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on Computational Economics |
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Issue of 2024‒07‒08
twenty papers chosen by |
| By: | Lei Fan; Justin Sirignano |
| Abstract: | Stochastic differential equation (SDE) models are the foundation for pricing and hedging financial derivatives. The drift and volatility functions in SDE models are typically chosen to be algebraic functions with a small number (less than 5) parameters which can be calibrated to market data. A more flexible approach is to use neural networks to model the drift and volatility functions, which provides more degrees-of-freedom to match observed market data. Training of models requires optimizing over an SDE, which is computationally challenging. For European options, we develop a fast stochastic gradient descent (SGD) algorithm for training the neural network-SDE model. Our SGD algorithm uses two independent SDE paths to obtain an unbiased estimate of the direction of steepest descent. For American options, we optimize over the corresponding Kolmogorov partial differential equation (PDE). The neural network appears as coefficient functions in the PDE. Models are trained on large datasets (many contracts), requiring either large simulations (many Monte Carlo samples for the stock price paths) or large numbers of PDEs (a PDE must be solved for each contract). Numerical results are presented for real market data including S&P 500 index options, S&P 100 index options, and single-stock American options. The neural-network-based SDE models are compared against the Black-Scholes model, the Dupire's local volatility model, and the Heston model. Models are evaluated in terms of how accurate they are at pricing out-of-sample financial derivatives, which is a core task in derivative pricing at financial institutions. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00459&r= |
| By: | Shidi Deng; Maximilian Schiffer; Martin Bichler |
| Abstract: | Nowadays, a significant share of the Business-to-Consumer sector is based on online platforms like Amazon and Alibaba and uses Artificial Intelligence for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra-competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk of collusion when Reinforcement Learning algorithms are used to decide on pricing strategies in competitive markets. Prior research in this field focused on Tabular Q-learning (TQL) and led to opposing views on whether learning-based algorithms can lead to supra-competitive prices. Our work contributes to this ongoing discussion by providing a more nuanced numerical study that goes beyond TQL by additionally capturing off- and on-policy Deep Reinforcement Learning (DRL) algorithms. We study multiple Bertrand oligopoly variants and show that algorithmic collusion depends on the algorithm used. In our experiments, TQL exhibits higher collusion and price dispersion phenomena compared to DRL algorithms. We show that the severity of collusion depends not only on the algorithm used but also on the characteristics of the market environment. We further find that Proximal Policy Optimization appears to be less sensitive to collusive outcomes compared to other state-of-the-art DRL algorithms. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.02437&r= |
| By: | Benjamin Avanzi; Eric Dong; Patrick J. Laub; Bernard Wong |
| Abstract: | A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalized Linear Models (GLMs; Nelder and Wedderburn, 1972) are commonly used, but challenges remain in developing models that can (i) allow covariates to flexibly impact different aspects of the conditional distribution, (ii) integrate developments in machine learning and AI to maximise the predictive power while considering (i), and, (iii) maintain a level of interpretability in the model to enhance trust in the model and its outputs, which is often compromised in efforts pursuing (i) and (ii). We tackle this problem by proposing a Distributional Refinement Network (DRN), which combines an inherently interpretable baseline model (such as GLMs) with a flexible neural network-a modified Deep Distribution Regression (DDR; Li et al., 2019) method. Inspired by the Combined Actuarial Neural Network (CANN; Schelldorfer and W{\''u}thrich, 2019), our approach flexibly refines the entire baseline distribution. As a result, the DRN captures varying effects of features across all quantiles, improving predictive performance while maintaining adequate interpretability. Using both synthetic and real-world data, we demonstrate the DRN's superior distributional forecasting capacity. The DRN has the potential to be a powerful distributional regression model in actuarial science and beyond. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00998&r= |
| By: | Mahdi Ebrahimi Kahou; James Yu; Jesse Perla; Geoff Pleiss |
| Abstract: | This paper examines the alignment of inductive biases in machine learning (ML) with structural models of economic dynamics. Unlike dynamical systems found in physical and life sciences, economics models are often specified by differential equations with a mixture of easy-to-enforce initial conditions and hard-to-enforce infinite horizon boundary conditions (e.g. transversality and no-ponzi-scheme conditions). Traditional methods for enforcing these constraints are computationally expensive and unstable. We investigate algorithms where those infinite horizon constraints are ignored, simply training unregularized kernel machines and neural networks to obey the differential equations. Despite the inherent underspecification of this approach, our findings reveal that the inductive biases of these ML models innately enforce the infinite-horizon conditions necessary for the well-posedness. We theoretically demonstrate that (approximate or exact) min-norm ML solutions to interpolation problems are sufficient conditions for these infinite-horizon boundary conditions in a wide class of problems. We then provide empirical evidence that deep learning and ridgeless kernel methods are not only theoretically sound with respect to economic assumptions, but may even dominate classic algorithms in low to medium dimensions. More importantly, these results give confidence that, despite solving seemingly ill-posed problems, there are reasons to trust the plethora of black-box ML algorithms used by economists to solve previously intractable, high-dimensional dynamical systems -- paving the way for future work on estimation of inverse problems with embedded optimal control problems. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.01898&r= |
| By: | Xi-Ning Zhuang; Zhao-Yun Chen; Cheng Xue; Xiao-Fan Xu; Chao Wang; Huan-Yu Liu; Tai-Ping Sun; Yun-Jie Wang; Yu-Chun Wu; Guo-Ping Guo |
| Abstract: | Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) that efficiently prepares and trains quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.01335&r= |
| By: | Iñaki Aldasoro; Leonardo Gambacorta; Anton Korinek; Vatsala Shreeti; Merlin Stein |
| Abstract: | At the core of the financial system is the processing and aggregation of vast amounts of information into price signals that coordinate participants in the economy. Throughout history, advances in information processing, from simple bookkeeping to artificial intelligence (AI), have transformed the financial sector. We use this framing to analyse how generative AI (GenAI) and emerging AI agents as well as, more speculatively, artificial general intelligence will impact finance. We focus on four functions of the financial system: financial intermediation, insurance, asset management and payments. We also assess the implications of advances in AI for financial stability and prudential policy. Moreover, we investigate potential spillover effects of AI on the real economy, examining both an optimistic and a disruptive AI scenario. To address the transformative impact of advances in AI on the financial system, we propose a framework for upgrading financial regulation based on well-established general principles for AI governance. |
| Keywords: | artificial intelligence, generative AI, AI agents, financial system, financial institutions |
| JEL: | E31 J24 O33 O40 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1194&r= |
| By: | Denish Omondi Otieno; Faranak Abri; Sima Siami-Namini; Akbar Siami Namin |
| Abstract: | Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human inputs. LLMs, can not only compose textual scripts such as emails and essays but also executable programming code. Contrary, the automated reasoning capability of these LLMs in performing statistically-driven descriptive analysis, particularly on user-specific data and as personal assistants to users with limited background knowledge in an application domain who would like to carry out basic, as well as advanced statistical and domain-specific analysis is not yet fully explored. More importantly, the performance of these LLMs has not been compared and discussed in detail when domain-specific data analysis tasks are needed. This study, consequently, explores whether LLMs can be used as generative AI-based personal assistants to users with minimal background knowledge in an application domain infer key data insights. To demonstrate the performance of the LLMs, the study reports a case study through which descriptive statistical analysis, as well as Natural Language Processing (NLP) based investigations, are performed on a number of phishing emails with the objective of comparing the accuracy of the results generated by LLMs to the ones produced by analysts. The experimental results show that LangChain and the Generative Pre-trained Transformer (GPT-4) excel in numerical reasoning tasks i.e., temporal statistical analysis, achieve competitive correlation with human judgments on feature engineering tasks while struggle to some extent on domain specific knowledge reasoning, where domain-specific knowledge is required. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.19578&r= |
| By: | Henri Fraisse; Christophe Hurlin |
| Abstract: | This note outlines the issues, risks and benefits of machine learning models for the design of internal credit risk assessment models used by banking institutions for the calculation of their own funds requirement ("Credit IRB Approach"). The use of ML models in IRB models is currently marginal. However, it could improve the predictive quality of models and in some cases lead to a reduction in capital requirements. However, ML models face a lack of interpretability that substitution or local approximation methods do not solve. <p> Cette note expose les enjeux, les risques et les avantages des modèles d’apprentissage automatique (« Machine Learning ») pour la conception des modèles internes d’évaluation de risque de crédit utilisés par les établissements bancaires dans le cadre du calcul de leur exigence en fond propre (« Approche IRB Crédit »). L’utilisation des modèles ML dans le cadre des modèles IRB est pour l’instant marginale. Elle pourrait pourtant permettre d’améliorer la qualité prédictive des modèles et dans certains cas conduire à une réduction des exigences de fonds propres. Toutefois les modèles ML se heurtent à un déficit d’interprétabilité que les méthodes par substitution ou d’approximation locale ne résolvent pas. |
| Keywords: | Machine Learning; banking prudential regulation; internal models; regulatory capital; Machine Learning ; réglementation prudentielle bancaire ; modèles internes ; capital réglementaire |
| JEL: | G21 G29 C10 C38 C55 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:decfin:44&r= |
| By: | Shumiao Ouyang; Hayong Yun; Xingjian Zheng |
| Abstract: | This study explores the risk preferences of Large Language Models (LLMs) and how the process of aligning them with human ethical standards influences their economic decision-making. By analyzing 30 LLMs, we uncover a broad range of inherent risk profiles ranging from risk-averse to risk-seeking. We then explore how different types of AI alignment, a process that ensures models act according to human values and that focuses on harmlessness, helpfulness, and honesty, alter these base risk preferences. Alignment significantly shifts LLMs towards risk aversion, with models that incorporate all three ethical dimensions exhibiting the most conservative investment behavior. Replicating a prior study that used LLMs to predict corporate investments from company earnings call transcripts, we demonstrate that although some alignment can improve the accuracy of investment forecasts, excessive alignment results in overly cautious predictions. These findings suggest that deploying excessively aligned LLMs in financial decision-making could lead to severe underinvestment. We underline the need for a nuanced approach that carefully balances the degree of ethical alignment with the specific requirements of economic domains when leveraging LLMs within finance. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.01168&r= |
| By: | Jinxin Xu; Kaixian Xu; Yue Wang; Qinyan Shen; Ruisi Li |
| Abstract: | Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today's society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the K-means algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.13076&r= |
| By: | Zhonghao Xian; Xing Yan; Cheuk Hang Leung; Qi Wu |
| Abstract: | We present a functional generative approach to extract risk-neutral densities from market prices of options. Specifically, we model the log-returns on the time-to-maturity continuum as a stochastic curve driven by standard normal. We then use neural nets to represent the term structures of the location, the scale, and the higher-order moments, and impose stringent conditions on the learning process to ensure the neural net-based curve representation is free of static arbitrage. This specification is structurally clear in that it separates the modeling of randomness from the modeling of the term structures of the parameters. It is data adaptive in that we use neural nets to represent the shape of the stochastic curve. It is also generative in that the functional form of the stochastic curve, although parameterized by neural nets, is an explicit and deterministic function of the standard normal. This explicitness allows for the efficient generation of samples to price options across strikes and maturities, without compromising data adaptability. We have validated the effectiveness of this approach by benchmarking it against a comprehensive set of baseline models. Experiments show that the extracted risk-neutral densities accommodate a diverse range of shapes. Its accuracy significantly outperforms the extensive set of baseline models--including three parametric models and nine stochastic process models--in terms of accuracy and stability. The success of this approach is attributed to its capacity to offer flexible term structures for risk-neutral skewness and kurtosis. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.17770&r= |
| By: | Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths |
| Abstract: | The observed similarities in the behavior of humans and Large Language Models (LLMs) have prompted researchers to consider the potential of using LLMs as models of human cognition. However, several significant challenges must be addressed before LLMs can be legitimately regarded as cognitive models. For instance, LLMs are trained on far more data than humans typically encounter, and may have been directly trained on human data in specific cognitive tasks or aligned with human preferences. Consequently, the origins of these behavioral similarities are not well understood. In this paper, we propose a novel way to enhance the utility of LLMs as cognitive models. This approach involves (i) leveraging computationally equivalent tasks that both an LLM and a rational agent need to master for solving a cognitive problem and (ii) examining the specific task distributions required for an LLM to exhibit human-like behaviors. We apply this approach to decision-making -- specifically risky and intertemporal choice -- where the key computationally equivalent task is the arithmetic of expected value calculations. We show that an LLM pretrained on an ecologically valid arithmetic dataset, which we call Arithmetic-GPT, predicts human behavior better than many traditional cognitive models. Pretraining LLMs on ecologically valid arithmetic datasets is sufficient to produce a strong correspondence between these models and human decision-making. Our results also suggest that LLMs used as cognitive models should be carefully investigated via ablation studies of the pretraining data. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.19313&r= |
| By: | Xuefeng Gao; Lingfei Li; Xun Yu Zhou |
| Abstract: | We study continuous-time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized exploratory control problem with stochastic policies to capture the exploration--exploitation balance essential for RL. Unlike the pure diffusion case initially studied by Wang et al. (2020), the derivation of the exploratory dynamics under jump-diffusions calls for a careful formulation of the jump part. Through a theoretical analysis, we find that one can simply use the same policy evaluation and q-learning algorithms in Jia and Zhou (2022a, 2023), originally developed for controlled diffusions, without needing to check a priori whether the underlying data come from a pure diffusion or a jump-diffusion. However, we show that the presence of jumps ought to affect parameterizations of actors and critics in general. Finally, we investigate as an application the mean-variance portfolio selection problem with stock price modelled as a jump-diffusion, and show that both RL algorithms and parameterizations are invariant with respect to jumps. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.16449&r= |
| By: | Ning Li; Huaikang Zhou; Kris Mikel-Hong |
| Abstract: | Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.17924&r= |
| By: | Andrzej Cichocki; Sergio Cruces; Auxiliadora Sarmiento; Toshihisa Tanaka |
| Abstract: | This paper introduces a novel family of generalized exponentiated gradient (EG) updates derived from an Alpha-Beta divergence regularization function. Collectively referred to as EGAB, the proposed updates belong to the category of multiplicative gradient algorithms for positive data and demonstrate considerable flexibility by controlling iteration behavior and performance through three hyperparameters: $\alpha$, $\beta$, and the learning rate $\eta$. To enforce a unit $l_1$ norm constraint for nonnegative weight vectors within generalized EGAB algorithms, we develop two slightly distinct approaches. One method exploits scale-invariant loss functions, while the other relies on gradient projections onto the feasible domain. As an illustration of their applicability, we evaluate the proposed updates in addressing the online portfolio selection problem (OLPS) using gradient-based methods. Here, they not only offer a unified perspective on the search directions of various OLPS algorithms (including the standard exponentiated gradient and diverse mean-reversion strategies), but also facilitate smooth interpolation and extension of these updates due to the flexibility in hyperparameter selection. Simulation results confirm that the adaptability of these generalized gradient updates can effectively enhance the performance for some portfolios, particularly in scenarios involving transaction costs. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00655&r= |
| By: | Umang Khetan; Jetson Leder-Luis; Jialan Wang; Yunrong Zhou |
| Abstract: | We study fraud in the unemployment insurance (UI) system using a dataset of 35 million debit card transactions. We apply machine learning techniques to cluster cards corresponding to varying levels of suspicious or potentially fraudulent activity. We then conduct a difference-in-differences analysis based on the staggered adoption of state-level identity verification systems between 2020 and 2021 to assess the effectiveness of screening for reducing fraud. Our findings suggest that identity verification reduced payouts to suspicious cards by 27%, while non-suspicious cards were largely unaffected by these technologies. Our results indicate that identity screening may be an effective mechanism for mitigating fraud in the UI system and for benefits programs more broadly. |
| JEL: | G51 H53 J65 K42 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32527&r= |
| By: | Christian Bayer; Ralph Luetticke; Maximilian Weiss; Yannik Winkelmann |
| Abstract: | The “histogram method” (Young, 2010), while the standard approach for analyzing distributional dynamics in heterogeneous agent models, is linear in optimal policies. We introduce a novel method that captures nonlinearities of distributional dynamics. This method solves the distributional dynamics by interpolation instead of integration, which is made possible by making the grid endogenous. It retains the tractability and speed of the histogram method, while increasing numerical efficiency even in the steady state and producing significant economic differences in scenarios with aggregate risk. We document this by studying aggregate investment risk with a third-order solution using perturbation techniques. |
| Keywords: | Numerical Methods, Distributions, Heterogeneous Agent Models, Linearization |
| JEL: | C46 C63 E32 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2024_548&r= |
| By: | Hui Li; Jian Ni; Fangzhu Yang |
| Abstract: | The development of Generative AI enables large-scale automation of product design. However, this automated process usually does not incorporate consumer preference information from a company's internal dataset. Meanwhile, external sources such as social media and user-generated content (UGC) websites often contain rich product design and consumer preference information, but such information is not utilized by companies in design generation. We propose a semi-supervised deep generative framework that integrates consumer preferences and external data into product design, allowing companies to generate consumer-preferred designs in a cost-effective and scalable way. We train a predictor model to learn consumer preferences and use predicted popularity levels as additional input labels to guide the training of a Continuous Conditional Generative Adversarial Network (CcGAN). The CcGAN can be instructed to generate new designs of a certain popularity level, enabling companies to efficiently create consumer-preferred designs and save resources by avoiding developing and testing unpopular designs. The framework also incorporates existing product designs and consumer preference information from external sources, which is particularly helpful for small or start-up companies who have limited internal data and face the "cold-start" problem. We apply the proposed framework to a real business setting by helping a large self-aided photography chain in China design new photo templates. We show that our proposed model performs well in generating appealing template designs for the company. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.15929&r= |
| By: | Christian Oliver Ewald; Charles Nolan |
| Abstract: | We show how the classical Lagrangian approach to solving constrained optimization problems from standard calculus can be extended to solve continuous time stochastic optimal control problems. Connections to mainstream approaches such as the Hamilton-Jacobi-Bellman equation and the stochastic maximum principle are drawn. Our approach is linked to the stochastic maximum principle, but more direct and tied to the classical Lagrangian principle, avoiding the use of backward stochastic differential equations in its formulation. Using infinite dimensional functional analysis, we formalize and extend the approach first outlined in Chow (1992) within a rigorous mathematical setting using infinite dimensional functional analysis. We provide examples that demonstrate the usefulness and effectiveness of our approach in practice. Further, we demonstrate the potential for numerical applications facilitating some of our key equations in combination with Monte Carlo backward simulation and linear regression, therefore illustrating a completely different and new avenue for the numerical application of Chow’s methods. |
| Keywords: | Lagrange formalism, continuous optimization, dynamic programming, economic growth models, stochastic processes, optimal control, regression-based Monte Carlo methods |
| JEL: | C61 C63 C65 E22 |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:gla:glaewp:2024_04&r= |
| By: | Prajwal Naga; Dinesh Balivada; Sharath Chandra Nirmala; Poornoday Tiruveedi |
| Abstract: | This research paper aims to investigate the efficacy of decision trees in constructing intraday trading strategies using existing technical indicators for individual equities in the NIFTY50 index. Unlike conventional methods that rely on a fixed set of rules based on combinations of technical indicators developed by a human trader through their analysis, the proposed approach leverages decision trees to create unique trading rules for each stock, potentially enhancing trading performance and saving time. By extensively backtesting the strategy for each stock, a trader can determine whether to employ the rules generated by the decision tree for that specific stock. While this method does not guarantee success for every stock, decision treebased strategies outperform the simple buy-and-hold strategy for many stocks. The results highlight the proficiency of decision trees as a valuable tool for enhancing intraday trading performance on a stock-by-stock basis and could be of interest to traders seeking to improve their trading strategies. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.13959&r= |