nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒09‒11
eighteen papers chosen by
Stan Miles, Thompson Rivers University

  1. DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data By Yancheng Liang; Jiajie Zhang; Hui Li; Xiaochen Liu; Yi Hu; Yong Wu; Jinyao Zhang; Yongyan Liu; Yi Wu
  2. Entity matching with similarity encoding: A supervised learning recommendation framework for linking (big) data By Karapanagiotis, Pantelis; Liebald, Marius
  3. A novel approach for quantum financial simulation and quantum state preparation By Yen-Jui Chang; Wei-Ting Wang; Hao-Yuan Chen; Shih-Wei Liao; Ching-Ray Chang
  4. CEO Stress, Aging, and Death By Borgschulte, Mark; Guenzel, Marius; Liu, Canyao; Malmendier, Ulrike
  5. How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan) By Tohid Atashbar
  6. Deep Learning from Implied Volatility Surfaces By Bryan T. Kelly; Boris Kuznetsov; Semyon Malamud; Teng Andrea Xu
  7. Machine Learning Advice in Managerial Decision-Making: The Overlooked Role of Decision Makers’ Advice Utilization By Sturm, Timo; Pumplun, Luisa; Gerlach, Jin; Kowalczyk, Martin; Buxmann, Peter
  8. Reinforcement Learning for Financial Index Tracking By Xianhua Peng; Chenyin Gong; Xue Dong He
  9. Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects By Chao Zhang; Xingyue Pu; Mihai Cucuringu; Xiaowen Dong
  10. Insurance pricing on price comparison websites via reinforcement learning By Tanut Treetanthiploet; Yufei Zhang; Lukasz Szpruch; Isaac Bowers-Barnard; Henrietta Ridley; James Hickey; Chris Pearce
  11. Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming By John V. Colias; Stella Park; Elizabeth Horn
  12. A Novel Credit Model Risk Measure: does more data lead to lower model risk in credit scoring models? By Valter T. Yoshida Jr; Alan de Genaro; Rafael Schiozer; Toni R. E. dos Santos
  13. GEOWEALTH: spatial wealth inequality data for the United States, 1960-2020 By Suss, Joel; Kemeny, Thomas; Connor, Dylan Shane
  14. Simulation stochastique du modèle FR-BDF et évaluation de l'incertitude entourant les prévisions conditionnelles By Turunen Harry; Zhutova Anastasia; Lemoine Matthieu
  15. AI exposure predicts unemployment risk By Morgan Frank; Yong-Yeol Ahn; Esteban Moro
  16. Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python By Du, Tianyu; Kanodia, Ayush; Athey, Susan
  17. Path Shadowing Monte-Carlo By Rudy Morel; St\'ephane Mallat; Jean-Philippe Bouchaud
  18. Managers and AI-driven decisions: Exploring Managers’ Sensemaking Processes in Digital Transformation Contexts By Fabrice Duval; Christophe Elie-Dit-Cosaque

  1. By: Yancheng Liang; Jiajie Zhang; Hui Li; Xiaochen Liu; Yi Hu; Yong Wu; Jinyao Zhang; Yongyan Liu; Yi Wu
    Abstract: Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods. Different from images or languages, real-world financial data are high-dimensional, sparse, noisy and extremely imbalanced, which makes deep neural network models particularly challenging to train and fragile in practice. In this work, we propose DeRisk, an effective deep learning risk prediction framework for credit risk prediction on real-world financial data. DeRisk is the first deep risk prediction model that outperforms statistical learning approaches deployed in our company's production system. We also perform extensive ablation studies on our method to present the most critical factors for the empirical success of DeRisk.
    Date: 2023–08
  2. By: Karapanagiotis, Pantelis; Liebald, Marius
    Abstract: In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives.
    Keywords: Entity matching, Entity resolution, Database linking, Machine learning, Record resolution, Similarity encoding
    JEL: C8
    Date: 2023
  3. By: Yen-Jui Chang; Wei-Ting Wang; Hao-Yuan Chen; Shih-Wei Liao; Ching-Ray Chang
    Abstract: Quantum state preparation is vital in quantum computing and information processing. The ability to accurately and reliably prepare specific quantum states is essential for various applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state representing the system we are trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Harnessing the advantages of quantum computation, the multi-SSQW models complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW's key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.
    Date: 2023–08
  4. By: Borgschulte, Mark (University of Illinois at Urbana-Champaign); Guenzel, Marius (Wharton School, University of Pennsylvania); Liu, Canyao (Yale University); Malmendier, Ulrike (University of California, Berkeley)
    Abstract: We assess the long-term effects of managerial stress on aging and mortality. First, we show that exposure to industry distress shocks during the Great Recession produces visible signs of aging in CEOs. Applying neural-network based machine-learning techniques to pre- and post-distress pictures, we estimate an increase in so-called apparent age by one year. Second, using data on CEOs since the mid-1970s, we estimate a 1.1-year decrease in life expectancy after an industry distress shock, but a two-year increase when anti-takeover laws insulate CEOs from market discipline. The estimated health costs are significant, also relative to other known health risks.
    Keywords: managerial stress, life expectancy, apparent-age estimation, job demands, industry distress, visual machine-learning, corporate governance
    JEL: G34 I12 M12
    Date: 2023–08
  5. By: Tohid Atashbar
    Abstract: In this study we introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data. Our analysis of the fragility data collected by the OECD for its States of Fragility index showed that the use of such techniques could provide further insights into the non-linear relationships and diverse drivers of state fragility, highlighting the importance of a nuanced and context-specific approach to understanding and addressing this multi-aspect issue. We also applied the methodology used in this paper to South Sudan, one of the most fragile countries in the world to analyze the dynamics behind the different aspects of fragility over time. The results could be used to improve the Fund’s country engagement strategy (CES) and efforts at the country.
    Keywords: Fragile and Conflict-Affected States; Fragility Trap; Fragility Syndrome; Machine Learning; Artificial Intelligence
    Date: 2023–08–11
  6. By: Bryan T. Kelly (Yale School of Management; AQR Capital Management; NBER); Boris Kuznetsov (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Semyon Malamud (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; and CEPR); Teng Andrea Xu (Ecole Polytechnique Fédérale de Lausanne)
    Abstract: We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML). The predictive information we identify is essentially uncorrelated with most of the existing option-implied characteristics, delivers a higher Sharpe ratio, and has a significant alpha relative to a battery of standard and option-implied factors. We show the virtue of ensemble complexity: Best results are achieved with a large ensemble of ML models, with the out-of-sample performance increasing in the ensemble size, saturating when the number of model parameters significantly exceeds the number of observations. We introduce principal linear features, an analog of principal components for ML and use them to show IV feature complexity: A low-rank rotation of the IV surface cannot explain the model performance. Our results are robust to short-sale constraints and transaction costs.
    Date: 2023–08
  7. By: Sturm, Timo; Pumplun, Luisa; Gerlach, Jin; Kowalczyk, Martin; Buxmann, Peter
    Abstract: Machine learning (ML) analyses offer great potential to craft profound advice for augmenting managerial decision-making. Yet, even the most promising ML advice cannot improve decision-making if it is not utilized by decision makers. We therefore investigate how ML analyses influence decision makers’ utilization of advice and resulting decision-making performance. By analyzing data from 239 ML-supported decisions in real-world organizational scenarios, we demonstrate that decision makers’ utilization of ML advice depends on the information quality and transparency of ML advice as well as decision makers’ trust in data scientists’ competence. Furthermore, we find that decision makers’ utilization of ML advice can lead to improved decision-making performance, which is, however, moderated by the decision makers’ management level. The study’s results can help organizations leverage ML advice to improve decision-making and promote the mutual consideration of technical and social aspects behind ML advice in research and practice as a basic requirement.
    Date: 2023–12
  8. By: Xianhua Peng; Chenyin Gong; Xue Dong He
    Abstract: We propose the first discrete-time infinite-horizon dynamic formulation of the financial index tracking problem under both return-based tracking error and value-based tracking error. The formulation overcomes the limitations of existing models by incorporating the intertemporal dynamics of market information variables not limited to prices, allowing exact calculation of transaction costs, accounting for the tradeoff between overall tracking error and transaction costs, allowing effective use of data in a long time period, etc. The formulation also allows novel decision variables of cash injection or withdraw. We propose to solve the portfolio rebalancing equation using a Banach fixed point iteration, which allows to accurately calculate the transaction costs specified as nonlinear functions of trading volumes in practice. We propose an extension of deep reinforcement learning (RL) method to solve the dynamic formulation. Our RL method resolves the issue of data limitation resulting from the availability of a single sample path of financial data by a novel training scheme. A comprehensive empirical study based on a 17-year-long testing set demonstrates that the proposed method outperforms a benchmark method in terms of tracking accuracy and has the potential for earning extra profit through cash withdraw strategy.
    Date: 2023–08
  9. By: Chao Zhang; Xingyue Pu; Mihai Cucuringu; Xiaowen Dong
    Abstract: We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. Moreover, our results consistently indicate that training with the Quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly-used mean squared error. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.
    Date: 2023–08
  10. By: Tanut Treetanthiploet; Yufei Zhang; Lukasz Szpruch; Isaac Bowers-Barnard; Henrietta Ridley; James Hickey; Chris Pearce
    Abstract: The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies. Operating on PCWs requires insurers to strike a delicate balance between competitive premiums and profitability, amidst obstacles such as low historical conversion rates, limited visibility of competitors' actions, and a dynamic market environment. In addition to this, the capital intensive nature of the business means pricing below the risk levels of customers can result in solvency issues for the insurer. To address these challenges, this paper introduces reinforcement learning (RL) framework that learns the optimal pricing policy by integrating model-based and model-free methods. The model-based component is used to train agents in an offline setting, avoiding cold-start issues, while model-free algorithms are then employed in a contextual bandit (CB) manner to dynamically update the pricing policy to maximise the expected revenue. This facilitates quick adaptation to evolving market dynamics and enhances algorithm efficiency and decision interpretability. The paper also highlights the importance of evaluating pricing policies using an offline dataset in a consistent fashion and demonstrates the superiority of the proposed methodology over existing off-the-shelf RL/CB approaches. We validate our methodology using synthetic data, generated to reflect private commercially available data within real-world insurers, and compare against 6 other benchmark approaches. Our hybrid agent outperforms these benchmarks in terms of sample efficiency and cumulative reward with the exception of an agent that has access to perfect market information which would not be available in a real-world set-up.
    Date: 2023–08
  11. By: John V. Colias (Decision Analyst); Stella Park (AT&T); Elizabeth Horn (Decision Analyst)
    Abstract: In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts.
    Date: 2023–08
  12. By: Valter T. Yoshida Jr; Alan de Genaro; Rafael Schiozer; Toni R. E. dos Santos
    Abstract: Large databases and Machine Learning have increased our ability to produce models with a different number of observations and explanatory variables. The credit scoring literature has focused on the optimization of classifications. Little attention has been paid to the inadequate use of models. This study fills this gap by focusing on model risk. It proposes a measure to assess credit scoring model risk. Its emphasis is on model misuse. The proposed model risk measure is ordinal, and it applies to many settings and types of loan portfolios, allowing comparisons of different specifications and situations (as in-sample or out-of-sample data). It allows practitioners and regulators to evaluate and compare different credit risk models in terms of model risk. We empirically test our measure in plugin LASSO default models and find that adding loans from different banks to increase the number of observations is not optimal, challenging the generally accepted assumption that more data leads to better predictions.
    Date: 2023–08
  13. By: Suss, Joel; Kemeny, Thomas; Connor, Dylan Shane
    Abstract: Wealth inequality has been sharply rising in the United States and across many other high-income countries. Due to a lack of data, we know little about how this trend has unfolded across locations within countries. Investigating this subnational geography of wealth is crucial, as from one generation to the next, wealth powerfully shapes opportunity and disadvantage across individuals and communities. Using machine-learning-based imputation to link newly assembled national historical surveys conducted by the U.S. Federal Reserve to population survey microdata, the data presented in this paper addresses this gap. The Geographic Wealth Inequality Database (“GEOWEALTH”) provides the first estimates of the level and distribution of wealth at various geographical scales within the United States from 1960 to 2020. The GEOWEALTH database enables new lines of investigation into the contribution of spatial wealth disparities to major societal challenges including wealth concentration, spatial income inequality, social mobility, housing unaffordability, and political polarization.
    JEL: J1 N0
    Date: 2023–08–01
  14. By: Turunen Harry; Zhutova Anastasia; Lemoine Matthieu
    Abstract: This paper presents a framework to introduce uncertainty into the FR-BDF model, used for macroeconomic projections and policy analysis at the Banque de France. Belonging to the semi-structural class of large-scale macroeconomic models, it is only fair to assume that FR-BDF may suffer from various types of misspecification. We do not seek to correct the latter, but instead we study its systematic nature using unobserved component models for the residuals of FR-BDF. Stochastic simulations based on random draws of innovations of these models allow us to work with applications that describe probabilities of events and risk in general. Applying this framework to the December 2022 forecast exercise of Banque de France, based on the available information at that time, the highest probability of observing a technical recession occurs in 2023Q2 and reaches 42%.
    Keywords: Semi-Structural Modelling, Stochastic Simulation, Unobserved Component Model
    JEL: C54 E37
    Date: 2023
  15. By: Morgan Frank; Yong-Yeol Ahn; Esteban Moro
    Abstract: Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at mo/.
    Date: 2023–08
  16. By: Du, Tianyu (Stanford U); Kanodia, Ayush (Stanford U); Athey, Susan (Stanford U)
    Abstract: The torch-choice is an open-source library for flexible, fast choice modeling with Python and PyTorch. torch-choice provides a ChoiceDataset data structure to manage databases flexibly and memory-efficiently. The paper demonstrates constructing a ChoiceDataset from databases of various formats and functionalities of ChoiceDataset. The package implements two widely used models, namely the multinomial logit and nested logit models, and supports regularization during model estimation. The package incorporates the option to take advantage of GPUs for estimation, allowing it to scale to massive datasets while being computationally efficient. Models can be initialized using either R-style formula strings or Python dictionaries. We conclude with a comparison of the computational efficiencies of torch-choice and mlogit in R as (1) the number of observations increases, (2) the number of covariates increases, and (3) the expansion of item sets. Finally, we demonstrate the scalability of torch-choice on large-scale datasets.
    Date: 2023–04
  17. By: Rudy Morel; St\'ephane Mallat; Jean-Philippe Bouchaud
    Abstract: We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or `shadows', the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called `Scattering Spectra'. This model promotes diversity of generated paths while reproducing the main statistical properties of financial prices, including stylized facts on volatility roughness. Our method yields state-of-the-art predictions for future realized volatility and allows one to determine conditional option smiles for the S\&P500 that outperform both the current version of the Path-Dependent Volatility model and the option market itself.
    Date: 2023–08
  18. By: Fabrice Duval (MEMIAD - Management, économie, modélisation, informatique et aide à la décision [UR7_3] - UA - Université des Antilles); Christophe Elie-Dit-Cosaque (MEMIAD - Management, économie, modélisation, informatique et aide à la décision [UR7_3] - UA - Université des Antilles)
    Abstract: Making effective decisions is vital for organisations to ensure their competitiveness and sustainability. Many expect decisions based on artificial intelligence (AI) to help revolutionise the business world. We know very little about how managers interpret, make sense of and respond to these digital transformation challenges. To address this issue and improve the understanding of how managers make sense of digital transformation, in particular AI-driven digital transformation, we propose to analyse their representations of AI-driven decisions and the forces at play in the sensemaking processes. To do so, we intend to conduct a qualitative study based on interviews with managers involved in digital transformation in Martinique, a Caribbean Island. The expected implications for research and practice are discussed.
    Abstract: Prendre des décisions efficaces est vital pour les organisations afin d'assurer leur compétitivité et leur pérennité. Beaucoup attendent des décisions fondées sur l'intelligence artificielle (IA) qu'elles contribuent à révolutionner le monde des affaires. Nous en savons très peu sur la façon dont les managers interprètent, donnent du sens et répondent à ces défis de transformation digitale. Afin de répondre à ce problème et d'améliorer la compréhension de la façon dont les managers donnent du sens à la transformation digitale, en particulier la transformation digitale axée sur l'intelligence artificielle, nous proposons d'analyser leurs représentations des décisions fondées sur l'IA et les forces en jeu dans les processus de construction de sens. Pour ce faire, nous comptons mener une étude qualitative fondée sur des entretiens avec des managers impliqués dans la transformation digitale en Martinique, une île des Caraïbes. Les implications attendues pour la recherche et la pratique sont discutées.
    Keywords: Sensemaking, AI-driven decisions, intuition, artificial intelligence, Construction de sens, transformation digitale, intelligence artificielle
    Date: 2023–05–29

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