effect of the TMU on the volatility and liquidity of equity markets is greater during the pandemic compared to the pre-pandemic period. Our results show that the information contained tweets are having a much larger effect on equity markets during the pandemic.

A comparative study of scoring systems by simulations
László Csató
arXiv

Scoring rules aggregate individual rankings by assigning some points to each position in each ranking such that the total sum of points provides the overall ranking of the alternatives. They are widely used in sports competitions consisting of multiple contests. We study the tradeoff between two risks in this setting: (1) the threat of early clinch when the title has been clinched before the last contest(s) of the competition take place; (2) the danger of winning the competition without finishing first in any contest. In particular, four historical points scoring systems of the Formula One World Championship are compared with the family of geometric scoring rules that have favourable axiomatic properties. The formers are found to be competitive or even better. The current scheme seems to be a reasonable compromise in optimising the above goals. Our results shed more light on the evolution of the Formula One points scoring systems and contribute to the issue of choosing the set of point values.

A self-organized criticality participative pricing mechanism for selling zero-marginal cost products
Daniel Fraiman
arXiv

In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's "correct" sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be designed, in particular ones that consider consumer preferences and reality. Current auction mechanisms establish a time deadline for the auction to take place. This deadline is set to increase the number of bidders and thus the final offering price. Consumers want to obtain the product as quickly as possible from the moment they become interested in it, and this time does not always coincide with the seller's deadline. Naturally, consumers also want to pay a price they consider "fair". Here we introduce an auction model where buyers continuously place bids and the challenge is to decide quickly whether or not to accept them. The model does not include a deadline for placing bids, and exhibits self-organized criticality; it presents a critical price from which a bid is accepted with probability one, and avalanches of sales above this value are observed. This model is of particular interest for startup companies interested in profit as well as making the product known on the market.

Abnormal Returns on Tourism Shares in the Chinese Stock Exchanges Amid the COVID-19 Pandemic
Liew, Venus Khim-Sen
SSRN
This study finds significant immediate adverse impact of the novel coronavirus (COVID-19) pandemic on tourism shares listed in the Shanghai and Shenzhen stock exchanges, in terms of breadth and depth. Overall, prices of these shares plunged by 20% in three consecutive days in response to pandemic fears, before technical rebound set in. Significant negative cumulative abnormal returns after the Wuhan lockdown are identified in 18 out of 21 tourism shares traded in the Chinese stock exchanges. These findings could serve as references for the China Security Regulatory Commission to monitor the market in future pandemic management. Investors are advised to avoid tourism shares the moment there is any suspicious development of virus outbreak in the future. Instead, they could look for opportunity to buy dip after massive market decline at the appropriate timing.

Aspects of a phase transition in high-dimensional random geometry
Axel Prüser,Imre Kondor,Andreas Engel
arXiv

A phase transition in high-dimensional random geometry is analyzed as it arises in a variety of problems. A prominent example is the feasibility of a minimax problem that represents the extremal case of a class of financial risk measures, among them the current regulatory market risk measure Expected Shortfall. Others include portfolio optimization with a ban on short selling, the storage capacity of the perceptron, the solvability of a set of linear equations with random coefficients, and competition for resources in an ecological system. These examples shed light on various aspects of the underlying geometric phase transition, create links between problems belonging to seemingly distant fields and offer the possibility for further ramifications.

Basel III in Nigeria: making it work
Ozili, Peterson K
SSRN
Basel III is a framework to preserve the stability of the international banking system. Nigeria adopts Basel capital framework for capital regulation in the banking sector. This article is a policy discussion on how to make Basel III work in Nigeria. The significance of Basel III is discussed, and some ideas to consider when implementing Basel III to make it work in Nigeria, are provided. Under Basel III, the Nigerian banking system should expect better capital quality, higher levels of capital, the imposition of minimum liquidity requirement for banks, reduced systemic risk, and a transitional arrangement for transitioning across Basel I and II. This article also emphasizes that (i) there should be enough time for the transition to Basel III in Nigeria, (ii) a combination of micro- and macro- prudential regulations is needed; and (iii) the need to repair the balance sheets of banks, in preparation for Basel III. The study recommends that the Nigerian regulator should enforce strict market discipline and ensure effective supervision under the Basel framework. There should be international cooperation between the domestic bank regulator and bank regulators in other countries. The regulator should have a contingency plan to reassure the public of the safety of their deposits, and there should be emergency liquidity solutions to support the financial system in bad times.

Design and Analysis of Robust Deep Learning Models for Stock Price Prediction
Jaydip Sen,Sidra Mehtab
arXiv

Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013- 2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.

Diversified reward-risk parity in portfolio construction
Jaehyung Choi,Hyangju Kim,Young Shin Kim
arXiv

We introduce diversified risk parity embedded with various reward-risk measures and more general allocation rules for portfolio construction. We empirically test advanced reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. All reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The Carhart four-factor analysis also indicates that the diversified reward-risk parity allocations gain superior performance.

Effects of Covid-19 Pandemic on Chinese Commodity Futures Markets
Ahmet Goncu
arXiv

In this study, empirical moments and the cointegration for all the liquid commodity futures traded in the Chinese futures markets are analyzed for the periods before and after Covid-19, which is important for trading strategies such as pairs trading. The results show that the positive change in the average returns of the products such as soybean, corn, corn starch, and iron ore futures are significantly stronger than other products in the post Covid-19 era, whereas the volatility increased most for silver, petroleum asphalt and egg futures after the pandemic started. The number of cointegrated pairs are reduced after the pandemic indicating the differentiation in returns due to the structural changes caused in the demand and supply conditions across commodities.

Hydrographic variability and biomass fluctuations of European anchovy (Engraulis encrasicolus) in the Central Mediterranean Sea: Monetary estimations and impacts on fishery from Lagrangian analysis
Antonio Di Cintio,Marco Torri,Federico Falcini,Raffaele Corrado,Guglielmo Lacorata,Angela Cuttitta,Bernardo Patti,Rosalia Santoleri
arXiv

During the last decades, scientific community has been investigating both biological and hydrographic processes that affect fisheries. Such an interdisciplinary and synergic approach is nowadays giving a fundamental contribution, in particular, in connecting the dots between hydrographic phenomena and biomass variability and distribution of small pelagic fish. Here we estimate impacts of hydrographic fluctuations on small pelagic fishery, focusing on the inter-annual variability that characterizes connectivity between spawning and recruiting areas for the European anchovy (Engraulis encrasicolus, Linnaues 1758), in the Northern side of the Sicily Channel (Mediterranean Sea). Results show that coastal transport dynamics of a specific year largely affect the biomass recorded the following year. Our work, moreover, quantifies the specific monetary impacts on landings of European anchovy fishery due to hydrodynamics variability, connecting biomass fluctuations with fishery economics in a highly dynamic and exploited marine environment as the Sicily Channel. In particular, we build a model that attributes a monetary value to the hydrographic phenomena (i.e., cross-shore vs. alongshore eggs and larvae transport), registered in the FAO Geographical Sub-Area (GSA) 16 (Southern Sicily). This allows us to provide a monetary estimation of catches, derived from different transport dynamics. Our results highlight the paramount importance that hydrographic phenomena can have over the socio-economic performance of a fishery.

Market Complete Option Valuation using a Jarrow-Rudd Pricing Tree with Skewness and Kurtosis
Yuan Hu,Abootaleb Shirvani,W. Brent Lindquist,Frank J. Fabozzi,Svetlozar T. Rachev
arXiv

Applying the Cherny-Shiryaev-Yor invariance principle, we introduce a generalized Jarrow-Rudd (GJR) option pricing model with uncertainty driven by a skew random walk. The GJR pricing tree exhibits skewness and kurtosis in both the natural and risk-neutral world. We construct implied surfaces for the parameters determining the GJR tree. Motivated by Merton's pricing tree incorporating transaction costs, we extend the GJR pricing model to include a hedging cost. We demonstrate ways to fit the GJR pricing model to a market driver that influences the price dynamics of the underlying asset. We supplement our findings with numerical examples.

Chenyanzi Yu,Tianyang Xie
arXiv

Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus on refining strategy rules instead of finding the optimal portfolio weight. In this paper, we brought up a simple yet profitable strategy called Volatility & Model Adaption Trade-off (VMAT) to leverage the issues. Experiment studies show its superior profit performance over baselines.

Order Flow Fragmentation and Flight-To-Transparency during Stressed Market Conditions: Evidence from COVID-19
Anselmi, Giulio,Petrella, Giovanni,Nimalendran, Mahendrarajah
SSRN
The proliferation of trading venues has resulted in a fragmented secondary markets landscape both in the US and Europe. Different factors drive the fragmentation of order flow during normal market conditions. This paper studies the composition of order flow during stressed market conditions based on the COVID-19 outbreak. We specifically investigate two related questions. Firstly, we assess whether stressed market conditions alter the level of fragmentation and, secondly, we study the choice of lit vs. dark trading when markets are under stress. As for the first question, we construct a measure to capture order flow fragmentation and find that fragmentation strongly decreases when markets are under stress (i.e., traders concentrate their order flow in fewer venues in times of market stress). This is especially the case for stocks experiencing a deeper shock in volatility. As for the second question, we find a migration of order flow from dark to lit venues when markets are under stress. Our interpretation of this finding is that traders tend to move to lit venues, where the probability of trading with an informed trader is less severe, during times of hyper-volatility. We call this evidence â€™flight-to-transparencyâ€™.

Planning in Financial Markets in Presence of Spikes: Using Machine Learning GBDT
Benhamou, Eric,Ohana, Jean-Jacques,Saltiel, David,Guez, Beatrice
SSRN
Planning in financial markets is a difficult task as the method needs to dramatically change its behavior when facing very rare black swan events like crises that shift market regime. In order to address this challenge, we present a gradient boosting decision trees (GBDT) approach to predict large price drops in equity indexes from a set of 150 technical, fundamental and macroeconomic features. We report an improved accu-racy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. We show that this model has a strong predic-tive power. We train the model from 2000 to 2014, a period where various crises have been observed and use a validation period of 3 years to find hyperparameters. The fitted model timely forecasts the Covid crisis giving us a planning method for early detection of potential future crises.

Politicians' Willingness to Agree: Evidence from the interactions in Twitter of Chilean Deputies
Pablo Henríquez,Jorge Sabat,José Patrìcio Sullivan
arXiv

Measuring the number of "likes" in Twitter and the number of bills voted in favor by the members of the Chilean Chambers of Deputies. We empirically study how signals of agreement in Twitter translates into cross-cutting voting during a high political polarization period of time. Our empirical analysis is guided by a spatial voting model that can help us to understand Twitter as a market of signals. Our model, which is standard for the public choice literature, introduces authenticity, an intrinsic factor that distort politicians' willigness to agree (Trilling, 2009). As our main contribution, we document empirical evidence that "likes" between opponents are positively related to the number of bills voted by the same pair of politicians in Congress, even when we control by politicians' time-invariant characteristics, coalition affiliation and following links in Twitter. Our results shed light into several contingent topics, such as polarization and disagreement within the public sphere.

Recurrent Neural Networks for Stochastic Control Problems with Delay
Jiequn Han,Ruimeng Hu
arXiv

Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions. In this paper, we propose and systematically study deep neural networks-based algorithms to solve stochastic control problems with delay features. Specifically, we employ neural networks for sequence modeling (\emph{e.g.}, recurrent neural networks such as long short-term memory) to parameterize the policy and optimize the objective function. The proposed algorithms are tested on three benchmark examples: a linear-quadratic problem, optimal consumption with fixed finite delay, and portfolio optimization with complete memory. Particularly, we notice that the architecture of recurrent neural networks naturally captures the path-dependent feature with much flexibility and yields better performance with more efficient and stable training of the network compared to feedforward networks. The superiority is even evident in the case of portfolio optimization with complete memory, which features infinite delay.

The Concept, Types and Structure of Corruption
Oleg Antonov,Ekaterina Lineva
arXiv

The article analyzes the essence of the phenomenon of corruption, highlights its main varieties and characteristics. The authors of the study apply historical analysis, emphasizing the long-term nature of corruption and its historical roots. The paper uses legal analysis to characterize the legal interpretation of corruption as an economic crime.

We formulate and solve a multi-player stochastic differential game between financial agents who seek to cost-efficiently liquidate their position in a risky asset in the presence of jointly aggregated transient price impact, along with taking into account a common general price predicting signal. The unique Nash-equilibrium strategies reveal how each agent's liquidation policy adjusts the predictive trading signal to the aggregated transient price impact induced by all other agents. This unfolds a quantitative relation between trading signals and the order flow in crowded markets. We also formulate and solve the corresponding mean field game in the limit of infinitely many agents. We prove that the equilibrium trading speed and the value function of an agent in the finite $N$-player game converges to the corresponding trading speed and value function in the mean field game at rate $O(N^{-2})$. In addition, we prove that the mean field optimal strategy provides an approximate Nash-equilibrium for the finite-player game.