nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒10‒03
23 papers chosen by
Stan Miles
Thompson Rivers University

  1. A review of Knowledge Graph and Graph Neural Network application By Elda Xhumari; Suela Maxhelaku; Endrit Xhina
  2. The Efficient Market Hypothesis for Bitcoin in the context of neural networks By Mike Kraehenbuehl; Joerg Osterrieder
  3. AI for trading strategies By Danijel Jevtic; Romain Deleze; Joerg Osterrieder
  4. Structured Macroeconomics: a self-deploying modeling and simulation approach By Martin Jaraiz
  5. How many inner simulations to compute conditional expectations with least-square Monte Carlo? By Aur\'elien Alfonsi; Bernard Lapeyre; J\'er\^ome Lelong
  6. Stock Market Prediction using Natural Language Processing -- A Survey By Om Mane; Saravanakumar kandasamy
  7. Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms By Arnoud V. den Boer; Janusz M. Meylahn; Maarten Pieter Schinkel
  8. The Shifting Attention of Political Leaders: Evidence from Two Centuries of Presidential Speeches By Oscar Calvo-Gonz\'alez; Axel Eizmendi; Germ\'an Reyes
  9. Mitigation Strategies to Improve Reproducibility of Poverty Estimations From Remote Sensing Images Using Deep Learning By J. Machicao; A. Ben Abbes; L. Meneguzzi; P L P Corrêa; A. Specht; Romain David; G. Subsol; D. Vellenich; R. Devillers; S. Stall; N. Mouquet; M. Chaumont; L Berti‐equille; D. Mouillot
  10. LINVER: The Linear Version of FRB/US By Flint Brayton; David L. Reifschneider
  11. Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices By Emanuel Kohlscheen
  12. Using Natural Language Processing to Measure COVID19-Induced Economic Policy Uncertainty for Canada and the US* By Shafiullah Qureshi; Ba Chu; Fanny S. Demers; Michel Demers
  13. Working Paper No. 355: The artificial intelligence (AI) data access regime: what are the factors affecting the access and sharing of industrial AI data? By Long, Vicky; Bjuggren, Per-Olof
  14. Klimawandelbedingte Ertragsveränderungen und Flächennutzung (KlimErtrag) By Söder, Mareike; Berg-Mohnicke, Michael; Bittner, Marlene; Ernst, Stefan; Feike, Til; Frühauf, Cathleen; Golla, Burkhard; Jänicke, Clemens; Jorzig, Christian; Leppelt, Thomas; Liedtke, Marco; Möller, Markus; Nendel, Claas; Offermann, Frank; Riedesel, Ludwig; Romanova, Vanya; Schmitt, Jonas; Schulz, Susanne; Sesermann, Diana-Maria; Rahman Shawon, Ashifur
  15. Energy price shocks and stabilization policies in a multi-agent macroeconomic model for the Euro Area By Turco, Enrico; Bazzana, Davide; Rizzati, Massimiliano; Ciola, Emanuele; Vergalli, Sergio
  16. Effective Fiscal-Monetary Interactions in Severe Recessions By Mr. Raphael A Espinoza; Jesper Lindé; Mr. Jiaqian Chen; Zoltan Jakab; Carlos Goncalves; Tryggvi Gudmundsson; Martina Hengge
  17. Accounting for climate transition risk in banks' capital requirements By Alessi, Lucia; Di Girolamo, Francesca Erica; Pagano, Andrea; Petracco Giudici, Marco
  18. Central Bank Policy Mix: Policy Perspectives and Modeling Issues By Juhro, Solikin M.; Sahminan, Sahminan; Wijoseno, Atet; Waluyo, Jati; Bathaluddin, M. Barik
  19. Constrained Optimization in Random Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions By Angun, Ebru; Kleijnen, Jack
  20. Integrated and Robust Storage Assignment: An E-Grocery Retailing Business Case By David Winkelmann; Frederik Tolkmitt; Matthias Ulrich; Michael R\"omer
  21. Does the Supply Network Shape the Firm Size Distribution? The Japanese case By Corrado DI GUILMI; FUJIWARA Yoshi
  22. What Cadastral Tax Rate Should Be Imposed on Farm Assets By Podstawka, Marian
  23. Optimal Bubble Riding: A Mean Field Game with Varying Entry Times By Ludovic Tangpi; Shichun Wang

  1. By: Elda Xhumari (University of Tirana, Faculty of Natural Sciences, Department of Informatics); Suela Maxhelaku (University of Tirana, Faculty of Natural Sciences, Department of Informatics); Endrit Xhina (University of Tirana, Faculty of Natural Sciences, Department of Informatics)
    Abstract: Many learning activities include working with graph data, which offers a wealth of relational information between parts. Modeling physical systems, learning molecular fingerprints, predicting protein interfaces, and diagnosing illnesses all need the use of a model that can learn from graph inputs. In other fields, such as learning from non-structural data such as texts and images, reasoning on extracted structures (such as phrase dependency trees and image scene graphs) is a major topic that requires graph reasoning models. Graph neural networks (GNNs) are neural models that use message transmission between graph nodes to represent graph dependency. Variants of GNNs have recently showed ground-breaking performance on a variety of deep learning tasks. This paper represents a review of the literature on Knowledge Graphs and Graph Neural Networks, with a particular focus on Graph Embeddings and Graph Neural Networks applications as a powerful tool for organizing structured data and making sense of unstructured data, which can be applied to a variety of real-world problems.
    Keywords: Knowledge Graph, Graph Neural Network, DeepWalk, Node2Vec, Structural Deep Network Embedding
    JEL: C45
    Date: 2022–07
  2. By: Mike Kraehenbuehl; Joerg Osterrieder
    Abstract: This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network. Due to the increasing popularity of cryptocurrencies in recent years, the question has arisen, as to whether market inefficiencies could be exploited in Bitcoin. Several studies we refer to here discuss this topic in the context of Bitcoin using either statistical tests or machine learning methods, mostly relying exclusively on data from Bitcoin itself. Results regarding market efficiency vary from study to study. In this study, however, the focus is on applying various asset-related input features in a neural network. The aim is to investigate whether the prediction accuracy improves when adding equity stock indices (S&P 500, Russell 2000), currencies (EURUSD), 10 Year US Treasury Note Yield as well as Gold&Silver producers index (XAU), in addition to using Bitcoin returns as input feature. As expected, the results show that more features lead to higher training performance from 54.6% prediction accuracy with one feature to 61% with six features. On the test set, we observe that with our neural network methodology, adding additional asset classes, no increase in prediction accuracy is achieved. One feature set is able to partially outperform a buy-and-hold strategy, but the performance drops again as soon as another feature is added. This leads us to the partial conclusion that weak market inefficiencies for Bitcoin cannot be detected using neural networks and the given asset classes as input. Therefore, based on this study, we find evidence that the Bitcoin market is efficient in the sense of the efficient market hypothesis during the sample period. We encourage further research in this area, as much depends on the sample period chosen, the input features, the model architecture, and the hyperparameters.
    Date: 2022–06
  3. By: Danijel Jevtic; Romain Deleze; Joerg Osterrieder
    Abstract: In this bachelor thesis, we show how four different machine learning methods (Long Short-Term Memory, Random Forest, Support Vector Machine Regression, and k-Nearest Neighbor) perform compared to already successfully applied trading strategies such as Cross Signal Trading and a conventional statistical time series model ARMA-GARCH. The aim is to show that machine learning methods perform better than conventional methods in the crude oil market when used correctly. A more detailed performance analysis was made, showing the performance of the different models in different market phases so that the robustness of individual models in high and low volatility phases could be examined more closely. For further investigation, these models would also have to be analyzed in other markets.
    Date: 2022–06
  4. By: Martin Jaraiz
    Abstract: This article presents an agent-based macroeconomics modeling framework that can read a Social Accounting Matrix (SAM) and build an economic system (active population, activity sectors acting as firms, a central bank, government, external sectors...) whose structure and activity replicate the economy at the time of the SAM snapshot. The main feature of the approach is the ability of the emergent macroeconomic system to adapt itself to subsequent changes, including the sustained dynamic evolution from initial models with simple behavioral rules towards models with increasingly complex behavior.
    Date: 2022–08
  5. By: Aur\'elien Alfonsi (MATHRISK, CERMICS); Bernard Lapeyre (MATHRISK, CERMICS); J\'er\^ome Lelong (DAO)
    Abstract: The problem of computing the conditional expectation E[f (Y)|X] with least-square Monte-Carlo is of general importance and has been widely studied. To solve this problem, it is usually assumed that one has as many samples of Y as of X. However, when samples are generated by computer simulation and the conditional law of Y given X can be simulated, it may be relevant to sample K $\in$ N values of Y for each sample of X. The present work determines the optimal value of K for a given computational budget, as well as a way to estimate it. The main take away message is that the computational gain can be all the more important that the computational cost of sampling Y given X is small with respect to the computational cost of sampling X. Numerical illustrations on the optimal choice of K and on the computational gain are given on different examples including one inspired by risk management.
    Date: 2022–09
  6. By: Om Mane; Saravanakumar kandasamy
    Abstract: The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this stock value offers enormous arbitrage profit opportunities. This attractiveness of finding a solution has prompted researchers to find a way past problems like volatility, seasonality, and dependence on time. This paper surveys recent literature in the domain of natural language processing and machine learning techniques used to predict stock market movements. The main contributions of this paper include the sophisticated categorizations of many recent articles and the illustration of the recent trends of research in stock market prediction and its related areas.
    Date: 2022–08
  7. By: Arnoud V. den Boer (University of Amsterdam); Janusz M. Meylahn (University of Twente); Maarten Pieter Schinkel (University of Amsterdam)
    Abstract: We examine recent claims that a particular Q-learning algorithm used by competitors ‘autonomously’ and systematically learns to collude, resulting in supracompetitive prices and extra profits for the firms sustained by collusive equilibria. A detailed analysis of the inner workings of this algorithm reveals that there is no immediate reason for alarm. We set out what is needed to demonstrate the existence of a colluding price algorithm that does form a threat to competition.
    Keywords: keywords
    JEL: C63 L13 L44 K21
    Date: 2022–09–21
  8. By: Oscar Calvo-Gonz\'alez; Axel Eizmendi; Germ\'an Reyes
    Abstract: This paper proposes a novel methodology to measure political leaders' attention that combines text data with machine learning algorithms. We use this method on a hand-collected database of presidential ``state-of-the-union''-type speeches spanning ten countries and two centuries to study the determinants of political attention, its shifts over time, and its impacts on countries' outcomes. We find that presidential attention can be characterized by a compact set of topics whose relative importance remains stable over long periods. Contrary to presidential rhetoric, using a differences-in-differences design, we show that presidents' attention has precisely-estimated null effects on growth and other policy outcomes.
    Date: 2022–09
  9. By: J. Machicao (USP - University of São Paulo, Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]); A. Ben Abbes (FRB - Fondation pour la recherche sur la Biodiversité , UMA - Université de la Manouba [Tunisie]); L. Meneguzzi (USP - University of São Paulo, Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]); P L P Corrêa (Polytechnic School of the University of São Paulo (Brazil) - USP - Universidade de São Paulo, Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]); A. Specht (USQ - University of Southern Queensland); Romain David (ERINHA-AISBL - European Research Infrastructure on Highly Pathogenic Agents); G. Subsol (UMR 228 Espace-Dev, Espace pour le développement - IRD - Institut de Recherche pour le Développement - UPVD - Université de Perpignan Via Domitia - AU - Avignon Université - UR - Université de La Réunion - UG - Université de Guyane - UA - Université des Antilles - UM - Université de Montpellier); D. Vellenich (USP - University of São Paulo, Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]); R. Devillers (UMR 228 Espace-Dev, Espace pour le développement - IRD - Institut de Recherche pour le Développement - UPVD - Université de Perpignan Via Domitia - AU - Avignon Université - UR - Université de La Réunion - UG - Université de Guyane - UA - Université des Antilles - UM - Université de Montpellier); S. Stall (American Geophysical Union); N. Mouquet (CESAB - Centre de Synthèse et d’Analyse sur la Biodiversité - FRB - Fondation pour la recherche sur la Biodiversité , UNIMES - Université de Nîmes); M. Chaumont (LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier); L Berti‐equille (UMR 228 Espace-Dev, Espace pour le développement - IRD - Institut de Recherche pour le Développement - UPVD - Université de Perpignan Via Domitia - AU - Avignon Université - UR - Université de La Réunion - UG - Université de Guyane - UA - Université des Antilles - UM - Université de Montpellier); D. Mouillot (LSEA MARBEC - Laboratoire Service d' Experimentations Aquacoles [Palavas les Flots] - UMR MARBEC - MARine Biodiversity Exploitation and Conservation - IRD - Institut de Recherche pour le Développement - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier, UM - Université de Montpellier)
    Abstract: The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g. wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyse visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the datasets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others.
    Keywords: Reproducibility,Replicability,Deep learning,Machine learning,FAIR,poverty indicators
    Date: 2022
  10. By: Flint Brayton; David L. Reifschneider
    Abstract: FRB/US, a large-scale, nonlinear macroeconomic model of the U.S., has been in use at the Federal Reserve Board for 25 years. For nearly as long, the FRB/US “project” has included a linear version of the model known as LINVER. A key reason that LINVER exists is the vast reduction in the computational costs that linearity confers when running experiments requiring large numbers of simulations under the assumption that expectations are model-consistent (MC). The public has been able to download FRB/US simulation code, documentation, and data from the Federal Reserve Board’s website since 2014. To further expand access to and understanding of the FRB/US project, a package devoted to LINVER is now available on the website. In this paper, we provide both a general introduction to LINVER and an overview of the contents and capabilities of its package. We review the ways that LINVER has been used in past research to study key policy issues; describe the package’s comprehensive set of programs for running simulations with MC expectations, with or without imposing the effective lower bound (ELB) on the federal funds rate and other nonlinear constraints; and illustrate how LINVER deterministic and stochastic simulations can be used to gauge the implications of the ELB for macroeconomic performance and to assess different strategies for mitigating its adverse effects.
    Keywords: Interest rates; Simulation; Econometric modeling; Monetary policy; Effective lower bound
    JEL: E52 E37 E58 E32
    Date: 2022–08–16
  11. By: Emanuel Kohlscheen
    Abstract: This study analyses oil price movements through the lens of an agnostic random forest model, which is based on 1,000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in sample root mean square errors by 65% relative to a standard linear least square model that uses the same set of 11 explanatory factors. In forecasting exercises the RMSE reduction ranges between 51% and 68%, highlighting the relevance of non linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models RMSE reduction in the post 2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is also considered as a risk factor, these shares become even larger.
    Date: 2022–08
  12. By: Shafiullah Qureshi (Department of Economics, Carleton University); Ba Chu (Department of Economics, Carleton University); Fanny S. Demers (Department of Economics, Carleton University); Michel Demers (Department of Economics, Carleton University)
    Abstract: We develop an economic policy uncertainty (EPU) index for Canada and the US using natural language processing (NLP) methods. Our EPU-NLP index is based on an application of several algorithms, including a rapid automatic keyword extraction algorithm (RAKE), a combination of the RoBERTa and the SentenceBERT algorithms, a PyLucene search engine, and the GrapeNLP local grammar engine. Classification-
    Date: 2022–01–18
  13. By: Long, Vicky (The Ratio Institute); Bjuggren, Per-Olof (The Ratio Institute)
    Abstract: This paper decomposes the factors that govern the access and sharing of machine-generated industrial data in the artificial intelligence era. Through a mapping of the key technological, institutional, and firm-level factors that affect the choice of governance structures, this study provides a synthesised view of AI data-sharing and coordination mechanisms. The question to be asked here is whether the hitherto de facto control—bilateral contracts and technical solution-dominating industrial practices in data sharing—can handle the long-run exchange needs or not.
    Keywords: Artificial intelligence (AI); governance structure; intellectual property rights (IPRs); data trade; industrial data
    JEL: D23 K10 K24 L14 L86 O30
    Date: 2022–05–05
  14. By: Söder, Mareike; Berg-Mohnicke, Michael; Bittner, Marlene; Ernst, Stefan; Feike, Til; Frühauf, Cathleen; Golla, Burkhard; Jänicke, Clemens; Jorzig, Christian; Leppelt, Thomas; Liedtke, Marco; Möller, Markus; Nendel, Claas; Offermann, Frank; Riedesel, Ludwig; Romanova, Vanya; Schmitt, Jonas; Schulz, Susanne; Sesermann, Diana-Maria; Rahman Shawon, Ashifur
    Abstract: We provide an overview of the state of knowledge on the climate change impacts on German crop production and generate model-based, quantitative and spatially differentiated simulations of the yield changes of the most important German arable crops, up to the middle of the century. To simulate yields, we use several agro-ecosystem models and provide a meta-analysis of the related scientific literature. In addition, we consider the effects of specific weather conditions such as heat and drought periods on yields in the past. In order to assess the future development, we use the data of different climate projections . On average, with regional differences, the simulations show no decline in yields until the middle of the century and no increase in yield variability. We observe a decrease in the effectiveness of the CO2 fertilization effect for yield increases of winter wheat over time. The yields of silage maize benefit the least from CO2 fertilization. For the past, we identify yield losses due to extreme summer and spring drought for almost all crops as well as due to heat events for winter wheat and partly for oilseed rape. Heat-related yield losses increase for winter wheat with increasing CO2 concentrations. However, we cannot identify an unambiguous increase in yield losses due to extreme drought or waterlogging in the future. Uncertainties in the results exist, amongst other reasons, due to a wide range of future precipitation development in the underlying climate models, in particular with regard to the reliability of the precipitation projection in spring. The simulations do not consider adaptation of production to climate change as well as negative yield effects due to potential increase in storms, hail storms, heavy rain or harmful organisms.
    Keywords: Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use, Risk and Uncertainty
    Date: 2022–09–13
  15. By: Turco, Enrico; Bazzana, Davide; Rizzati, Massimiliano; Ciola, Emanuele; Vergalli, Sergio
    Abstract: Soaring energy prices since fall 2021 have prompted European governments to introduce policy measures to support households and businesses. In this paper, we employ the MATRIX model, a multi-sector and multi-agent macroeconomic model calibrated on the Euro Area, to analyze the economic and distributional effects of different types of macro-stabilization policies in response to energy price shocks. Simulation results show that, in the absence of stabilization policies, an increase in fossil fuel price would lead to a sharp growth in price inflation and a severe contraction in real GDP, followed by a slow but steady recovery. We find no significant effects of generalized tax cuts and household subsidies, while firm subsidies promote a faster recovery but at the expense of greater financial instability in the medium term due to the resulting market distortions. If timely adopted, government-funded energy tariff reduction is the most effective policy in mitigating GDP losses at relatively low public costs, especially if coupled with an extra-profit tax on energy firms. Energy entrepreneurs benefit from rising fuel prices in all policy scenarios, but to a lesser extent under energy tariff cuts and windfall profits tax, favouring, in that case, workers and downstream firms owners.
    Keywords: Demand and Price Analysis, Public Economics, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy
    Date: 2022–09–09
  16. By: Mr. Raphael A Espinoza; Jesper Lindé; Mr. Jiaqian Chen; Zoltan Jakab; Carlos Goncalves; Tryggvi Gudmundsson; Martina Hengge
    Abstract: The COVID-19 pandemic and the subsequent need for policy support have called the traditional separation between fiscal and monetary policies into question. Based on simulations of an open economy DSGE model calibrated to emerging and advance economies and case study evidence, the analysis shows when constraints are binding a more integrated approach of looking at policies can lead to a better policy mix and ultimately better macroeconomic outcomes under certain circumstances. Nonetheless, such an approach entails risks, necessitating a clear assessment of each country’s circumstances as well as safeguards to protect the credibility of the existing institutional framework.
    Date: 2022–09–02
  17. By: Alessi, Lucia (European Commission); Di Girolamo, Francesca Erica (European Commission); Pagano, Andrea (European Commission); Petracco Giudici, Marco (European Commission)
    Abstract: This paper uses a stylized simulation model to assess the potential impact of transition risk on banks' balance sheets and establishes a basis for calibrating relevant macro-prudential instruments. We show that even in the short run, a fire-sale mechanism could amplify an initially contained shock on high-carbon assets into a systemic crisis with significant losses for the EU banking sector. We calculate that an additional capital buffer of 0.5% RWA on average would be sufficient to protect the system. Moreover, under an orderly transition, the decrease in banks’ transition risk exposure due to the greening of the economy would reduce the effect of a fire-sale by a factor of 10.
    Keywords: Green transition risk, dynamic balance sheet, banking crisis
    JEL: C15 G2 Q54
    Date: 2022–06
  18. By: Juhro, Solikin M.; Sahminan, Sahminan; Wijoseno, Atet; Waluyo, Jati; Bathaluddin, M. Barik
    Abstract: This paper discusses the core model of Bank Indonesia policy mix (BIPOLMIX), a macroeconomic modeling breakthrough designed for economic and financial projections and policy simulations. The BIPOLMIX model captures the integrated central bank policy responses, e.g. monetary, macroprudential, and payment system policies, and considers the role of fiscal policy. The strategy of developing the model is flexible, dynamic, and forward-looking to make the model relevant as the basis for Bank Indonesia policy transformation in coping with challenges in a rapidly changing environment. In this regard, the model takes into account various economic dynamics and policy instrument mix in optimizing the achievement of macroeconomic and financial system stability. Amid main issues related to the model parameter consistency, in line with theoretical and technical considerations, the modeling framework is believed to be useful as a pivotal reference by the central banks in EMEs in developing core models to support optimal policy responses.
    Keywords: Central Bank Policy Mix, Policy Modeling, Projections and Simulations, Bank Indonesia.
    JEL: C51 E37 E58
    Date: 2022–09
  19. By: Angun, Ebru; Kleijnen, Jack (Tilburg University, School of Economics and Management)
    Date: 2022
  20. By: David Winkelmann; Frederik Tolkmitt; Matthias Ulrich; Michael R\"omer
    Abstract: In this paper, we deal with a storage assignment problem arising in a fulfilment centre of a major European e-grocery retailer. The centre can be characterised as a hybrid warehouse consisting of a highly efficient and partially automated fast-picking area designed as a pick-and-pass system with multiple stations and a picker-to-parts area. The storage assignment problem considered in this paper comprises the decisions to select the products to be allocated to the fast-picking area, the assignment of the products to picking stations and the determination of a shelf within the assigned station. The objective is to achieve a high level of picking efficiency while respecting station workload balancing and precedence order constraints. We propose to solve this three-level problem using an integrated MILP model. In computational experiments with real-world data, we show that using the proposed integrated approach yields significantly better results than a sequential approach in which the selection of products to be included in the fast-picking area is solved before assigning station and shelf. Furthermore, we provide an extension to the integrated storage assignment model that explicitly accounts for within-week demand variation. In a set of experiments with day-of-week-dependent demands we show that while a storage assignment that is based on average demand figures tends to exhibit a highly imbalanced workload on certain days of the week, the augmented model yields robust storage assignments that are well balanced on each day of the week without compromising the quality of the solutions in terms of picking efficiency.
    Date: 2022–09
  21. By: Corrado DI GUILMI; FUJIWARA Yoshi
    Abstract: The paper presents an investigation on how the upward transmission of demand shocks in the Japanese supply network influences the growth rates of firms and, consequently, shapes their size distribution. Through an empirical analysis, analytical decomposition of the growth rates' volatility, and numerical simulations, we obtain several original results. We find that the Japanese supply network has a bow-tie structure in which firms located in the upstream layers display a larger volatility in their growth rates. As a result, the Gibrat's law breaks down for upstream firms, whereas downstream firms are more likely to be located in the power law tail of the size distribution. This pattern is determined by the amplification of demand shocks hitting downstream firms, and the magnitude of this amplification depends on the network structure and on the relative market power of downstream firms. Finally, we observe that in an almost perfectly hierarchical network, the power-law tail in firm size distribution disappears. The paper shows that aggregate demand shocks can affect the economy directly through the reduction in output for downstream firms and indirectly by shaping the firm size distribution.
    Date: 2022–08
  22. By: Podstawka, Marian
    Abstract: The aim of the study was to determine cadastral tax rate calculated on farm assets, which would allow for replacing the current wealth taxes without increasing the tax burden for farms. The research was based on data from FADN (Farm Accountancy Data Network) farms. The method of financial analysis simulation was used. The research shows that the total wealth tax burden related to farm income is small. The taxes are the greatest burden to the income of very small farms whose economic size is between EUR 2,000 and 8,000 annually (7.37%) and farms dealing with field crops (4.36%). Meanwhile, farms dealing with horticulture (0.69%) and poultry production (0.54%), as well as large farms with an annual economic size of EUR 100,000–500,000 (1.93%) and very large farms with an economic size of more than EUR 500,000 (1.13%) currently pay relatively lower taxes. It was also found that significant changes occurred in the structure of farm assets. While in the 1970s the largest share (approx. 84%) of the assets of individual farms at that time concerned buildings, currently the share of buildings in assets decreased to approx. 19%. There is a relatively larger share of buildings in assets among farms specialized in horticulture and poultry amounting to 43.2 and 37.8%, respectively. The research allowed for determining the rate of a possible cadastral tax, while maintaining the current tax burden for farms. The tax rate may not exceed 0.22%. Relating it to the value of buildings, permanent crops, and land, it will not increase the current tax burden for farms.
    Keywords: Agricultural Finance, Financial Economics
    Date: 2022–06–28
  23. By: Ludovic Tangpi; Shichun Wang
    Abstract: Recent financial bubbles such as the emergence of cryptocurrencies and "meme stocks" have gained increasing attention from both retail and institutional investors. In this paper, we propose a game-theoretic model on optimal liquidation in the presence of an asset bubble. Our setup allows the influx of players to fuel the price of the asset. Moreover, traders will enter the market at possibly different times and take advantage of the uptrend at the risk of an inevitable crash. In particular, we consider two types of crashes: an endogenous burst which results from excessive selling, and an exogenous burst which cannot be anticipated and is independent from the actions of the traders. The popularity of asset bubbles suggests a large-population setting, which naturally leads to a mean field game (MFG) formulation. We introduce a class of MFGs with varying entry times. In particular, an equilibrium will depend on the entry-weighted average of conditional optimal strategies. To incorporate the exogenous burst time, we adopt the method of progressive enlargement of filtrations. We prove existence of MFG equilibria using the weak formulation in a generalized setup, and we show that the equilibrium strategy can be decomposed into before-and-after-burst segments, each part containing only the market information. We also perform numerical simulations of the solution, which allow us to provide some intriguing results on the relationship between the bubble burst and equilibrium strategies.
    Date: 2022–09

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