nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒12‒05
twenty-six papers chosen by
Stan Miles
Thompson Rivers University

  1. Hybrid Convolutional Neural Network Components By John, Otumu
  2. A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization By Alcántara Mata, Antonio; Ruiz Mora, Carlos
  3. Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models By Maciej Wysocki; Paweł Sakowski
  4. Nowcasting GDP using machine learning methods By Dennis Kant; Andreas Pick; Jasper de Winter
  5. A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy By Illia Baranochnikov; Robert Ślepaczuk
  6. The smart green nudge: Reducing product returns through enriched digital footprints & causal machine learning By von Zahn, Moritz; Bauer, Kevin; Mihale-Wilson, Cristina; Jagow, Johanna; Speicher, Max; Hinz, Oliver
  7. A survey on machine learning methods for churn prediction By Louis Geiler; Séverine Affeldt; Mohamed Nadif
  8. Examining the influence of user engagement on tourist virtual reality behavioral response from the human-computer interaction perspective: A PLSSEM-IMP-NN hybrid machine learning approach By Shang, Dawei
  9. How Communication Makes the Difference between a Cartel and Tacit Collusion: A Machine Learning Approach By Maximilian Andres; Lisa Bruttel; Jana Friedrichsen
  10. Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models By Francis X. Diebold; Maximilian Gobel; Philippe Goulet Coulombe
  11. A Data-driven Case-based Reasoning in Bankruptcy Prediction By Wei Li; Wolfgang Karl H\"ardle; Stefan Lessmann
  12. Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty By Yugo Fujimotol; Kei Nakagawa; Kentaro Imajo; Kentaro Minami
  13. Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index By Katarzyna Kryńska; Robert Ślepaczuk
  14. The effects of mandatory speed limits on crash frequency: A causal machine learning approach By Metz-Peeters, Maike
  15. Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces By Susan Athey; Dean Karlan; Emil Palikot; Yuan Yuan
  16. State-dependent Asset Allocation Using Neural Networks By Reza Bradrania; Davood Pirayesh Neghab
  17. A level-set approach to the control of state-constrained McKean-Vlasov equations: application to renewable energy storage and portfolio selection By Maximilien Germain; Huyên Pham; Xavier Warin
  18. Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning By Tristan Lim
  19. Flexible machine learning estimation of conditional average treatment effects: a blessing and a curse By Richard Post; Isabel van den Heuvel; Marko Petkovic; Edwin van den Heuvel
  20. (Machine) Learning from the COVID-19 Lockdown about Electricity Market Performance with a Large Share of Renewables By Christoph Graf; Federico Quaglia; Frank A. Wolak
  21. Deep Learning for Inflexible Multi-Asset Hedging of incomplete market By Ruochen Xiao; Qiaochu Feng; Ruxin Deng
  22. The Who, What, When, and How of Industrial Policy: A Text-Based Approach By Juhász, Réka; Lane, Nathaniel; Oehlsen, Emily; Pérez, Verónica C.
  24. Can maker-taker fees prevent algorithmic cooperation in market making? By Bingyan Han
  25. Measuring sustainable urban development using novel neighborhood classification By Ala-Mantila, Sanna; Kurvinen, Antti; Karhula, Aleksi
  26. Shape it better than skip it: mapping the territory of quantum computing and its transformative potential By Imed Boughzala; Nesrine Ben Yahia; Narjès Bellamine Ben Saoud; Wissem Eljaoued

  1. By: John, Otumu
    Abstract: Hybrid Convolutional Neural Network Components
    Date: 2022–10–29
  2. By: Alcántara Mata, Antonio; Ruiz Mora, Carlos
    Abstract: The use of machine learning methods helps to improve decision making in different fields. In particular, the idea of bridging predictions (machine learning models) and prescriptions (optimization problems) is gaining attention within the scientific community. One of the main ideas to address this trade-off is the so-called Constraint Learning (CL) methodology, where the structures of the machine learning model can be treated as a set of constraints to be embedded within the optimization problem, establishing therelationship between a direct decision variable x and a response variable y. However, most CL approaches have focused on making point predictions for a certain variable, not taking into account the statistical and external uncertainty faced in the modeling process. In this paper, we extend the CL methodology to deal with uncertainty in the response variable y. The novel Distributional Constraint Learning (DCL) methodology makes use of a piece-wise linearizable neural network-based model to estimate the parametersof the conditional distribution of y (dependent on decisions x and contextualinformation), which can be embedded within mixed-integer optimization problems. In particular, we formulate a stochastic optimization problem by sampling random values from the estimated distribution by using a linear set of constraints. In this sense, DCL combines both the high predictive performance of the neural network method and the possibility of generating scenarios to account for uncertainty within a tractable optimization model. The behavior of the proposed methodology is tested in a real-worldproblem in the context of electricity systems, where a Virtual Power Plant seeks to optimize its operation, subject to different forms of uncertainty, and with price-responsive consumers.
    Keywords: Stochastic Optimization; Constraint Learning; Distribution Estimation; Neural Networks; Mixed-Integer Optimization
    Date: 2022–11–21
  3. By: Maciej Wysocki (University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group); Paweł Sakowski (University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group)
    Abstract: This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. In this study we propose a novel framework for variance-covariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use these forecasts to optimize portfolios that consisted of stocks and cryptocurrencies. Our analysis presents results across different combinations of observation windows and rebalancing periods to compare performances of classical and deep learning variance-covariance estimation methods. The conclusions of the study are that although the strategies (portfolios) performance differed significantly between different combinations of parameters, generally the best results in terms of the information ratio and annualized returns are obtained using the LSTM-RNN models. Moreover, longer observation windows translate into better performance of the deep learning models indicating that these methods require longer windows to be able to efficiently capture the long-term dependencies of the variance-covariance matrix structure. Strategies with less frequent rebalancing typically perform better than these with the shortest rebalancing windows across all considered methods.
    Keywords: Portfolio Optimization, Deep Learning, Variance-Covariance Matrix Forecasting, Investment Strategies, Recurrent Neural Networks, Long Short-Term Memory Neural Networks
    JEL: C4 C14 C45 C53 C58 G11
    Date: 2022
  4. By: Dennis Kant; Andreas Pick; Jasper de Winter
    Abstract: This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the years 1992-2018 using a broad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of regressors. We find that the random forest forecast provides the most accurate nowcasts while using the different variables in a relative stable and equal manner.
    Keywords: factor models; forecasting competition; machine learning methods; nowcasting.
    JEL: C32 C53 E37
    Date: 2022–11
  5. By: Illia Baranochnikov (University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance; Quantitative Finance Research Group)
    Abstract: The aim of this work is to build a profitable algorithmic investment strategy on various types of assets. The algorithm is built using recurrent neural networks (LSTM and GRU) as the primary source of signals to buy/sell financial instruments. LSTM and GRU architectures are compared in terms of obtaining the best results and beating the market. The algorithm is tested for four financial instruments (Bitcoin, Tesla, Brent Oil and Gold) on daily and hourly data frequencies. The out-of-sample period is from 1 January 2021 to 1 April 2022. A walk-forward process is responsible for training models and selecting the best model to forecast asset prices in the future. Ten model architectures with various hyperparameters are trained during each step of the walk-forward process. The model architecture with the highest Information Ratio (IR*) in the validation period is used for forecasting in the out-of-sample period. For each strategy, the performance metrics are calculated based on which the profitability of the algorithm is evaluated. At the end, a detailed sensitivity analysis with regards to the main hyperparameters is conducted. The results reveal that LSTM outperforms GRU in most of the cases and that investment strategy built based on LSTM/GRU architecture is able to beat the market only on 50% of tested cases.
    Keywords: deep learning, recurrent neural networks, algorithm, trading strategy, LSTM, GRU, walk-forward process
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2022
  6. By: von Zahn, Moritz; Bauer, Kevin; Mihale-Wilson, Cristina; Jagow, Johanna; Speicher, Max; Hinz, Oliver
    Abstract: With free delivery of products virtually being a standard in E-commerce, product returns pose a major challenge for online retailers and society. For retailers, product returns involve significant transportation, labor, disposal, and administrative costs. From a societal perspective, product returns contribute to greenhouse gas emissions and packaging disposal and are often a waste of natural resources. Therefore, reducing product returns has become a key challenge. This paper develops and validates a novel smart green nudging approach to tackle the problem of product returns during customers' online shopping processes. We combine a green nudge with a novel data enrichment strategy and a modern causal machine learning method. We first run a large-scale randomized field experiment in the online shop of a German fashion retailer to test the efficacy of a novel green nudge. Subsequently, we fuse the data from about 50,000 customers with publicly-available aggregate data to create what we call enriched digital footprints and train a causal machine learning system capable of optimizing the administration of the green nudge. We report two main findings: First, our field study shows that the large-scale deployment of a simple, low-cost green nudge can significantly reduce product returns while increasing retailer profits. Second, we show how a causal machine learning system trained on the enriched digital footprint can amplify the effectiveness of the green nudge by "smartly" administering it only to certain types of customers. Overall, this paper demonstrates how combining a low-cost marketing instrument, a privacy-preserving data enrichment strategy, and a causal machine learning method can create a win-win situation from both an environmental and economic perspective by simultaneously reducing product returns and increasing retailers' profits.
    Keywords: Product returns,Green Nudging,Causal Machine Learning,Enriched Digital Footprint
    Date: 2022
  7. By: Louis Geiler (CB - CB - Centre Borelli - UMR 9010 - Service de Santé des Armées - INSERM - Institut National de la Santé et de la Recherche Médicale - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay - UPCité - Université Paris Cité); Séverine Affeldt (CB - CB - Centre Borelli - UMR 9010 - Service de Santé des Armées - INSERM - Institut National de la Santé et de la Recherche Médicale - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay - UPCité - Université Paris Cité); Mohamed Nadif (CB - CB - Centre Borelli - UMR 9010 - Service de Santé des Armées - INSERM - Institut National de la Santé et de la Recherche Médicale - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay - UPCité - Université Paris Cité)
    Abstract: The diversity and specificities of today's businesses have leveraged a wide range of prediction techniques. In particular, churn prediction is a major economic concern for many companies. The purpose of this study is to draw general guidelines from a benchmark of supervised machine learning techniques in association with widely used data sampling approaches on publicly available datasets in the context of churn prediction. Choosing a priori the most appropriate sampling method as well as the most suitable classification model is not trivial, as it strongly depends on the data intrinsic characteristics. In this paper we study the behavior of eleven supervised and semi-supervised learning methods and seven sampling approaches on sixteen diverse and publicly available churn-like datasets. Our evaluations, reported in terms of the Area Under the Curve (AUC) metric, explore the influence of sampling approaches and data characteristics on the performance of the studied learning methods. Besides, we propose Nemenyi test and Correspondence Analysis as means of comparison and visualization of the association between classification algorithms, sampling methods and datasets. Most importantly, our experiments lead to a practical recommendation for a prediction pipeline based on an ensemble approach. Our proposal can be successfully applied to a wide range of churn-like datasets.
    Keywords: churn prediction,machine learning,ensemble technique
    Date: 2022
  8. By: Shang, Dawei
    Abstract: Due to the impact of the COVID-19 pandemic, new attraction ways are tended to be adapted by compelling sites to provide tours product and services, such as virtual reality (VR) to visitors. Based on human-computer interaction (HCI) user engagement and domain segmentation innovativeness theory, we develop and test a theoretical framework using a hybrid partial least squares structural equation model (PLSSEM) with Importance Performance Matrix (IMP) and neural network machine learning approach (PLSSEM-IMP-NN) that examines key user engagement drivers of visitors’ attitude toward VR (ATT) and in-person tour intentions (ITI) during COVID-19. According to a sample of visitors' response, the results demonstrate that a) user engagement including aesthetic appeal, focused attention, perceived usability, and reward experience, raise attitude toward VR; b) product-possessing innovativeness positively moderates the relationships between ATT and ITI; c) information-possessing innovativeness negatively moderates the relationships between ATT and ITI; d) ATT exert the mediating effect between user engagement and ITI. The proposed new PLSSEM-IMP-NN approach has been examined and denotes its efficient and effective in HCI and behavioral response assessment. Other contributions to theories and practical implications are discussed accordingly.
    Date: 2022–10–15
  9. By: Maximilian Andres; Lisa Bruttel; Jana Friedrichsen
    Abstract: This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices.
    Keywords: cartel, collusion, communication, machine learning, experiment
    JEL: C92 D43 L41
    Date: 2022
  10. By: Francis X. Diebold (University of Pennsylvania); Maximilian Gobel (University of Lisbon); Philippe Goulet Coulombe (University of Quebec)
    Abstract: We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Gobel (2022), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the frequent appearance of predictability thresholds, which differ across months, meaning that accuracy initially fails to improve as the target date is approached but then increases progressively once a threshold lead time is crossed. Also, we find that FEML can improve appreciably over FELR when forecasting "turning point" months in the annual cycle at horizons of one to three months ahead.
    Keywords: Seasonal climate forecasting, forecast evaluation and comparison, prediction
    JEL: Q54 C22 C52 C53
    Date: 2022–06–23
  11. By: Wei Li; Wolfgang Karl H\"ardle; Stefan Lessmann
    Abstract: There has been intensive research regarding machine learning models for predicting bankruptcy in recent years. However, the lack of interpretability limits their growth and practical implementation. This study proposes a data-driven explainable case-based reasoning (CBR) system for bankruptcy prediction. Empirical results from a comparative study show that the proposed approach performs superior to existing, alternative CBR systems and is competitive with state-of-the-art machine learning models. We also demonstrate that the asymmetrical feature similarity comparison mechanism in the proposed CBR system can effectively capture the asymmetrically distributed nature of financial attributes, such as a few companies controlling more cash than the majority, hence improving both the accuracy and explainability of predictions. In addition, we delicately examine the explainability of the CBR system in the decision-making process of bankruptcy prediction. While much research suggests a trade-off between improving prediction accuracy and explainability, our findings show a prospective research avenue in which an explainable model that thoroughly incorporates data attributes by design can reconcile the dilemma.
    Date: 2022–11
  12. By: Yugo Fujimotol; Kei Nakagawa; Kentaro Imajo; Kentaro Minami
    Abstract: Machine learning is an increasingly popular tool with some success in predicting stock prices. One promising method is the Trader-Company~(TC) method, which takes into account the dynamism of the stock market and has both high predictive power and interpretability. Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction. However, point prediction in the absence of uncertainty estimates lacks credibility quantification and raises concerns about safety. The challenge in this paper is to make an investment strategy that combines high predictive power and the ability to quantify uncertainty. We propose a novel approach called Uncertainty Aware Trader-Company Method~(UTC) method. The core idea of this approach is to combine the strengths of both frameworks by merging the TC method with the probabilistic modeling, which provides probabilistic predictions and uncertainty estimations. We expect this to retain the predictive power and interpretability of the TC method while capturing the uncertainty. We theoretically prove that the proposed method estimates the posterior variance and does not introduce additional biases from the original TC method. We conduct a comprehensive evaluation of our approach based on the synthetic and real market datasets. We confirm with synthetic data that the UTC method can detect situations where the uncertainty increases and the prediction is difficult. We also confirmed that the UTC method can detect abrupt changes in data generating distributions. We demonstrate with real market data that the UTC method can achieve higher returns and lower risks than baselines.
    Date: 2022–10
  13. By: Katarzyna Kryńska (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)
    Abstract: This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four approaches are tested: two are regression problems (price level prediction) and the other two are classification problems (prediction of price direction). All approaches are applied to daily, hourly, and 15-minute data and are using a walk-forward optimization procedure. The out-of-sample period for the S&P 500 Index is from February 6, 2014 to November 27, 2020, and for Bitcoin it is from January 15, 2014 to December 1, 2020. We discover that classification techniques beat regression methods on average, but we cannot determine if intra-day models outperform inter-day models. We come to the conclusion that the ensembling of models does not always have a positive impact on performance. Finally, a sensitivity analysis is performed to determine how changes in the main hyperparameters of the LSTM model affect strategy performance.
    Keywords: machine learning, deep learning, recurrent neural networks, LSTM, algorithmic trading, ensemble investment strategy, intra-day trading, S&P 500 Index, Bitcoin
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2022
  14. By: Metz-Peeters, Maike
    Abstract: This study analyzes the effects of binding speed limits on crash frequency on German motorways. Various geo-spatial data sources are merged to a new data set providing rich information on roadway characteristics for 500-meter segments of large parts of the German motorway network. The empirical analysis uses a causal forest, which allows to estimate the effects of speed limits on crash frequency under fairly weak assumptions about the underlying data generating process and provides insights into treatment effect heterogeneity. The paper is the first to explicitly discuss possible pitfalls and potential solutions when applying causal forests to geo-spatial data. Substantial negative effects of three levels of speed limits on accident rates are found, being largest for severe, and especially fatal crash rates, while effects on light crash rates are rather moderate. The heterogeneity analysis suggest that the effects are larger for less congested roads, as well as for roads with entrance and exit ramps, while heterogeneity regarding shares of heavy traffic is inconclusive.
    Keywords: Crash frequency,speed limits,German Autobahn,causal machine learning,causal forest,spatial machine learning
    JEL: R41 R42 R48
    Date: 2022
  15. By: Susan Athey; Dean Karlan; Emil Palikot; Yuan Yuan
    Abstract: Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by borrowers creating online profiles impact both of these objectives. We further support this conclusion with a web-based randomized survey experiment. In the experiment, we create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. We then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension.
    JEL: D0 D40 J0 O1
    Date: 2022–11
  16. By: Reza Bradrania; Davood Pirayesh Neghab
    Abstract: Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.
    Date: 2022–11
  17. By: Maximilien Germain (EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF, EDF R&D - EDF R&D - EDF - EDF, EDF - EDF, LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Huyên Pham (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, 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, FiME Lab - Laboratoire de Finance des Marchés d'Energie - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CREST - EDF R&D - EDF R&D - EDF - EDF); Xavier Warin (EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF, EDF R&D - EDF R&D - EDF - EDF, EDF - EDF, FiME Lab - Laboratoire de Finance des Marchés d'Energie - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CREST - EDF R&D - EDF R&D - EDF - EDF)
    Abstract: We consider the control of McKean-Vlasov dynamics (or mean-field control) with probabilistic state constraints. We rely on a level-set approach which provides a representation of the constrained problem in terms of an unconstrained one with exact penalization and running maximum or integral cost. The method is then extended to the common noise setting. Our work extends (Bokanowski, Picarelli, and Zidani, SIAM J. Control Optim. 54.5 (2016), pp. 2568–2593) and (Bokanowski, Picarelli, and Zidani, Appl. Math. Optim. 71 (2015), pp. 125–163) to a mean-field setting. The reformulation as an unconstrained problem is particularly suitable for the numerical resolution of the problem, that is achieved from an extension of a machine learning algorithm from (Carmona, Laurière, arXiv:1908.01613 to appear in Ann. Appl. Prob., 2019). A first application concerns the storage of renewable electricity in the presence of mean-field price impact and another one focuses on a mean-variance portfolio selection problem with probabilistic constraints on the wealth. We also illustrate our approach for a direct numerical resolution of the primal Markowitz continuous-time problem without relying on duality.
    Keywords: mean-field control,state constraints,neural networks
    Date: 2022
  18. By: Tristan Lim
    Abstract: The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
    Date: 2022–09
  19. By: Richard Post; Isabel van den Heuvel; Marko Petkovic; Edwin van den Heuvel
    Abstract: Causal inference from observational data requires untestable assumptions. If these assumptions apply, machine learning (ML) methods can be used to study complex forms of causal-effect heterogeneity. Several ML methods were developed recently to estimate the conditional average treatment effect (CATE). If the features at hand cannot explain all heterogeneity, the individual treatment effects (ITEs) can seriously deviate from the CATE. In this work, we demonstrate how the distributions of the ITE and the estimated CATE can differ when a causal random forest (CRF) is applied. We extend the CRF to estimate the difference in conditional variance between treated and controls. If the ITE distribution equals the CATE distribution, this difference in variance should be small. If they differ, an additional causal assumption is necessary to quantify the heterogeneity not captured by the CATE distribution. The conditional variance of the ITE can be identified when the individual effect is independent of the outcome under no treatment given the measured features. Then, in the cases where the ITE and CATE distributions differ, the extended CRF can appropriately estimate the characteristics of the ITE distribution while the CRF fails to do so.
    Date: 2022–10
  20. By: Christoph Graf; Federico Quaglia; Frank A. Wolak
    Abstract: The negative demand shock due to the COVID-19 lockdown has reduced net demand for electricity -- system demand less amount of energy produced by intermittent renewables, hydroelectric units, and net imports -- that must be served by controllable generation units. Under normal demand conditions, introducing additional renewable generation capacity reduces net demand. Consequently, the lockdown can provide insights about electricity market performance with a large share of renewables. We find that although the lockdown reduced average day-ahead prices in Italy by 45%, re-dispatch costs increased by 73%, both relative to the average of the same magnitude for the same period in previous years. We estimate a deep-learning model using data from 2017--2019 and find that predicted re-dispatch costs during the lockdown period are only 26% higher than the same period in previous years. We argue that the difference between actual and predicted lockdown period re-dispatch costs is the result of increased opportunities for suppliers with controllable units to exercise market power in the re-dispatch market in these persistently low net demand conditions. Our results imply that without grid investments and other technologies to manage low net demand conditions, an increased share of intermittent renewables is likely to increase costs of maintaining a reliable grid.
    Date: 2022–11
  21. By: Ruochen Xiao; Qiaochu Feng; Ruxin Deng
    Abstract: Models trained under assumptions in the complete market usually don't take effect in the incomplete market. This paper solves the hedging problem in incomplete market with three sources of incompleteness: risk factor, illiquidity, and discrete transaction dates. A new jump-diffusion model is proposed to describe stochastic asset prices. Three neutral networks, including RNN, LSTM, Mogrifier-LSTM are used to attain hedging strategies with MSE Loss and Huber Loss implemented and compared.As a result, Mogrifier-LSTM is the fastest model with the best results under MSE and Huber Loss.
    Date: 2022–11
  22. By: Juhász, Réka; Lane, Nathaniel (University of Oxford); Oehlsen, Emily; Pérez, Verónica C.
    Abstract: Although questions surrounding industrial policy are fundamental, we lack both measures and comprehensive data on industrial policy. Consequently, scholars and practitioners lack a systematic picture of industrial policy practice. This paper provides a new, text-based approach to measuring industrial policy. We take the tools of supervised machine learning to a comprehensive, English-language database of economic policy to construct measures of industrial policy at the country, industry, and year level. We use this data to establish four fundamental facts about global industrial policy from 2009 to 2020. First, IP is common (25 percent of policies in our database) and has been trending upward since 2010. Second, industrial policy is technocratic and granular, taking the form of subsidies and export promotion measures targeted at individual firms, instead of tariffs. Third, the countries engaged most in IP tend to be wealthier (top income quintile) liberal democracies, and IP is very rare among the poorest nations (bottom quintile). Fourth, IP tends to be targeted toward a small share of industries, and targeting is highly correlated with an industry’s revealed comparative advantage. Thus, we find contemporary practice is a far cry from industrial policy’s past and tends toward selective, export-oriented policies used by the world’s most developed economies.
    Date: 2022–08–14
  23. By: , SHANIMON S Dr; Mathew, Seena Mary
    Abstract: Artificial intelligence (AI) is now widely acknowledged as one of the most important digital transformation enablers across a significant number of industries. Artificial intelligence (AI) has the potential to facilitate enterprises. become more imaginative, versatile, and adaptable than they have ever been. AI is already being applied to enhance productivity and competitiveness while also driving digital transformation in a range of organizations. AI is supporting Indian banks in upgrading their operations across the board, from accounting to sales to contracts and cybersecurity. This is a case study based on virtual assistant of SBI-SIA. Recent developments and emergence of virtual banking and the trends in the modern banking systems explained in this study.
    Date: 2022–07–28
  24. By: Bingyan Han
    Abstract: In a semi-realistic market simulator, independent reinforcement learning algorithms may facilitate market makers to maintain wide spreads even without communication. This unexpected outcome challenges the current antitrust law framework. We study the effectiveness of maker-taker fee models in preventing cooperation via algorithms. After modeling market making as a repeated general-sum game, we experimentally show that the relation between net transaction costs and maker rebates is not necessarily monotone. Besides an upper bound on taker fees, we may also need a lower bound on maker rebates to destabilize the cooperation. We also consider the taker-maker model and the effects of mid-price volatility, inventory risk, and the number of agents.
    Date: 2022–11
  25. By: Ala-Mantila, Sanna; Kurvinen, Antti; Karhula, Aleksi
    Abstract: As a result of the ongoing urbanization megatrend, cities have an increasingly critical role in the search for sustainability. To create sustainable strategies for cities and to follow up if they induce desired effects proper metrics on the development of neighborhoods are needed. In this paper, we introduce a neighborhood classification framework and demonstrate its use through an analysis of the 20 largest cities in Finland. The high-quality data available for Finland provided solid grounds for development, but the framework is widely applicable to other locations. The classification is freely available for use and has a multitude of potential applications.
    Date: 2022–09–22
  26. By: Imed Boughzala (LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], TIM - Département Technologies, Information & Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]); Nesrine Ben Yahia (University of Manouba, Tunisia - RIADI Laboratory, National School of Computer Science); Narjès Bellamine Ben Saoud (University of Manouba, Tunisia - RIADI Laboratory, National School of Computer Science); Wissem Eljaoued (University of Manouba, Tunisia - RIADI Laboratory, National School of Computer Science)
    Abstract: Quantum Computing (QC) is an emerging and fast-growing research field that combines computer science with quantum mechanics such as quantum superposition and quantum entanglement. In order to contribute to a clarification of this field, the objective of this paper is twofold. Firstly, it aims to map the territory in which most relevant QC researches, scientific communities and related domains are stated and its relationship with classical computing. Secondly, it aims to examine the future research agenda according to different perspectives. We will do so by conducting a systematic literature review (SLR) based on the most important databases from 2010 to 2022. Our findings demonstrate that there is still room for understanding QC and how it transforms business, society and learning.
    Keywords: Quantum Computing,SLR,Mapping the Territory,Digital intelligence
    Date: 2022–08–31

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