This paper examines the use of Large Language Models (LLMs), specifically BERT-based models and GPT-3.5, in the sentiment analysis of Korean financial analyst reports. Due to the specialized language in these reports, traditional natural language ...

Rough volatility models have garnered considerable attention among practitioners due to their remarkable empirical fit. However, their non-Markovian nature arises from the presence of a kernel (leading to so-called Voltera diffusions), which complicates ...

In the field of portfolio management using reinforcement learning, existing approaches have mainly focused on cash-only trading, overlooking the potential benefits and risks of margin trading. Incorporating margin accounts and their constraints, ...

Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges. Recent studies have explored image-driven approaches using computer vision models to address these challenges, often ...

In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other. Uncovering such relationships is valuable in downstream tasks, such as control, forecasting, and ...

Graph-based deep learning is a rapidly evolving and practical field due to the ubiquity of graph data and its flexible topology. Although many graph learning frameworks show impressive capabilities, their outputs begin to deteriorate for sufficiently ...

Solving partial differential equations (PDEs) is a frequent necessity in numerous domains, ranging from complex systems simulation to financial derivatives pricing and continuous-time optimisation tasks. The challenging nature of PDEs, especially in ...

Missing data imputation is crucial in finance to ensure accurate financial analysis, risk management, investment strategies, and other financial applications. Recently, tensor factorization and completion have gained momentum in many finance data ...

Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we propose the first end-to-end autoregressive generative model that generates ...

Automated credit risk assessment plays an important role in agricultural lending. However, credit risk assessment in the agricultural domain has unique challenges due to the impact of weather, pest outbreaks, commodities market dynamics, and other ...

Non Fungible Tokens (NFTs) are blockchain-based unique digital assets defining ownership deeds. They can characterize various different objects such as collectible, art, and in-game items. In general, NFTs are encoded by blockchains smart contracts, and ...

In this paper, we present CoMeTS-GAN (Correlated Multivariate Time Series GAN), a framework based on Conditional Generative Adversarial Networks (C-GANs), designed to generate mid-prices and volumes time series of correlated stocks. This tool provides a ...

This paper addresses the challenges of risk management in the financial market through a data-driven approach. In investment management, it is important to detect and avoid market anomalies, defined as significant deviations from typical stock price ...

Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains. Few-shot methods offer an alternative, utilizing contrastive learning techniques that can be effective with as little as 20 examples ...

Research in adversarial robustness has primarily focused on neural networks in domains like computer vision, neglecting heterogeneous tabular datasets prevalent in finance. The financial domain, in particular, faces a heightened risk, where malicious ...

Calibrating agent-based models (ABMs) in economics and finance typically involves a derivative-free search in a very large parameter space. In this work, we benchmark a number of search methods in the calibration of a well-known macroeconomic ABM on ...

Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to ...

The Cumulative Prospect Theory (CPT) is a popular behavioral decision-making model that has been shown to reflect humans’ risk-sensitive behavior. This work develops an end-to-end CPT-based DRL trading agent. For our architecture, we draw on the ...

The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. The core methodology these models use is to learn the distribution of the underlying data, ...

Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positive ...