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on Computational Economics |
| By: | Jian Guo; Saizhuo Wang; Yiyan Qi |
| Abstract: | Multi-stage decision-making is crucial in various real-world artificial intelligence applications, including recommendation systems, autonomous driving, and quantitative investment systems. In quantitative investment, for example, the process typically involves several sequential stages such as factor mining, alpha prediction, portfolio optimization, and sometimes order execution. While state-of-the-art end-to-end modeling aims to unify these stages into a single global framework, it faces significant challenges: (1) training such a unified neural network consisting of multiple stages between initial inputs and final outputs often leads to suboptimal solutions, or even collapse, and (2) many decision-making scenarios are not easily reducible to standard prediction problems. To overcome these challenges, we propose Guided Learning, a novel methodological framework designed to enhance end-to-end learning in multi-stage decision-making. We introduce the concept of a ``guide'', a function that induces the training of intermediate neural network layers towards some phased goals, directing gradients away from suboptimal collapse. For decision scenarios lacking explicit supervisory labels, we incorporate a utility function that quantifies the ``reward'' of the throughout decision. Additionally, we explore the connections between Guided Learning and classic machine learning paradigms such as supervised, unsupervised, semi-supervised, multi-task, and reinforcement learning. Experiments on quantitative investment strategy building demonstrate that guided learning significantly outperforms both traditional stage-wise approaches and existing end-to-end methods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10496 |
| By: | Ananya Unnikrishnan |
| Abstract: | Reinforcement learning (RL) has emerged as a transformative approach for financial trading, enabling dynamic strategy optimization in complex markets. This study explores the integration of sentiment analysis, derived from large language models (LLMs), into RL frameworks to enhance trading performance. Experiments were conducted on single-stock trading with Apple Inc. (AAPL) and portfolio trading with the ING Corporate Leaders Trust Series B (LEXCX). The sentiment-enhanced RL models demonstrated superior net worth and cumulative profit compared to RL models without sentiment and, in the portfolio experiment, outperformed the actual LEXCX portfolio's buy-and-hold strategy. These results highlight the potential of incorporating qualitative market signals to improve decision-making, bridging the gap between quantitative and qualitative approaches in financial trading. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11059 |
| By: | Joshua Aslett; Stuart Hamilton; Ignacio Gonzalez; David Hadwick; Michael A Hardy; Azael Pérez |
| Abstract: | This technical note provides an overview of current thinking on artificial intelligence (AI) in tax and customs administration. Written primarily for senior officials, the intent of the note is to provide an awareness of AI that can help inform decision making and planning. The note opens with an exploration of historic and ongoing AI developments. It then provides an overview of legal and ethical concerns, AI use cases, guidance on how to promote AI's responsible use, and logic for introducing AI use cases into an operational setting. The note closes by presenting a selection of questions being debated by experts. In its annexes, the note includes (1) an example of an AI policy; (2) references to help develop AI strategy; and (3) methodology to risk assess AI use cases. |
| Keywords: | tax administration; customs administration; artificial intelligence; generative artificial intelligence; AI regulation; strategy and planning; use case; AI strategy; understanding AI; AI Policy; risk assessment; Tax administration core functions; Customs administration core functions; Training and development in revenue administration; Europe; Global |
| Date: | 2024–11–21 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2024/006 |
| By: | Xin Zhang; Zhen Xu; Yue Liu; Mengfang Sun; Tong Zhou; Wenying Sun |
| Abstract: | In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11848 |
| By: | Tsogt-Ochir Enkhbayar |
| Abstract: | This paper advances the computational efficiency of Deep Hedging frameworks through the novel integration of Kronecker-Factored Approximate Curvature (K-FAC) optimization. While recent literature has established Deep Hedging as a data-driven alternative to traditional risk management strategies, the computational burden of training neural networks with first-order methods remains a significant impediment to practical implementation. The proposed architecture couples Long Short-Term Memory (LSTM) networks with K-FAC second-order optimization, specifically addressing the challenges of sequential financial data and curvature estimation in recurrent networks. Empirical validation using simulated paths from a calibrated Heston stochastic volatility model demonstrates that the K-FAC implementation achieves marked improvements in convergence dynamics and hedging efficacy. The methodology yields a 78.3% reduction in transaction costs ($t = 56.88$, $p |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.15002 |
| By: | Sorouralsadat Fatemi; Yuheng Hu |
| Abstract: | Financial trading has been a challenging task, as it requires the integration of vast amounts of data from various modalities. Traditional deep learning and reinforcement learning methods require large training data and often involve encoding various data types into numerical formats for model input, which limits the explainability of model behavior. Recently, LLM-based agents have demonstrated remarkable advancements in handling multi-modal data, enabling them to execute complex, multi-step decision-making tasks while providing insights into their thought processes. This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks. Our framework employs a team of specialized LLM-based agents, each adept at processing and interpreting various forms of financial data, such as textual news reports, candlestick charts, and trading signal charts. A key feature of our approach is the integration of a reflection module, which conducts analyses of historical trading signals and their outcomes. This reflective process is instrumental in enhancing the decision-making capabilities of the system for future trading scenarios. Furthermore, the ablation studies indicate that the visual reflection module plays a crucial role in enhancing the decision-making capabilities of our framework. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08899 |
| By: | Marco Hening-Tallarico; Pablo Olivares |
| Abstract: | The objective of the paper is to price weather derivative contracts based on temperature and precipitation as underlying climate variables. We use a neural network approach combined with time series forecast to value Pacific Rim index in Toronto and Chicago |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12013 |
| By: | Junjie Guo |
| Abstract: | This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock portfolios. Two datasets comprising randomly selected stocks from the S&P500 and NASDAQ indices, each spanning a decade of daily data, are utilized. The models predict daily stock returns based on historical features such as past returns, Relative Strength Index (RSI), trading volume, and volatility. Portfolios are dynamically adjusted by longing stocks with positive predicted returns and shorting those with negative predictions, with equal asset weights. Performance is evaluated over a two-year testing period, focusing on return, Sharpe ratio, and maximum drawdown metrics. The results demonstrate the efficacy of deep learning models in enhancing long-short stock portfolio performance. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13555 |
| By: | Jue Xiao; Tingting Deng; Shuochen Bi |
| Abstract: | In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05790 |
| By: | Bartosz Bieganowski; Robert \'Slepaczuk |
| Abstract: | This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focuses on Bitcoin, Litecoin, and Ethereum as the traded assets from January 1, 2016, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12753 |
| By: | Shuochen Bi; Tingting Deng; Jue Xiao |
| Abstract: | The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also highlight the continued improvement in data processing capabilities. Explore how AI is reshaping financial services with precisely tAIlored services that can more precisely meet the diverse needs of individual investors. The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection. Analyze the limitations of current AI technology in financial forecasting and its potential impact on the future financial industry landscape, including changes in the job market, the emergence of new financial institutions, and user interface innovations. Emphasizing the importance of increasing investor understanding and awareness of AI and looking ahead to future trends in AI tools for user experience to drive wider adoption of AI in financial decision making. The huge potential, challenges, and future directions of AI in the financial sector highlight the critical role of AI technology in driving transformation and innovation in the financial sector |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13562 |
| By: | Mabsur Fatin Bin Hossain; Lubna Zahan Lamia; Md Mahmudur Rahman; Md Mosaddek Khan |
| Abstract: | Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12748 |
| By: | Karel Janda (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic & Department of Banking and Insurance, Faculty of Finance and Accounting, Prague University of Economics and Business, Czech Republic); Mathieu Petit (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic) |
| Abstract: | This study addresses the economic rationale behind algorithmic trading in the Electric Vehicle (EV) sector, enhancing the interpretability of Q-learning agents. By integrating EV-specific data, such as Tesla´s stock fundamentals and key supply chain players such as Albemarle and Panasonic Holdings Corporation, this paper uses a Q-Reinforcement Learning (Q-RL) framework to generate a profitable trading agent. The agent´s decisions are analyzed and interpreted using a decision tree to reveal the influence of supply chain dynamics. Tested on a holdout period, the agent achieves monthly profitability above a 2% threshold. The agent shows sensitivity to supply chain instability and identifies potential disruptions impacting Tesla by treating supplier stock movements as proxies for broader economic and market conditions. Indirectly, this approach improves understanding and trust in Q-RL-based algorithmic trading within the EV market. |
| Keywords: | Electric Vehicle Supply Chain, Algorithmic Trading, Machine Learning, Q-Reinforcement Learning, Interpretability |
| JEL: | G17 Q42 C45 Q55 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2024_40 |
| By: | Simon D Angus (Monash University) |
| Abstract: | Democratic resilience is as much about the narratives of our nation we affirm, as the institutions that enshrine our values and laws, a fact re-affirmed by scholarship across many branches of social science in recent decades. For policymakers and quantitative social scientists, analysing or tracking public discourse through the lens of narrative and framing has historically involved the annotation of texts by hand, placing severe limitations on the scale and modality of discourse under inquiry. In this study, we consider a variety of tools from the field of computational linguistics, which either automate the standard approach to textual annotation, or introduce entirely new ways of conceptualising `text as data', opening up new horizons for the tracking of public narratives of democratic resilience. In particular, we assess the regime-shift occurring in natural language processing and artificial intelligence brought about by the advent of the transformer architecture. These new tools offer, perhaps for the first time, the `holy grail' of the quantitative social scientist: the ability to identify, accurately, and efficiently, nuanced narratives in text at scale. We conclude by contributing data and research recommendations for public stakeholders who wish to see these opportunities realised. |
| Keywords: | Computational linguistics, Political discourse analysis, Natural Language Processing, Quantitative social science, AI in policy research |
| JEL: | C45 C83 D72 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ajr:sodwps:2024-07 |
| By: | Claudia Biancotti; Carolina Camassa; Andrea Coletta; Oliver Giudice; Aldo Glielmo |
| Abstract: | Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental framework to assess whether LLMs adhere to ethical and legal standards in the relatively unexplored context of finance. We prompt nine LLMs to impersonate the CEO of a financial institution and test their willingness to misuse customer assets to repay outstanding corporate debt. Beginning with a baseline configuration, we adjust preferences, incentives and constraints, analyzing the impact of each adjustment with logistic regression. Our findings reveal significant heterogeneity in the baseline propensity for unethical behavior of LLMs. Factors such as risk aversion, profit expectations, and regulatory environment consistently influence misalignment in ways predicted by economic theory, although the magnitude of these effects varies across LLMs. This paper highlights both the benefits and limitations of simulation-based, ex post safety testing. While it can inform financial authorities and institutions aiming to ensure LLM safety, there is a clear trade-off between generality and cost. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11853 |
| By: | Peter Dixon; Michael Jerie; Dean Mustakinov; Maureen T. Rimmer; Nicholas Sheard; Florian Schiffmann; Glyn Wittwer |
| Abstract: | This paper describes a destructive events tool (DET) for anticipating the national and regional economic effects of a destructive event occurring at any latitude/longitude in a country. The event is characterized by areas of complete destruction and evacuation. The event could be a natural disaster, major industrial accident, or terrorist attack. The key ingredient for a DET is data showing population and employment by industry in small rectangular areas (SRAs). In the Poland DET, motivating the paper, there are 600, 000 SRAs, each 0.5 sq km. This spatial resolution greatly improves the accuracy of the estimation of the economic impacts of events where physical impacts vary substantially across small areas. The second ingredient is an economic model with sufficient regional/industrial definition to translate shocks at an SRA level into implications at the sub-national and national levels. This requirement is met by a multi-regional computable general equilibrium (CGE) model. The final ingredient is an approximation for the model's reduced form. This is necessary so that the DET can be applied by organizations, without in-house CGE expertise, that need quick turnaround in a secure environment. We implement an approximation method for CGE reduced forms based on Neural Networks. |
| Keywords: | Destructive events tool, Small rectangular areas, Multi-regional computable general equilibrium models, Neural network approximations to reduced forms |
| JEL: | C81 C68 C45 H84 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:cop:wpaper:g-349 |
| By: | Lars Fluri; A. Ege Yilmaz; Denis Bieri; Thomas Ankenbrand; Aurelio Perucca |
| Abstract: | This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for planned extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating the current functioning of the investment platform using an ABM specified with empirical data. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13381 |
| By: | Yahui Bai; Yuhe Gao; Runzhe Wan; Sheng Zhang; Rui Song |
| Abstract: | In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications. This approach has shown great potential to solve decision-making tasks in finance. In this survey, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared to traditional methods. Moreover, we identify challenges including explainability, Markov Decision Process (MDP) modeling, and robustness that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12746 |
| By: | Sahand Hassanizorgabad |
| Abstract: | Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence them. This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. This paper will introduce the development of a novel trading system methodology by proposing a two-layer Composing Ensembles architecture, optimized through grid search, to predict whether the price will rise or fall the next day. This strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark, representing a standard investment strategy. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13559 |
| By: | James B. Glattfelder; Thomas Houweling |
| Abstract: | We present a series of equations that track the total realized and unrealized profits and losses at any time, incorporating the spread. The resulting formalism is ideally suited to evaluate the performance of trading model algorithms. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.14068 |
| By: | Junhua Liu |
| Abstract: | Financial AI empowers sophisticated approaches to financial market forecasting, portfolio optimization, and automated trading. This survey provides a systematic analysis of these developments across three primary dimensions: predictive models that capture complex market dynamics, decision-making frameworks that optimize trading and investment strategies, and knowledge augmentation systems that leverage unstructured financial information. We examine significant innovations including foundation models for financial time series, graph-based architectures for market relationship modeling, and hierarchical frameworks for portfolio optimization. Analysis reveals crucial trade-offs between model sophistication and practical constraints, particularly in high-frequency trading applications. We identify critical gaps and open challenges between theoretical advances and industrial implementation, outlining open challenges and opportunities for improving both model performance and practical applicability. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12747 |
| By: | Martin Arnaiz Iglesias (UP1 UFR27); Adil Rengim Cetingoz (UP1 UFR27); Noufel Frikha (UP1 UFR27) |
| Abstract: | This paper introduces and examines numerical approximation schemes for computing risk budgeting portfolios associated to positive homogeneous and sub-additive risk measures. We employ Mirror Descent algorithms to determine the optimal risk budgeting weights in both deterministic and stochastic settings, establishing convergence along with an explicit non-asymptotic quantitative rate for the averaged algorithm. A comprehensive numerical analysis follows, illustrating our theoretical findings across various risk measures -- including standard deviation, Expected Shortfall, deviation measures, and Variantiles -- and comparing the performance with that of the standard stochastic gradient descent method recently proposed in the literature. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12323 |