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on Computational Economics |
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Issue of 2024‒03‒04
twenty papers chosen by |
| By: | Franck Ramaharo; Gerzhino Rasolofomanana |
| Abstract: | We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1--2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.10255&r=cmp |
| By: | Pierre Renucci |
| Abstract: | This study presents an unsupervised machine learning approach for optimizing Profit and Loss (PnL) in quantitative finance. Our algorithm, akin to an unsupervised variant of linear regression, maximizes the Sharpe Ratio of PnL generated from signals constructed linearly from exogenous variables. The methodology employs a linear relationship between exogenous variables and the trading signal, with the objective of maximizing the Sharpe Ratio through parameter optimization. Empirical application on an ETF representing U.S. Treasury bonds demonstrates the model's effectiveness, supported by regularization techniques to mitigate overfitting. The study concludes with potential avenues for further development, including generalized time steps and enhanced corrective terms. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05337&r=cmp |
| By: | Weidong Lin; Abderrahim Taamouti |
| Abstract: | The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns, and it rules out, by construction, systemic risk, which can negatively affect its out-of-sample performance. In the present work, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios, we simulate a large number of returns via a Monte Carlo method. This is done by Örst obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting, and then combining them with a Ötted copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both proÖtability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs. |
| Keywords: | Portfolio optimization; probability forecasting; quantile regression neural network; extreme scenarios; big data. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:liv:livedp:202310&r=cmp |
| By: | Kim, Dongin |
| Keywords: | Agribusiness, Agricultural Finance, International Relations/Trade, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iats22:339469&r=cmp |
| By: | Gordeev, Stepan; Jelliffe, Jeremy; Kim, Dongin; Steinbach, Sandro |
| Keywords: | International Relations/Trade, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iats23:339533&r=cmp |
| By: | David Almog; Romain Gauriot; Lionel Page; Daniel Martin |
| Abstract: | Powered by the increasing predictive capabilities of machine learning algorithms, artificial intelligence (AI) systems have begun to be used to overrule human mistakes in many settings. We provide the first field evidence this AI oversight carries psychological costs that can impact human decision-making. We investigate one of the highest visibility settings in which AI oversight has occurred: the Hawk-Eye review of umpires in top tennis tournaments. We find that umpires lowered their overall mistake rate after the introduction of Hawk-Eye review, in line with rational inattention given psychological costs of being overruled by AI. We also find that umpires increased the rate at which they called balls in, which produced a shift from making Type II errors (calling a ball out when in) to Type I errors (calling a ball in when out). We structurally estimate the psychological costs of being overruled by AI using a model of rational inattentive umpires, and our results suggest that because of these costs, umpires cared twice as much about Type II errors under AI oversight. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.16754&r=cmp |
| By: | Diwas Paudel; Tapas K. Das |
| Abstract: | Fast-charging hubs for electric vehicles will soon become part of the newly built infrastructure for transportation electrification across the world. These hubs are expected to host many DC fast-charging stations and will admit EVs only for charging. Like the gasoline refueling stations, fast-charging hubs in a neighborhood will dynamically vary their prices to compete for the same pool of EV owners. These hubs will interact with the electric power network by making purchase commitments for a significant part of their power needs in the day-ahead (DA) electricity market and meeting the difference from the real-time (RT) market. Hubs may have supplemental battery storage systems (BSS), which they will use for arbitrage. In this paper, we develop a two-step data-driven dynamic pricing methodology for hubs in price competition. We first obtain the DA commitment by solving a stochastic DA commitment model. Thereafter we obtain the hub pricing strategies by modeling the game as a competitive Markov decision process (CMDP) and solving it using a multi-agent deep reinforcement learning (MADRL) approach. We develop a numerical case study for a pricing game between two charging hubs. We solve the case study with our methodology by using combinations of two different DRL algorithms, DQN and SAC, and two different neural networks (NN) architectures, a feed-forward (FF) neural network, and a multi-head attention (MHA) neural network. We construct a measure of collusion (index) using the hub profits. A value of zero for this index indicates no collusion (perfect competition) and a value of one indicates full collusion (monopolistic behavior). Our results show that the collusion index varies approximately between 0.14 and 0.45 depending on the combinations of the algorithms and the architectures chosen by the hubs. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.15108&r=cmp |
| By: | Mario Sanz-Guerrero; Javier Arroyo |
| Abstract: | Peer-to-peer (P2P) lending has emerged as a distinctive financing mechanism, linking borrowers with lenders through online platforms. However, P2P lending faces the challenge of information asymmetry, as lenders often lack sufficient data to assess the creditworthiness of borrowers. This paper proposes a novel approach to address this issue by leveraging the textual descriptions provided by borrowers during the loan application process. Our methodology involves processing these textual descriptions using a Large Language Model (LLM), a powerful tool capable of discerning patterns and semantics within the text. Transfer learning is applied to adapt the LLM to the specific task at hand. Our results derived from the analysis of the Lending Club dataset show that the risk score generated by BERT, a widely used LLM, significantly improves the performance of credit risk classifiers. However, the inherent opacity of LLM-based systems, coupled with uncertainties about potential biases, underscores critical considerations for regulatory frameworks and engenders trust-related concerns among end-users, opening new avenues for future research in the dynamic landscape of P2P lending and artificial intelligence. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.16458&r=cmp |
| By: | Bartosz Bieganowski (UUniversity 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 paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors. |
| Keywords: | machine learning, algorithmic investment strategy, supervised autoencoders, financial time series, trading strategy, risk-adjusted return |
| JEL: | C4 C14 C45 C53 C58 G13 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2024-03&r=cmp |
| By: | Almeida, Derick (University of Coimbra); Naudé, Wim (RWTH Aachen University); Sequeira, Tiago Neves (University of Coimbra) |
| Abstract: | Theory predicts that global economic growth will stagnate and even come to an end due to slower and eventually negative growth in population. It has been claimed, however, that Artificial Intelligence (AI) may counter this and even cause an economic growth explosion. In this paper, we critically analyse this claim. We clarify how AI affects the ideas production function (IPF) and propose three models relating innovation, AI and population: AI as a research-augmenting technology; AI as researcher scale enhancing technology; and AI as a facilitator of innovation. We show, performing model simulations calibrated on USA data, that AI on its own may not be sufficient to accelerate the growth rate of ideas production indefinitely. Overall, our simulations suggests that an economic growth explosion would only be possible under very specific and perhaps unlikely combinations of parameter values. Hence we conclude that it is not imminent. |
| Keywords: | automation, artificial intelligence, economic growth, innovation, ideas production function |
| JEL: | O31 O33 O40 J11 J24 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16766&r=cmp |
| By: | Dengxin Huang |
| Abstract: | This document presents a stock market analysis conducted on a dataset consisting of 750 instances and 16 attributes donated in 2014-10-23. The analysis includes an exploratory data analysis (EDA) section, feature engineering, data preparation, model selection, and insights from the analysis. The Fama French 3-factor model is also utilized in the analysis. The results of the analysis are presented, with linear regression being the best-performing model. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.10903&r=cmp |
| By: | Churchill, Alexander; Pichika, Shamitha; Xu, Chengxin (Seattle University) |
| Abstract: | Supervised content encoding applies a given codebook to a larger non-numerical dataset and is central to empirical research in public administration. Not only is it a key analytical approach for qualitative studies, but the method also allows researchers to measure constructs using non-numerical data, which can then be applied to quantitative description and causal inference. Despite its utility, supervised content encoding faces challenges including high cost and low reproducibility. In this report, we test if large language models (LLM), specifically generative pre-trained transformers (GPT), can solve these problems. Using email messages collected from a national corresponding experiment in the U.S. nursing home market as an example, we demonstrate that although we found some disparities between GPT and human coding results, the disagreement is acceptable for certain research design, which makes GPT encoding a potential substitute for human encoders. Practical suggestions for encoding with GPT are provided at the end of the letter. |
| Date: | 2024–01–25 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:6fpgj&r=cmp |
| By: | Zhiyu Quan; Changyue Hu; Panyi Dong; Emiliano A. Valdez |
| Abstract: | Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This paper presents a groundbreaking effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies' risk management. Our study further utilizes various machine learning techniques to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house. The quantification process provides a deeper understanding of the value of the InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic-industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.16723&r=cmp |
| By: | Helena Chuliá (Riskcenter- IREA and Department of Econometrics and Statistics, University of Barcelona.); Sabuhi Khalili (Department of Econometrics and Statistics, University of Barcelona.); Jorge M. Uribe (Faculty of Economics and Business Studies, Open University of Catalonia.) |
| Abstract: | SWe propose generative artificial intelligence to measure systemic risk in the global markets of sovereign debt and foreign exchange. Through a comparative analysis, we explore three novel models to the economics literature and integrate them with traditional factor models. These models are: Time Variational Autoencoders, Time Generative Adversarial Networks, and Transformer-based Time-series Generative Adversarial Networks. Our empirical results provide evidence in support of the Variational Autoencoder. Results here indicate that both the Credit Default Swaps and foreign exchange markets are susceptible to systemic risk, with a historically high probability of distress observed by the end of 2022, as measured by both the Joint Probability of Distress and the Expected Proportion of Markets in Distress. Our results provide insights for governments in both developed and developing countries, since the realistic counterfactual scenarios generated by the AI, yet to occur in global markets, underscore the potential worst-case scenarios that may unfold if systemic risk materializes. Considering such scenarios is crucial when designing macroprudential policies aimed at preserving financial stability and when measuring the effectiveness of the implemented policies. |
| Keywords: | Twin Ds, Sovereign Debt, Credit Risk, TimeGANs, Transformers, TimeVAEs, Autoencoders, Variational Inference. JEL classification: C45, C53, F31, F37. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:202402&r=cmp |
| By: | M. Shabani; M. Magris; George Tzagkarakis (IRGO - Institut de Recherche en Gestion des Organisations - UB - Université de Bordeaux - Institut d'Administration des Entreprises (IAE) - Bordeaux); J. Kanniainen; A. Iosifidis |
| Abstract: | Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in the time domain of two time series and for determining their state of synchronization. We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state based on features extracted from dynamically sub-sampled cross recurrence plots. We provide extensive experiments on several stocks, major constituents of the S &P100 index, to empirically validate our approach. We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance (84% F1-score, on average). © 2023, The Author(s). |
| Keywords: | Cross recurrence plot, Synchronization, Kernel convolutional neural network, Financial time series |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04415269&r=cmp |
| By: | Hauke Licht (University of Cologne, Cologne Center for Comparative Politics); Ronja Sczepanksi (Sciences Po Paris, Center for European Studies and Comparative Research) |
| Abstract: | Politicians appeal to social groups to court their electoral support. However, quantifying which groups politicians refer to, claim to represent, or address in their public communication presents researchers with challenges. We propose a novel supervised learning approach for extracting group mentions in political texts. We first collect human annotations to determine the exact text passages that refer to social groups. We then fine-tune a Transformer language model for contextualized supervised classification at the word level. Applied to unlabeled texts, our approach enables researchers to automatically detect and extract word spans that contain group mentions. We illustrate our approach in three applications, generating new empirical insights how British parties use social groups in their rhetoric. Our methodological innovation allows to detect and extract mentions of social groups from various sources of texts, creating new possibilities for empirical research in political science. |
| Keywords: | social groups, political rhetoric, computational text analysis, supervised classification |
| JEL: | C45 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ajk:ajkdps:277&r=cmp |
| By: | Roberto Baviera; Pietro Manzoni |
| Abstract: | L\'evy-driven Ornstein-Uhlenbeck (OU) processes represent an intriguing class of stochastic processes that have garnered interest in the energy sector for their ability to capture typical features of market dynamics. However, in the current state-of-the-art, Monte Carlo simulations of these processes are not straightforward for two main reasons: i) algorithms are available only for some particular processes within this class; ii) they are often computationally expensive. In this paper, we introduce a new simulation technique designed to address both challenges. It relies on the numerical inversion of the characteristic function, offering a general methodology applicable to all L\'evy-driven OU processes. Moreover, leveraging FFT, the proposed methodology ensures fast and accurate simulations, providing a solid basis for the widespread adoption of these processes in the energy sector. Lastly, the algorithm allows an optimal control of the numerical error. We apply the technique to the pricing of energy derivatives, comparing the results with existing benchmarks. Our findings indicate that the proposed methodology is at least one order of magnitude faster than existing algorithms, all while maintaining an equivalent level of accuracy. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.15483&r=cmp |
| By: | Wesley H. Holliday; Alexander Kristoffersen; Eric Pacuit |
| Abstract: | By classic results in social choice theory, any reasonable preferential voting method sometimes gives individuals an incentive to report an insincere preference. The extent to which different voting methods are more or less resistant to such strategic manipulation has become a key consideration for comparing voting methods. Here we measure resistance to manipulation by whether neural networks of varying sizes can learn to profitably manipulate a given voting method in expectation, given different types of limited information about how other voters will vote. We trained nearly 40, 000 neural networks of 26 sizes to manipulate against 8 different voting methods, under 6 types of limited information, in committee-sized elections with 5-21 voters and 3-6 candidates. We find that some voting methods, such as Borda, are highly manipulable by networks with limited information, while others, such as Instant Runoff, are not, despite being quite profitably manipulated by an ideal manipulator with full information. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.16412&r=cmp |
| By: | Greiner, Ben; Grünwald, Philipp; Lindner, Thomas; Lintner, Georg; Wiernsperger, Martin |
| Abstract: | Managerial decision-makers are increasingly supported by advanced data analytics and other AI-based technologies, but are often found to be hesitant to follow the algorithmic advice. We examine how compensation contract design and framing of an AI algorithm influence decision-makers’ reliance on algorithmic advice and performance in a price estimation task. Based on a large sample of almost 1, 500 participants, we find that compared to a fixed compensation, both compensation contracts based on individual performance and tournament contracts lead to an increase in effort duration and to more reliance on algorithmic advice. We further find that using an AI algorithm that is framed as incorporating also human expertise has positive effects on advice utilization, especially for decision-makers with fixed pay contracts. By showing how widely used control practices such as incentives and task framing influence the interaction of human decision-makers with AI algorithms, our findings have direct implications for managerial practice. |
| Keywords: | artificial intelligence; algorithmic advice; human-augmented algorithmic advice; trust; financial incentives; decision-making |
| Date: | 2024–01–31 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus055:60237853&r=cmp |
| By: | Tin Cheuk Leung; Koleman Strumpf |
| Abstract: | In this study, we propose a novel approach to detect supply-side media bias, independent of external factors like ownership or editors’ ideological leanings. Analyzing over 100, 000 articles from The New York Times (NYT) and The Wall Street Journal (WSJ), complemented by data from 22 million tweets, we assess the factors influencing article duration on their digital homepages. By flexibly controlling for demand-side preferences, we attribute extended homepage presence of ideologically slanted articles to supply-side biases. Utilizing a machine learning model, we assign “pro-Democrat” scores to articles, revealing that both tweets count and ideological orientation significantly impact homepage longevity. Our findings show that liberal articles tend to remain longer on the NYT homepage, while conservative ones persist on the WSJ. Further analysis into articles’ transition to print and podcasts suggests that increased competition may reduce media bias, indicating a potential direction for future theoretical exploration. |
| Keywords: | media bias, media economics, social media, machine learning |
| JEL: | D22 D72 D83 L82 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10890&r=cmp |