Research articles for the 2020-04-26

A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models
Sidra Mehtab,Jaydip Sen

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. Design of such predictive models requires choice of appropriate variables, right transformation methods of the variables, and tuning of the parameters of the models. In this work, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building and training the forecasting models. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data. We build eight classification and eight regression models based on statistical and machine learning approaches. In addition to these models, a deep learning regression model using a long-and-short-term memory (LSTM) network is also built. Extensive results have been presented on the performance of these models, and the results are critically analyzed.

Cryptocurrency Trading: A Comprehensive Survey
Fan Fang,Carmine Ventre,Michail Basios,Hoiliong Kong,Leslie Kanthan,Lingbo Li,David Martinez-Regoband,Fan Wu

Since the inception of cryptocurrencies, an increasing number of financial institutions are getting involved in cryptocurrency trading. It is therefore important to summarise existing research papers and results on cryptocurrency trading. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 118 research papers on various aspects of cryptocurrency trading (e.g.,cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return,crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with promising opportunities in cryptocurrency trading.

Deaths, Panic, Lockdowns and US Equity Markets: The Case of COVID-19 Pandemic
Baig, Ahmed,Butt, Hassan A.,Haroon, Omair,Rizvi, Syed Aun R.
This study investigates the impact of COVID-19 pandemic on the microstructure of US equity markets. In particular, we attempt to explain the liquidity and volatility dynamics via indexes that capture multiple dimensions of the pandemic. Our results suggest that increases in confirmed cases and deaths due to coronavirus are associated with a significant increase in market illiquidity and volatility. Similarly, declining sentiment and the implementations of restrictions and lockdowns contribute to the deterioration of the liquidity and stability of markets.

Did the Paycheck Protection Program Hit the Target?
Granja, Joao,Makridis, Christos,Yannelis, Constantine,Zwick, Eric
This paper takes an early look at the Paycheck Protection Program (PPP), a large and novel small business support program that was part of the initial policy response to the COVID-19 pandemic. We use new data on the distribution of PPP loans and high-frequency micro-level employment data to consider two dimensions of program targeting. First, we do not find evidence that funds flowed to areas more adversely affected by the economic effects of the pandemic, as measured by declines in hours worked or business shutdowns. If anything, funds flowed to areas less hard hit. Second, we find significant heterogeneity across banks in terms of disbursing PPP funds, which does not only reflect differences in underlying loan demand. The top-4 banks alone account for 36% of total pre-policy small business loans, but disbursed less than 3% of all PPP loans. Areas that were significantly more exposed to low-PPP banks received much lower loan allocations. As data become available, we will study employment and establishment responses to the program and the impact of PPP support on the economic recovery. Measuring these responses is critical for evaluating the social insurance value of the PPP and similar policies.

Environmental Economics and Uncertainty: Review and a Machine Learning Outlook
Ruda Zhang,Patrick Wingo,Rodrigo Duran,Kelly Rose,Jennifer Bauer,Roger Ghanem

Economic assessment in environmental science concerns the measurement or valuation of environmental impacts, adaptation, and vulnerability. Integrated assessment modeling is a unifying framework of environmental economics, which attempts to combine key elements of physical, ecological, and socioeconomic systems. Uncertainty characterization in integrated assessment varies by component models: uncertainties associated with mechanistic physical models are often assessed with an ensemble of simulations or Monte Carlo sampling, while uncertainties associated with impact models are evaluated by conjecture or econometric analysis.

Manifold sampling is a machine learning technique that constructs a joint probability model of all relevant variables which may be concentrated on a low-dimensional geometric structure. Compared with traditional density estimation methods, manifold sampling is more efficient especially when the data is generated by a few latent variables. The manifold-constrained joint probability model helps answer policy-making questions from prediction, to response, and prevention. Manifold sampling is applied to assess risk of offshore drilling in the Gulf of Mexico.

Financial Inclusion and Fintech during COVID-19 Crisis: Policy Solutions
Ozili, Peterson K
This article offers a number of policy solutions to improve financial inclusion during the COVID-19 crisis. Covid-19 is a global problem to which some of the usual global solutions, like greater financial inclusion, can help. Financial inclusion remains a powerful development tool to improve access to finance, and to support vulnerable individuals and households during the coronavirus â€" covid-19 â€" crisis. The documented policy solutions for financial inclusion can help mitigate the effect of the Covid-19 crisis through the combined use of Fintech and short-term policy solutions.

Financial contagion during COVIDâ€"19 crisis
Akhtaruzzaman, Md,Boubaker, Sabri,Sensoy, Ahmet
This study examines how financial contagion occurs through financial and nonfinancial firms between China and G7 countries during the COVIDâ€"19 period. The empirical results show that listed firms across these countries, financial and non-financial firms alike, experience significant increase in dynamic conditional correlations between their stock returns. However, the magnitude of increase in these correlations is considerably higher for financial firms during the COVID-19 outbreak, indicating the importance of their role in financial contagion transmission. They also show that optimal hedge ratios increase significantly in most cases, implying higher hedging costs during the COVID-19 period.

High-dimensional macroeconomic forecasting using message passing algorithms
Dimitris Korobilis

This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either towards zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a Generalized Approximate Message Passing (GAMP) algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well.

Inside the Mind of Investors During the COVID-19 Pandemic: Evidence from the StockTwits Data
Hasan Fallahgoul

We study the investor beliefs, sentiment and disagreement, about stock market returns during the COVID-19 pandemic using a large number of messages of investors on a social media investing platform, \textit{StockTwits}. The rich and multimodal features of StockTwits data allow us to explore the evolution of sentiment and disagreement within and across investors, sectors, and even industries. We find that the sentiment (disagreement) has a sharp decrease (increase) across all investors with any investment philosophy, horizon, and experience between February 19, 2020, and March 23, 2020, where a historical market high followed by a record drop. Surprisingly, these measures have a sharp reverse toward the end of March. However, the performance of these measures across various sectors is heterogeneous. Financial and healthcare sectors are the most pessimistic and optimistic divisions, respectively.

Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises
Ahelegbey, Daniel Felix,Giudici, Paolo
We construct a network-based turbulence score for analyzing the relationship between individual market volatility, interconnectedness, and global risk, and for identifying systemically important institutions, with the highest contribution to financial turbulence. We apply our measure to study the integration among the major world markets over the first two decades of the 21st century, particularly during the tech, sub-prime, and COVID-19 crises. The result shows that the interconnectedness of the markets amplifies initial risks (on average almost four times), to cause financial turbulence. We also found evidence that the United States is central to global market turbulence, followed by Brazil, France, Hong Kong, and Germany.

On Game-Theoretic Risk Management (Part One) -- Towards a Theory of Games with Payoffs that are Probability-Distributions
Stefan Rass

Optimal behavior in (competitive) situation is traditionally determined with the help of utility functions that measure the payoff of different actions. Given an ordering on the space of revenues (payoffs), the classical axiomatic approach of von Neumann and Morgenstern establishes the existence of suitable utility functions, and yields to game-theory as the most prominent materialization of a theory to determine optimal behavior. Although this appears to be a most natural approach to risk management too, applications in critical infrastructures often violate the implicit assumption of actions leading to deterministic consequences. In that sense, the gameplay in a critical infrastructure risk control competition is intrinsically random in the sense of actions having uncertain consequences. Mathematically, this takes us to utility functions that are probability-distribution-valued, in which case we loose the canonic (in fact every possible) ordering on the space of payoffs, and the original techniques of von Neumann and Morgenstern no longer apply.

This work introduces a new kind of game in which uncertainty applies to the payoff functions rather than the player's actions (a setting that has been widely studied in the literature, yielding to celebrated notions like the trembling hands equilibrium or the purification theorem). In detail, we show how to fix the non-existence of a (canonic) ordering on the space of probability distributions by only mildly restricting the full set to a subset that can be totally ordered. Our vehicle to define the ordering and establish basic game-theory is non-standard analysis and hyperreal numbers.

Polarization under rising inequality and economic decline
Alexander J. Stewart,Nolan McCarty,Joanna J. Bryson

Social and political polarization is a significant source of conflict and poor governance in many societies. Thus, understanding its causes has become a priority of scholars across many disciplines. Here we demonstrate that shifts in socialization strategies analogous to political polarization and identity politics can arise as a locally-beneficial response to both rising wealth inequality and economic decline. Adopting a perspective of cultural evolution, we develop a framework to study the emergence of polarization under shifting economic environments. In many contexts, interacting with diverse out-groups confers benefits from innovation and exploration greater than those that arise from interacting exclusively with a homogeneous in-group. However, when the economic environment favors risk-aversion, a strategy of seeking low-risk interactions can be important to maintaining individual solvency. To capture this dynamic, we assume that in-group interactions have a lower expected outcome, but a more certain one. Thus in-group interactions are less risky than out-group interactions. Our model shows that under conditions of economic decline or increasing wealth inequality, some members of the population benefit from adopting a risk-averse, in-group favoring strategy. Moreover, we show that such in-group polarization can spread rapidly to the whole population and persist even when the conditions that produced it have reversed. Finally we offer empirical support for the role of income inequality as a driver of affective polarization in the United States, mirroring findings on a panel of developed democracies. Our work provides a framework for studying how disparate forces interplay, via cultural evolution, to shape patterns of identity, and unifies what are often seen as conflicting explanations for political polarization: identity threat versus economic anxiety.

Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling
Roy Cerqueti,Massimiliano Giacalone,Raffaele Mattera

Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in terms of market capitalization. At this aim, we consider non-Gaussian GARCH volatility models, which form a class of stochastic recursive systems commonly adopted for financial predictions. Results show that the best specification and forecasting accuracy are achieved under the Skewed Generalized Error Distribution when Bitcoin/USD and Litecoin/USD exchange rates are considered, while the best performances are obtained for skewed Distribution in the case of Ethereum/USD exchange rate. The obtain findings state the effectiveness -- in terms of prediction performance -- of relaxing the normality assumption and considering skewed distributions.

Sustainability and Fairness Simulations Based on Decision-Making Model of Utility Function and Norm Function
Takeshi Kato,Yasuyuki Kudo,Junichi Miyakoshi,Jun Otsuka,Hayato Saigo,Kaori Karasawa,Hiroyuki Yamaguchi,Yoshinori Hiroi,Yasuo Deguchi

We introduced a decision-making model based on value functions that included individualistic utility function and socio-constructivistic norm function and proposed a norm-fostering process that recursively updates norm function through mutual recognition between the self and others. As an example, we looked at the resource-sharing problem typical of economic activities and assumed the distribution of individual actions to define the (1) norm function fostered through mutual comparison of value/action ratio based on the equity theory (progressive tax-like), (2) norm function proportional to resource utilization (proportional tax-like) and (3) fixed norm function independent of resource utilization (fixed tax-like). By carrying out numerical simulation, we showed that the progressive tax-like norm function (i) does not increase disparity for the distribution of the actions, unlike the other norm functions, and (ii) has high resource productivity and low Gini coefficient. Therefore the progressive tax-like norm function has the highest sustainability and fairness.

The COVID-19 Bailouts
Meier, Jean-Marie A.,Smith, Jake
The COVID-19 pandemic hit the world economy with unprecedented force. The response by US policy makers to this pandemic has been similarly unprecedented, especially with respect to the bailouts of the private sector, which are the focus of this paper. We collect data on all bailout funds received by publicly listed firms in the US using corporate filings with the Securities and Exchange Commission. We provide summary statistics on the bailout funds and their recipients. We document several empirical facts that are of importance to policy makers and the general public regarding the design of existing and potential policy interventions. We discuss potential policy implications.

The GRACE Act: One Way to Flatten the Curve of the Financial Pandemic
Angel, James
The pandemic-induced shutdowns are leading to a financial pandemic. When the unemployed worker or small business can’t pay the rent, then the landlord can’t pay the mortgage. Even when a creditor provides forbearance on a loan, the borrower’s credit is damaged for years. What is needed is a way to stop the chain reaction and give everyone enough breathing room to recover without costing the taxpayer trillions of dollars. The GRACE (General Recovery and Credit Extension) Act would provide renters and borrowers an automatic ability to defer rent, credit card, and installment debt payments for six months. They would still have to pay the money back later, with affordable monthly payments stretched over a long-enough period to reduce the pain.Payment deferral without any other action would create cash flow difficulties for the creditors. To alleviate the problem, the creditors would be able to use the receivables created by the deferred payments as collateral for immediate loans from any bank for the full amount of the expected deferrals. The loans would be non-recourse loans backed by the U.S. government. Banks may be skittish about expanding their lending because of concerns about complying with bank capital standards. To incentivize banks to make such loans, they would carry a zero-risk weight for purposes of calculating Risk-Weighted Assets (RWA) and the total leverage ratio, and they would be counted as High Quality Liquid Assets (HQLA). In order to avoid long-term damage to a borrower’s credit, the GRACE Act would require that the deferred payments be reported as current on credit reports, and that lenders would not be permitted to deny credit based on a borrower’s use of the deferrals. The process resembles a pre-packaged Chapter 11: The borrower seeking deferment fills out an online form listing the payments to be deferred with contact information for the creditors. The creditors then receive a notification (either electronically or in writing). The notification can be used immediately as collateral for a loan from any lender. The GRACE Act is designed to give much-needed breathing room to our workers and businesses. It is not designed to keep zombie businesses alive or rescue those who were already bankrupt before the pandemic. Accordingly, the deferrals should be restricted to payments that were on-time as of February 1, 2020. This will limit credit losses to the federal government. To avoid the negative publicity of bailouts for billionaires, there should be a limit on the total payments any one person or business can defer. The GRACE Act is a WIN-WIN-WIN-WIN for borrowers, creditors, taxpayers, and the US economy. It will simplify the resolution of the wave of defaults that threatens to paralyze the economy for years to come. It will flatten the curve of the financial pandemic and prevent millions unnecessary evictions and bankruptcies. However, the GRACE Act is not a panacea that will cure all of the economic fallout from the pandemic. Additional efforts will be needed to address other areas of the economy. The GRACE Act should be an integral part of the next rescue package.

The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets
Annika Kemper,Maren D. Schmeck,Anna Kh. Balci

In electricity markets, futures contracts typically function as a swap since they deliver the underlying over a period of time. In this paper, we introduce a market price for the delivery periods of electricity swaps, thereby opening an arbitrage-free pricing framework for derivatives based on these contracts. Furthermore, we use a weighted geometric averaging of an artificial geometric futures price over the corresponding delivery period. Without any need for approximations, this averaging results in geometric swap price dynamics. Our framework allows for including typical features as the Samuelson effect, seasonalities, and stochastic volatility. In particular, we investigate the pricing procedures for electricity swaps and options in line with Arismendi et al. (2016), Schneider and Tavin (2018), and Fanelli and Schmeck (2019). A numerical study highlights the differences between these models depending on the delivery period.

The Relationship between Cryptocurrencies and COVID-19 Pandemic
Demir, Ender,Bilgin, Mehmet Huseyin,Karabulut, Gokhan,Doker, Aslı Cansın
We examine the relationship between cryptocurrencies (namely Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP)) and COVID-19 cases/deaths. The Wavelet coherence analysis indicates that there is initially a negative relationship between Bitcoin and the number of reported cases and deaths; however, the relationship becomes positive during the later period. The findings for Ethereum and Ripple are also similar but with weaker interactions. This shows the hedging role of cryptocurrencies against the uncertainty raised by COVID-19.