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on Computational Economics |
| By: | Daksh Dave; Gauransh Sawhney; Vikhyat Chauhan |
| Abstract: | This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention-based models. These models are assessed for their ability to capture complex temporal dependencies inherent in stock market data. Our findings show that attention-based models outperform other architectures, achieving the highest accuracy by capturing both short and long-term dependencies. This study contributes valuable insights into AI-driven financial forecasting, offering practical guidance for developing more accurate and efficient trading systems. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15853 |
| By: | Guojun Xiong; Zhiyang Deng; Keyi Wang; Yupeng Cao; Haohang Li; Yangyang Yu; Xueqing Peng; Mingquan Lin; Kaleb E Smith; Xiao-Yang Liu; Jimin Huang; Sophia Ananiadou; Qianqian Xie |
| Abstract: | Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to improve decision-making. To address this, we propose \textsc{FLAG-Trader}, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization, in which a partially fine-tuned LLM acts as the policy network, leveraging pre-trained knowledge while adapting to the financial domain through parameter-efficient fine-tuning. Through policy gradient optimization driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other financial-domain tasks. We present extensive empirical evidence to validate these enhancements. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.11433 |
| By: | Zhipeng Liu; Peibo Duan; Mingyang Geng; Bin Zhang |
| Abstract: | Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensively employed in stock prediction due to their powerful capability to capture spatiotemporal dependencies of stocks. However, despite the efforts of various GNN stock predictors to enhance predictive performance, the improvements remain limited, as they focus solely on analyzing historical spatiotemporal dependencies, overlooking the correlation between historical and future patterns. In this study, we propose a novel distillation-based future-aware GNN framework (DishFT-GNN) for stock trend prediction. Specifically, DishFT-GNN trains a teacher model and a student model, iteratively. The teacher model learns to capture the correlation between distribution shifts of historical and future data, which is then utilized as intermediate supervision to guide the student model to learn future-aware spatiotemporal embeddings for accurate prediction. Through extensive experiments on two real-world datasets, we verify the state-of-the-art performance of DishFT-GNN. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.10776 |
| By: | Yan Zhang; Lin Chen; Yixiang Tian |
| Abstract: | Interpretability analysis methods for artificial intelligence models, such as LIME and SHAP, are widely used, though they primarily serve as post-model for analyzing model outputs. While it is commonly believed that the transparency and interpretability of AI models diminish as their complexity increases, currently there is no standardized method for assessing the inherent interpretability of the models themselves. This paper uses bond market default prediction as a case study, applying commonly used machine learning algorithms within AI models. First, the classification performance of these algorithms in default prediction is evaluated. Then, leveraging LIME and SHAP to assess the contribution of sample features to prediction outcomes, the paper proposes a novel method for evaluating the interpretability of the models themselves. The results of this analysis are consistent with the intuitive understanding and logical expectations regarding the interpretability of these models. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.19615 |
| By: | Lei Zhao; Lin Cai |
| Abstract: | Deep hedging represents a cutting-edge approach to risk management for financial derivatives by leveraging the power of deep learning. However, existing methods often face challenges related to computational inefficiency, sensitivity to noisy data, and optimization complexity, limiting their practical applicability in dynamic and volatile markets. To address these limitations, we propose Deep Hedging with Linearized-objective Neural Network (DHLNN), a robust and generalizable framework that enhances the training procedure of deep learning models. By integrating a periodic fixed-gradient optimization method with linearized training dynamics, DHLNN stabilizes the training process, accelerates convergence, and improves robustness to noisy financial data. The framework incorporates trajectory-wide optimization and Black-Scholes Delta anchoring, ensuring alignment with established financial theory while maintaining flexibility to adapt to real-world market conditions. Extensive experiments on synthetic and real market data validate the effectiveness of DHLNN, demonstrating its ability to achieve faster convergence, improved stability, and superior hedging performance across diverse market scenarios. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17757 |
| By: | Leite, Walter; Zhang, Huibin; collier, zachary; Chawla, Kamal; , l.kong@ufl.edu; Lee, Yongseok (University of Florida); Quan, Jia; Soyoye, Olushola |
| Abstract: | Machine learning has become a common approach for estimating propensity scores for quasi-experimental research using matching, weighting, or stratification on the propensity score. This systematic review examined machine learning applications for propensity score estimation across different fields, such as health, education, social sciences, and business over 40 years. The results show that the gradient boosting machine (GBM) is the most frequently used method, followed by random forest. Classification and regression trees (CART), neural networks, and the super learner were also used in more than five percent of studies. The most frequently used packages to estimate propensity scores were twang, gbm and randomforest in the R statistical software. The review identified many hyperparameter configurations used for machine learning methods. However, it also shows that hyperparameters are frequently under-reported, as well as critical steps of the propensity score analysis, such as the covariate balance evaluation. A set of guidelines for reporting the use of machine learning for propensity score estimation is provided. |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:gmrk7_v1 |
| By: | Zichuan Guo; Mihai Cucuringu; Alexander Y. Shestopaloff |
| Abstract: | We tackle the challenges of modeling high-dimensional data sets, particularly those with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships. Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression. Our approach introduces PCA and Soft PCA layers, which can be embedded at any stage of a neural network architecture, allowing the model to alternate between factor modeling and non-linear transformations. This flexibility makes our method especially effective for processing hierarchical compositional data. We explore ours and other techniques for imposing low-rank structures on neural networks and examine how architectural design impacts model performance. The effectiveness of our method is demonstrated through simulation studies, as well as applications to forecasting future price movements of equity ETF indices and nowcasting with macroeconomic data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.11310 |
| By: | Lei Zhao; Lin Cai; Wu-Sheng Lu |
| Abstract: | In the field of financial derivatives trading, managing volatility risk is crucial for protecting investment portfolios from market changes. Traditional Vega hedging strategies, which often rely on basic and rule-based models, are hard to adapt well to rapidly changing market conditions. We introduce a new framework for dynamic Vega hedging, the Adaptive Nesterov Accelerated Distributional Deep Hedging (ANADDH), which combines distributional reinforcement learning with a tailored design based on adaptive Nesterov acceleration. This approach improves the learning process in complex financial environments by modeling the hedging efficiency distribution, providing a more accurate and responsive hedging strategy. The design of adaptive Nesterov acceleration refines gradient momentum adjustments, significantly enhancing the stability and speed of convergence of the model. Through empirical analysis and comparisons, our method demonstrates substantial performance gains over existing hedging techniques. Our results confirm that this innovative combination of distributional reinforcement learning with the proposed optimization techniques improves financial risk management and highlights the practical benefits of implementing advanced neural network architectures in the finance sector. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17777 |
| By: | Zhao, Yu |
| Abstract: | Accurately forecasting whether a real estate transaction will close is crucial for agents, lenders, and investors, impacting resource allocation, risk management, and client satisfaction. This task, however, is complex due to a combination of economic, procedural, and behavioral factors that influence transaction outcomes. Traditional machine learning approaches, particularly gradient boosting models like Gradient Boost Decision Tree, have proven effective for tabular data, outperforming deep learning models on structured datasets. However, recent advances in attention-based deep learning models present new opportunities to capture temporal dependencies and complex interactions within transaction data, potentially enhancing prediction accuracy. This article explores the challenges of forecasting real estate transaction closures, compares the performance of machine learning models, and examines how attention-based models can improve predictive insights in this critical area of real estate analytics. |
| Date: | 2024–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:sxmq2_v1 |
| By: | Meet Satishbhai Sonani; Atta Badii; Armin Moin |
| Abstract: | This paper presents a novel hybrid model that integrates long-short-term memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the accuracy of stock market predictions. The LSTM component adeptly captures temporal patterns in stock price data, effectively modeling the time series dynamics of financial markets. Concurrently, the GNN component leverages Pearson correlation and association analysis to model inter-stock relational data, capturing complex nonlinear polyadic dependencies influencing stock prices. The model is trained and evaluated using an expanding window validation approach, enabling continuous learning from increasing amounts of data and adaptation to evolving market conditions. Extensive experiments conducted on historical stock data demonstrate that our hybrid LSTM-GNN model achieves a mean square error (MSE) of 0.00144, representing a substantial reduction of 10.6% compared to the MSE of the standalone LSTM model of 0.00161. Furthermore, the hybrid model outperforms traditional and advanced benchmarks, including linear regression, convolutional neural networks (CNN), and dense networks. These compelling results underscore the significant potential of combining temporal and relational data through a hybrid approach, offering a powerful tool for real-time trading and financial analysis. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15813 |
| By: | Furkan Karada\c{s}; Bahaeddin Eravc{\i}; Ahmet Murat \"Ozbayo\u{g}lu |
| Abstract: | In an era where financial markets are heavily influenced by many static and dynamic factors, it has become increasingly critical to carefully integrate diverse data sources with machine learning for accurate stock price prediction. This paper explores a multimodal machine learning approach for stock price prediction by combining data from diverse sources, including traditional financial metrics, tweets, and news articles. We capture real-time market dynamics and investor mood through sentiment analysis on these textual data using both ChatGPT-4o and FinBERT models. We look at how these integrated data streams augment predictions made with a standard Long Short-Term Memory (LSTM model) to illustrate the extent of performance gains. Our study's results indicate that incorporating the mentioned data sources considerably increases the forecast effectiveness of the reference model by up to 5%. We also provide insights into the individual and combined predictive capacities of these modalities, highlighting the substantial impact of incorporating sentiment analysis from tweets and news articles. This research offers a systematic and effective framework for applying multimodal data analytics techniques in financial time series forecasting that provides a new view for investors to leverage data for decision-making. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.05186 |
| By: | Luca Lalor; Anatoliy Swishchuk |
| Abstract: | In this paper, we propose an event-driven Limit Order Book (LOB) model that captures twelve of the most observed LOB events in exchange-based financial markets. To model these events, we propose using the state-of-the-art Neural Hawkes process, a more robust alternative to traditional Hawkes process models. More specifically, this model captures the dynamic relationships between different event types, particularly their long- and short-term interactions, using a Long Short-Term Memory neural network. Using this framework, we construct a midprice process that captures the event-driven behavior of the LOB by simulating high-frequency dynamics like how they appear in real financial markets. The empirical results show that our model captures many of the broader characteristics of the price fluctuations, particularly in terms of their overall volatility. We apply this LOB simulation model within a Deep Reinforcement Learning Market-Making framework, where the trading agent can now complete trade order fills in a manner that closely resembles real-market trade execution. Here, we also compare the results of the simulated model with those from real data, highlighting how the overall performance and the distribution of trade order fills closely align with the same analysis on real data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17417 |
| By: | Xiangyu Li; Yawen Zeng; Xiaofen Xing; Jin Xu; Xiangmin Xu |
| Abstract: | As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations, impeding their practical application. Hence, there is an imperative to explore a more robust and resilient framework. This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ``hedging'' strategies. In this well-balanced system, an array of hedging agents has been tailored, where HedgeAgents consist of a central fund manager and multiple hedging experts specializing in various financial asset classes. These agents leverage LLMs' cognitive capabilities to make decisions and coordinate through three types of conferences. Benefiting from the powerful understanding of LLMs, our HedgeAgents attained a 70% annualized return and a 400% total return over a period of 3 years. Moreover, we have observed with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/). |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.13165 |
| By: | Remi Genet |
| Abstract: | The execution of Volume Weighted Average Price (VWAP) orders remains a critical challenge in modern financial markets, particularly as trading volumes and market complexity continue to increase. In my previous work arXiv:2502.13722, I introduced a novel deep learning approach that demonstrated significant improvements over traditional VWAP execution methods by directly optimizing the execution problem rather than relying on volume curve predictions. However, that model was static because it employed the fully linear approach described in arXiv:2410.21448, which is not designed for dynamic adjustment. This paper extends that foundation by developing a dynamic neural VWAP framework that adapts to evolving market conditions in real time. We introduce two key innovations: first, the integration of recurrent neural networks to capture complex temporal dependencies in market dynamics, and second, a sophisticated dynamic adjustment mechanism that continuously optimizes execution decisions based on market feedback. The empirical analysis, conducted across five major cryptocurrency markets, demonstrates that this dynamic approach achieves substantial improvements over both traditional methods and our previous static implementation, with execution performance gains of 10 to 15% in liquid markets and consistent outperformance across varying conditions. These results suggest that adaptive neural architectures can effectively address the challenges of modern VWAP execution while maintaining computational efficiency suitable for practical deployment. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.18177 |
| By: | Enoch H. Kang; Hema Yoganarasimhan; Lalit Jain |
| Abstract: | We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline MaxEnt-IRL) in machine learning. The objective is to recover reward or $Q^*$ functions that govern agent behavior from offline behavior data. In this paper, we propose a globally convergent gradient-based method for solving these problems without the restrictive assumption of linearly parameterized rewards. The novelty of our approach lies in introducing the Empirical Risk Minimization (ERM) based IRL/DDC framework, which circumvents the need for explicit state transition probability estimation in the Bellman equation. Furthermore, our method is compatible with non-parametric estimation techniques such as neural networks. Therefore, the proposed method has the potential to be scaled to high-dimensional, infinite state spaces. A key theoretical insight underlying our approach is that the Bellman residual satisfies the Polyak-Lojasiewicz (PL) condition -- a property that, while weaker than strong convexity, is sufficient to ensure fast global convergence guarantees. Through a series of synthetic experiments, we demonstrate that our approach consistently outperforms benchmark methods and state-of-the-art alternatives. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14131 |
| By: | Leonardo Berti; Bardh Prenkaj; Paola Velardi |
| Abstract: | Financial markets are complex systems characterized by high statistical noise, nonlinearity, and constant evolution. Thus, modeling them is extremely hard. We address the task of generating realistic and responsive Limit Order Book (LOB) market simulations, which are fundamental for calibrating and testing trading strategies, performing market impact experiments, and generating synthetic market data. Previous works lack realism, usefulness, and responsiveness of the generated simulations. To bridge this gap, we propose a novel TRAnsformer-based Denoising Diffusion Probabilistic Engine for LOB Simulations (TRADES). TRADES generates realistic order flows conditioned on the state of the market, leveraging a transformer-based architecture that captures the temporal and spatial characteristics of high-frequency market data. There is a notable absence of quantitative metrics for evaluating generative market simulation models in the literature. To tackle this problem, we adapt the predictive score, a metric measured as an MAE, by training a stock price predictive model on synthetic data and testing it on real data. We compare TRADES with previous works on two stocks, reporting an x3.27 and x3.47 improvement over SoTA according to the predictive score, demonstrating that we generate useful synthetic market data for financial downstream tasks. We assess TRADES's market simulation realism and responsiveness, showing that it effectively learns the conditional data distribution and successfully reacts to an experimental agent, giving sprout to possible calibrations and evaluations of trading strategies and market impact experiments. We developed DeepMarket, the first open-source Python framework for market simulation with deep learning. Our repository includes a synthetic LOB dataset composed of TRADES's generates simulations. We release the code at github.com/LeonardoBerti00/DeepMarket. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.07071 |
| By: | Hota, Ashish |
| Abstract: | This paper explores the evolution of AI-driven pricing strategies in the automotive and financial services sectors, focusing on dynamic and deal-based pricing models that adapt in real time to shifts in consumer behavior, supply chain limitations, and market fluctuations. We examine how advanced machine learning techniques, including deep learning and reinforcement learning, enable predictive and adaptive pricing solutions that drive customer loyalty, revenue optimization, and transparency. Explainable AI also features prominently, offering transparency to consumers and regulators alike. |
| Date: | 2024–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:emgpv_v1 |
| By: | Ankur Sinha; Chaitanya Agarwal; Pekka Malo |
| Abstract: | Large language models (LLMs) excel at generating human-like responses but often struggle with interactive tasks that require access to real-time information. This limitation poses challenges in finance, where models must access up-to-date information, such as recent news or price movements, to support decision-making. To address this, we introduce Financial Agent, a knowledge-grounding approach for LLMs to handle financial queries using real-time text and tabular data. Our contributions are threefold: First, we develop a Financial Context Dataset of over 50, 000 financial queries paired with the required context. Second, we train FinBloom 7B, a custom 7 billion parameter LLM, on 14 million financial news articles from Reuters and Deutsche Presse-Agentur, alongside 12 million Securities and Exchange Commission (SEC) filings. Third, we fine-tune FinBloom 7B using the Financial Context Dataset to serve as a Financial Agent. This agent generates relevant financial context, enabling efficient real-time data retrieval to answer user queries. By reducing latency and eliminating the need for users to manually provide accurate data, our approach significantly enhances the capability of LLMs to handle dynamic financial tasks. Our proposed approach makes real-time financial decisions, algorithmic trading and other related tasks streamlined, and is valuable in contexts with high-velocity data flows. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.18471 |
| By: | Jibang Wu; Chenghao Yang; Simon Mahns; Chaoqi Wang; Hao Zhu; Fei Fang; Haifeng Xu |
| Abstract: | This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin. Our findings suggest a promising LLM-based agentic framework to automate large-scale targeted marketing while ensuring responsible generation using only facts. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.16810 |
| By: | Sangram Deshpande; Elin Ranjan Das; Frank Mueller |
| Abstract: | Currency arbitrage capitalizes on price discrepancies in currency exchange rates between markets to produce profits with minimal risk. By employing a combinatorial optimization problem, one can ascertain optimal paths within directed graphs, thereby facilitating the efficient identification of profitable trading routes. This research investigates the methodologies of quantum annealing and gate-based quantum computing in relation to the currency arbitrage problem. In this study, we implement the Quantum Approximate Optimization Algorithm (QAOA) utilizing Qiskit version 1.2. In order to optimize the parameters of QAOA, we perform simulations utilizing the AerSimulator and carry out experiments in simulation. Furthermore, we present an NchooseK-based methodology utilizing D-Wave's Ocean suite. This methodology enables a comparison of the effectiveness of quantum techniques in identifying optimal arbitrage paths. The results of our study enhance the existing literature on the application of quantum computing in financial optimization challenges, emphasizing both the prospective benefits and the present limitations of these developing technologies in real-world scenarios. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15742 |
| By: | Hota, Ashish |
| Abstract: | The integration of behavioral data analysis and machine learning (ML) within unified systems has become increasingly vital for enhanced decision-making and system optimization across various industries, including healthcare, marketing, and finance. Behavioral data—comprising user actions, preferences, and interactions—provides valuable insights into emerging trends, enabling adaptive and intelligent system functionalities. Coupling this with ML allows systems to continuously learn and improve their performance. This paper presents a comprehensive approach to integrating behavioral data analysis and ML within unified systems, covering key methodologies, technical challenges, applications, and a roadmap for future developments. Additionally, the article includes technical facts, tables, diagrams, and comparisons to aid in understanding the technical aspects and advantages of this integration. |
| Date: | 2024–12–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:rjpxs_v1 |
| By: | Lippens, Louis (Ghent University) |
| Abstract: | The advent of large language models (LLMs) may reshape hiring in the labour market. This paper investigates how generative pre-trained transformers (GPTs)—i.e. OpenAI’s GPT-3.5, GPT-4, and GPT-4o—can aid hiring decisions. In a direct comparison between humans and GPTs on an identical hiring task, I show that GPTs tend to select candidates more liberally than humans but exhibit less ethnic bias. GPT-4 even slightly favours certain ethnic minorities. While LLMs may complement humans in hiring by making a (relatively extensive) pre-selection of job candidates, the findings suggest that they may miss-select due to a lack of contextual understanding and may reproduce pre-trained human bias at scale. |
| Date: | 2024–07–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:zxf5y_v1 |
| By: | Sario, Azhar ul Haque |
| Abstract: | This third volume in the “Stock Predictions” series builds on the success of the first edition, “Stock Price Predictions: An Introduction to Probabilistic Models” (ISBN 979-8223912712), and the second edition, “Forecasting Stock Prices: Mathematics of Probabilistic Models” (ISBN 979-8223038993). This new edition delves deeper into the complex world of quantitative finance, providing readers with a comprehensive guide to advanced financial models used in stock price prediction. The book covers a wide array of models, beginning with the foundational concept of Brownian Motion, which represents the random movement of stock prices and underpins many financial models. It then progresses to Geometric Brownian Motion, a model that accounts for the exponential growth often observed in stock prices. Mean Reversion Models are introduced to capture the tendency of stock prices to revert to their long-term average, offering a counterpoint to trend-following strategies. The book explores the world of volatility modeling with GARCH models, which capture the clustering and persistence of volatility in financial markets, crucial for risk management and option pricing. Extensions of GARCH, such as EGARCH and TGARCH, are examined to address the asymmetric impact of positive and negative news on volatility. In the latter part of the book, the focus shifts to Machine Learning, demonstrating how techniques like Support Vector Machines and Neural Networks can uncover complex patterns in financial data and enhance prediction accuracy. Recurrent Neural Networks, particularly LSTMs, are highlighted for their ability to model sequential data, making them ideal for capturing the temporal dynamics of stock prices. Monte Carlo simulations are discussed as a powerful tool for generating a range of possible future outcomes, enabling investors to assess risk and make informed decisions. Finally, Copula Models are introduced to model the dependence structure between multiple assets, critical for portfolio management and risk assessment. Throughout the book, each model is presented with a clear explanation of its mathematical formulation, parameter estimation techniques, and practical applications in stock price prediction. The book emphasizes the strengths and limitations of each model, equipping readers with the knowledge to select the most appropriate model for their specific needs. This book is an invaluable resource for students, researchers, and practitioners in finance and investments seeking to master the quantitative tools used in stock price prediction. With its rigorous yet accessible approach, this book empowers readers to leverage advanced financial models and make informed investment decisions in today’s dynamic markets. The book is based on 95 research studies, which are listed on the references page and uploaded on Harvard University’s Dataverse for transparency. As a published book, it has undergone review for originality. |
| Date: | 2024–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:pk7w3_v1 |
| By: | Aldo Glielmo; Mitja Devetak; Adriano Meligrana; Sebastian Poledna |
| Abstract: | BeforeIT is an open-source software for building and simulating state-of-the-art macroeconomic agent-based models (macro ABMs) based on the recently introduced macro ABM developed in [1] and here referred to as the base model. Written in Julia, it combines extraordinary computational efficiency with user-friendliness and extensibility. We present the main structure of the software, demonstrate its ease of use with illustrative examples, and benchmark its performance. Our benchmarks show that the base model built with BeforeIT is orders of magnitude faster than a Matlab version, and significantly faster than Matlab-generated C code. BeforeIT is designed to facilitate reproducibility, extensibility, and experimentation. As the first open-source, industry-grade software to build macro ABMs of the type of the base model, BeforeIT can significantly foster collaboration and innovation in the field of agent-based macroeconomic modelling. The package, along with its documentation, is freely available at https://github.com/bancaditalia/BeforeIT.jl under the AGPL-3.0. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.13267 |
| By: | Munipalle, Pravith |
| Abstract: | Bot trading, or algorithmic trading, has transformed modern financial markets by using advanced technologies like artificial intelligence and machine learning to execute trades with unparalleled speed and efficiency. This paper examines the mechanisms and types of trading bots, their impact on market liquidity, efficiency, and stability, and the ethical and regulatory challenges they pose. Key findings highlight the dual nature of bot trading—enhancing market performance while introducing systemic risks, such as those observed during the 2010 Flash Crash. Emerging technologies like blockchain and predictive analytics, along with advancements in AI, present opportunities for innovation but also underscore the need for robust regulations and ethical design. To provide deeper insights, we conducted an experiment analyzing the performance of different trading bot strategies in simulated market conditions, revealing the potential and pitfalls of these systems under varying scenarios. |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:p98zv_v1 |
| By: | Ricardo Masini; Marcelo Medeiros |
| Abstract: | Traditional parametric econometric models often rely on rigid functional forms, while nonparametric techniques, despite their flexibility, frequently lack interpretability. This paper proposes a parsimonious alternative by modeling the outcome $Y$ as a linear function of a vector of variables of interest $\boldsymbol{X}$, conditional on additional covariates $\boldsymbol{Z}$. Specifically, the conditional expectation is expressed as $\mathbb{E}[Y|\boldsymbol{X}, \boldsymbol{Z}]=\boldsymbol{X}^{T}\boldsymbol{\beta}(\boldsymbol{Z})$, where $\boldsymbol{\beta}(\cdot)$ is an unknown Lipschitz-continuous function. We introduce an adaptation of the Random Forest (RF) algorithm to estimate this model, balancing the flexibility of machine learning methods with the interpretability of traditional linear models. This approach addresses a key challenge in applied econometrics by accommodating heterogeneity in the relationship between covariates and outcomes. Furthermore, the heterogeneous partial effects of $\boldsymbol{X}$ on $Y$ are represented by $\boldsymbol{\beta}(\cdot)$ and can be directly estimated using our proposed method. Our framework effectively unifies established parametric and nonparametric models, including varying-coefficient, switching regression, and additive models. We provide theoretical guarantees, such as pointwise and $L^p$-norm rates of convergence for the estimator, and establish a pointwise central limit theorem through subsampling, aiding inference on the function $\boldsymbol\beta(\cdot)$. We present Monte Carlo simulation results to assess the finite-sample performance of the method. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.13438 |
| By: | Yuzhi Hao (Department of Economics, The Hong Kong University of Science and Technology); Danyang Xie (Thrust of Innovation, Policy, and Entrepreneurship, the Society Hub, The Hong Kong University of Science and Technology) |
| Abstract: | This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple Large Language Models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs' economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: with explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a Multi-LLM-Agent-Based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest-income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs' human-like reasoning capabilities and computational power. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.16879 |
| By: | Thomas Henning; Siddhartha M. Ojha; Ross Spoon; Jiatong Han; Colin F. Camerer |
| Abstract: | This paper explores how Large Language Models (LLMs) behave in a classic experimental finance paradigm widely known for eliciting bubbles and crashes in human participants. We adapt an established trading design, where traders buy and sell a risky asset with a known fundamental value, and introduce several LLM-based agents, both in single-model markets (all traders are instances of the same LLM) and in mixed-model "battle royale" settings (multiple LLMs competing in the same market). Our findings reveal that LLMs generally exhibit a "textbook-rational" approach, pricing the asset near its fundamental value, and show only a muted tendency toward bubble formation. Further analyses indicate that LLM-based agents display less trading strategy variance in contrast to humans. Taken together, these results highlight the risk of relying on LLM-only data to replicate human-driven market phenomena, as key behavioral features, such as large emergent bubbles, were not robustly reproduced. While LLMs clearly possess the capacity for strategic decision-making, their relative consistency and rationality suggest that they do not accurately mimic human market dynamics. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15800 |
| By: | Zheli Xiong |
| Abstract: | This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance. By combining RL algorithms such as A2C, PPO, and SAC with traditional classifiers like Support Vector Machines (SVM), Decision Trees, and Logistic Regression, we investigate how different classifier groups can be integrated to improve risk-return trade-offs. The study evaluates the effectiveness of various ensemble methods, comparing them with individual RL models across key financial metrics, including Cumulative Returns, Sharpe Ratios (SR), Calmar Ratios, and Maximum Drawdown (MDD). Our results demonstrate that ensemble methods consistently outperform base models in terms of risk-adjusted returns, providing better management of drawdowns and overall stability. However, we identify the sensitivity of ensemble performance to the choice of variance threshold {\tau}, highlighting the importance of dynamic {\tau} adjustment to achieve optimal performance. This study emphasizes the value of combining RL with classifiers for adaptive decision-making, with implications for financial trading, robotics, and other dynamic environments. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17518 |
| By: | Jian Chen; Guohao Tang; Guofu Zhou; Wu Zhu |
| Abstract: | We study whether ChatGPT and DeepSeek can extract information from the Wall Street Journal to predict the stock market and the macroeconomy. We find that ChatGPT has predictive power. DeepSeek underperforms ChatGPT, which is trained more extensively in English. Other large language models also underperform. Consistent with financial theories, the predictability is driven by investors' underreaction to positive news, especially during periods of economic downturn and high information uncertainty. Negative news correlates with returns but lacks predictive value. At present, ChatGPT appears to be the only model capable of capturing economic news that links to the market risk premium. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.10008 |
| By: | Lee, Heungmin |
| Abstract: | The rapid advancements in large language models (LLMs) have ushered in a new era of transformative potential for the finance industry. This paper explores the latest developments in the application of LLMs across key areas of the finance domain, highlighting their significant impact and future implications. In the realm of financial analysis and modelling, LLMs have demonstrated the ability to outperform traditional models in tasks such as stock price prediction, portfolio optimization, and risk assessment. By processing vast amounts of financial data and leveraging their natural language understanding capabilities, these models can generate insightful analyses, identify patterns, and provide data-driven recommendations to support decision-making processes. The conversational capabilities of LLMs have also revolutionized the customer service landscape in finance. LLMs can engage in natural language dialogues, addressing customer inquiries, providing personalized financial advice, and even handling complex tasks like loan applications and investment planning. This integration of LLMs into financial institutions has the potential to enhance customer experiences, improve response times, and reduce the workload of human customer service representatives. Furthermore, LLMs are making significant strides in the realm of risk management and compliance. These models can analyze complex legal and regulatory documents, identify potential risks, and suggest appropriate remedial actions. By automating routine compliance tasks, such as anti-money laundering (AML) checks and fraud detection, LLMs can help financial institutions enhance their risk management practices and ensure better compliance, mitigating the risk of costly penalties or reputational damage. As the finance industry continues to embrace the transformative potential of LLMs, it will be crucial to address the challenges surrounding data privacy, algorithmic bias, and the responsible development of these technologies. By navigating these considerations, the finance sector can harness the full capabilities of LLMs to drive innovation, improve efficiency, and ultimately, enhance the overall financial ecosystem. |
| Date: | 2025–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:ahkd3_v1 |
| By: | Luiz Tavares; Jose Mazzon; Francisco Paletta; Fabio Barros |
| Abstract: | The marketing departments of financial institutions strive to craft products and services that cater to the diverse needs of businesses of all sizes. However, it is evident upon analysis that larger corporations often receive a more substantial portion of available funds. This disparity arises from the relative ease of assessing the risk of default and bankruptcy in these more prominent companies. Historically, risk analysis studies have focused on data from publicly traded or stock exchange-listed companies, leaving a gap in knowledge about small and medium-sized enterprises (SMEs). Addressing this gap, this study introduces a method for evaluating SMEs by generating images for processing via a convolutional neural network (CNN). To this end, more than 10, 000 images, one for each company in the sample, were created to identify scenarios in which the CNN can operate with higher assertiveness and reduced training error probability. The findings demonstrate a significant predictive capacity, achieving 97.8% accuracy, when a substantial number of images are utilized. Moreover, the image creation method paves the way for potential applications of this technique in various sectors and for different analytical purposes. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15726 |
| By: | Horst Treiblmaier |
| Abstract: | Dee Hock, the founder of Visa, coined the term 'chaordic' to describe simultaneously chaotic and ordered systems. Based on his reasoning, we introduce the Theory of Chaordic Economics to explain how economic systems are transformed by two disruptive technologies: namely Artificial Intelligence and Blockchain. Artificial intelligence can generate novel output through algorithmic yet rather unpredictable processes. Blockchain creates deterministic results without central authorities and relies on elaborated protocols that prescribe how consensus can be reached within a network of peers. The amalgamation of chaos and order produces chaordic economic systems and can yield hitherto unthinkable economic structures. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.16596 |
| By: | Kevin He; Ran Shorrer; Mengjia Xia |
| Abstract: | We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI's choices and human choices. In every problem, human subjects' average prediction about GenAI's choice is substantially closer to the average human-subject choice than it is to the GenAI choice. At the individual level, different subjects' predictions about GenAI's choice in a given problem are highly correlated with their own choices in the same problem. We explore the implications of people overestimating GenAI alignment in a simple theoretical model. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14708 |
| By: | Muffert, Johanna (FAU Erlangen Nuremberg); Winkler, Erwin (University of Erlangen-Nuremberg) |
| Abstract: | We study how the effects of exports on earnings vary across individual workers, depending on a wide range of worker, firm, and job characteristics. To this end, we combine a generalized random forest with an instrumental variable strategy. Analyzing Germany's exports to China and Eastern Europe, we document sharp disparities: workers in the bottom quartile (ranked by the size of the effect) experience little to no earnings gains due to exports, while those in the top quartile see considerable earnings increases. As expected, the workers who benefit the most on average are employed in larger firms and have higher skill levels. Importantly, however, we also find that workers with the largest earnings gains tend to be male, younger, and more specialized in their industry. These factors have received little attention in the previous literature. Finally, we provide evidence that the contribution to overall earnings inequality is smaller than expected. |
| Keywords: | machine learning, earnings, inequality, exports, skills, labor market |
| JEL: | C52 F14 J23 J24 J32 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17667 |
| By: | Guanyuan Yu; Qing Li; Yu Zhao; Jun Wang; YiJun Chen; Shaolei Chen |
| Abstract: | Financial risks can propagate across both tightly coupled temporal and spatial dimensions, posing significant threats to financial stability. Moreover, risks embedded in unlabeled data are often difficult to detect. To address these challenges, we introduce GraphShield, a novel approach with three key innovations: Enhanced Cross-Domain Infor mation Learning: We propose a dynamic graph learning module to improve information learning across temporal and spatial domains. Advanced Risk Recognition: By leveraging the clustering characteristics of risks, we construct a risk recognizing module to enhance the identification of hidden threats. Risk Propagation Visualization: We provide a visualization tool for quantifying and validating nodes that trigger widespread cascading risks. Extensive experiments on two real-world and two open-source datasets demonstrate the robust performance of our framework. Our approach represents a significant advancement in leveraging artificial intelligence to enhance financial stability, offering a powerful solution to mitigate the spread of risks within financial networks. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.13979 |
| By: | Pandit, Harshvardhan J.; Rintamäki, Tytti |
| Abstract: | The recently published EU Artificial Intelligence Act (AI Act) is a landmark regulation that regulates the use of AI technologies. One of its novel requirements is the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), where organisations in the role of deployers must assess the risks of their AI system regarding health, safety, and fundamental rights. Another novelty in the AI Act is the requirement to create a questionnaire and an automated tool to support organisations in their FRIA obligations. Such automated tools will require a machine-readable form of information involved within the FRIA process, and additionally also require machine-readable documentation to enable further compliance tools to be created. In this article, we present our novel representation of the FRIA as an ontology based on semantic web standards. Our work builds upon the existing state of the art, notably the Data Privacy Vocabulary (DPV), where similar works have been established to create tools for GDPR's Data Protection Impact Assessments (DPIA) and other obligations. Through our ontology, we enable the creation and management of FRIA, and the use of automated tool in its various steps. |
| Date: | 2024–12–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:tm74p_v1 |
| By: | Zichen Chen; Jiaao Chen; Jianda Chen; Misha Sra |
| Abstract: | Current financial LLM agent benchmarks are inadequate. They prioritize task performance while ignoring fundamental safety risks. Threats like hallucinations, temporal misalignment, and adversarial vulnerabilities pose systemic risks in high-stakes financial environments, yet existing evaluation frameworks fail to capture these risks. We take a firm position: traditional benchmarks are insufficient to ensure the reliability of LLM agents in finance. To address this, we analyze existing financial LLM agent benchmarks, finding safety gaps and introducing ten risk-aware evaluation metrics. Through an empirical evaluation of both API-based and open-weight LLM agents, we reveal hidden vulnerabilities that remain undetected by conventional assessments. To move the field forward, we propose the Safety-Aware Evaluation Agent (SAEA), grounded in a three-level evaluation framework that assesses agents at the model level (intrinsic capabilities), workflow level (multi-step process reliability), and system level (integration robustness). Our findings highlight the urgent need to redefine LLM agent evaluation standards by shifting the focus from raw performance to safety, robustness, and real world resilience. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15865 |
| By: | Giacomo Case |
| Abstract: | This study presents a comparative analysis of Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods in the context of derivative pricing, emphasizing convergence rates and the curse of dimensionality. After a concise overview of traditional Monte Carlo techniques for evaluating expectations of derivative securities, the paper introduces quasi-Monte Carlo methods, which leverage low-discrepancy sequences to achieve more uniformly distributed sample points without relying on randomness. Theoretical insights highlight that QMC methods can attain superior convergence rates of $O(1/n^{1-\epsilon})$ compared to the standard MC rate of $O(1/\sqrt{n})$, where $\epsilon>0$. Numerical experiments are conducted on two types of options: geometric basket call options and Asian call options. For the geometric basket options, a five-dimensional setting under the Black-Scholes framework is utilized, comparing the performance of Sobol' and Faure low-discrepancy sequences against standard Monte Carlo simulations. Results demonstrate a significant reduction in root mean square error for QMC methods as the number of sample points increases. Similarly, for Asian call options, incorporating a Brownian bridge construction with RQMC further enhances accuracy and convergence efficiency. The findings confirm that quasi-Monte Carlo methods offer substantial improvements over traditional Monte Carlo approaches in derivative pricing, particularly in scenarios with moderate dimensionality. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17731 |
| By: | Denizalp Goktas; Amy Greenwald; Sadie Zhao; Alec Koppel; Sumitra Ganesh |
| Abstract: | In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which we develop polynomial-time algorithms to solve, the former of which relies on an exact first-order oracle, and the latter, a stochastic one. We extend our approach to solve inverse multiagent simulacral learning in polynomial time and number of samples. In these problems, we seek a simulacrum, meaning parameters and an associated equilibrium that replicate the given observations in expectation. We find that our approach outperforms the widely-used ARIMA method in predicting prices in Spanish electricity markets based on time-series data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14160 |
| By: | Jia, Fernando; Zheng, Jade; Li, Florence |
| Abstract: | In the rapidly evolving landscape of GameFi, a fusion of gaming and decentralized finance (DeFi), there exists a critical need to enhance player engagement and economic interaction within gaming ecosystems. Our GameFi ecosystem aims to fundamentally transform this landscape by integrating advanced embodied AI agents into GameFi platforms. These AI agents, developed using cutting-edge large language models (LLMs), such as GPT-4 and Claude AI, are capable of proactive, adaptive, and contextually rich interactions with players. By going beyond traditional scripted responses, these agents become integral participants in the game's narrative and economic systems, directly influencing player strategies and in-game economies. We address the limitations of current GameFi platforms, which often lack immersive AI interactions and mechanisms for community engagement or creator monetization. Through the deep integration of AI agents with blockchain technology, we establish a consensus-driven, decentralized GameFi ecosystem. This ecosystem empowers creators to monetize their contributions and fosters democratic collaboration among players and creators. Furthermore, by embedding DeFi mechanisms into the gaming experience, we enhance economic participation and provide new opportunities for financial interactions within the game. Our approach enhances player immersion and retention and advances the GameFi ecosystem by bridging traditional gaming with Web3 technologies. By integrating sophisticated AI and DeFi elements, we contribute to the development of more engaging, economically robust, and community-centric gaming environments. This project represents a significant advancement in the state-of-the-art in GameFi, offering insights and methodologies that can be applied throughout the gaming industry. |
| Date: | 2025–01–14 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:tn5rx_v1 |
| By: | Froolik, Alderd J. (Quest Sales & Marketing Automations BV) |
| Abstract: | Chat Gerative Pre-trained Transformers are used more and more. OpenAI’s ChatGPT is the most used AI solution as of today. In the marketing ChatGPT’s role is creasing on a never seen before scale. Utilyzing ChatGPT on a iPaaS (integration Platform as a Service) will change the way of running a business. iPaaS platforms can take over tasks in various departments. When implemented in the marketing department, it can help to create content from almost any source, distribute it to almost any source, analyze the outcome and all at scale too. Combined with other applications images, videos and podcasts can be created automatically. |
| Date: | 2024–04–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:m3a8x_v1 |
| By: | Schoeffer, Jakob; Jakubik, Johannes; Vössing, Michael; Kühl, Niklas; Satzger, Gerhard |
| Abstract: | In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In practice, however, we often see that humans cannot assess the correctness of AI recommendations and, as a result, adhere to wrong or override correct advice. Different ways of relying on AI recommendations have immediate, yet distinct, implications for decision quality. Unfortunately, reliance and decision quality are often inappropriately conflated in the current literature on AI-assisted decision-making. In this work, we disentangle and formalize the relationship between reliance and decision quality, and we characterize the conditions under which human-AI complementarity is achievable. To illustrate how reliance and decision quality relate to one another, we propose a visual framework and demonstrate its usefulness for interpreting empirical findings, including the effects of interventions like explanations. Overall, our research highlights the importance of distinguishing between reliance behavior and decision quality in AI-assisted decision-making. |
| Date: | 2024–08–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:cekm9_v1 |