nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒10‒24
eighteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Realistic Neural Networks By L. Ingber
  2. Generalized Gloves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance By Dangxing Chen; Weicheng Ye
  3. Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring By Dangxing Chen; Weicheng Ye
  4. Interpretable Selective Learning in Credit Risk By Dangxing Chen; Weicheng Ye; Jiahui Ye
  5. Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys By Milien Dhorne; Claire Nicolas; Christopher Arderne; Juliette Besnard
  6. Physics-Informed Convolutional Transformer for Predicting Volatility Surface By Soohan Kim; Seok-Bae Yun; Hyeong-Ohk Bae; Muhyun Lee; Youngjoon Hong
  7. Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics By Yicong Liu; Kaili Wang; Patrick Loa; Khandker Nurul Habib
  8. Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation By Christophe Geissler; Nicolas Morizet; Matteo Rizzato; Julien Wallart
  9. Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting By Peter Christensen; Paul Francisco; Erica Myers; Hansen Shao; Mateus Souza
  10. Communicating with vocal emotions By Oleksandr Talavera; Shuxing Yin; Mao Zhang
  11. Model-Free Reinforcement Learning for Asset Allocation By Adebayo Oshingbesan; Eniola Ajiboye; Peruth Kamashazi; Timothy Mbaka
  12. Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks By Stéphane Crépey; Lehdili Noureddine; Nisrine Madhar; Maud Thomas
  13. Distributional Effects of Tax Reforms in Japan: Micro-simulation Approach By Takuma Hisanaga
  14. Editorial: Artificial Intelligence (AI) and Data Sharing in Manufacturing, Production and Operations Management Research By Thanos Papadopoulos; Uthayasankar Sivarajah; Konstantina Spanaki; Stella Despoudi; Angappa Gunasekaran
  15. Environmental impacts of enlarging the market share of electric vehicles By Daniel de Wolf; Ngagne Diop; Moez Kilani
  16. Stackelberg competition in groundwater resources with multiple uses By Julia de Frutos Cachorro; Guiomar Martín-Herrán; Mabel Tidball
  17. The multiple dimensions of selection into employment By Kenza Elass
  18. Insurance Contract for High Renewable Energy Integration By Dongwei Zhao; Hao Wang; Jianwei Huang; Xiaojun Lin

  1. By: L. Ingber
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:lei:ingber:22rn&r=
  2. By: Dangxing Chen; Weicheng Ye
    Abstract: For many years, machine learning methods have been used in a wide range of fields, including computer vision and natural language processing. While machine learning methods have significantly improved model performance over traditional methods, their black-box structure makes it difficult for researchers to interpret results. For highly regulated financial industries, transparency, explainability, and fairness are equally, if not more, important than accuracy. Without meeting regulated requirements, even highly accurate machine learning methods are unlikely to be accepted. We address this issue by introducing a novel class of transparent and interpretable machine learning algorithms known as generalized gloves of neural additive models. The generalized gloves of neural additive models separate features into three categories: linear features, individual nonlinear features, and interacted nonlinear features. Additionally, interactions in the last category are only local. The linear and nonlinear components are distinguished by a stepwise selection algorithm, and interacted groups are carefully verified by applying additive separation criteria. Empirical results demonstrate that generalized gloves of neural additive models provide optimal accuracy with the simplest architecture, allowing for a highly accurate, transparent, and explainable approach to machine learning.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10082&r=
  3. By: Dangxing Chen; Weicheng Ye
    Abstract: The forecasting of credit default risk has been an active research field for several decades. Historically, logistic regression has been used as a major tool due to its compliance with regulatory requirements: transparency, explainability, and fairness. In recent years, researchers have increasingly used complex and advanced machine learning methods to improve prediction accuracy. Even though a machine learning method could potentially improve the model accuracy, it complicates simple logistic regression, deteriorates explainability, and often violates fairness. In the absence of compliance with regulatory requirements, even highly accurate machine learning methods are unlikely to be accepted by companies for credit scoring. In this paper, we introduce a novel class of monotonic neural additive models, which meet regulatory requirements by simplifying neural network architecture and enforcing monotonicity. By utilizing the special architectural features of the neural additive model, the monotonic neural additive model penalizes monotonicity violations effectively. Consequently, the computational cost of training a monotonic neural additive model is similar to that of training a neural additive model, as a free lunch. We demonstrate through empirical results that our new model is as accurate as black-box fully-connected neural networks, providing a highly accurate and regulated machine learning method.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10070&r=
  4. By: Dangxing Chen; Weicheng Ye; Jiahui Ye
    Abstract: The forecasting of the credit default risk has been an important research field for several decades. Traditionally, logistic regression has been widely recognized as a solution due to its accuracy and interpretability. As a recent trend, researchers tend to use more complex and advanced machine learning methods to improve the accuracy of the prediction. Although certain non-linear machine learning methods have better predictive power, they are often considered to lack interpretability by financial regulators. Thus, they have not been widely applied in credit risk assessment. We introduce a neural network with the selective option to increase interpretability by distinguishing whether the datasets can be explained by the linear models or not. We find that, for most of the datasets, logistic regression will be sufficient, with reasonable accuracy; meanwhile, for some specific data portions, a shallow neural network model leads to much better accuracy without significantly sacrificing the interpretability.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10127&r=
  5. By: Milien Dhorne; Claire Nicolas; Christopher Arderne; Juliette Besnard
    Keywords: Energy - Electric Power Energy - Energy Conservation & Efficiency
    Date: 2021–04
    URL: http://d.repec.org/n?u=RePEc:wbk:wboper:35473&r=
  6. By: Soohan Kim; Seok-Bae Yun; Hyeong-Ohk Bae; Muhyun Lee; Youngjoon Hong
    Abstract: Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The Black-Scholes option pricing model is one of the most widely used models by market participants. Notwithstanding, the Black-Scholes model is based on heavily criticized theoretical premises, one of which is the constant volatility assumption. The dynamics of the volatility surface is difficult to estimate. In this paper, we establish a novel architecture based on physics-informed neural networks and convolutional transformers. The performance of the new architecture is directly compared to other well-known deep-learning architectures, such as standard physics-informed neural networks, convolutional long-short term memory (ConvLSTM), and self-attention ConvLSTM. Numerical evidence indicates that the proposed physics-informed convolutional transformer network achieves a superior performance than other methods.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10771&r=
  7. By: Yicong Liu; Kaili Wang; Patrick Loa; Khandker Nurul Habib
    Abstract: The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10664&r=
  8. By: Christophe Geissler; Nicolas Morizet; Matteo Rizzato; Julien Wallart
    Abstract: The finance industry is producing an increasing amount of datasets that investment professionals can consider to be influential on the price of financial assets. These datasets were initially mainly limited to exchange data, namely price, capitalization and volume. Their coverage has now considerably expanded to include, for example, macroeconomic data, supply and demand of commodities, balance sheet data and more recently extra-financial data such as ESG scores. This broadening of the factors retained as influential constitutes a serious challenge for statistical modeling. Indeed, the instability of the correlations between these factors makes it practically impossible to identify the joint laws needed to construct scenarios. Fortunately, spectacular advances in Deep Learning field in recent years have given rise to GANs. GANs are a type of generative machine learning models that produce new data samples with the same characteristics as a training data distribution in an unsupervised way, avoiding data assumptions and human induced biases. In this work, we are exploring the use of GANs for synthetic financial scenarios generation. This pilot study is the result of a collaboration between Fujitsu and Advestis and it will be followed by a thorough exploration of the use cases that can benefit from the proposed solution. We propose a GANs-based algorithm that allows the replication of multivariate data representing several properties (including, but not limited to, price, market capitalization, ESG score, controversy score,. . .) of a set of stocks. This approach differs from examples in the financial literature, which are mainly focused on the reproduction of temporal asset price scenarios. We also propose several metrics to evaluate the quality of the data generated by the GANs. This approach is well fit for the generation of scenarios, the time direction simply arising as a subsequent (eventually conditioned) generation of data points drawn from the learned distribution. Our method will allow to simulate high dimensional scenarios (compared to $\lesssim 10$ features currently employed in most recent use cases) where network complexity is reduced thanks to a wisely performed feature engineering and selection. Complete results will be presented in a forthcoming study.
    Date: 2022–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.03935&r=
  9. By: Peter Christensen; Paul Francisco; Erica Myers; Hansen Shao; Mateus Souza
    Abstract: Building energy efficiency has been a cornerstone of greenhouse gas mitigation strategies for decades. However, impact evaluations have revealed that energy savings typically fall short of engineering model forecasts that currently guide funding decisions. This creates a resource allocation problem that impedes progress on climate change. Using data from the largest U.S. energy efficiency program, we demonstrate that a data-driven approach to predicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models. Targeting high-return interventions based on these predictions dramatically increases net social benefits, from $0.93 to $1.23 per dollar invested.
    JEL: H50 Q4
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:30467&r=
  10. By: Oleksandr Talavera (University of Birmingham); Shuxing Yin (University of Sheffield); Mao Zhang (University of St Andrews)
    Abstract: Using machine learning techniques, we extract vocal emotions from audio files of earnings conference calls and examine how managers communicate with analysts in question-and-answer (Q&A) sessions. Focusing on these conversations, we find that the vocal emotion of managers (answering questions) is affected by how each question is asked and who asks the question. Managers, who dialogues with a positive emotive analyst or a female analyst, exhibit a more positive vocal response. Our data also provide evidence of distinctive manager-specific vocal communication styles. Female managers and younger managers are more likely to display negative vocal emotions as compared to male and older colleagues. Stock prices respond to managers' vocal emotions in a timely manner. Analysts also incorporate such vocal emotions into near-term earnings forecasts.
    Keywords: conference calls; vocal emotion; manager-analyst conversation; gender; market reaction
    JEL: G10 G14 G41 G30 J16 M14
    Date: 2022–10
    URL: http://d.repec.org/n?u=RePEc:bir:birmec:22-11&r=
  11. By: Adebayo Oshingbesan; Eniola Ajiboye; Peruth Kamashazi; Timothy Mbaka
    Abstract: Asset allocation (or portfolio management) is the task of determining how to optimally allocate funds of a finite budget into a range of financial instruments/assets such as stocks. This study investigated the performance of reinforcement learning (RL) when applied to portfolio management using model-free deep RL agents. We trained several RL agents on real-world stock prices to learn how to perform asset allocation. We compared the performance of these RL agents against some baseline agents. We also compared the RL agents among themselves to understand which classes of agents performed better. From our analysis, RL agents can perform the task of portfolio management since they significantly outperformed two of the baseline agents (random allocation and uniform allocation). Four RL agents (A2C, SAC, PPO, and TRPO) outperformed the best baseline, MPT, overall. This shows the abilities of RL agents to uncover more profitable trading strategies. Furthermore, there were no significant performance differences between value-based and policy-based RL agents. Actor-critic agents performed better than other types of agents. Also, on-policy agents performed better than off-policy agents because they are better at policy evaluation and sample efficiency is not a significant problem in portfolio management. This study shows that RL agents can substantially improve asset allocation since they outperform strong baselines. On-policy, actor-critic RL agents showed the most promise based on our analysis.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10458&r=
  12. By: Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité); Lehdili Noureddine (Natixis); Nisrine Madhar (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité, Natixis); Maud Thomas (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, SU - Sorbonne Université)
    Abstract: We consider time series representing a wide variety of risk factors in the context of financial risk management. A major issue of these data is the presence of anomalies that induce a miscalibration of the models used to quantify and manage risk, whence potentially erroneous risk measures on their basis. Therefore, the detection of anomalies is of utmost importance in financial risk management. We propose an approach that aims at improving anomaly detection on financial time series, overcoming most of the inherent difficulties. One first concern is to extract from the time series valuable features that ease the anomaly detection task. This step is ensured through a compression and reconstruction of the data with the application of principal component analysis. We define an anomaly score using a feed-forward neural network. A time series is deemed contaminated when its anomaly score exceeds a given cutoff. This cutoff value is not a hand-set parameter, instead it is calibrated as a parameter of the neural network throughout the minimisation of a customized loss function. The efficiency of the proposed model with respect to several well-known anomaly detection algorithms is numerically demonstrated. We show on a practical case of value-at-risk estimation, that the estimation errors are reduced when the proposed anomaly detection model is used, together with a naive imputation approach to correct the anomaly.
    Keywords: anomaly detection,financial time series,principal component analysis,neural network,density estimation,missing data,market risk,value at risk
    Date: 2022–09–15
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03777995&r=
  13. By: Takuma Hisanaga
    Abstract: This paper conducts micro-simulations to study the distributional effects of several tax measures in Japan, considering households’ heterogeneity in terms of both income and wealth. Simulation results suggest that increasing the consumption tax rate and strengthening the recurrent tax on immovable property would weigh more heavily on low-income households with large wealth than on those of comparable incomes with small wealth, and that introduction of a consumption tax credit would be effective in containing a rise in tax burden of low-income households.
    Keywords: Tax Policy; Japan; Inequality; Micro-simulation; consumption tax credit; employment income deduction; capital income tax tax rate; reform option; pension income deduction; Consumption taxes; Income; Tax incidence; Tax allowances; Personal income tax; resident tax; income decile
    Date: 2022–07–22
    URL: http://d.repec.org/n?u=RePEc:imf:imfwpa:2022/150&r=
  14. By: Thanos Papadopoulos (University of Kent [Canterbury]); Uthayasankar Sivarajah (School of Management [Bradford] - University of Bradford); Konstantina Spanaki (Audencia Business School); Stella Despoudi (Aston Business School - Aston University [Birmingham]); Angappa Gunasekaran (CSUB - California State University [Bakersfield])
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03766170&r=
  15. By: Daniel de Wolf (TVES - Territoires, Villes, Environnement & Société - ULR 4477 - ULCO - Université du Littoral Côte d'Opale - Université de Lille); Ngagne Diop (TVES - Territoires, Villes, Environnement & Société - ULR 4477 - ULCO - Université du Littoral Côte d'Opale - Université de Lille); Moez Kilani (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
    Abstract: The model, which is described in detail in Kilani et al. covers the North of France and includes both urban and intercity trips. It is a multi-agents simulation based on the MATsim framework and calibrated on observed traffic flows. We find that the decrease in emissions of pollutant gases decreases in comparable proportion to the market share of the electric vehicles. When only users with shorter trips switch to electric vehicles the impact is limited and demand for charging stations is small since most users will charge by night at home. When the government is able to target users with longer trips, the impact can be higher by more than a factor of two. But, in this case, our model shows that it is important to increase the number of charging stations with an optimized deployment for their accessibility.
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03763391&r=
  16. By: Julia de Frutos Cachorro (Universitat de Barcelona and BEAT); Guiomar Martín-Herrán (IMUVA and Universidad de Valladolid); Mabel Tidball (CEE-M and University of Montpellier)
    Abstract: We study a problem of exploitation of a groundwater resource, mainly used for irrigation, in which a water agency is needed in order to manage an exceptional and priority extraction of water for an alternative/new use (e.g. domestic water). To this goal, we build a two-stage discrete Stackelberg game in which the leader (the water agency) just intervenes when the new use takes place (in the second stage) and the follower is a representative agent of the regular users of the aquifer, i.e. the agricultural users. We study two types of Stackelberg equilibrium, which can arise depending on the agents' commitment behavior, namely openloop (commitment) equilibrium and feedback (non-commitment) equilibrium. We analyze and compare extraction behaviors of the different agents for the different equilibria and the consequences of these extraction behaviors for the final state of the resource and the agents' profits. For some hypotheses on the parameters, theoretical results show that commitment strategies lead to higher stock levels than non-commitment strategies when the leader's weight assigned to the profits from the agricultural use is lower or equal than the one assigned to the profits from the non-agricultural use. However, performing numerical simulations relaxing previous economic assumptions, we show that there are situations in which non-commitment strategies could be more favorable than commitment strategies not only in terms of final stock of the resource but also in terms of users' profits.
    Keywords: Groundwater resource, multiple uses, Stackelberg dynamic game, information structures.
    JEL: Q25 C72
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:ewp:wpaper:431web&r=
  17. By: Kenza Elass (Aix-Marseille Univ, CNRS, AMSE, Marseille, France.)
    Abstract: A vast literature on gender wage gaps has examined the importance of selection into employment. However, most analyses have focused only on female labour force participation and gaps at the median. The Great Recession questions this approach both because of the major shift in male employment that it implied but also because women's decisions to participate seem to have been different along the distribution, particularly due to an "added worker effect". This paper uses the methodology proposed by Arellano and Bonhomme (2017) to estimate a quantile selection model over the period 2007-2018. Using a tax and benefit microsimulation model, I compute an instrument capturing the male selection induced by the crisis as well as female decisions: the potential out-of-work income. Since my instrument is crucially determined by the welfare state, I consider three countries with notably different benefit systems-the UK, France and Finland. My results imply different selection patterns across countries and a sizeable male selection in France and the UK. Correction for selection bias lowers the gender wage gap and, in most recent years, reveals an increasing shape of gender gap distribution with a substantial glass ceiling for the three countries.
    Keywords: gender wage gap, sample selection, quantile selection model, wage inequality, quantiles, selection, glass ceilings, sticky floors
    JEL: J31 J21 J16 C21
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:aim:wpaimx:2219&r=
  18. By: Dongwei Zhao; Hao Wang; Jianwei Huang; Xiaojun Lin
    Abstract: The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2209.10363&r=

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