nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒02‒14
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach By Iuri H. Ferreira; Marcelo C. Medeiros
  2. Optimal monetary policy using reinforcement learning By Hinterlang, Natascha; Tänzer, Alina
  3. DeepSets and their derivative networks for solving symmetric PDEs * By Maximilien Germain; Mathieu Laurière; Huyên Pham; Xavier Warin
  4. A Survey of Quantum Computing for Finance By Dylan Herman; Cody Googin; Xiaoyuan Liu; Alexey Galda; Ilya Safro; Yue Sun; Marco Pistoia; Yuri Alexeev
  5. Machine Learning for Labour Market Matching By Mühlbauer, Sabrina; Weber, Enzo
  6. Uncovering the Source of Machine Bias By Xiyang Hu; Yan Huang; Beibei Li; Tian Lu
  7. Purchasing decisions on alternative fuel vehicles within the agent-based model By Arkadiusz Jędrzejewski; Katarzyna Sznajd-Weron; Jakub Pawłowski; Anna Kowalska-Pyzalska
  8. StableSims: Optimizing MakerDAO Liquidations 2.0 Incentives via Agent-Based Modeling By Andrew Kirillov; Sehyun Chung
  9. Economic development, weather shocks and child marriage in South Asia: A machine learning approach By Dietrich, Stephan; Meysonnat, Aline; Rosales, Francisco; Cebotari, Victor; Gassmann, Franziska
  10. AN AGENT-BASED MODEL OF TRICKLE-UP GROWTH AND INCOME INEQUALITY Documents de travail GREDEG GREDEG Working Papers Series By Elisa Palagi; Mauro Napoletano; Andrea Roventini; Jean-Luc Gaffard
  11. Monetary policy, Twitter and financial markets: evidence from social media traffic By Donato Masciandaro; Davide Romelli; Gaia Rubera
  12. The impact of research independence on PhD students' careers: Large-scale evidence from France By Patsali, Sofia; Pezzoni, Michele; Visentin, Fabiana
  13. Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules By Narita, Yusuke; Yata, Kohei
  14. Sellin' in the Rain: Weather, Climate, and Retail Sales By Brigitte Roth Tran

  1. By: Iuri H. Ferreira; Marcelo C. Medeiros
    Abstract: In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through the LSTM model. This model also improves significantly the performance of the lagged market return as predictive variable. Finally, intraday RF estimation outputs indicate that there is no performance improvement with this method, and it may even worsen the results in some cases.
    Date: 2021–12
  2. By: Hinterlang, Natascha; Tänzer, Alina
    Abstract: This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures or asymmetric preferences. We use quarterly U.S. data from1987:Q3-2007:Q2 to estimate (nonlinear) model transition equations, train optimal policies and perform counterfactual analyses to evaluate them, assuming that the transition equations remain unchanged. All of our resulting policy rules outperform other common rules as well as the actual federal funds rate. Given a neural network representation of the economy, our optimized nonlinear policy rules reduce the central bank's loss by over43 %. A DSGE model comparison exercise further indicates robustness of the optimized rules.
    Keywords: Optimal Monetary Policy,Reinforcement Learning,Artificial Neural Network,Machine Learning,Reaction Function
    JEL: C45 C61 E52 E58
    Date: 2021
  3. By: Maximilien Germain (EDF - EDF, LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UP - Université de Paris, EDF R&D - EDF R&D - EDF - EDF, EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF); Mathieu Laurière (ORFE - Department of Operations Research and Financial Engineering - Princeton University, School of Engineering and Applied Science); Huyên Pham (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, FiME Lab - Laboratoire de Finance des Marchés d'Energie - EDF R&D - EDF R&D - EDF - EDF - CREST - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique); Xavier Warin (EDF - EDF, FiME Lab - Laboratoire de Finance des Marchés d'Energie - EDF R&D - EDF R&D - EDF - EDF - CREST - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres, EDF R&D - EDF R&D - EDF - EDF, EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF)
    Abstract: Machine learning methods for solving nonlinear partial differential equations (PDEs) are hot topical issues, and different algorithms proposed in the literature show efficient numerical approximation in high dimension. In this paper, we introduce a class of PDEs that are invariant to permutations, and called symmetric PDEs. Such problems are widespread, ranging from cosmology to quantum mechanics, and option pricing/hedging in multi-asset market with exchangeable payoff. Our main application comes actually from the particles approximation of mean-field control problems. We design deep learning algorithms based on certain types of neural networks, named PointNet and DeepSet (and their associated derivative networks), for computing simultaneously an approximation of the solution and its gradient to symmetric PDEs. We illustrate the performance and accuracy of the PointNet/DeepSet networks compared to classical feedforward ones, and provide several numerical results of our algorithm for the examples of a mean-field systemic risk, mean-variance problem and a min/max linear quadratic McKean-Vlasov control problem.
    Keywords: Permutation-invariant PDEs,symmetric neural networks,exchangeability,deep backward scheme,mean-field control
    Date: 2022
  4. By: Dylan Herman; Cody Googin; Xiaoyuan Liu; Alexey Galda; Ilya Safro; Yue Sun; Marco Pistoia; Yuri Alexeev
    Abstract: Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on Monte Carlo integration, optimization, and machine learning, showing how these solutions, adapted to work on a quantum computer, can help solve more efficiently and accurately problems such as derivative pricing, risk analysis, portfolio optimization, natural language processing, and fraud detection. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.
    Date: 2022–01
  5. By: Mühlbauer, Sabrina (Institute for Employment Research (IAB), Nuremberg, Germany); Weber, Enzo (Institute for Employment Research (IAB), Nuremberg, Germany)
    Abstract: "This paper develops a large-scale application to improve the labour market matching process with model- and algorithm-based statistical methods. We use comprehensive administrative data on employment biographies covering individual and job-related information of workers in Germany. We estimate the probability that a job seeker gets employed in a certain occupational field. For this purpose, we make predictions with common statistical methods and machine learning (ML) methods. The findings suggest that ML performs better than the other methods regarding the out-of-sample classification error. In terms of the unemployment rate, the advantage of ML would stand for a difference of 2.9 - 3.6 percentage points." (Author's abstract, IAB-Doku) ((en))
    Keywords: IAB-Open-Access-Publikation
    JEL: C14 C45 J64 C55
    Date: 2022–02–02
  6. By: Xiyang Hu; Yan Huang; Beibei Li; Tian Lu
    Abstract: We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias.
    Date: 2022–01
  7. By: Arkadiusz Jędrzejewski; Katarzyna Sznajd-Weron; Jakub Pawłowski; Anna Kowalska-Pyzalska
    Abstract: We develop an empirically grounded agent-based model to explore the purchasing decisions of mutually interacting agents (consumers) between three types of alternative fuel vehicles. We calibrate the model with recently published empirical data on consumer preferences towards such vehicles. Furthermore, running the Monte Carlo simulations, we show possible scenarios for the development of the alternative fuel vehicle market depending on the marketing strategies employed.
    Keywords: Agent-based model; Diffusion; Alternative fuel vehicles
    Date: 2022
  8. By: Andrew Kirillov; Sehyun Chung
    Abstract: The StableSims project set out to determine optimal parameters for the new auction mechanism, Liquidations 2.0, used by MakerDAO, a protocol built on Ethereum offering a decentralized, collateralized stablecoin called Dai. We developed an agent-based simulation that emulates both the Maker protocol smart contract logic, and how profit-motivated agents ("keepers") will act in the real world when faced with decisions such as liquidating "vaults" (collateralized debt positions) and bidding on collateral auctions. This research focuses on the incentive structure introduced in Liquidations 2.0, which implements both a constant fee (tip) and a fee proportional to vault size (chip) paid to keepers that liquidate vaults or restart stale collateral auctions. We sought to minimize the amount paid in incentives while maximizing the speed with which undercollateralized vaults were liquidated. Our findings indicate that it is more cost-effective to increase the constant fee, as opposed to the proportional fee, in order to decrease the time it takes for keepers to liquidate vaults.
    Date: 2022–01
  9. By: Dietrich, Stephan (UNU-MERIT, Maastricht University); Meysonnat, Aline (University of Washington, Daniel J. Evans School of Public Policy and Governance); Rosales, Francisco (ESAN Graduate School of Business, Lima); Cebotari, Victor (University of Luxembourg); Gassmann, Franziska (UNU-MERIT, Maastricht University)
    Abstract: Globally, 21 percent of young women are married before their 18th birthday. Despite some progress in addressing child marriage, it remains a widespread practice, in particular in South Asia. While household predictors of child marriage have been studied extensively in the literature, the evidence base on macro-economic factors contributing to child marriage and models that predict where child marriage cases are most likely to occur remains limited. In this paper we aim to fill this gap and explore region-level indicators to predict the persistence of child marriage in four countries in South Asia, namely Bangladesh, India, Nepal and Pakistan. We apply machine learning techniques to child marriage data and develop a prediction model that relies largely on regional and local inputs such as droughts, floods, population growth and nightlight data to model the incidence of child marriages. We find that our gradient boosting model is able to identify a large proportion of the true child marriage cases and correctly classifies 78% of the true marriage cases, with a higher accuracy in Bangladesh (90% of the cases) and a lower accuracy in Nepal (71% of cases). In addition, all countries contain in their top 10 variables for classification nighttime light growth, a shock index of drought over the previous and the last two years and the regional level of education, suggesting that income shocks, the regional economic activity and regional education levels play a significant role in predicting child marriage. Given the accuracy of the model to predict child marriage, our model is a valuable tool to support policy design in countries where household-level data remains limited.
    Keywords: child marriage, income shocks, machine learning, South Asia
    JEL: J1 J12 O15 Q54 R11
    Date: 2021–09–10
  10. By: Elisa Palagi (SSSUP - Scuola Universitaria Superiore Sant'Anna [Pisa]); Mauro Napoletano (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (... - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur, OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po, SKEMA Business School, SSSUP - Scuola Universitaria Superiore Sant'Anna [Pisa]); Andrea Roventini (SSSUP - Scuola Universitaria Superiore Sant'Anna [Pisa], OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po); Jean-Luc Gaffard (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (... - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur, OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po, SKEMA Business School)
    Abstract: We build an agent-based model to study how coordination failures, credit con- straints and unequal access to investment opportunities affect inequality and aggre- gate income dynamics. The economy is populated by households who can invest in alternative projects associated with different productivity growth rates. Access to investment projects also depends on credit availability. The income of each house- hold is determined by the output of the project but also by aggregate demand conditions. We show that aggregate dynamics is affected by income distribution. Moreover, we show that the model features a trickle-up growth dynamics. Redis- tribution towards poorer households raises aggregate demand and is beneficial for the income growth of all agents in the economy. Extensive numerical simulations show that our model is able to reproduce several stylized facts concerning income inequality and social mobility. Finally, we test the impact of redistributive fiscal policies, showing that fiscal policies facilitating access to investment opportunities by poor households have the largest impact in terms of raising long-run aggregate income and decreasing income inequality. Moreover, policy timing is important: fiscal policies that are implemented too late may have no significant effects on in- equality.
    Keywords: income inequality,social mobility,credit constraints,coordination failures,effective demand,trickle-up growth,fiscal policy JEL classification: C63,D31,E63,E21
    Date: 2022–01–04
  11. By: Donato Masciandaro; Davide Romelli; Gaia Rubera
    Abstract: How does central bank communication affect financial markets? This paper shows that the monetary policy announcements of three major central banks, i.e. the European Central Bank, the Federal Reserve and the Bank of England, trigger significant discussions on monetary policy on Twitter. Using machine learning techniques we identify Twitter messages related to monetary policy around the release of monetary policy decisions and we build a metric of the similarity between the policy announcement and Twitter traffic before and after the announcement. We interpret large changes in the similarity of tweets and announcements as a proxy for monetary policy surprise and show that market volatility spikes after the announcement whenever changes in similarity are high. These findings suggest that social media discussions on central bank communication are aligned with bond and stock market reactions.
    Keywords: monetary policy, central bank communication, financial markets, social media, Twitter, Federal Reserve, European Central Bank, Bank of England
    JEL: E44 E52 E58 G14 G15 G41
    Date: 2021
  12. By: Patsali, Sofia (Université Côte d'Azur, GREDEG, and Université de Strasbourg, BETA, CNRS France); Pezzoni, Michele (Université Côte d'Azur, GREDEG, CNRS, Observatoire des Sciences et Techniques, HCERES, OFCE, Sciences Po, and ICRIOS, Bocconi University, Italy); Visentin, Fabiana (UNU-MERIT, Maastricht University)
    Abstract: This study investigates the effect of research independence during the PhD period on students' career outcomes. We use a unique and detailed dataset on the French population of STEM PhD students who graduated between 1995 and 2013. To measure research independence, we compare the PhD thesis content with the supervisor's research. We employ advanced neural network text analysis techniques evaluating the similarity between the student's thesis abstract and supervisor's publications during the PhD period. After exploring which characteristics of the PhD training experience and supervisor explain the level of research similarity, we estimate how similarity associates with the likelihood of pursuing a research career. We find that the student thesis's similarity with her supervisor's research work is negatively associated with starting a career in academia and patenting probability. Increasing the PhD-supervisor similarity score by one standard deviation is associated with a 2.1 percentage point decrease in the probability of obtaining an academic position and a 0.57 percentage point decrease in the probability of patenting. However, conditional on starting an academic career, PhD-supervisor similarity is associated with a higher student's productivity after graduation as measured by citations received, network size, and probability of moving to a foreign or US-based affiliation.
    Keywords: Research independence, Early career researchers, Scientific career outcomes, Neural network text analysis
    JEL: D22 O30 O33 O38
    Date: 2021–10–15
  13. By: Narita, Yusuke; Yata, Kohei
    Abstract: Algorithms produce a growing portion of decisions and recommendations both in policy and business. Such algorithmic decisions are natural experiments (conditionally quasirandomly assigned instruments) since the algorithms make decisions based only on observable input variables. We use this observation to develop a treatment-effect estimator for a class of stochastic and deterministic decision-making algorithms. Our estimator is shown to be consistent and asymptotically normal for well-defined causal effects. A key special case of our estimator is a multidimensional regression discontinuity design. We apply our estimator to evaluate the effect of the Coronavirus Aid, Relief, and Economic Security (CARES) Act, where hundreds of billions of dollars worth of relief funding is allocated to hospitals via an algorithmic rule. Our estimates suggest that the relief funding has little effect on COVID- 19-related hospital activity levels. Naive OLS and IV estimates exhibit substantial selection bias.
    Date: 2022–01
  14. By: Brigitte Roth Tran
    Abstract: I apply a novel machine-learning based “weather index” method to daily store- level sales data for a national apparel and sporting goods brand to examine short-run responses to weather and long-run adaptation to climate. I find that even when considering potentially offsetting shifts of sales between outdoor and indoor stores, to the firm's website, or over time, weather has significant persistent effects on sales. This suggests that weather may increase sales volatility as more severe weather shocks be- come more frequent under climate change. Consistent with adaptation to climate, I find that sensitivity of sales to weather decreases with historical experience for precipitation, snow, and cold weather events, but-surprisingly-not for extreme heat events. This suggests that adaptation may moderate some but not all of the adverse impacts of climate change on sales. Retailers can respond by adjusting their staffing, inventory, promotion events, compensation, and financial reporting.
    Keywords: adaptation; climate change; weather; machine learning; retail; sales
    JEL: Q54 L81 D12
    Date: 2022–01–21

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