Research articles for the 2021-03-21

Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction
Yi Wei

Neural networks for stock price prediction(NNSPP) have been popular for decades. However, most of its study results remain in the research paper and cannot truly play a role in the securities market. One of the main reasons leading to this situation is that the prediction error(PE) based evaluation results have statistical flaws. Its prediction results cannot represent the most critical financial direction attributes. So it cannot provide investors with convincing, interpretable, and consistent model performance evaluation results for practical applications in the securities market. To illustrate, we have used data selected from 20 stock datasets over six years from the Shanghai and Shenzhen stock market in China, and 20 stock datasets from NASDAQ and NYSE in the USA. We implement six shallow and deep neural networks to predict stock prices and use four prediction error measures for evaluation. The results show that the prediction error value only partially reflects the model accuracy of the stock price prediction, and cannot reflect the change in the direction of the model predicted stock price. This characteristic determines that PE is not suitable as an evaluation indicator of NNSPP. Otherwise, it will bring huge potential risks to investors. Therefore, this paper establishes an experiment platform to confirm that the PE method is not suitable for the NNSPP evaluation, and provides a theoretical basis for the necessity of creating a new NNSPP evaluation method in the future.

Analyst Information Intermediation during the COVID-19 Pandemic
Bilinski, Pawel
We use the COVID-19 pandemic to examine how this tail risk event affected analysts research production and their information intermediation role. Analysts markedly increase their research activity in the initial months of the pandemic: the number of quarterly earnings forecasts increases by 72%, revenue forecasts by 80%, cash flow by 59%, dividend by 11%, target prices by 154% and stock recommendations by 88% in March 2020 compared to the same pre-pandemic month. Forecasts issued during the pandemic associate with significantly higher errors compared to a comparable pre-pandemic period. Analysts aggressively revise their forecasts during the pandemic compared to the pre-pandemic period: the average absolute revisions range between 142% for revenue forecasts and 9% for dividend estimates. Price reactions to revisions in analyst forecasts and stock recommendation are incrementally higher during the pandemic though lower for target prices. This effect is magnified in periods where investors actively search for information about the pandemic and the stock market as captured by google searches. Investors value more analyst private information discovery role than their role in interpreting public information during the pandemic.

Blockchain Technology: A Way Ahead in Insurance Sector
Shah, Tanay,Shah, Dr. Megha
Blockchain is considered as one of the most important technology of the future. To do transaction in virtual money or digital currencies like bitcoin, gram, libra which is to be considered as the future money, would become possible as a result of the technologies like blockchain. Blockchain technology can be considered as one of the most important technology that can be implemented in insurance sector in near future. Everything that looks shining stars in the beginning that always have risk associated with it. Risk like huge expenditure on infrastructure, risk of adoption of technology by the investors/customers, risk of safety or security, risk that competitor will get first mover advantage. These are just a few examples, but lot more risk can be associated with it. Blockchain technology can either create the revolution in insurance sector or it can also be replaced just like the previous many technology. Even due to the pandemic situation dependency on technology can easily vouched. When transactions become digital then to record the same transaction is much more important. Blockchain technology is one of the most important technology that record the transaction through creating a block which encrypt the data so that it can only accessed by the authorized personal. Insurance sector in which paying the premium, putting a claim for the losses, settlement of claim etc. which required fast process, blockchain can play a vital role in this. It reduces time along with maintaining the secrecy. But as every coin have two sides, blockchain requires knowledge of the technology, investment in huge infrastructure as well as providing training to the associated parties. One should be very vigilant before applying the blockchain technology.This research paper is an attempt to provide the future scenario of blockchain technology in insurance sector.

COVID-19 and the Energy Trade: Evidence from Tanker Trade Routes
Michail, Nektarios,Melas, Konstantinos D.
We employ a cointegration setup to explore route-specific off-equilibrium deviations related to COVID-19 that have affected clean (petroleum products) and dirty (crude oil) tanker freight rates, over and above the expected macroeconomic reactions. We find that the additional deviation caused by COVID-19 is route-specific. In particular, deviation caused by COVID-19 is found to be more significant for clean tankers, given that these products are more reliant on economic developments as a result of their uses. The clean tanker impact is more evident in Japan-related routes, while no specific pattern can be extracted with regards to the additional off-equilibrium COVID-19 deviation for dirty tanker routes.

Does the Shiller CAPE Predict a Crash of the S&P 500?
Lechner, Gerhard
Stock markets have rebounded to record highs after the March 2020 crash. First, the Nasdaq hit a high, followed by the S&P 500 and the Dow Jones. This paper deals with the question of the valuation of the S&P 500. For this purpose the CAPE Shiller and thus, a forecast for the next 1 to 2 years is to be derived from the current assessment. The current level of the CAPE of the S&P 500 indicates a worrying overvaluation of the index. However, if you look at the US Fed's expansionary monetary policy over the past 20 years, the overvaluation should not be overstated. The author expects the historical peak (December 1999) of CAPE S&P 500 to be surpassed in 2022. After that, the risk of a crash increases significantly.

Financing Development: Private Capital Mobilization and Institutional Investors
Inderst, Georg
This report discusses key issues around the mobilization of private capital for development. Investment requirements are huge, especially for infrastructure, climate and other SDG-related investments. External finance for developing countries stagnated in the years before the pandemic, followed by a major setback in 2020/2021. The focus is in particular on institutional investors, whose exposure to less-developed countries is still very low, even more so in unlisted assets and projects. There is a potential for progress as asset owners seek new diversification opportunities in growth markets. The main burden is on governments to create favourable business conditions for investable long-term assets. Policy makers, development finance institutions and investors should utilize the full spectrum of investment vehicles - commercial, impact and blended finance.

Functional portfolio optimization in stochastic portfolio theory
Steven Campbell,Ting-Kam Leonard Wong

In this paper we develop a concrete and fully implementable approach to the optimization of functionally generated portfolios in stochastic portfolio theory. The main idea is to optimize over a family of rank-based portfolios parameterized by an exponentially concave function on the unit interval. This choice can be motivated by the long term stability of the capital distribution observed in large equity markets, and allows us to circumvent the curse of dimensionality. The resulting optimization problem, which is convex, is flexible as various regularizations and constraints can be imposed on the generating function. We prove that the optimization problem is well-posed and provide a stability estimate in terms of a Wasserstein metric of the input measure. We then give a careful treatment of its discretization and the optimization algorithm. Finally, we present empirical examples using CRSP data from the US stock market.

Implied Equity Duration: A Measure of Pandemic Shutdown Risk
Dechow, Patricia,Erhard, Ryan,Sloan, Richard G.,Soliman, Mark T.
Implied equity duration was originally developed to analyze the sensitivity of equity prices to discount rate changes. We demonstrate that implied equity duration is also useful for analyzing the sensitivity of equity prices to pandemic shutdowns. Pandemic shutdowns primarily impact short‐term cash flows, thus they have a greater impact on low‐duration equities. We show that implied equity duration has a strong positive relation to U.S. equity returns and analyst forecast revisions during the onset of the 2020 COVID‐19 shutdown. Our analysis also demonstrates that the underperformance of “value” stocks during this period is a rational response to their lower durations.

Liquidity Connectedness of Cryptocurrencies and Stock Markets during COVID-19: A Wavelet Coherence Approach
Umar, Muhammad ,Rubbaniy, Ghulame, XU, Yan
Using wavelet coherence framework on five major cryptocurrencies and three major stock market indices over the COVID-19 period from January 1st, 2020 to February 8th, 2021, our study concludes that SSEC index liquidity co-moves with liquidity of all the cryptocurrencies, while liquidities of Nikkei 225 and NYSE indices very weakly or not at all co-move with the sampled cryptocurrencies over most of our sample period. Our findings show that SSEC index liquidity positively co-moves with liquidities of all sampled crypto currencies over a limited time span and generally at short-term frequency band of 0-8 days; however, Ripple liquidity positively co-moves with liquidity of SSEC index at both shorter-horizon and long-term. Overall, our study provides useful insights that the choice of the crypto currency can play a significant role in portfolio liquidity diversification for investors investing in Nikkei 225 or NYSE index.

Locational Marginal Pricing: Towards a Free Market in Power
Martin Higgins

Nothing has done more to empower the free market, enterprise, and meritocracy than the spread of electricity and power to everyone. The power system has been the precursor to the greatest period of innovation in our history and has meant that visionaries with revolutionary ideas can compete with those with capital, political power, and means. Electricity, therefore, has been the great equalising force of the last 150 years, enhancing the productivity of the masses and granting prosperity to whole swathes of our nation. Whilst electricity has been one of the single largest innovations in enhancing the power of free markets, it is somewhat ironic that the way power is sold to consumers is largely unfree. The market is highly regulated, centralised, and is often used for political football by cynical politicians on both sides of the political spectrum. Introducing Locational Marginal Pricing into the UK grid system will increase economic freedom in the consumer markets for power, reduce prices for the poorest in the UK, decrease transmission losses, increase the permeation of low carbon generation in the grid, and incentivise investment in the UK's Northern Powerhouse initiative.

Misaligned Incentives in Markets: Envisioning Finance That Benefits All of Society
Clements, Ryan
The modern financial system is plagued by misaligned incentives that allow some firms to extract distributive profits, and direct wealth transfers in their favor, without producing anything of value, or improving society with enhanced employment or socially useful innovation. Many modern financial products and activities serve no underlying economic or productive purpose. The system is creating market intermediaries of astounding size, power, profitability, and economic and regulatory policy influence. Some financial firms expressly profit from heightened interconnection and complexity, while others benefit directly from increased volatility. Yet we all bear the costs of this evolved financial system when it unravels due to its interconnectedness with the real economy, and our increased reliance on markets. This article advocates for a financial system that is de-financialized, de-complexified, more transparent, and better orientated to productive ends in a way that benefits all of society, not just the firms who reap asymmetrical payoffs in a complex system, intermediate capital, create financial products, or run the plumbing in a system that ultimately serves them best.This article gives support to Hyman Minsky’s “money manager capitalism” hypothesis by showing how the financial system has evolved since the 2008 crisis because of misaligned incentives. In support of this contention the article profiles numerous post-crisis trends and events in financial markets where misaligned incentives emerge, including moral hazard in debt origination, how some financial firms benefit from volatility; the real winners of the Game Stop “meme stock” saga; problems from price dislocations in credit exchange traded funds (ETFs) during the coronavirus pandemic crash; conflicts in the construction and composition of indices; market disruption from volatility-linked exchange traded products (ETPs); misaligned incentives in special purpose acquisition companies (SPACs) and evolved private equity (PE) business models; fragilities in pension administration; environmental, social, governance (ESG) opacity and greenwashing in investment funds; and governance conflicts from economic and proxy voting power of mega-asset managers.

Multi-Period Portfolio Optimization using Model Predictive Control with Mean-Variance and Risk Parity Frameworks
Xiaoyue Li,A. Sinem Uysal,John M. Mulvey

We employ model predictive control for a multi-period portfolio optimization problem. In addition to the mean-variance objective, we construct a portfolio whose allocation is given by model predictive control with a risk-parity objective, and provide a successive convex program algorithm that provides 30 times faster and robust solutions in the experiments. Computational results on the multi-asset universe show that multi-period models perform better than their single period counterparts in out-of-sample period, 2006-2020. The out-of-sample risk-adjusted performance of both mean-variance and risk-parity formulations beat the fix-mix benchmark, and achieve Sharpe ratio of 0.64 and 0.97, respectively.

Optimal Clearing Payments in a Financial Contagion Model
Giuseppe Calafiore,Giulia Fracastoro,Anton V. Proskurnikov

Modern financial networks are characterized by complex structures of mutual obligations. Such interconnections may propagate and amplificate individual defaults, leading in some cases to financial disaster. For this reason, mathematical models for the study and control of systemic risk (the risk of severe instabilities on the system as a whole, due to default of single entities) have attracted considerable research attention in recent years. One important line of research is concerned with mechanisms of clearing, that is, the mechanism by which mutual debts are repaid, in the regular regime, or in a default regime. One of the first models of a clearing mechanism was proposed by Eisenberg and Noe and is based on the three rules: limited liability, the priority of debt claims over the shareholders' interests, and the equal priority of debts (pro-rata rule). These three principles naturally lead to the concept of clearing vector (the vector of the entities' total payments). In this paper, we propose a necessary and sufficient condition for the uniqueness of clearing vector applicable to an arbitrary topology of the financial network. Further, we show that the overall system loss can be reduced if one relaxes the pro-rata rule and replaces the clearing vector by a matrix of clearing payments. This approach shifts the focus from the individual interest to the system, or social, interest, in order to control and contain the adverse effects of cascaded failures.

Quantum propensity in economics
David Orrell,Monireh Houshmand

This paper describes an approach to economics that is inspired by quantum computing, and is motivated by the need to develop a consistent quantum mathematical framework for economics. The traditional neoclassical approach assumes that rational utility-optimisers drive market prices to a stable equilibrium, subject to external perturbations. While this approach has been highly influential, it has come under increasing criticism following the financial crisis of 2007/8. The quantum approach, in contrast, is inherently probabilistic and dynamic. Decision-makers are described, not by a utility function, but by a propensity function which specifies the probability of transacting. We show how a number of cognitive phenomena such as preference reversal and the disjunction effect can be modelled by using a simple quantum circuit to generate an appropriate propensity function. Conversely, a general propensity function can be quantized to incorporate effects such as interference and entanglement that characterise human decision-making. Applications to some common problems in economics and finance are discussed.

Renters vs. Homeowners: Income and Liquid Asset Trends during COVID-19
Greig, Fiona,Zhao, Chen,Lefevre, Alexandra
The COVID-19 recession hit the lowest-paid sectors of the economy the hardest, resulting in lower-income Americans losing their jobs and income at higher rates. Therefore, renters are more likely to have been impacted negatively by the economic shocks of the pandemic. While the Coronavirus Aid, Relief, and Economic Security (CARES) Act that passed in March 2020 provided one year of easily attainable mortgage forbearance for most homeowners, renters did not receive any form of federally-provided rent relief until the Coronavirus Response and Relief Supplemental Appropriations Act of 2021, which provided $25 billion in rental assistance through the Emergency Rental Assistance Program to cover current or past due balances. We examine income and savings patterns for renters and mortgages holders during the pandemic and ask: is there evidence that renters needed more assistance than they received? We find evidence that renters indeed needed more of a financial safety net than was available during the pandemic. The renters we analyze are much more affluent than typical renters and even among this population, they were more likely than mortgage holders to have lost their job and suffered large swings in their labor income, including large drops. Even with unusually generous UI benefits and stimulus checks, more than one in five renters experienced a greater than 10 percent drop in their total income. These income swings were more negative relative to the pre-COVID period. Finally, renters not only had lower incomes than mortgage holders, they also had much less of a savings buffer entering the pandemic. While more generous UI benefits and stimulus checks dramatically boosted their savings, they had depleted most of the additional savings by the end of the year, their position relative to mortgage holders did not improve significantly, and nearly one in four renters saw their savings decrease in 2020.

Universal Trading for Order Execution with Oracle Policy Distillation
Yuchen Fang,Kan Ren,Weiqing Liu,Dong Zhou,Weinan Zhang,Jiang Bian,Yong Yu,Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.