nep-cmp New Economics Papers
on Computational Economics
Issue of 2020‒09‒14
38 papers chosen by
Stan Miles
Thompson Rivers University

  1. GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading By Zezheng Zhang; Matloob Khushi
  2. Pattern recognition of financial institutions’ payment behavior By Carlos León; Paolo Barucca; Oscar Acero; Gerardo Gage; Fabio Ortega
  3. Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA By Linyu Zheng; Hongmei He
  4. Learning low-frequency temporal patterns for quantitative trading By Joel da Costa; Tim Gebbie
  5. Heterogeneous speculators and stock market dynamics: A simple agent-based computational model By Schmitt, Noemi; Schwartz, Ivonne; Westerhoff, Frank H.
  6. Predicting monetary policy using artificial neural networks By Hinterlang, Natascha
  7. A Generalized Time Iteration Method for Solving Dynamic Optimization Problems with Occasionally Binding Constraints By Ayse Kabukcuoglu; Enrique Martínez-García
  8. Deep Learning for Constrained Utility Maximisation By Ashley Davey; Harry Zheng
  9. Assessing the Macroeconomic Impacts of Financing Options for Renewable-Energy Policy in Nigeria: Insights from a CGE Model By Oluwasola Emmanuel Omoju; Lulit Mitik Beyene; Emily Edoisa Ikhide; Stephen Kelechi Dimwobi; Augustina Ehimare
  10. DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data By Aiusha Sangadiev; Rodrigo Rivera-Castro; Kirill Stepanov; Andrey Poddubny; Kirill Bubenchikov; Nikita Bekezin; Polina Pilyugina; Evgeny Burnaev
  11. Convergence of Deep Fictitious Play for Stochastic Differential Games By Jiequn Han; Ruimeng Hu; Jihao Long
  12. Distributional and Economy-Wide Effects of Post-Conflict Policy in Colombia By Dora Elena Jiménez Giraldo Author-Name: Adrián Saldarriaga-Isaza Author-Name: Martín Cicowiez
  13. A solution method for the shared Resource Constrained Multi-Shortest Path Problem By Alonso Ayuso, Antonio; Laguna, Manuel; Molina Ferragut, Elisenda; García Heredia, David
  14. InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification By Konstantin T. Matchev; Prasanth Shyamsundar
  15. Note on simulation pricing of $\pi$-options By Zbigniew Palmowski; Tomasz Serafin
  16. Finding Core Members of Cooperative Games using Agent-Based Modeling By Daniele Vernon-Bido; Andrew J. Collins
  17. Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations By Tomasz Antczak; Rafal Weron; Jacek Zabawa
  18. Hybrid quantum-classical optimization for financial index tracking By Samuel Fern\'andez-Lorenzo; Diego Porras; Juan Jos\'e Garc\'ia-Ripoll
  19. Exchange-Rate Policy in a Dollarized Economy: Implications for Growth and Employment in Bolivia By Martin Cicowiez Author-Name: Carlos Gustavo Machicado Author-Name: Beatriz Muriel Author-Name: Alejandro Herrera Jiménez Author-Name: Alejandra Goytia
  20. Impact on the Mongolian Economy of Foreign Direct Investment in the Coal-Export Sector By Ragchaasuren Galindev; Nyambaatar Batbayar; Lulit Mitik Beyene; Oyunzul Tserendorj; Unurjargal Davaa
  21. Market-making with reinforcement-learning (SAC) By Alexey Bakshaev
  22. Projecting Unemployment Durations: A Factor-Flows Simulation Approach With Application to the COVID-19 Recession By Gabriel Chodorow-Reich; John Coglianese
  23. The 2014 Mongolian Social Accounting Matrix By Ragchaasuren Galindev; Tsolmon Baatarzorig; Nyambaatar Batbayar; Delgermaa Begz; Unurjargal Davaa; Oyunzul Tserendorj
  24. Uncertainties in the Mongolian Economy in the Near Future By Ragchaasuren Galindev; Delgermaa Begz; Tsolmon Baatarzorig; Unurjargal Davaa; Nyambaatar Batbayar; Oyunzul Tserendorj
  25. Mastering the Art of Cookbook Medicine: Machine Learning, Randomized Trials, and Misallocation By Jason Abaluck; Leila Agha; David C. Chan Jr; Daniel Singer; Diana Zhu
  26. Data vs collateral By Leonardo Gambacorta; Yiping Huang; Zhenhua Li; Han Qiu; Shu Chen
  27. How is Machine Learning Useful for Macroeconomic Forecasting? By Philippe Goulet Coulombe; Maxime Leroux; Dalibor Stevanovic; St\'ephane Surprenant
  28. Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning By Ned Augenblick; Jonathan T. Kolstad; Ziad Obermeyer; Ao Wang
  29. News-driven inflation expectations and information rigidities By Vegard H. Larsen; Leif Anders Thorsrud; Julia Zhulanova
  30. Computing the distribution: Adaptive finite volume methods for economic models with heterogeneous agents By SeHyoun Ahn
  31. A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction By Xiao Li; Weili Wu
  32. Image Processing Tools for Financial Time Series Classification By Bairui Du; Paolo Barucca
  33. Numerical Scheme for Game Options in Local Volatility models By Benjamin Gottesman Berdah
  34. Analysing a built-in advantage in asymmetric darts contests using causal machine learning By Goller, Daniel
  35. Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms By Jeffrey Cohen; Alex Khan; Clark Alexander
  36. Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer By Jeffrey Cohen; Alex Khan; Clark Alexander
  37. High-Resolution Poverty Maps in Sub-Saharan Africa By Kamwoo Lee; Jeanine Braithwaite
  38. Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020 By Nik Dawson; Sacha Molitorisz; Marian-Andrei Rizoiu; Peter Fray

  1. By: Zezheng Zhang; Matloob Khushi
    Abstract: Foreign exchange is the largest financial market in the world, and it is also one of the most volatile markets. Technical analysis plays an important role in the forex market and trading algorithms are designed utilizing machine learning techniques. Most literature used historical price information and technical indicators for training. However, the noisy nature of the market affects the consistency and profitability of the algorithms. To address this problem, we designed trading rule features that are derived from technical indicators and trading rules. The parameters of technical indicators are optimized to maximize trading performance. We also proposed a novel cost function that computes the risk-adjusted return, Sharpe and Sterling Ratio (SSR), in an effort to reduce the variance and the magnitude of drawdowns. An automatic robotic trading (RoboTrading) strategy is designed with the proposed Genetic Algorithm Maximizing Sharpe and Sterling Ratio model (GA-MSSR) model. The experiment was conducted on intraday data of 6 major currency pairs from 2018 to 2019. The results consistently showed significant positive returns and the performance of the trading system is superior using the optimized rule-based features. The highest return obtained was 320% annually using 5-minute AUDUSD currency pair. Besides, the proposed model achieves the best performance on risk factors, including maximum drawdowns and variance in return, comparing to benchmark models. The code can be accessed at https://github.com/zzzac/rule-based-fore xtrading-system
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.09471&r=all
  2. By: Carlos León (Banco de la República de Colombia); Paolo Barucca (University College London, United Kingdom); Oscar Acero (Banco de la República de Colombia); Gerardo Gage (Centro de Estudios Monetarios Latinoamericanos (CEMLA), México); Fabio Ortega (Banco de la República de Colombia)
    Abstract: We present a general supervised machine learning methodology to represent the payment behavior of financial institutions starting from a database of transactions in the Colombian large-value payment system. The methodology learns a feedforward artificial neural network parameterization to represent the payment patterns through 113 features corresponding to financial institutions’ contribution to payments, funding habits, payments timing, payments concentration, centrality in the payments network, and systemic impact due to failure to pay. The representation is then used to test the coherence of out-of-sample payment patterns of the same institution to its characteristic patterns. The performance is remarkable, with an out-of-sample classification error around three percent. The performance is robust to reductions in the number of features by unsupervised feature selection. Also, we test that network centrality and systemic impact features contribute to enhancing the performance of the methodology definitively. For financial authorities, this is the first step towards the automated detection of individual financial institutions’ anomalous behavior in payment systems. **** RESUMEN: Presentamos una metodología general de aprendizaje automático supervisado para representar el comportamiento de pago de las instituciones financieras a partir de una base de datos de transacciones del sistema de pagos de alto valor de Colombia. La metodología utiliza una red neuronal artificial para representar los patrones de pago de instituciones financieras a través de 113 características que corresponden a su contribución a los pagos, hábitos de fondeo, momento de pagos, concentración de pagos, centralidad en la red de pagos, e impacto sistémico debido a la imposibilidad de pagar. Esta representación es utilizada para probar la coherencia de los patrones de pago fuera de muestra de una institución financiera con sus patrones de pago característicos. El desempeño del modelo es notable, con un error de clasificación fuera de muestra cercano a tres por ciento. El desempeño es robusto a reducciones en el número de características con base en la selección no supervisada de características. También se comprueba que la centralidad en la red de pagos y el impacto sistémico son características que efectivamente mejoran el desempeño de la metodología. Para las autoridades financieras este es un primer paso hacia la detección automatizada de anomalías en el comportamiento de las instituciones financieras como participantes en sistemas de pago.
    Keywords: Payments, neural networks, feature selection, machine learning, pattern recognition, pagos, redes neuronales, selección de características, aprendizaje automático, reconocimiento de patrones
    JEL: C45 E42 G21
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:bdr:borrec:1130&r=all
  3. By: Linyu Zheng; Hongmei He
    Abstract: The capital market plays a vital role in marketing operations for aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. We investigated two types of aerospace industries (manufacturer and operator). The experimental results show that PCA could improve both accuracy and efficiency of prediction. Various factors could influence the performance of prediction models, such as finance data, extracted features, optimisation algorithms, and parameters of the prediction model. The selection of features may depend on the stability of historical data: technical features could be the first option when the share price is stable, whereas fundamental features could be better when the share price has high fluctuation. The delays of RNN also depend on the stability of historical data for different types of companies. It would be more accurate through using short-term historical data for aerospace manufacturers, whereas using long-term historical data for aerospace operating airlines. The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities in terms of predicted future share price, thus improving the return on investment. Currently, COVID-19 severely influences aerospace industries. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.11788&r=all
  4. By: Joel da Costa; Tim Gebbie
    Abstract: We consider the viability of a modularised mechanistic online machine learning framework to learn signals in low-frequency financial time series data. The framework is proved on daily sampled closing time-series data from JSE equity markets. The input patterns are vectors of pre-processed sequences of daily, weekly and monthly or quarterly sampled feature changes. The data processing is split into a batch processed step where features are learnt using a stacked autoencoder via unsupervised learning, and then both batch and online supervised learning are carried out using these learnt features, with the output being a point prediction of measured time-series feature fluctuations. Weight initializations are implemented with restricted Boltzmann machine pre-training, and variance based initializations. Historical simulations are then run using an online feedforward neural network initialised with the weights from the batch training and validation step. The validity of results are considered under a rigorous assessment of backtest overfitting using both combinatorially symmetrical cross validation and probabilistic and deflated Sharpe ratios. Results are used to develop a view on the phenomenology of financial markets and the value of complex historical data-analysis for trading under the unstable adaptive dynamics that characterise financial markets.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.09481&r=all
  5. By: Schmitt, Noemi; Schwartz, Ivonne; Westerhoff, Frank H.
    Abstract: We propose a simple agent-based computational model in which speculators' trading behavior may cause bubbles and crashes, excess volatility, serially uncorrelated returns, fat-tailed return distributions and volatility clustering, thereby replicating five important stylized facts of stock markets. Since each speculator bets on his own (technical and fundamental) trading signals, stock prices are excessively volatile and oscillate erratically around their fundamental value. However, speculators' heterogeneity occasionally vanishes, e.g. due to panic-induced herding behavior, yielding extreme returns. Lasting regimes with high volatility originate from the fact that speculators extract stronger trading signals out of past stock price movements when stock prices fluctuate strongly. Simulations furthermore suggest that circuit breakers may be an effective tool to combat financial market turbulences.
    Keywords: stock markets,stylized facts,agent-based computational models,technical and fundamental analysis,circuit breakers,econophysics
    JEL: C63 D84 G15
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:bamber:160&r=all
  6. By: Hinterlang, Natascha
    Abstract: This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that artificial neural networks are able to predict the nominal interest rate better than linear and nonlinearTaylor rule models as well as univariate processes. While in-sample measures usually imply a forward-looking behaviour of the central bank, using nowcasts of the explanatory variables seems to be better suited for forecasting purposes. Overall, evidence suggests that U.S. monetary policy behaviour between1987-2012 is nonlinear.
    Keywords: Forecasting,Monetary Policy,Artificial Neural Network,Taylor Rule,Reaction Function
    JEL: C45 E47 E52
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:bubdps:442020&r=all
  7. By: Ayse Kabukcuoglu; Enrique Martínez-García
    Abstract: We study a generalized version of Coleman (1990)’s time iteration method (GTI) for solving dynamic optimization problems. Our benchmark framework is an irreversible investment model with labor-leisure choice. The GTI algorithm is simple to implement and provides advantages in terms of speed relative to Howard (1960)’s improvement algorithm. A second application on a heterogeneous-agents incomplete-markets model further explores the performance of GTI.
    Keywords: General equilibrium models; Occasionally binding constraints; Computational methods; Time iteration; Policy function iteration; Endogenous grid
    JEL: C6 C61 C63 C68
    Date: 2020–08–21
    URL: http://d.repec.org/n?u=RePEc:fip:feddgw:88641&r=all
  8. By: Ashley Davey; Harry Zheng
    Abstract: This paper proposes two algorithms for solving stochastic control problems with deep reinforcement learning, with a focus on the utility maximisation problem. The first algorithm solves Markovian problems via the Hamilton Jacobi Bellman (HJB) equation. We solve this highly nonlinear partial differential equation (PDE) with a second order backward stochastic differential equation (2BSDE) formulation. The convex structure of the problem allows us to describe a dual problem that can either verify the original primal approach or bypass some of the complexity. The second algorithm utilises the full power of the duality method to solve non-Markovian problems, which are often beyond the scope of stochastic control solvers in the existing literature. We solve an adjoint BSDE that satisfies the dual optimality conditions. We apply these algorithms to problems with power, log and non-HARA utilities in the Black-Scholes, the Heston stochastic volatility, and path dependent volatility models. Numerical experiments show highly accurate results with low computational cost, supporting our proposed algorithms.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.11757&r=all
  9. By: Oluwasola Emmanuel Omoju; Lulit Mitik Beyene; Emily Edoisa Ikhide; Stephen Kelechi Dimwobi; Augustina Ehimare
    Abstract: In 2015, Nigeria formulated its Renewable Energy and Energy Efficiency Policy (NREEEP) to promote the development of renewable-energy systems in line with the Paris Climate Agreement and Sustainable Development Goals. With that as inspiration, we examined the effectiveness and macroeconomic impacts of Nigeria’s renewable-energy policy using a computable general equilibrium (CGE) model. We calibrated the PEP-1-1 CGE model on Nigeria’s updated social accounting matrix (SAM) and ascertained the effects on key energy, economic, and environmental variables. We found that a production subsidy was effective in developing the renewable-electricity sector and encouraging the use of renewable electricity, regardless of how the subsidy was financed. The fiscal incentive for the renewable electricity sector had positive impacts on such key macroeconomic and welfare variables as employment, real GDP, household income, and welfare if the subsidy was financed by government deficit. Macroeconomic impacts were unfavourable, however, if the subsidy was financed by adjustments in government expenditures.
    Keywords: Renewable Energy, Energy Policy and the Macroeconomy, CGE models, Nigeria
    JEL: Q42 Q43 L94 C68
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:lvl:mpiacr:2020-01&r=all
  10. By: Aiusha Sangadiev; Rodrigo Rivera-Castro; Kirill Stepanov; Andrey Poddubny; Kirill Bubenchikov; Nikita Bekezin; Polina Pilyugina; Evgeny Burnaev
    Abstract: This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.12152&r=all
  11. By: Jiequn Han; Ruimeng Hu; Jihao Long
    Abstract: Stochastic differential games have been used extensively to model agents' competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets. The recently proposed machine learning algorithm, deep fictitious play, provides a novel efficient tool for finding Markovian Nash equilibrium of large $N$-player asymmetric stochastic differential games [J. Han and R. Hu, Mathematical and Scientific Machine Learning Conference, 2020]. By incorporating the idea of fictitious play, the algorithm decouples the game into $N$ sub-optimization problems, and identifies each player's optimal strategy with the deep backward stochastic differential equation (BSDE) method parallelly and repeatedly. In this paper, under appropriate conditions, we prove the convergence of deep fictitious play (DFP) to the true Nash equilibrium. We can also show that the strategy based on DFP forms an $\epsilon$-Nash equilibrium. We generalize the algorithm by proposing a new approach to decouple the games, and present numerical results of large population games showing the empirical convergence of the algorithm beyond the technical assumptions in the theorems.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.05519&r=all
  12. By: Dora Elena Jiménez Giraldo Author-Name: Adrián Saldarriaga-Isaza Author-Name: Martín Cicowiez
    Abstract: As part of the 2016 peace accord in Columbia, agricultural policies were proposed for rural regions most affected by an armed conflict that had gone on for decades. We evaluated the effects of these policies with particular attention to their economy-wide and distributional effects. We used a newly built 2014 social accounting matrix for Colombia to calibrate an extended version of the well-known PEP 1-1 Computable General Equilibrium model. The policies we considered were an increase in total factorial productivity because of infrastructure construction and greater technical assistance and employment subsidies intended to promote the substitution of illicit crops. We found that value added, demand for labor, and factor incomes increased in the areas most affected by the conflict while the opposite occurred in the other areas. Moreover, total rural income increased as long as the financing mechanism did not involve an increase in the taxation of rural incomes. In general, distributional effects were strongly conditional on the financing mechanism adopted by the government.
    Keywords: post-conflict, agricultural policy, CGE modeling, distributional effects
    JEL: Q18 C68 D58 R12
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:lvl:mpiacr:2020-12&r=all
  13. By: Alonso Ayuso, Antonio; Laguna, Manuel; Molina Ferragut, Elisenda; García Heredia, David
    Abstract: We tackle the problem of finding, for each network within a collection, the shortest path betweentwo given nodes, while not exceeding the limits of a set of shared resources. We present an integer programming (IP) formulation of this problem and propose a parallelizable matheuristic consistingof three phases: 1) generation of feasible solutions, 2) combination of solutions, and 3) solution improvement. We show that the shortest paths found with our procedure correspond to the solution of the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and that a particular case of the RCMPSP occurs in Air Traffic Flow Management (ATFM). Our computational results include finding optimal solutions to small and medium-size ATFM instances by applying Gurobi to the IP formulation. We use those solutions to assess the quality of the output produced by our proposed matheuristic. For the largest instances, which correspond to actual flight plans in ATFM, exact methods fail and we assess the quality of our solutions by means of Lagrangian bounds. Computational results suggest that the proposed procedure is an effective approach to the family of shortest path problems that we discuss here.
    Keywords: Air Traffic Flow Management; Resource-Constrained Multi-Project Scheduling Problem; Shortest Path; Matheuristics
    Date: 2020–09–04
    URL: http://d.repec.org/n?u=RePEc:cte:wsrepe:30793&r=all
  14. By: Konstantin T. Matchev; Prasanth Shyamsundar
    Abstract: We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional independence mixture models (CIMMs). We approach the estimation of a CIMM as a multi-class classification problem, since dividing the dataset into different categories naturally leads to the estimation of the mixture model. InClass nets consist of multiple independent classifier neural networks (NNs), each of which handles one of the variates of the CIMM. Fitting the CIMM to the data is performed by simultaneously training the individual NNs using suitable cost functions. The ability of NNs to approximate arbitrary functions makes our technique nonparametric. Further leveraging the power of NNs, we allow the conditionally independent variates of the model to be individually high-dimensional, which is the main advantage of our technique over existing non-machine-learning-based approaches. We derive some new results on the nonparametric identifiability of bivariate CIMMs, in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We provide a public implementation of InClass nets as a Python package called RainDancesVI and validate our InClass nets technique with several worked out examples. Our method also has applications in unsupervised and semi-supervised classification problems.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2009.00131&r=all
  15. By: Zbigniew Palmowski; Tomasz Serafin
    Abstract: In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $\pi$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces lower and upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $\pi$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.
    Date: 2020–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2007.02076&r=all
  16. By: Daniele Vernon-Bido; Andrew J. Collins
    Abstract: Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2009.00519&r=all
  17. By: Tomasz Antczak; Rafal Weron; Jacek Zabawa
    Abstract: We build a realistic agent-based model for simulating customer decisions of picking lines in supermarkets. The model is calibrated to actual point of sale (POS) data from three supermarkets of one of major European retail chains and is implemented in the open-access NetLogo simulation platform. The model can provide insights as to the impact of individual customer decisions of picking lines on the overall efficiency of the checkout process. In particular, we show that when customers pick a line by minimizing the expected waiting time, not only is this choice beneficial for the customers themselves, as it leads to shorter waiting times in queues, but also for the supermarket management, since it yields shorter working times of the cashiers.
    Keywords: Retail operations; Customer analytics; Decision support; Checkout process; Queue management system; Agent-based simulation; NetLogo
    Date: 2020–08–29
    URL: http://d.repec.org/n?u=RePEc:ahh:wpaper:worms2016&r=all
  18. By: Samuel Fern\'andez-Lorenzo; Diego Porras; Juan Jos\'e Garc\'ia-Ripoll
    Abstract: Tracking a financial index boils down to replicating its trajectory of returns for a well-defined time span by investing in a weighted subset of the securities included in the benchmark. Picking the optimal combination of assets becomes a challenging NP-hard problem even for moderately large indices consisting of dozens or hundreds of assets, thereby requiring heuristic methods to find approximate solutions. Hybrid quantum-classical optimization with variational gate-based quantum circuits arises as a plausible method to improve performance of current schemes. In this work we introduce a heuristic pruning algorithm to find weighted combinations of assets subject to cardinality constraints. We further consider different strategies to respect such constraints and compare the performance of relevant quantum ans\"{a}tze and classical optimizers through numerical simulations.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.12050&r=all
  19. By: Martin Cicowiez Author-Name: Carlos Gustavo Machicado Author-Name: Beatriz Muriel Author-Name: Alejandro Herrera Jiménez Author-Name: Alejandra Goytia
    Abstract: We analyzed the impact of currency devaluation on the Bolivian economy, employing a dynamic and extended version of the PEP 1-1 standard model to simulate effects impact on both the main macroeconomic aggregates and the financial stocks and flows of economic agents. We built a new Financial Social Accounting Matrix for the year 2014 and calibrated the model to it. Besides simulating a devaluation of the nominal exchange rate, we also analyzed a policy-response scenario, an external-shock scenario, and a gradual-devaluation scenario. In the policy-response scenario, devaluation was accompanied by a reduction in government expenses (fiscal adjustment); in the external-shock scenario, devaluation came with an increase in the export price of gas (main export commodity); and, in the gradual-devaluation scenario, the exchange-rate policy relaxed gradually. The external-shock scenario dominated the other scenarios in terms of higher average growth and less average unemployment. The fiscal-adjustment scenario, however, dominated in terms of inflation, though it implied an inflationary shock in 2020.
    Keywords: Foreign exchange policy, macroeconomic policy, CGE modelling, Bolivia
    JEL: C68 E61 O24 O54
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:lvl:mpiacr:2020-08&r=all
  20. By: Ragchaasuren Galindev; Nyambaatar Batbayar; Lulit Mitik Beyene; Oyunzul Tserendorj; Unurjargal Davaa
    Abstract: This paper developed a recursive dynamic Computable General Equilibrium model to examine the impact on the Mongolian economy of Foreign Direct Investment (FDI) in the coal-export sector. Based on a 2014 Social Accounting Matrix, the model simulated two scenarios during the 2016-2025 period: 1) a business-as-usual scenario as a reference case that replicated the latest IMF projections for main macroeconomic variables; and 2) an FDI scenario in which the coal-export sector received 1 trillion MNT over four years between 2019-2022, which increased production capacity in 2023. Although FDI increased GDP, employment, private consumption, and investment while decreasing budget deficits and public debt, it altered the structure of the economy and created a Dutch disease effect.
    Keywords: Mining sector, Dutch disease, CGE model, Mongolian economy
    JEL: D58 Q33
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:lvl:mpiacr:2019-25&r=all
  21. By: Alexey Bakshaev
    Abstract: The paper explores the application of a continuous action space soft actor-critic (SAC) reinforcement learning model to the area of automated market-making. The reinforcement learning agent receives a simulated flow of client trades, thus accruing a position in an asset, and learns to offset this risk by either hedging at simulated "exchange" spreads or by attracting an offsetting client flow by changing offered client spreads (skewing the offered prices). The question of learning minimum spreads that compensate for the risk of taking the position is being investigated. Finally, the agent is posed with a problem of learning to hedge a blended client trade flow resulting from independent price processes (a "portfolio" position). The position penalty method is introduced to improve the convergence. An Open-AI gym-compatible hedge environment is introduced and the Open AI SAC baseline RL engine is being used as a learning baseline.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.12275&r=all
  22. By: Gabriel Chodorow-Reich; John Coglianese
    Abstract: We propose a three-step factor-flows simulation-based approach to forecast the duration distribution of unemployment. Step 1: estimate individual transition hazards across employment, temporary layoff, permanent layoff, quitter, entrant, and out of the labor force, with each hazard depending on an aggregate component as well as an individual's labor force history. Step 2: relate the aggregate components to the overall unemployment rate using a factor model. Step 3: combine the individual duration dependence, factor structure, and an auxiliary forecast of the unemployment rate to simulate a panel of individual labor force histories. Applying our approach to the July Blue Chip forecast of the COVID-19 recession, we project that 1.6 million workers laid off in April 2020 remain unemployed six months later. Total long-term unemployment rises thereafter and eventually reaches more 4.5 million individuals unemployed for more than 26 weeks and almost 2 million individuals unemployed for more than 46 weeks. Long-term unemployment rises even more in a more pessimistic recovery scenario, but remains below the level in the Great Recession due to a high amount of labor market churn.
    JEL: E27 J64
    Date: 2020–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:27566&r=all
  23. By: Ragchaasuren Galindev; Tsolmon Baatarzorig; Nyambaatar Batbayar; Delgermaa Begz; Unurjargal Davaa; Oyunzul Tserendorj
    Abstract: The construction of the Mongolian Social Accounting Matrix (SAM) for 2014 is described. The SAM included fifty-six sectors, seventy commodities, two types of production factors (capital and labor), three types of institutions (households, government and the rest of the world) along with capital accounts, three types of taxes (direct taxes, import duties and indirect taxes on commodities) and investment accounts (public investment, private investment and changes in inventories).
    Keywords: Social Accounting Matrix, CGE model
    JEL: D58
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:lvl:mpiacr:2019-23&r=all
  24. By: Ragchaasuren Galindev; Delgermaa Begz; Tsolmon Baatarzorig; Unurjargal Davaa; Nyambaatar Batbayar; Oyunzul Tserendorj
    Abstract: This paper examines the impact of three shocks (a commodity-price drop, fiscal expansion, and the termination of the biggest mine development) looming in Mongolia’s near future. We modified the PEP-1-t model and calibrated it to the IMF’s recent projections in a business-as-usual scenario. The alternative scenarios for the Mongolian economy, considering these shocks, suggest that the impacts may be significant.
    Keywords: CGE model, Mongolian economy, Commodity price, Fiscal policy
    JEL: D58 E62 I32 Q33
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:lvl:mpiacr:2019-26&r=all
  25. By: Jason Abaluck; Leila Agha; David C. Chan Jr; Daniel Singer; Diana Zhu
    Abstract: The application of machine learning (ML) to randomized controlled trials (RCTs) can quantify and improve misallocation in healthcare. We study the decision to prescribe anticoagulants for atrial fibrillation patients; anticoagulation reduces stroke risk but increases hemorrhage risk. We combine observational data on treatment choice and guideline use with ML estimates of heterogeneous treatment effects from eight RCTs. When physicians adopt a clinical guideline, treatment decisions shift towards the recommendation but adherence remains far from perfect. Improving guideline adherence would produce larger gains than informing physicians about guidelines. Adherence to an optimal rule would prevent 47% more strokes without increasing hemorrhages.
    JEL: I11 I18 O33
    Date: 2020–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:27467&r=all
  26. By: Leonardo Gambacorta; Yiping Huang; Zhenhua Li; Han Qiu; Shu Chen
    Abstract: The use of massive amounts of data by large technology firms (big techs) to assess firms’ creditworthiness could reduce the need for collateral in solving asymmetric information problems in credit markets. Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices and firm characteristics. We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that a greater use of big tech credit – granted on the basis of machine learning and big data – could reduce the importance of collateral in credit markets and potentially weaken the financial accelerator mechanism.
    Keywords: big tech, big data, collateral, banks, asymmetric information, credit markets
    JEL: D22 G31 R30
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:bis:biswps:881&r=all
  27. By: Philippe Goulet Coulombe; Maxime Leroux; Dalibor Stevanovic; St\'ephane Surprenant
    Abstract: We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting?" by adding the "how". The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. In contrast, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify the "treatment" effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K-fold cross-validation is the best practice and (iv) the $L_2$ is preferred to the $\bar \epsilon$-insensitive in-sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.12477&r=all
  28. By: Ned Augenblick; Jonathan T. Kolstad; Ziad Obermeyer; Ao Wang
    Abstract: Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around √x rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further efficiency. Accounting for this decline in intra-group spread, we show that increasing frequency can paradoxically reduce the total testing cost. Second, we show that group size and efficiency increases with intra-group risk correlation, which is expected in natural test groupings based on proximity. Third, because optimal groupings depend on uncertain risk and correlation, we show how better estimates from machine learning can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.
    JEL: I1 I18
    Date: 2020–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:27457&r=all
  29. By: Vegard H. Larsen (Norges Bank and Centre for Applied Macroeconomics and Commodity Prices, BI Norwegian Business School); Leif Anders Thorsrud (Norges Bank and Centre for Applied Macroeconomics and Commodity Prices, BI Norwegian Business School); Julia Zhulanova (Centre for Applied Macroeconomics and Commodity Prices, BI Norwegian Business School)
    Abstract: We investigate the role played by the media in the expectations formation process of households. Using a novel news-topic-based approach we show that news types the media choose to report on, e.g., fiscal policy, health, and politics, are good predictors of households' stated inflation expectations. In turn, in a noisy information model setting, augmented with a simple media channel, we document that the underlying time series properties of relevant news topics explain the time-varying information rigidity among households. As such, we not only provide a novel estimate showing the degree to which information rigidities among households varies across time, but also provide, using a large news corpus and machine learning algorithms, robust and new evidence highlighting the role of the media for understanding inflation expectations and information rigidities.
    Keywords: expectations, media, machine learning, inflation
    JEL: C11 C53 D83 D84 E13 E31 E37
    Date: 2019–02–20
    URL: http://d.repec.org/n?u=RePEc:bno:worpap:2019_05&r=all
  30. By: SeHyoun Ahn (Norges Bank)
    Abstract: Solving economic models with heterogeneous agents requires computing aggregate dynamics consistent with individual behaviours. This paper introduces the ?nite volume method from the mathe-matics literature to enlarge the set of numerical methods available to compute dynamics in continuous time. Finite volume discretization methods allow theoretically consistent dimensional and local adaptivity that guarantee the mass conservation and positivity of the distribution function of the discretized system. This paper shows examples of 1) the Ornstein-Uhlenbeck process 2) the Aiyagari-Bewley-Huggett (wealth+income heterogeneity) model and 3) the lifecycle (wealth+income+age heterogeneity) model. The numerical exercises show that for the current dimensionality of the problems in economics, the ?nite volume method (with or without adaptivity) outperforms pre-existing methods. This paper further provides a companion open-source implementation of the ?nite volume method at github.com/sehyoun/adaptive_finite_volume to reduce the testing time of the ?nite volume method.
    Date: 2019–06–13
    URL: http://d.repec.org/n?u=RePEc:bno:worpap:2019_10&r=all
  31. By: Xiao Li; Weili Wu
    Abstract: Bitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information, finance and social media sentiments. The feature engineering not only requires much human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mining the abundant patterns encoded in bitcoin transactions, and propose k-order transaction graph to reveal patterns under different scope. We propose the transaction graph based feature to automatically encode the patterns. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period. The results of comparison experiments demonstrate that the proposed method outperforms the most recent state-of-art methods.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.09667&r=all
  32. By: Bairui Du; Paolo Barucca
    Abstract: Time series prediction is a challenge for many complex systems, yet in finance predictions are hindered by the very nature of how financial markets work. In efficient markets, the opportunities for stock price predictions leading to profitable trades are supposed to rapidly disappear. In the growing industry of high-frequency trading, the competition over extracting predictions on stock prices from the increasing amount of available information for performing profitable trades is becoming more and more severe. With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, through machine learning. Spot prices of stocks provide a simple snapshot representation of a financial market. Stock prices fluctuate over time, affected by numerous factors, and the prediction of their changes is at the core of both long-term and short-term financial investing. The collective patterns of price movements are generally referred to as market states. As a paramount example, when stock prices follow an upward trend, it is called a bull market, and when stock prices follow a downward trend is called a bear market
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.06042&r=all
  33. By: Benjamin Gottesman Berdah
    Abstract: In this paper we introduce a numerical method for optimal stopping in the framework of one dimensional diffusion. We use the Skorokhod embedding in order to construct recombining tree approximations for diffusions with general coefficients. This technique allows us to determine convergence rates and construct nearly optimal stopping times which are optimal at the same rate. Finally, we demonstrate the efficiency of our scheme with several examples of game options.
    Date: 2020–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2007.02323&r=all
  34. By: Goller, Daniel
    Abstract: We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we analyse the built-in advantage, which is the first-mover having potentially more but never less moves. Our empirical findings suggest that the first-mover has an 8.6 percentage points higher probability to win the match induced by the technical advantage. Contestants with low performance measures and little experience have the highest built-in advantage. With regard to the fairness principle that contestants with equal abilities should have equal winning probabilities, this contest is ex-ante fair in the case of equal built-in advantages for both competitors and a randomized starting right. Nevertheless, the contest design produces unequal probabilities of winning for equally skilled contestants because of asymmetries in the built-in advantage associated with social pressure for contestants competing at home and away.
    Keywords: Causal machine learning, heterogeneity, contest design, social pressure, built-in advantage, incentives, performance, darts
    JEL: C14 D02 D20 Z20
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:usg:econwp:2020:13&r=all
  35. By: Jeffrey Cohen; Alex Khan; Clark Alexander
    Abstract: We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.08669&r=all
  36. By: Jeffrey Cohen; Alex Khan; Clark Alexander
    Abstract: We investigate the use of quantum computers for building a portfolio out of a universe of U.S. listed, liquid equities that contains an optimal set of stocks. Starting from historical market data, we look at various problem formulations on the D-Wave Systems Inc. D-Wave 2000Q(TM) System (hereafter called DWave) to find the optimal risk vs return portfolio; an optimized portfolio based on the Markowitz formulation and the Sharpe ratio, a simplified Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We approach this first classically, then by our new method on DWave. Our results show that practitioners can use a DWave to select attractive portfolios out of 40 U.S. liquid equities.
    Date: 2020–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2007.01430&r=all
  37. By: Kamwoo Lee; Jeanine Braithwaite
    Abstract: Up-to-date poverty maps are an important tool for policymakers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of the 25 countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many Sub-Saharan African countries and other low- and middle-income countries.
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2009.00544&r=all
  38. By: Nik Dawson; Sacha Molitorisz; Marian-Andrei Rizoiu; Peter Fray
    Abstract: In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3,698 journalist job ads from a corpus of over 6.7 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We can also tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that the skills sought by journalism job ads increasingly include social media and generalist communications.
    Date: 2020–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2008.12459&r=all

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