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on Computational Economics |
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Issue of 2024‒04‒15
eighteen papers chosen by |
| By: | Njiru, Ruth; Appel, Franziska; Dong, Changxing; Balmann, Alfons |
| Abstract: | In light of the dynamic challenges facing agricultural land markets, the conventional analytical frameworks fall short in capturing the intricate interplay of strategic decisions and evolving complexities. This necessitates the development of a novel method, integrating deep learning into Agent-based Modelling, to provide a more realistic and nuanced understanding of land market dynamics, enabling informed policy assessments and contributing to a comprehensive discourse on agricultural structural change. In this paper, different deep learning models are tested and evaluated, as emulators of AgriPoliS (Agricultural Policy Simulator). AgriPoliS is an agent-based model used to model the evolution of structural change in agriculture resultant on the change in the policy environment. This study is part of preliminary works towards integrating deep learning methods and predictions with AgriPoliS to capture strategic decision making and actions of agents in land markets. The paper tests the models on their suitability, computational requirements and run-time complexities. The output from AgriPoliS serves as the input features for the deep learning models. Models are evaluated using a combination of coefficient of determination (R2 score), mean absolute error, visual displays and runtime. The models were able to replicate the variable of interest with a high degree of accuracy with R2 score of more than 90%. The CNN was the most suited for replicating the data. Through this work, we learned the required complexities, computational and training efforts needed to integrate deep learning and AgriPoliS to capture strategic decision-making. |
| Keywords: | Land Economics/Use |
| Date: | 2024–03–26 |
| URL: | http://d.repec.org/n?u=RePEc:ags:bokufo:340874&r=cmp |
| By: | Junyi Ye; Bhaskar Goswami; Jingyi Gu; Ajim Uddin; Guiling Wang |
| Abstract: | This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.06779&r=cmp |
| By: | Yuxiang Sun; Jingyi Li; Mengdie Lu; Zongying Guo |
| Abstract: | Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the accounting and auditing fields can harness big data's potential while ensuring accuracy, transparency, and integrity in financial reporting. Keywords: Big Data, Accounting, Audit, Data Privacy, AI, Machine Learning, Transparency. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.07180&r=cmp |
| By: | Mueller, H.; Rauh, C.; Seimon, B. |
| Abstract: | This article provides a structured description of openly available news topics and forecasts for armed conflict at the national and grid cell level starting January 2010. The news topics as well as the forecasts are updated monthly at conflictforecast.org and provide coverage for more than 170 countries and about 65, 000 grid cells of size 55x55km worldwide. The forecasts rely on Natural Language Processing (NLP) and machine learning techniques to leverage a large corpus of newspaper text for predicting sudden onsets of violence in peaceful countries. Our goals are to: a) support conflict prevention efforts by making our risk forecasts available to practitioners and research teams worldwide, b) facilitate additional research that can utilise risk forecasts for causal identification, and to c) provide an overview of the news landscape. |
| Keywords: | Civil War, Conflict, Forecasting, Machine Learning, News Topics, Random Forest, Topic Models |
| Date: | 2024–02–02 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2404&r=cmp |
| By: | Tsendsuren Batsuuri; Shan He; Ruofei Hu; Jonathan Leslie; Flora Lutz |
| Abstract: | This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical. |
| Keywords: | Early warning systems; IMF Lending; Machine Learning |
| Date: | 2024–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/054&r=cmp |
| By: | Xinyi Wang; Lang Tong |
| Abstract: | This paper presents a novel generative probabilistic forecasting approach derived from the Wiener-Kallianpur innovation representation of nonparametric time series. Under the paradigm of generative artificial intelligence, the proposed forecasting architecture includes an autoencoder that transforms nonparametric multivariate random processes into canonical innovation sequences, from which future time series samples are generated according to their probability distributions conditioned on past samples. A novel deep-learning algorithm is proposed that constrains the latent process to be an independent and identically distributed sequence with matching autoencoder input-output conditional probability distributions. Asymptotic optimality and structural convergence properties of the proposed generative forecasting approach are established. Three applications involving highly dynamic and volatile time series in real-time market operations are considered: (i) locational marginal price forecasting for merchant storage participants, {(ii) interregional price spread forecasting for interchange markets, } and (iii) area control error forecasting for frequency regulations. Numerical studies based on market data from multiple independent system operators demonstrate superior performance against leading traditional and machine learning-based forecasting techniques under both probabilistic and point forecast metrics. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.05743&r=cmp |
| By: | Berg, Gerard J. van den (University of Groningen, University Medical Center Groningen ; IFAU Uppsala ; ZEW ; IZA ; CEPR); Kunaschk, Max (Institute for Employment Research (IAB), Nuremberg, Germany); Lang, Julia (Institute for Employment Research (IAB), Nuremberg, Germany); Stephan, Gesine (Institute for Employment Research (IAB), Nuremberg, Germany); Uhlendorff, Arne (Institute for Employment Research (IAB), Nuremberg, Germany) |
| Abstract: | "We analyze unique data on three sources of information on the probability of re-employment within 6 months (RE6), for the same individuals sampled from the inflow into unemployment. First, they were asked for their perceived probability of RE6. Second, their caseworkers revealed whether they expected RE6. Third, random-forest machine learning methods are trained on administrative data on the full inflow, to predict individual RE6. We compare the predictive performance of these measures and consider how combinations improve this performance. We show that self-reported (and to a lesser extent caseworker) assessments sometimes contain information not captured by the machine learning algorithm." (Author's abstract, IAB-Doku) ((en)) |
| Keywords: | Bundesrepublik Deutschland ; IAB-Open-Access-Publikation ; berufliche Reintegration ; Fremdbild ; Integrierte Erwerbsbiografien ; Langzeitarbeitslosigkeit ; Profiling ; Prognosegenauigkeit ; Risikoabschätzung ; Selbsteinschätzung ; Arbeitsberater ; Machine learning ; Arbeitslose ; Arbeitslosenversicherung ; Arbeitslosigkeitsdauer ; Arbeitsmarktchancen ; 2012-2013 |
| JEL: | C21 C41 C53 J64 J65 C55 |
| Date: | 2024–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:iab:iabdpa:202403&r=cmp |
| By: | Matteo Rizzato (Advestis); Julien Wallart (Fujitsu Systems Europe); Christophe Geissler (Advestis); Nicolas Morizet (Advestis); Noureddine Boumlaik |
| 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 ≲ 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. |
| Keywords: | Data Augmentation, Financial Scenarios, Risk Management, Generative Adversarial Networks |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03716692&r=cmp |
| By: | Severin Reissl; Luca E. Fierro; Francesco Lamperti; Andrea Roventini |
| Abstract: | We present an updated, stock-flow consistent version of the 'Dystopian Schumpeter meeting Keynes' agent-based integrated assessment model. By embedding the model in a fully specified accounting system, all balance sheet items and financial flows can be explicitly and consistently tracked throughout a simulation. This allows for an improved analysis of climate change and climate policy scenarios in terms of their systemic implications for agent and sector-level balance sheet dynamics and financial stability. We provide an extensive description of the updated model, representing the most detailed outline of a model from the well-established 'Keynes + Schumpeter' family available to date. Following a discussion of calibration and validation, we present a range of example scenarios. |
| Keywords: | Climate change; Agent-based models; Integrated assessment |
| Date: | 2024–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2024/09&r=cmp |
| By: | Koresh Galil (BGU); Ami Hauptman (Computer Science Department of Sapir College); Rosit Levy Rosenboim (Applied Economics Department of Sapir College) |
| Keywords: | Corporate Ratings, Machine Learning, Classification and Regression Tree, Support Vector Regression, CART, SVR, Size |
| JEL: | C45 C53 G24 G32 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:bgu:wpaper:2308&r=cmp |
| By: | Buczak, Philip; Horn, Daniel; Pauly, Markus |
| Abstract: | There is a long tradition of modeling ordinal response data with parametric models such as the proportional odds model. With the advent of machine learning (ML), however, the classical stream of parametric models has been increasingly challenged by a more recent stream of tree ensemble (TE) methods extending popular ML algorithms such as random forest to ordinal response data. Despite selective efforts, the current literature lacks an encompassing comparison between the two methodological streams. In this work, we fill this gap by investigating under which circumstances a proportional odds model is competitive with TE methods regarding its predictive performance, and when TE should be preferred. Additionally, we study whether the optimization of the numeric scores assigned to ordinal response categories, as in Ordinal Forest (OF; Hornung, 2019), is worth the associated computational burden. To this end, we further contribute to the literature by proposing the Ordinal Score Optimization Algorithm (OSOA). Similar, to OF, OSOA optimizes the numeric scores assigned to the ordinal response categories, but aims to enhance the optimization procedure used in OF by employing a non-linear optimization algorithm. Our comparison results show that while TE approaches outperformed the proportional odds model in the presence of strong non-linear effects, the latter was competitive for small sample sizes even under medium non-linear effects. Regarding the TE methods, only subtle differences emerged between the individual methods, showing that the benefit of score optimization was situational. We analyze potential reasons for the mixed benefits of score optimization to motivate further methodological research. Based on our results, we derive practical recommendations for researchers and practitioners. |
| Date: | 2024–03–29 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:v7bcf&r=cmp |
| By: | João A. Bastos; Maria Inês Bernardes |
| Abstract: | Customer profiling in e-commerce is a powerful tool that enables organizations to create personalized offers through direct marketing. One crucial objective of customer profiling is to predict whether a website visitor will make a purchase, thereby generating revenue. Machine learning models are the most accurate means to achieve this objective. However, the opaque nature of these models may deter companies from adopting them. Instead, they may prefer simpler models that allow for a clear understanding of the customer attributes that contribute to a purchase. In this study, we show that companies need not compromise on prediction accuracy to understand their online customers. By leveraging website data from a multinational communications service provider, we establish that the most pertinent customer attributes can be readily extracted from a black-box model. Specifically, we show that features measuring customer activity within the e-commerce platform are the most reliable predictors of conversions. Moreover, we uncover significant non-linear relationships between customer features and the likelihood of conversion. |
| Keywords: | Customer Profiling; Conversion; Direct marketing; Explainable artificial intelligence; SHAP value; Accumulated local effects. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp03132024&r=cmp |
| By: | Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium); Casas, Alex (Detralytics) |
| Abstract: | In absence of a closed form expression such as in the Heston model, the option pricing is computationally intensive when calibrating a model to market quotes. this article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs). A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman-Kac (FK) equation, which is a partial differential equation (PDE) governing the derivative price in the Heston model. We focus on the valuation of European options and show that PINNs constitute an efficient alternative for pricing options with various specifications and parameters without the need for retraining. |
| Keywords: | Neural networks ; options ; Heston model ; Feynman-Kac equation |
| Date: | 2024–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:aiz:louvad:2024002&r=cmp |
| By: | Gianluca Pallante; Mattia Guerini; Mauro Napoletano; Andrea Roventini |
| Abstract: | We extend the Schumpeter meeting Keynes (K+S; see Dosi et al., 2010, 2013, 2015) to model the emergence and the dynamics of an interbank network in the money market. The extended model allows banks to directly exchange funds, while evaluating their interbank positions using a network- based clearing mechanism (NEVA, see Barucca et al., 2020). These novel adds on, allow us to better measure financial contagion and systemic risk events in the model and to study the possible interactions between micro-prudential and macro-prudential policies. We find that the model can replicate new stylized facts concerning the topology of the interbank network, as well as the dynamics of individual banks’ balance sheets. Policy results suggest that the economic system at large can benefit from the introduction of a micro-prudential regulation that takes into account the interbank network relationships. Such a policy decreases the incidence of systemic risk events and the bankruptcies of financial institutions. Moreover, a trade-off between financial stability and macroeconomic performance does not emerge in a two-pillar regulatory framework grounded on i) a Basel III macro-prudential regulation and ii) a NEVA-based micro-prudential policy. Indeed, the NEVA allows the economic system to achieve financial stability without overly stringent capital requirements. |
| Keywords: | Financial contagion, Systemic risk, Micro-prudential policy, Macro-prudential policy, Macroeconomic stability, Agent-based computational economics |
| Date: | 2024–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2024/08&r=cmp |
| By: | Pinski, Marc; Benlian, Alexander |
| Date: | 2024–03–13 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:143748&r=cmp |
| By: | Brendan J. Chapuis; John Coglianese |
| Abstract: | In this note, we introduce a measure of unemployment risk, the likelihood of a worker becoming unemployed within the next twelve months. By using nonparametric machine learning applied to data on millions of workers in the US, we can estimate how unemployment risk varies across individuals and over time. |
| Date: | 2024–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2024-03-08-1&r=cmp |
| By: | Valerio Capraro; Roberto Di Paolo; Matjaz Perc; Veronica Pizziol |
| Abstract: | Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology, and artificial intelligence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain elusive. While traditional models try to explain human behaviour as a function of the outcomes of available actions, recent experimental research reveals that linguistic content significantly impacts decision-making, thus prompting a paradigm shift from outcome-based to language-based utility functions. This shift is more urgent than ever, given the advancement of generative AI, which has the potential to support humans in making critical decisions through language-based interactions. We propose sentiment analysis as a fundamental tool for this shift and take an initial step by analyzing 61 experimental instructions from the dictator game, an economic game capturing the balance between self-interest and the interest of others, which is at the core of many social interactions. Our meta-analysis shows that sentiment analysis can explain human behaviour beyond economic outcomes. We discuss future research directions. We hope this work sets the stage for a novel game theoretical approach that emphasizes the importance of language in human decisions. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.08944&r=cmp |
| By: | Charles Hoffreumon; Chris CM Forman; Nicolas van Zeebroeck |
| Date: | 2024–03–05 |
| URL: | http://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/369623&r=cmp |