In-Context Operator Learning for Linear Propagator Models ArXiv ID: 2501.15106 “View on arXiv” Authors: Unknown Abstract We study operator learning in the context of linear propagator models for optimal order execution problems with transient price impact à la Bouchaud et al. (2004) and Gatheral (2010). Transient price impact persists and decays over time according to some propagator kernel. Specifically, we propose to use In-Context Operator Networks (ICON), a novel transformer-based neural network architecture introduced by Yang et al. (2023), which facilitates data-driven learning of operators by merging offline pre-training with an online few-shot prompting inference. First, we train ICON to learn the operator from various propagator models that maps the trading rate to the induced transient price impact. The inference step is then based on in-context prediction, where ICON is presented only with a few examples. We illustrate that ICON is capable of accurately inferring the underlying price impact model from the data prompts, even with propagator kernels not seen in the training data. In a second step, we employ the pre-trained ICON model provided with context as a surrogate operator in solving an optimal order execution problem via a neural network control policy, and demonstrate that the exact optimal execution strategies from Abi Jaber and Neuman (2022) for the models generating the context are correctly retrieved. Our introduced methodology is very general, offering a new approach to solving optimal stochastic control problems with unknown state dynamics, inferred data-efficiently from a limited number of examples by leveraging the few-shot and transfer learning capabilities of transformer networks. ...

A Space Mapping approach for the calibration of financial models with the application to the Heston model ArXiv ID: 2501.14521 “View on arXiv” Authors: Unknown Abstract We present a novel approach for parameter calibration of the Heston model for pricing an Asian put option, namely space mapping. Since few parameters of the Heston model can be directly extracted from real market data, calibration to real market data is implicit and therefore a challenging task. In addition, some of the parameters in the model are non-linear, which makes it difficult to find the global minimum of the optimization problem within the calibration. Our approach is based on the idea of space mapping, exploiting the residuum of a coarse surrogate model that allows optimization and a fine model that needs to be calibrated. In our case, the pricing of an Asian option using the Heston model SDE is the fine model, and the surrogate is chosen to be the Heston model PDE pricing a European option. We formally derive a gradient descent algorithm for the PDE constrained calibration model using well-known techniques from optimization with PDEs. Our main goal is to provide evidence that the space mapping approach can be useful in financial calibration tasks. Numerical results underline the feasibility of our approach. ...

Optimal Investment under Mutual Strategy Influence among Agents ArXiv ID: 2501.14259 “View on arXiv” Authors: Unknown Abstract In financial markets, agents often mutually influence each other’s investment strategies and adjust their strategies to align with others. However, there is limited quantitative study of agents’ investment strategies in such scenarios. In this work, we formulate the optimal investment differential game problem to study the mutual influence among agents. We derive the analytical solutions for agents’ optimal strategies and propose a fast algorithm to find approximate solutions with low computational complexity. We theoretically analyze the impact of mutual influence on agents’ optimal strategies and terminal wealth. When the mutual influence is strong and approaches infinity, we show that agents’ optimal strategies converge to the asymptotic strategy. Furthermore, in general cases, we prove that agents’ optimal strategies are linear combinations of the asymptotic strategy and their rational strategies without others’ influence. We validate the performance of the fast algorithm and verify the correctness of our analysis using numerical experiments. This work is crucial to comprehend mutual influence among agents and design effective mechanisms to guide their strategies in financial markets. ...

AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics ArXiv ID: 2502.00029 “View on arXiv” Authors: Unknown Abstract Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional metrics often struggle with robustness and generalization, particularly in dynamic and volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve and optimize financial metrics to discover enhanced risk-return metrics that outperform traditional approaches in robustness and correlation with future performance metrics by employing iterative crossover, mutation, and evaluation. Key contributions of this work include: (1) a novel use of LLMs to generate and refine financial metrics with implicit domain-specific knowledge, (2) a scoring mechanism to ensure that evolved metrics generalize effectively to unseen data, and (3) an empirical demonstration of 3x predictive power for future risk-returns, and 2x portfolio performance. Experimental results in a real-world dataset highlight the superiority of discovered metrics, making them highly relevant to portfolio managers and financial decision-makers. This framework not only addresses the limitations of existing metrics but also showcases the potential of LLMs in advancing financial analytics, paving the way for informed and robust investment strategies. ...

Multimodal Stock Price Prediction ArXiv ID: 2502.05186 “View on arXiv” Authors: Unknown 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. ...

Optimizing Portfolios with Pakistan-Exposed ETFs: Risk and Performance Insight ArXiv ID: 2501.13901 “View on arXiv” Authors: Unknown Abstract This study examines the investment landscape of Pakistan as an emerging and frontier market, focusing on implications for international investors, particularly those in the United States, through exchange-traded funds (ETFs) with exposure to Pakistan. The analysis encompasses 30 ETFs with varying degrees of exposure to Pakistan, covering the period from January 1, 2016, to February 2024. This research highlights the potential benefits and risks associated with investing in these ETFs, emphasizing the importance of thorough risk assessments and portfolio performance comparisons. By providing descriptive statistics and performance metrics based on historical optimization, this paper aims to equip investors with the necessary insights to make informed decisions when optimizing their portfolios with Pakistan-exposed ETFs. The second part of the paper introduces and assesses dynamic optimization methodologies. This section is designed to explore the adaptability and performance metrics of dynamic optimization techniques in comparison with conventional historical optimization methods. By integrating dynamic optimization into the investigation, this research aims to offer insights into the efficacy of these contrasting methodologies in the context of Pakistan-exposed ETFs. The findings underscore the significance of Pakistan’s market dynamics within the broader context of emerging markets, offering a pathway for diversification and potential growth in investment strategies. ...

(Non-Monotonic) Effects of Productivity and Credit Constraints on Equilibrium Aggregate Production in General Equilibrium Models with Heterogeneous Producers ArXiv ID: 2501.12700 “View on arXiv” Authors: Unknown Abstract We show that, in a market economy, the aggregate production level depends not only on the aggregate variables but also on the distribution of individual characteristics (e.g., productivity, credit limit, …). We prove that, due to financial frictions, the equilibrium aggregate production may be non-monotonic in both individual productivity and credit limit. We provide conditions (based on exogenous parameters) under which this phenomenon happens. By consequence, improving productivity or relaxing credit limit of firms may not necessarily be beneficial to economic development. ...

AI-Powered (Finance) Scholarship ArXiv ID: ssrn-5103553 “View on arXiv” Authors: Unknown Abstract This paper describes a process for automatically generating academic finance papers using large language models (LLMs). It demonstrates the process’ efficacy by Keywords: Generative AI, Large Language Models (LLMs), Automated Research, Financial Modeling, NLP, Technology Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper focuses on the process of using LLMs to generate academic content, lacking advanced mathematical derivations, while showing minimal evidence of backtesting or implementation-heavy data analysis. flowchart TD A["Research Goal<br>Automate Finance Paper Generation"] --> B["Inputs<br>Financial Data + LLM Prompts"] B --> C{"Methodology<br>Multi-Step Chain-of-Thought"} C --> D["Computational Process<br>LLM Synthesis & Modeling"] D --> E{"Evaluation<br>Human Expert Review"} E --> F["Outcomes<br>High-Quality Finance Papers"] E --> G["Outcomes<br>Validation of LLM Efficacy"] F --> H["Final Result<br>AI-Powered Scholarship Pipeline"] G --> H

Breaking the Dimensional Barrier for Constrained Dynamic Portfolio Choice ArXiv ID: 2501.12600 “View on arXiv” Authors: Unknown Abstract We propose a scalable, policy-centric framework for continuous-time multi-asset portfolio-consumption optimization under inequality constraints. Our method integrates neural policies with Pontryagin’s Maximum Principle (PMP) and enforces feasibility by maximizing a log-barrier-regularized Hamiltonian at each time-state pair, thereby satisfying KKT conditions without value-function grids. Theoretically, we show that the barrier-regularized Hamiltonian yields O($ε$) policy error and a linear Hamiltonian gap (quadratic when the KKT solution is interior), and we extend the BPTT-PMP correspondence to constrained settings with stable costate convergence. Empirically, PG-DPO and its projected variant (P-PGDPO) recover KKT-optimal policies in canonical short-sale and consumption-cap problems while maintaining strict feasibility across dimensions; unlike PDE/BSDE solvers, runtime scales linearly with the number of assets and remains practical at n=100. These results provide a rigorous and scalable foundation for high-dimensional constrained continuous-time portfolio optimization. ...

Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach ArXiv ID: 2501.13136 “View on arXiv” Authors: Unknown Abstract Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63% accuracy for predicting the next day and 64%, 67% and 82% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72% to 2.85% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature. ...