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on Computational Economics |
| By: | Zeda Xu; John Liechty; Sebastian Benthall; Nicholas Skar-Gislinge; Christopher McComb |
| Abstract: | Volatility, which indicates the dispersion of returns, is a crucial measure of risk and is hence used extensively for pricing and discriminating between different financial investments. As a result, accurate volatility prediction receives extensive attention. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its succeeding variants are well established models for stock volatility forecasting. More recently, deep learning models have gained popularity in volatility prediction as they demonstrated promising accuracy in certain time series prediction tasks. Inspired by Physics-Informed Neural Networks (PINN), we constructed a new, hybrid Deep Learning model that combines the strengths of GARCH with the flexibility of a Long Short-Term Memory (LSTM) Deep Neural Network (DNN), thus capturing and forecasting market volatility more accurately than either class of models are capable of on their own. We refer to this novel model as a GARCH-Informed Neural Network (GINN). When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination ($R^2$), Mean Squared Error (MSE), and Mean Absolute Error (MAE). |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00288 |
| By: | John Phan; Hung-Fu Chang |
| Abstract: | This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company's financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.03913 |
| By: | Köbis, Nils; Rahwan, Zoe (Max Planck Institute for Human Development); Bersch, Clara; Ajaj, Tamer; Bonnefon, Jean-François (Toulouse School of Economics); Rahwan, Iyad |
| Abstract: | While artificial intelligence (AI) enables significant productivity gains from delegating tasks to machines, it can also facilitate the delegation of unethical behaviour. Here, we demonstrate this risk by having human principals instruct machine agents to perform a task with an incentive to cheat. Principals’ requests for cheating behaviour increased when the interface implicitly afforded unethical conduct: Machine agents programmed via supervised learning or goal specification evoked more cheating than those programmed with explicit rules. Cheating propensity was unaffected by whether delegation was mandatory or voluntary. Given the recent rise of large language model-based chatbots, we also explored delegation via natural language. Here, cheating requests did not vary between human and machine agents, but compliance diverged: When principals intended agents to cheat to the fullest extent, the majority of human agents did not comply, despite incentives to do so. In contrast, GPT4, a state-of-the-art machine agent, nearly fully complied. Our results highlight ethical risks in delegating tasks to intelligent machines, and suggest design principles and policy responses to mitigate such risks. |
| Date: | 2024–10–04 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:dnjgz |
| By: | Ananya Unnikrishnan |
| Abstract: | An accurate valuation of American call options is critical in most financial decision making environments. However, traditional models like the Barone-Adesi Whaley (B-AW) and Binomial Option Pricing (BOP) methods fall short in handling the complexities of early exercise and market dynamics present in American options. This paper proposes a Modular Neural Network (MNN) model which aims to capture the key aspects of American options pricing. By dividing the prediction process into specialized modules, the MNN effectively models the non-linear interactions that drive American call options pricing. Experimental results indicate that the MNN model outperform both traditional models as well as a simpler Feed-forward Neural Network (FNN) across multiple stocks (AAPL, NVDA, QQQ), with significantly lower RMSE and nRMSE (by mean). These findings highlight the potential of MNNs as a powerful tool to improve the accuracy of predicting option prices. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.19706 |
| By: | Pascal K\"undig; Fabio Sigrist |
| Abstract: | We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This allows for modeling non-linearities and interactions among predictor variables in a flexible data-driven manner and for accounting for spatio-temporal variation that is not explained by observable predictor variables. We also show how estimation and prediction can be done in a computationally efficient manner. In an application to a large U.S. mortgage credit risk data set, we find that both predictive default probabilities for individual loans and predictive loan portfolio loss distributions obtained with our novel approach are more accurate compared to conventional independent linear hazard models and also linear spatio-temporal models. Using interpretability tools for machine learning models, we find that the likely reasons for this outperformance are strong interaction and non-linear effects in the predictor variables and the presence of large spatio-temporal frailty effects. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.02846 |
| By: | Daniele Condorelli; Massimiliano Furlan |
| Abstract: | We train two neural networks adversarially to play normal-form games. At each iteration, a row and column network take a new randomly generated game and output individual mixed strategies. The parameters of each network are independently updated via stochastic gradient descent to minimize expected regret given the opponent's strategy. Our simulations demonstrate that the joint behavior of the networks converges to strategies close to Nash equilibria in almost all games. For all $2 \times 2$ and in 80% of $3 \times 3$ games with multiple equilibria, the networks select the risk-dominant equilibrium. Our results show how Nash equilibrium emerges from learning across heterogeneous games. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.15197 |
| By: | Masanori Hirano; Kentaro Imajo |
| Abstract: | This paper proposes a novel method for constructing instruction-tuned large language models (LLMs) for finance without instruction data. Traditionally, developing such domain-specific LLMs has been resource-intensive, requiring a large dataset and significant computational power for continual pretraining and instruction tuning. Our study proposes a simpler approach that combines domain-specific continual pretraining with model merging. Given that general-purpose pretrained LLMs and their instruction-tuned LLMs are often publicly available, they can be leveraged to obtain the necessary instruction task vector. By merging this with a domain-specific pretrained vector, we can effectively create instruction-tuned LLMs for finance without additional instruction data. Our process involves two steps: first, we perform continual pretraining on financial data; second, we merge the instruction-tuned vector with the domain-specific pretrained vector. Our experiments demonstrate the successful construction of instruction-tuned LLMs for finance. One major advantage of our method is that the instruction-tuned and domain-specific pretrained vectors are nearly independent. This independence makes our approach highly effective. The Japanese financial instruction-tuned LLMs we developed in this study are available at https://huggingface.co/pfnet/nekomata-14 b-pfn-qfin-inst-merge. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.19854 |
| By: | Diego Vallarino |
| Abstract: | This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00002 |
| By: | Bingyao Liu; Iris Li; Jianhua Yao; Yuan Chen; Guanming Huang; Jiajing Wang |
| Abstract: | This paper takes the graph neural network as the technical framework, integrates the intrinsic connections between enterprise financial indicators, and proposes a model for enterprise credit risk assessment. The main research work includes: Firstly, based on the experience of predecessors, we selected 29 enterprise financial data indicators, abstracted each indicator as a vertex, deeply analyzed the relationships between the indicators, constructed a similarity matrix of indicators, and used the maximum spanning tree algorithm to achieve the graph structure mapping of enterprises; secondly, in the representation learning phase of the mapped graph, a graph neural network model was built to obtain its embedded representation. The feature vector of each node was expanded to 32 dimensions, and three GraphSAGE operations were performed on the graph, with the results pooled using the Pool operation, and the final output of three feature vectors was averaged to obtain the graph's embedded representation; finally, a classifier was constructed using a two-layer fully connected network to complete the prediction task. Experimental results on real enterprise data show that the model proposed in this paper can well complete the multi-level credit level estimation of enterprises. Furthermore, the tree-structured graph mapping deeply portrays the intrinsic connections of various indicator data of the company, and according to the ROC and other evaluation criteria, the model's classification effect is significant and has good "robustness". |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17909 |
| By: | Ryan Y. Lin; Siddhartha Ojha; Kevin Cai; Maxwell F. Chen |
| Abstract: | Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can independently engage in anti-competitive practices such as collusion or, more specifically, market division. Our findings demonstrate that LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies, thereby maximizing profitability without direct human input or explicit collusion commands. These results pose unique challenges and opportunities for businesses looking to integrate AI into strategic roles and for regulatory bodies tasked with maintaining fair and competitive markets. The study provides a foundation for further exploration into the ramifications of deferring high-stakes decisions to LLM-based agents. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00031 |
| By: | Zheng Cao; Xinhao Lin |
| Abstract: | This study focuses on the application of the Heston model to option pricing, employing both theoretical derivations and empirical validations. The Heston model, known for its ability to incorporate stochastic volatility, is derived and analyzed to evaluate its effectiveness in pricing options. For practical application, we utilize Monte Carlo simulations alongside market data from the Crude Oil WTI market to test the model's accuracy. Machine learning based optimization methods are also applied for the estimation of the five Heston parameters. By calibrating the model with real-world data, we assess its robustness and relevance in current financial markets, aiming to bridge the gap between theoretical finance models and their practical implementations. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.12453 |
| By: | John Armstrong; George Tatlow |
| Abstract: | We train neural networks to learn optimal replication strategies for an option when two replicating instruments are available, namely the underlying and a hedging option. If the price of the hedging option matches that of the Black--Scholes model then we find the network will successfully learn the Black-Scholes gamma hedging strategy, even if the dynamics of the underlying do not match the Black--Scholes model, so long as we choose a loss function that rewards coping with model uncertainty. Our results suggest that the reason gamma hedging is used in practice is to account for model uncertainty rather than to reduce the impact of transaction costs. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13567 |
| By: | Gustavo de Freitas Fonseca; Lucas Coelho e Silva; Paulo Andr\'e Lima de Castro |
| Abstract: | In Reinforcement Learning (RL), multi-armed Bandit (MAB) problems have found applications across diverse domains such as recommender systems, healthcare, and finance. Traditional MAB algorithms typically assume stationary reward distributions, which limits their effectiveness in real-world scenarios characterized by non-stationary dynamics. This paper addresses this limitation by introducing and evaluating novel Bandit algorithms designed for non-stationary environments. First, we present the \textit{Adaptive Discounted Thompson Sampling} (ADTS) algorithm, which enhances adaptability through relaxed discounting and sliding window mechanisms to better respond to changes in reward distributions. We then extend this approach to the Portfolio Optimization problem by introducing the \textit{Combinatorial Adaptive Discounted Thompson Sampling} (CADTS) algorithm, which addresses computational challenges within Combinatorial Bandits and improves dynamic asset allocation. Additionally, we propose a novel architecture called Bandit Networks, which integrates the outputs of ADTS and CADTS, thereby mitigating computational limitations in stock selection. Through extensive experiments using real financial market data, we demonstrate the potential of these algorithms and architectures in adapting to dynamic environments and optimizing decision-making processes. For instance, the proposed bandit network instances present superior performance when compared to classic portfolio optimization approaches, such as capital asset pricing model, equal weights, risk parity, and Markovitz, with the best network presenting an out-of-sample Sharpe Ratio 20\% higher than the best performing classical model. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.04217 |
| By: | Xiang Li; Lan Zhao; Junhao Ren; Yajuan Sun; Chuan Fu Tan; Zhiquan Yeo; Gaoxi Xiao |
| Abstract: | Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloguing historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge, this paper leverages Large Language Models (LLMs) to classify textual data describing economic activities into the International Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository. Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18988 |
| By: | Sukjin Han |
| Abstract: | The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying its validity (especially exclusion restrictions) is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can accelerate this process exponentially and explore an extremely large search space. We demonstrate how to construct prompts to search for potentially valid IVs. We argue that multi-step prompting is useful and role-playing prompts are suitable for mimicking the endogenous decisions of economic agents. We apply our method to three well-known examples in economics: returns to schooling, production functions, and peer effects. We then extend our strategy to finding (i) control variables in regression and difference-in-differences and (ii) running variables in regression discontinuity designs. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.14202 |
| By: | Shuaiyu Chen; T. Clifton Green; Huseyin Gulen; Dexin Zhou |
| Abstract: | We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.11540 |
| By: | Manish Jha; Jialin Qian; Michael Weber; Baozhong Yang |
| Abstract: | We use generative AI to extract managerial expectations about their economic outlook from over 120, 000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment, both in the short term and to 10 quarters. This predictive power is incremental to that of existing measures, including survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. Our findings suggest that managerial expectations carry unique insights about economic activities, with implications for both macroeconomic and microeconomic decision-making. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.03897 |
| By: | Azhari Amine (FSJES Agadir) |
| Abstract: | Résumé Cette étude explore l'impact de l'intégration de l'intelligence artificielle sur la qualité de l'information comptable. En s'appuyant sur un échantillon de 86 observations auprès de professionnels du domaine de la comptabilité, et en utilisant des modèles d'équations structurelles, nous avons analysé comment l'IA améliore la prévision financière et la gestion des risques, contribuant ainsi à la précision et à la fiabilité des informations comptables. Les résultats montrent que la prévision financière assistée par l'IA permet une anticipation plus précise des flux de trésorerie et des besoins en financement, tout en optimisant la planification stratégique. De plus, la gestion des risques optimisée par l'IA améliore la détection des anomalies et l'évaluation proactive des risques, renforçant la stabilité financière des entreprises. Ces découvertes soulignent l'importance stratégique de l'IA dans la comptabilité moderne, tout en mettant en évidence les défis liés à son adoption, tels que la formation continue et la sécurité des données. Nos conclusions fournissent des recommandations pratiques pour les entreprises souhaitant intégrer ces technologies et ouvrent la voie à de futures recherches sur l'impact de l'IA en comptabilité. Mots clés : Intelligence artificielle, Prévision financière, Gestion des risques, Modèles d'équations structurelles, Qualité de l'information comptable Abstract This study explores the impact of integrating artificial intelligence (AI) on the quality of accounting information. Based on a sample of 86 observations from professionals in the accounting field, and using structural equation modeling (SEM), we analyzed how AI enhances financial forecasting and risk management, contributing to the accuracy and reliability of accounting information. The results show that AI-assisted financial forecasting allows for more precise anticipation of cash flows and financing needs, while optimizing strategic planning. Additionally, AI-optimized risk management improves anomaly detection and proactive risk assessment, strengthening companies' financial stability. These findings highlight the strategic importance of AI in modern accounting, while also addressing challenges related to its adoption, such as continuous training and data security. Our conclusions provide practical recommendations for companies looking to integrate these technologies and pave the way for future research on AI's impact on accounting. Keywords: Artificial intelligence, Financial forecasting, Risk management, Structural equation modeling, Quality of accounting information |
| Abstract: | Résumé Cette étude explore l'impact de l'intégration de l'intelligence artificielle sur la qualité de l'information comptable. En s'appuyant sur un échantillon de 86 observations auprès de professionnels du domaine de la comptabilité, et en utilisant des modèles d'équations structurelles, nous avons analysé comment l'IA améliore la prévision financière et la gestion des risques, contribuant ainsi à la précision et à la fiabilité des informations comptables. Les résultats montrent que la prévision financière assistée par l'IA permet une anticipation plus précise des flux de trésorerie et des besoins en financement, tout en optimisant la planification stratégique. De plus, la gestion des risques optimisée par l'IA améliore la détection des anomalies et l'évaluation proactive des risques, renforçant la stabilité financière des entreprises. Ces découvertes soulignent l'importance stratégique de l'IA dans la comptabilité moderne, tout en mettant en évidence les défis liés à son adoption, tels que la formation continue et la sécurité des données. Nos conclusions fournissent des recommandations pratiques pour les entreprises souhaitant intégrer ces technologies et ouvrent la voie à de futures recherches sur l'impact de l'IA en comptabilité. Mots clés : Intelligence artificielle, Prévision financière, Gestion des risques, Modèles d'équations structurelles, Qualité de l'information comptable Abstract This study explores the impact of integrating artificial intelligence (AI) on the quality of accounting information. Based on a sample of 86 observations from professionals in the accounting field, and using structural equation modeling (SEM), we analyzed how AI enhances financial forecasting and risk management, contributing to the accuracy and reliability of accounting information. The results show that AI-assisted financial forecasting allows for more precise anticipation of cash flows and financing needs, while optimizing strategic planning. Additionally, AI-optimized risk management improves anomaly detection and proactive risk assessment, strengthening companies' financial stability. These findings highlight the strategic importance of AI in modern accounting, while also addressing challenges related to its adoption, such as continuous training and data security. Our conclusions provide practical recommendations for companies looking to integrate these technologies and pave the way for future research on AI's impact on accounting. Keywords: Artificial intelligence, Financial forecasting, Risk management, Structural equation modeling, Quality of accounting information |
| Keywords: | Intelligence artificielle, Prévision financière, Gestion des risques, Modèles d'équations structurelles, Qualité de l'information comptable, African Scientific Journal, Intelligence artificielle Prévision financière Gestion des risques Modèles d'équations structurelles Qualité de l Artificial intelligence Financial forecasting Risk management Structural equation modeling Quality of accounting information, Qualité de l Artificial intelligence, Financial forecasting, Risk management, Structural equation modeling, Quality of accounting information |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04694939 |
| By: | Corentin Lobet; Patrick Llerena; André Lorentz |
| Abstract: | We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explore their emergence through simulations. The model incorporates productivity dynamics (learning and depreciation) and is extended with time-saving process innovations. Specifically, we assume workers possess inherent creativity that flourishes during idle periods. The firm, rather than laying off idle workers, is assumed to harness this potential by involving them in the innovation process. Results show that a firm's organizational and managerial decisions, the temporal structure of the production system, the degree of workers' learning and forgetting, and the pace of innovation are critical factors influencing production efficiency in both the short and long term. The co-evolution of production and innovation processes emerges in our model through the two-sided effects of idleness: the loss of skills through forgetting and the deflection of time from the production of goods to the production of ideas giving birth to idleness-driven innovations. In doing so, it allows us to question the status of labour as an adjustment variable in a productive organisation. The paper concludes by discussing potential solutions to this issue and suggesting avenues for future research. |
| Keywords: | Production Theory; Firm Theory; Agent-based model; Idleness; Innovation; Fund-flow |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/27 |