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on Computational Economics |
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Issue of 2024‒02‒26
nineteen papers chosen by |
| By: | Mestiri, Sami |
| Abstract: | In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, i present the different Machine Learning techniques used, and provide some suggestions on the choice of methods in financial applications. We refer the reader to the R packages that can be used to compute the Machine learning methods |
| Keywords: | Financial applications; Machine learning ; R software. |
| JEL: | C45 G00 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120045&r=cmp |
| By: | d’Aspremont, Alexandre; Arous, Simon Ben; Bricongne, Jean-Charles; Lietti, Benjamin; Meunier, Baptiste |
| Abstract: | This paper exploits daily infrared images taken from satellites to track economic activity in advanced and emerging countries. We first develop a framework to read, clean, and exploit satellite images. Our algorithm uses the laws of physics (Planck’s law) and machine learning to detect the heat produced by cement plants in activity. This allows us to monitor in real-time whether a cement plant is working. Using this information on around 500 plants, we construct a satellite-based index tracking activity. We show that using this satellite index outperforms benchmark models and alternative indicators for nowcasting the production of the cement industry as well as the activity in the construction sector. Comparing across methods, we find neural networks yields significantly more accurate predictions as they allow to exploit the granularity of our daily and plant-level data. Overall, we show that combining satellite images and machine learning allows to track economic activity accurately. JEL Classification: C51, C81, E23, E37 |
| Keywords: | big data, construction, data science, high-frequency data, machine learning |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242900&r=cmp |
| By: | Tanmay Ghosh; Nithin Nagaraj |
| Abstract: | The decision making involved behind the mode choice is critical for transportation planning. While statistical learning techniques like discrete choice models have been used traditionally, machine learning (ML) models have gained traction recently among the transportation planners due to their higher predictive performance. However, the black box nature of ML models pose significant interpretability challenges, limiting their practical application in decision and policy making. This study utilised a dataset of $1350$ households belonging to low and low-middle income bracket in the city of Bengaluru to investigate mode choice decision making behaviour using Multinomial logit model and ML classifiers like decision trees, random forests, extreme gradient boosting and support vector machines. In terms of accuracy, random forest model performed the best ($0.788$ on training data and $0.605$ on testing data) compared to all the other models. This research has adopted modern interpretability techniques like feature importance and individual conditional expectation plots to explain the decision making behaviour using ML models. A higher travel costs significantly reduce the predicted probability of bus usage compared to other modes (a $0.66\%$ and $0.34\%$ reduction using Random Forests and XGBoost model for $10\%$ increase in travel cost). However, reducing travel time by $10\%$ increases the preference for the metro ($0.16\%$ in Random Forests and 0.42% in XGBoost). This research augments the ongoing research on mode choice analysis using machine learning techniques, which would help in improving the understanding of the performance of these models with real-world data in terms of both accuracy and interpretability. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.13977&r=cmp |
| By: | Andrew Ye; James Xu; Yi Wang; Yifan Yu; Daniel Yan; Ryan Chen; Bosheng Dong; Vipin Chaudhary; Shuai Xu |
| Abstract: | We propose the integration of sentiment analysis and deep-reinforcement learning ensemble algorithms for stock trading, and design a strategy capable of dynamically altering its employed agent given concurrent market sentiment. In particular, we create a simple-yet-effective method for extracting news sentiment and combine this with general improvements upon existing works, resulting in automated trading agents that effectively consider both qualitative market factors and quantitative stock data. We show that our approach results in a strategy that is profitable, robust, and risk-minimal -- outperforming the traditional ensemble strategy as well as single agent algorithms and market metrics. Our findings determine that the conventional practice of switching ensemble agents every fixed-number of months is sub-optimal, and that a dynamic sentiment-based framework greatly unlocks additional performance within these agents. Furthermore, as we have designed our algorithm with simplicity and efficiency in mind, we hypothesize that the transition of our method from historical evaluation towards real-time trading with live data should be relatively simple. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.01441&r=cmp |
| By: | Valentina Aparicio; Daniel Gordon; Sebastian G. Huayamares; Yuhuai Luo |
| Abstract: | Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences. Developing and training a new LLM can be very computationally expensive, so it is becoming a common practice to take existing LLMs and finetune them with carefully curated datasets for desired applications in different fields. Here, we present BioFinBERT, a finetuned LLM to perform financial sentiment analysis of public text associated with stocks of companies in the biotechnology sector. The stocks of biotech companies developing highly innovative and risky therapeutic drugs tend to respond very positively or negatively upon a successful or failed clinical readout or regulatory approval of their drug, respectively. These clinical or regulatory results are disclosed by the biotech companies via press releases, which are followed by a significant stock response in many cases. In our attempt to design a LLM capable of analyzing the sentiment of these press releases, we first finetuned BioBERT, a biomedical language representation model designed for biomedical text mining, using financial textual databases. Our finetuned model, termed BioFinBERT, was then used to perform financial sentiment analysis of various biotech-related press releases and financial text around inflection points that significantly affected the price of biotech stocks. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.11011&r=cmp |
| By: | Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Boris Kuznetsov (Swiss Finance Institute; EPFL); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Teng Andrea Xu (École Polytechnique Fédérale de Lausanne) |
| Abstract: | We open up the black box behind Deep Learning for portfolio optimization and prove that a sufficiently wide and arbitrarily deep neural network (DNN) trained to maximize the Sharpe ratio of the Stochastic Discount Factor (SDF) is equivalent to a large factor model (LFM): A linear factor pricing model that uses many non-linear characteristics. The nature of these characteristics depends on the architecture of the DNN in an explicit, tractable fashion. This makes it possible to derive end-to-end trained DNN-based SDFs in closed form for the first time. We evaluate LFMs empirically and show how various architectural choices impact SDF performance. We document the virtue of depth complexity: With enough data, the out-of-sample performance of DNNSDF is increasing in the NN depth, saturating at huge depths of around 100 hidden layers. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23121&r=cmp |
| By: | F. Bolivar; M. A. Duran; A. Lozano-Vivas |
| Abstract: | To examine the relation between profitability and business models (BMs) across bank sizes, the paper proposes a research strategy based on machine learning techniques. This strategy allows for analyzing whether size and profit performance underlie BM heterogeneity, with BM identification being based on how the components of the bank portfolio contribute to profitability. The empirical exercise focuses on the European Union banking system. Our results suggest that banks with analogous levels of performance and different sizes share strategic features. Additionally, high capital ratios seem compatible with high profitability if banks, relative to their size peers, adopt a standard retail BM. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.12323&r=cmp |
| By: | Shubham Singh; Mayur Bhat |
| Abstract: | The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.08077&r=cmp |
| By: | Nora Bearth; Michael Lechner |
| Abstract: | It is valuable for any decision maker to know the impact of decisions (treatments) on average and for subgroups. The causal machine learning literature has recently provided tools for estimating group average treatment effects (GATE) to understand treatment heterogeneity better. This paper addresses the challenge of interpreting such differences in treatment effects between groups while accounting for variations in other covariates. We propose a new parameter, the balanced group average treatment effect (BGATE), which measures a GATE with a specific distribution of a priori-determined covariates. By taking the difference of two BGATEs, we can analyse heterogeneity more meaningfully than by comparing two GATEs. The estimation strategy for this parameter is based on double/debiased machine learning for discrete treatments in an unconfoundedness setting, and the estimator is shown to be $\sqrt{N}$-consistent and asymptotically normal under standard conditions. Adding additional identifying assumptions allows specific balanced differences in treatment effects between groups to be interpreted causally, leading to the causal balanced group average treatment effect. We explore the finite sample properties in a small-scale simulation study and demonstrate the usefulness of these parameters in an empirical example. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.08290&r=cmp |
| By: | O. Didkovskyi; N. Jean; G. Le Pera; C. Nordio |
| Abstract: | This paper introduces a credit risk rating model for credit risk assessment in quantitative finance, aiming to categorize borrowers based on their behavioral data. The model is trained on data from Experian, a widely recognized credit bureau, to effectively identify instances of loan defaults among bank customers. Employing state-of-the-art statistical and machine learning techniques ensures the model's predictive accuracy. Furthermore, we assess the model's transferability by testing it on behavioral data from the Bank of Italy, demonstrating its potential applicability across diverse datasets during prediction. This study highlights the benefits of incorporating external behavioral data to improve credit risk assessment in financial institutions. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.09778&r=cmp |
| By: | Henri Arno; Klaas Mulier; Joke Baeck; Thomas Demeester |
| Abstract: | In this paper, we present ECL, a novel multi-modal dataset containing the textual and numerical data from corporate 10K filings and associated binary bankruptcy labels. Furthermore, we develop and critically evaluate several classical and neural bankruptcy prediction models using this dataset. Our findings suggest that the information contained in each data modality is complementary for bankruptcy prediction. We also see that the binary bankruptcy prediction target does not enable our models to distinguish next year bankruptcy from an unhealthy financial situation resulting in bankruptcy in later years. Finally, we explore the use of LLMs in the context of our task. We show how GPT-based models can be used to extract meaningful summaries from the textual data but zero-shot bankruptcy prediction results are poor. All resources required to access and update the dataset or replicate our experiments are available on github.com/henriarnoUG/ECL. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.12652&r=cmp |
| By: | Busch, Malte; Duwe, Daniel |
| Abstract: | Artificial Intelligence (AI) is playing an increasingly important role in innovation processes. Using the example of an innovation research institute, this paper examines the role that AI plays in the institute's innovation processes, how experts from the various departments assess the impact of AI and what challenges they see. The paper brings together findings from systematically analysed AI and innovation literature with the qualitative assessments of employees. |
| Abstract: | Künstliche Intelligenz (KI) spielt eine immer wichtigere Rolle in Innovationsprozessen. Am Beispiel eines Innovationsforschungsinstitut untersucht dieses Papier, welche Rolle KI in den Innovationsprozessen des Instituts spielt, wie die Experten aus den unterschiedlichen Abteilungen die Auswirkungen von KI einschätzen und welche Herausforderungen sie sehen. Das Papier bringt Erkenntnisse aus systematisch analysierter KI- und Innovationsliteratur mit den qualitativen Einschätzungen der Mitarbeiter zusammen. |
| Keywords: | Innovation process, Artificial intelligence (AI), Research Institute, Innovationsprozess, Künstliche Intelligenz (KI), Forschungsinsitut |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esrepo:281981&r=cmp |
| By: | Mathieu Chevrier (Université Côte d'Azur, CNRS, GREDEG, France); Brice Corgnet (Emlyon Business School, GATE UMR 5824, France); Eric Guerci (Université Côte d'Azur, CNRS, GREDEG, France); Julie Rosaz (CEREN EA 7477, Burgundy School of Business, Université Bourgogne Franche-Comté, Dijon, France) |
| Abstract: | This study examines algorithm credulity by which people rely on faulty algorithmic advice without critical evaluation. Using a prediction task comparing human and algorithm advisors, we find that participants are more likely to follow the same deficient advice when issued by an algorithm than by a human. We show that algorithm credulity reduces expected earnings by 13%. To explain this finding, we propose the Algo-Intelligibility-Credulity Model, which posits that people are more likely to perceive as intelligible an unpredictable and deficient piece of advice when produced by an algorithm than by a human. These results imply that humans might be particularly susceptible to the influence of malicious algorithmic advice, potentially due to limitations in our evolved epistemic vigilance when applied to interactions with automated agents. |
| Keywords: | Algorithm credulity, algorithmic advice, intelligibility, trust, laboratory experiments |
| JEL: | C92 D91 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2024-03&r=cmp |
| By: | João A. Bastos |
| Abstract: | The uncertainty associated with option price predictions has largely been overlooked in the literature. This paper aims to fill this gap by quantifying such uncertainty using conformal prediction. Conformal prediction is a model-agnostic procedure that constructs prediction intervals, ensuring valid coverage in finite samples without relying on distributional assumptions. Through the simulation of synthetic option prices, we find that conformal prediction generates prediction intervals for gradient boosting machines with an empirical coverage close to the nominal level. Conversely, non-conformal prediction intervals exhibit empirical coverage levels that fall short of the nominal target. In other words, they fail to contain the actual option price more frequently than expected for a given coverage level. As anticipated, we also observe a decrease in the width of prediction intervals as the size of the training data increases. However, we uncover significant variations in the width of these intervals across different options. Specifically, out-of-the-money options and those with a short time-to-maturity exhibit relatively wider prediction intervals. Then, we perform an empirical study using American call and put options on individual stocks. We find that the empirical results replicate those obtained in the simulation experiment. |
| Keywords: | Conformal prediction; Machine learning; Option price; Quantile regression; American options. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp03042023&r=cmp |
| By: | Marie Obidzinski (Université Paris Panthéon Assas, CRED UR 7321, 75005 Paris, France); Yves Oytana (CRESE UR3190, Univ. Bourgogne Franche-Comté, F-25000 Besançon, France) |
| Abstract: | We characterize the socially optimal liability sharing rule in a situation where a manufacturer develops an artificial intelligence (AI) system that is then used by a human operator (or user). First, the manufacturer invests to increase the autonomy of the AI (i.e., the set of situations that the AI can handle without human intervention) and sets a selling price. The user then decides whether or not to buy the AI. Since the autonomy of the AI remains limited, the human operator must sometimes intervene even when the AI is in use. Our main assumption is that users are subject to behavioral inattention. Behavioral inattention reduces the effectiveness of user intervention and increases the expected harm. Only some users are aware of their own attentional limits. Under the assumption that AI outperforms users, we show that policymakers may face a trade-off when choosing how to allocate liability between the manufacturer and the user. Indeed, the manufacturer may underinvest in the autonomy of the AI. If this is the case, the policymaker can incentivize the latter to invest more by increasing his share of liability. On the other hand, increasing the liability of the manufacturer may come at the cost of slowing down the diffusion of AI technology. |
| Keywords: | liability rules, artificial intelligence, inattention |
| JEL: | K4 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:crb:wpaper:2024-08&r=cmp |
| By: | Anubha Goel (Tampere University - Faculty of Information Technology and Communication Sciences); Damir Filipović (École Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Puneet Pasricha (Indian Institute of Technology Ropar) |
| Abstract: | This paper uses topological data analysis (TDA) tools and introduces a data-driven clustering based stock selection strategy tailored for sparse portfolio construction. Our asset selection strategy exploits the topological features of stock price movements to select a subset of topologically similar (different) assets for a sparse index tracking (Markowitz) portfolio. We introduce new distance measures, which serve as an input to the clustering algorithm, on the space of persistence diagrams and landscapes that consider the time component of a time series. We conduct an empirical analysis on the S&P index from 2009 to 2020, including a study on the COVID-19 data to validate the robustness of our methodology. Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios. |
| Keywords: | Topological Data Analysis, Clustering, Index Tracking, Mean-Variance Portfolio, Global Minimum Variance Portfolio, Sparse Portfolios |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2407&r=cmp |
| By: | Gmyrek, Pawel,; Lutz, Christoph,; Newlands, Gemma, |
| Abstract: | Despite initial research about the biases and perceptions of Large Language Models (LLMs), we lack evidence on how LLMs evaluate occupations, especially in comparison to human evaluators. In this paper, we present a systematic comparison of occupational evaluations by GPT-4 with those from an in-depth, high-quality and recent human respondents survey in the United Kingdom. Covering the full ISCO-08 occupational landscape, with 580 occupations and two distinct metrics (prestige and social value), our findings indicate that GPT-4 and human scores are highly correlated across all ISCO-08 major groups. In absolute terms, GPT-4 scores are more generous than those of the human respondents. At the same time, GPT-4 substantially under or overestimates the occupational prestige and social value of many occupations, particularly for emerging digital and stigmatized occupations. |
| Keywords: | occupational classification, occupational qualification, artificial intelligence, survey |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ilo:ilowps:995347793502676&r=cmp |
| By: | Hannes Wallimann; Silvio Sticher |
| Abstract: | We present a classroom game that integrates economics and data-science competencies. In the first two parts of the game, participants assume the roles of firms in a procurement market, where they must either adopt competitive behaviors or have the option to engage in collusion. Success in these parts hinges on their comprehension of market dynamics. In the third part of the game, participants transition to the role of competition-authority members. Drawing from recent literature on machine-learning-based cartel detection, they analyze the bids for patterns indicative of collusive (cartel) behavior. In this part of the game, success depends on data-science skills. We offer a detailed discussion on implementing the game, emphasizing considerations for accommodating diverging levels of preexisting knowledge in data science. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.14757&r=cmp |
| By: | Lars Ericson; Xuejun Zhu; Xusi Han; Rao Fu; Shuang Li; Steve Guo; Ping Hu |
| Abstract: | In the financial services industry, forecasting the risk factor distribution conditional on the history and the current market environment is the key to market risk modeling in general and value at risk (VaR) model in particular. As one of the most widely adopted VaR models in commercial banks, Historical simulation (HS) uses the empirical distribution of daily returns in a historical window as the forecast distribution of risk factor returns in the next day. The objectives for financial time series generation are to generate synthetic data paths with good variety, and similar distribution and dynamics to the original historical data. In this paper, we apply multiple existing deep generative methods (e.g., CGAN, CWGAN, Diffusion, and Signature WGAN) for conditional time series generation, and propose and test two new methods for conditional multi-step time series generation, namely Encoder-Decoder CGAN and Conditional TimeVAE. Furthermore, we introduce a comprehensive framework with a set of KPIs to measure the quality of the generated time series for financial modeling. The KPIs cover distribution distance, autocorrelation and backtesting. All models (HS, parametric and neural networks) are tested on both historical USD yield curve data and additional data simulated from GARCH and CIR processes. The study shows that top performing models are HS, GARCH and CWGAN models. Future research directions in this area are also discussed. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.10370&r=cmp |