nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒12‒19
twenty-two papers chosen by

  1. FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis By Shayan Halder
  2. Online Investor Sentiment via Machine Learnings By Zongwu Cai; Pixiong Chen
  3. Spatial Machine Learning – New Opportunities for Regional Science By Katarzyna Kopczewska
  4. On the application of Machine Learning in telecommunications forecasting: A comparison By Petre, Konstantin; Varoutas, Dimitris
  5. A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches By Kakuho Furukawa; Ryohei Hisano; Yukio Minoura; Tomoyuki Yagi
  6. Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm By Wei-Chang Yeh; Yu-Hsin Hsieh; Chia-Ling Huang
  7. Redirect the Probability Approach in Econometrics Towards PAC Learning By Duo Qin
  8. On Pricing of Discrete Asian and Lookback Options under the Heston Model By Leonardo Perotti; Lech A. Grzelak
  9. Intellectual Property Protection Lost and Competition: An Examination Using Machine Learning By Utku U. Acikalin; Tolga Caskurlu; Gerard Hoberg; Gordon M. Phillips
  10. Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks By Chong Mo; Song Li; Geoffrey K. F. Tso; Jiandong Zhou; Yiyan Qi; Mingjie Zhu
  11. A review of macroeconomic models for the WEFE nexus assessment By Castelli, Chiara; Castellini, Marta; Ciola, Emanuele; Gusperti, Camilla; Romani, Ilenia Gaia; Vergalli, Sergio
  12. Enhanced Bayesian Neural Networks for Macroeconomics and Finance By Niko Hauzenberger; Florian Huber; Karin Klieber; Massimiliano Marcellino
  13. A deep solver for BSDEs with jumps By Alessandro Gnoatto; Marco Patacca; Athena Picarelli
  14. Numerical Simulations of Reaching a Steady State: No Need to Generate Any Rational Expectations By Harashima, Taiji
  15. Using multimodal learning and deep generative models for corporate bankruptcy prediction By Rogelio A. Mancisidor
  16. Consumer credit in the age of AI: Beyond anti-discrimination law By Langenbucher, Katja
  17. Humans Feel Too Special for Machines to Score Their Morals By Purcell, Zoe; Bonnefon, Jean-François
  18. Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare By Krämer, Jan; Shekhar, Shiva; Hofmann, Janina
  19. A Model for Cooperative Intelligent Transport Diffusion Simulation in the European Vehicle Fleet By Degrande, Thibault; Vannieuwenborg, Frederic; Verbrugge, Sofie; Colle, Didier
  20. Effects of Artificial Intelligence, Big Data Analytics, and Business Intelligence on Digital Transformation in UAE Telecommunication Firms By , editor2021; Younus, Ahmed Muayad
  21. The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest By Naudé, Wim
  22. Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution By Simon Hirsch; Florian Ziel

  1. By: Shayan Halder
    Abstract: Economy is severely dependent on the stock market. An uptrend usually corresponds to prosperity while a downtrend correlates to recession. Predicting the stock market has thus been a centre of research and experiment for a long time. Being able to predict short term movements in the market enables investors to reap greater returns on their investments. Stock prices are extremely volatile and sensitive to financial market. In this paper we use Deep Learning networks to predict stock prices, assimilating financial, business and technology news articles which present information about the market. First, we create a simple Multilayer Perceptron (MLP) network and then expand into more complex Recurrent Neural Network (RNN) like Long Short Term Memory (LSTM), and finally propose FinBERT-LSTM model, which integrates news article sentiments to predict stock price with greater accuracy by analysing short-term market information. We then train the model on NASDAQ-100 index stock data and New York Times news articles to evaluate the performance of MLP, LSTM, FinBERT-LSTM models using mean absolute error (MAE), mean absolute percentage error (MAPE) and accuracy metrics.
    Date: 2022–11
  2. By: Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Pixiong Chen (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: In this paper, we propose to utilize machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment. Our empirical studies provide a strong evidence that some machine learning methods, such as the extreme gradient boosting or random forest, show significant predictive ability in terms of out-of-sample R-square with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which reveal a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value that it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of certainty equivalent return gain and Sharpe ratio.
    JEL: C45 C55 C58 G11 G17
    Date: 2022–11
  3. By: Katarzyna Kopczewska (Faculty of Economic Sciences, University of Warsaw)
    Abstract: This paper is a methodological guide on using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data and supervised learning, which displaces classical spatial econometrics. It shows the potential and traps of using this developing methodology. It catalogues and comments on the usage of spatial clustering methods (for locations and values, separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling, and density indicators. It shows details of spatial machine learning models, combined with spatial data integration, modelling, model fine-tuning and predictions, to deal with spatial autocorrelation and big data. The paper delineates "already available" and "forthcoming" methods and gives inspirations to transplant modern quantitative methods from other thematic areas to research in regional science.
    Keywords: spatial machine learning, clustering, spatial covariates, spatial cross-validation, spatial autocorrelation
    JEL: C31 R10 C49
    Date: 2021
  4. By: Petre, Konstantin; Varoutas, Dimitris
    Abstract: Over the past few decades, a large number of research papers has published focused on forecasting ICT products using various diffusion models like logistic, Gompertz, Bass, etc. Much less research work has been done towards the application of time series forecasting in ICT such as ARIMA model which seems to be an attractive alternative. More recently with the advancement in computational power, machine learning and artificial intelligence have become popular due to superior performance than classical models in many areas of concern. In this paper, broadband penetration is analysed separately for all OECD countries, trying to figure out which model is superior in most cases and phases in time. Although diffusion models are dedicated for this purpose, the ARIMA model has nevertheless shown an enormous influence as a good alternative in many previous works. In this study, a new approach using LSTM networks stands out to be a promising method for projecting high technology innovations diffusion.
    Keywords: Diffusion models,ARIMA,LSTM,broadband penetration forecasting
    Date: 2022
  5. By: Kakuho Furukawa (Bank of Japan); Ryohei Hisano (The University of Tokyo); Yukio Minoura (Bank of Japan); Tomoyuki Yagi (Bank of Japan)
    Abstract: Recent years have seen a growing trend to utilize "alternative data" in addition to traditional statistical data in order to understand and assess economic conditions in real time. In this paper, we construct a nowcasting model for the Indices of Industrial Production (IIP), which measure production activity in the manufacturing sector in Japan. The model has the following characteristics: First, it uses alternative data (mobility data and electricity demand data) that is available in real-time and can nowcast the IIP one to two months before their official release. Second, the model employs machine learning techniques to improve the nowcasting accuracy by endogenously changing the mixing ratio of nowcast values based on traditional economic statistics (the Indices of Industrial Production Forecast) and nowcast values based on alternative data, depending on the economic situation. The estimation results show that by applying machine learning techniques to alternative data, production activity can be nowcasted with high accuracy, including when it went through large fluctuations during the spread of the COVID-19 pandemic.
    Keywords: Industrial production; Mobility data; Electricity data; Nowcasting; Machine learning; COVID-19
    JEL: C49 C55 E23 E27
    Date: 2022–11–25
  6. By: Wei-Chang Yeh; Yu-Hsin Hsieh; Chia-Ling Huang
    Abstract: In modern society, the trading methods and strategies used in financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by a pre-programmed computer programs because the continuous development of network and computer computing technology. The quantitative trading, which the main purpose is to automatically formulate people's investment decisions into a fixed and quantifiable operation logic that eliminates all emotional interference and the influence of subjective thoughts and applies this logic to financial market activities in order to obtain excess profits above average returns, has led a lot of attentions in financial market. The development of self-adjustment programming algorithms for automatically trading in financial market has transformed a top priority for academic research and financial practice. Thus, a new flexible grid trading model combined with the Simplified Swarm Optimization (SSO) algorithm for optimizing parameters for various market situations as input values and the fully connected neural network (FNN) and Long Short-Term Memory (LSTM) model for training a quantitative trading model to automatically calculate and adjust the optimal trading parameters for trading after inputting the existing market situation is developed and studied in this work. The proposed model provides a self-adjust model to reduce investors' effort in the trading market, obtains outperformed investment return rate and model robustness, and can properly control the balance between risk and return.
    Date: 2022–09
  7. By: Duo Qin (Department of Economics, SOAS University of London)
    Abstract: Infiltration of machine learning (ML) methods into econometrics has remained relatively slow, compared with their extensive applications in many other disciplines. The bottleneck is traced to two key factors – a communal nescience of the theoretical foundation of ML and an outdated probability foundation. The present study ventures on an overhaul of the probability approach by Haavelmo (1944) in light of ML theories of learnibility, centred upon the notion of probably approximately correct (PAC) learning. The study argues for a reorientation of the probability approach towards assisting decision making for model learning and selection purposes. The first part of the study is presented here.
    Keywords: probability; uncertainty; machine learning; hypothesis testing; knowledge; representation
    JEL: C10 C18 B40
    Date: 2022–03
  8. By: Leonardo Perotti; Lech A. Grzelak
    Abstract: We propose a new, data-driven approach for efficient pricing of - fixed- and float-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics. The method proposed in this article constitutes an extension of our previous work, where the problem of sampling from time-integrated stochastic bridges was addressed. The model relies on the Seven-League scheme, where artificial neural networks are employed to "learn" the distribution of the random variable of interest utilizing stochastic collocation points. The method results in a robust procedure for Monte Carlo pricing. Furthermore, semi-analytic formulae for option pricing are provided in a simplified, yet general, framework. The model guarantees high accuracy and a reduction of the computational time up to thousands of times compared to classical Monte Carlo pricing schemes.
    Date: 2022–11
  9. By: Utku U. Acikalin; Tolga Caskurlu; Gerard Hoberg; Gordon M. Phillips
    Abstract: We examine the impact of lost intellectual property protection on innovation, competition, acquisitions, lawsuits and employment agreements. We consider firms whose ability to protect intellectual property (IP) using patents is weakened following the Alice Corp. vs. CLS Bank International Supreme Court decision. This decision has impacted patents in multiple areas including business methods, software, and bioinformatics. We use state-of-the-art machine learning techniques to identify firms’ existing patent portfolios’ potential exposure to the Alice decision. While all affected firms decrease patenting post-Alice, we find an unequal impact of decreased patent protection. Large affected firms benefit as their sales and market valuations increase, and their exposure to lawsuits decreases. They also acquire fewer firms post-Alice. Small affected firms lose as they face increased competition, product-market encroachment, and lower profits and valuations. They increase R&D and have their employees sign more nondisclosure agreements.
    JEL: D43 G34 O31 O33 O34
    Date: 2022–11
  10. By: Chong Mo; Song Li; Geoffrey K. F. Tso; Jiandong Zhou; Yiyan Qi; Mingjie Zhu
    Abstract: Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.
    Date: 2022–11
  11. By: Castelli, Chiara; Castellini, Marta; Ciola, Emanuele; Gusperti, Camilla; Romani, Ilenia Gaia; Vergalli, Sergio
    Abstract: The Water, Energy, Food and Ecosystems (WEFE) nexus refers to the system of complex and highly non-linear interconnections between these four elements. It now represents the basic framework to assess and design policies characterized by an holistic environmental end economical perspective. In this work, we provide a systematic review of the macroeconomic models investigating its components as well as combinations of them and their interlinkages with the economic system. We focus on four different types of macroeconomic models: Computable General Equilibrium (CGE) models, Integrated Assessment Models (IAMs), Agent-based Models (ABMs), and Dynamic Stochastic General Equilibrium (DSGE) models. On the basis of our review, we find that the structure of IAMs is currently the most used to represent the nexus complexity, while DSGE models focus only on single components but appear to be better suited to account for the randomization of exogenous shocks. CGE models and ABMs could be more effective on the side of the policy perspective. Indeed, the former can account for interlinkages across sectors and countries, while the latter can define theoretical frameworks that better approximate reality.
    Keywords: Environmental Economics and Policy, Land Economics/Use, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy
    Date: 2022–11–28
  12. By: Niko Hauzenberger; Florian Huber; Karin Klieber; Massimiliano Marcellino
    Abstract: We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities and time variation for (possibly large sets of) macroeconomic and financial variables. From a methodological point of view, we allow for a general specification of networks that can be applied to either dense or sparse datasets, and combines various activation functions, a possibly very large number of neurons, and stochastic volatility (SV) for the error term. From a computational point of view, we develop fast and efficient estimation algorithms for the general BNNs we introduce. From an empirical point of view, we show both with simulated data and with a set of common macro and financial applications that our BNNs can be of practical use, particularly so for observations in the tails of the cross-sectional or time series distributions of the target variables.
    Date: 2022–11
  13. By: Alessandro Gnoatto; Marco Patacca; Athena Picarelli
    Abstract: The aim of this work is to propose an extension of the Deep BSDE solver by Han, E, Jentzen (2017) to the case of FBSDEs with jumps. As in the aforementioned solver, starting from a discretized version of the BSDE and parametrizing the (high dimensional) control processes by means of a family of ANNs, the BSDE is viewed as model-based reinforcement learning problem and the ANN parameters are fitted so as to minimize a prescribed loss function. We take into account both finite and infinite jump activity by introducing, in the latter case, an approximation with finitely many jumps of the forward process.
    Date: 2022–11
  14. By: Harashima, Taiji
    Abstract: It is not easy to numerically simulate the path to a steady state because there is no closed form solution in dynamic economic growth models in which households behave generating rational expectations. In contrast, it is easy if households are supposed to behave under the MDC (maximum degree of comfortability)-based procedure. In such a simulation, a household increases or decreases its consumption according to simple formulae. In this paper, I simulate the path when households behave under the MDC-based procedure, and the results of simulations indicate that households can easily reach a stabilized (steady) state without generating any rational expectations by behaving according to their feelings and guesses about their preferences and the state of the entire economy.
    Keywords: Balanced growth path; Economic growth model; Government transfer; Heterogeneity; Simulation; Steady state
    JEL: C60 D60 E10 H30 I30
    Date: 2022–11–16
  15. By: Rogelio A. Mancisidor
    Abstract: This research introduces for the first time the concept of multimodal learning in bankruptcy prediction models. We use the Conditional Multimodal Discriminative (CMMD) model to learn multimodal representations that embed information from accounting, market, and textual modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions. This fact makes the use of bankruptcy prediction models using textual data realistic and possible, since accounting and market data are available for all companies unlike textual data. The empirical results in this research show that the classification performance of our proposed methodology is superior compared to that of a large number of traditional classifier models. We also show that our proposed methodology solves the limitation of previous bankruptcy models using textual data, as they can only make predictions for a small proportion of companies. Finally, based on multimodal representations, we introduce an index that is able to capture the uncertainty of the financial situation of companies during periods of financial distress.
    Date: 2022–10
  16. By: Langenbucher, Katja
    Abstract: Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
    Keywords: credit scoring methodology,AI enabled credit scoring,AI borrower classification,responsible lending,credit scoring regulation,financial privacy,statistical discrimination
    JEL: C18 C32 K12 K23 K33 K40 J14 O31 O33
    Date: 2022
  17. By: Purcell, Zoe; Bonnefon, Jean-François
    Abstract: Artificial Intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems —enabling people and organizations to form judgements of others at scale. However, it also poses significant ethical challenges and is, subsequently, the subject of wide debate. As these technologies are developed and governing bodies face regulatory decisions, it is crucial that we understand the attraction or resistance that people have for AI moral scoring. Across four experiments, we show that the acceptability of moral scoring by AI is related to expectations about the quality of those scores, but that expectations about quality are compromised by people's tendency to see themselves as morally peculiar. We demonstrate that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI.
    Keywords: Artificial Intelligence; social credit scoring, ethics; consumer psychology
    Date: 2022–11
  18. By: Krämer, Jan; Shekhar, Shiva; Hofmann, Janina
    Abstract: Algorithmic learning gives rise to a data-driven network effects, which allow a dominant platform to reinforce its dominant market position. Data-driven network effects can also spill over to related markets and thereby allow to leverage a dominant position. This has led policymakers to propose data siloing and mandated data sharing remedies for dominant data-driven platforms in order to keep digital markets open and contestable. While data siloing seeks to prevent the spillover of data-driven network effects generated by algorithmic learning to other markets, data sharing seeks to share this externality with rival firms. Using a game-theoretic model, we investigate the impacts of both types of regulation. Our results bear important policy implications, as we demonstrate that data siloing and data sharing are potentially harmful remedies, which can reduce the innovation incentives of the regulated platform, and can lead overall lower consumer surplus and total welfare.
    Keywords: Data-driven network effects,algorithmic learning,regulation,data sharing,data siloing
    Date: 2022
  19. By: Degrande, Thibault; Vannieuwenborg, Frederic; Verbrugge, Sofie; Colle, Didier
    Abstract: To date, the European Commission is working hard on amending Directive 2010/40/EU, in which a new, technologyneutral framework for Cooperative Intelligent Transportation Systems (C-ITS) is proposed. C-ITS promise to reduce traffic congestion, lessen the environmental impact of transportation, and reduce the number of traffic mortalities. To realize those societal goals, adoption of C-ITS in passenger cars is essential. As passenger cars are no fast-moving consumer goods and the European fleet is subject to principles such as production cycles, traditional adoption models are not suitable to estimate expected penetration of C-ITS the fleet. Therefore, this paper provides a model to simulate penetration rates of C-ITS equipped cars in the European passenger car fleet, allowing simulation of both ITS-G5 and C-V2X technology diffusion separately. The model enables the assessment of the impact of policies such as a mandate, market decisions, and other scenarios, on the penetration of CITS in the European fleet. Insights into C-ITS penetration are valuable for a number of stakeholders, such as national and local governments, road authorities, car manufacturers and (network) technology providers, to appraise policy and business decisions. Lastly, the model can also be used to estimate penetration of other new technologies in passenger cars.
    Keywords: techno-economic,adoption,cooperative intelligent,transport systems
    Date: 2022
  20. By: , editor2021; Younus, Ahmed Muayad
    Abstract: This research’s primary objective is to investigate the impact of artificial intelligence, big data analytics, and business intelligence on digital transformation in UAE telecommunications companies. Following the completion of the sample checking procedure, 200 samples were collected. The Amos program was used to process all the collected data in the research study. The findings of the research demonstrate a set of relationships and linkages that can enhance digital transformation. Moreover, a summary of the findings revealed that all three hypotheses H1, H2, and H3 were found to be valid and significant. This study concluded that artificial intelligence, big data analytics, and business intelligence have a positive impact on developing and enhancing for digital transformation.
    Date: 2022–06–06
  21. By: Naudé, Wim (RWTH Aachen University)
    Abstract: This paper contributes to the economics of AI by exploring three topics neglected by economists: (i) the notion of a Singularity (and Singleton), (ii) the existential risks that AI may pose to humanity, including that from an extraterrestrial AI in a Dark Forest universe; and (iii) the relevance of economics' Mythical Agent (homo economicus) for the design of value-aligned AI-systems. From the perspective of expected utility maximization, which both the fields of AI and economics share, these three topics are interrelated. By exploring these topics, several future avenues for economic research on AI becomes apparent, and areas where economic theory may benefit from a greater understanding of AI can be identified. Two further conclusions that emerge are first that a Singularity and existential risk from AI are still science fiction: which, however, should not preclude economics from bearing on the issues (it does not deter philosophers); and two, that economists should weigh in more on existential risk, and not leave this topic to lose credibility because of the Pascalian fanaticism of longtermism.
    Keywords: technology, artificial intelligence, economics, growth, existential risk, longtermism, Fermi Paradox, Grabby Aliens
    JEL: O40 O33 D01 D64
    Date: 2022–11
  22. By: Simon Hirsch; Florian Ziel
    Abstract: During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.
    Date: 2022–11

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