ICAIF

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 ...

Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the ...

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market frictions, which are challenging to address ...

Limit order books are a fundamental and widespread market mechanism. This paper investigates the use of conditional generative models for order book simulation. For developing a trading agent, this approach has drawn recent attention as an alternative ...

We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN), a method for the generation of time-series data that is designed to support decision-making. The framework adopts a multi-Wasserstein loss on decision-...

The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic ...

Active investing aims to construct a portfolio of assets that are expected to be relatively profitable in the markets. A popular strategy involves the use of factor-based methods. Recently, efforts have increased to apply deep learning to identify “deep ...

The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations. These challenges often hinder the ability of both academics and ...

The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper ...

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 ...

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 ...

As Large Language Model (LLM) based chatbots are becoming more accessible, users are relying on these chatbots for reliable and personalized recommendations in diverse domains, ranging from code generation to financial advisement. In this context, we ...

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 ...

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 ...

Deep hedging is a promising direction in quantitative finance, incorporating models and techniques from deep learning research. While giving excellent hedging strategies, models inherently requires careful treatment in designing architectures for neural ...

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 ...

Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineered features, which relies heavily on the availability of labelled data. ...

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 ...

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, ...

Spatio-temporal modeling is an essential lens to understand many real-world phenomena from traffic to finance. There has been exciting work that explores spatio-temporal modeling with temporal graph convolutional networks. Often these methods assume ...

Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous ...

We propose a graph learning mechanism in a unique but very practical heterogeneous setting, where multiple types of relations can exist between pair of nodes of same or different type. Such problem setting is ubiquitous in digital product/service ...

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 ...

Dynamic Bipartite graph is naturally suited for modeling temporally evolving interaction in several domains, including digital payment and social media. Though dynamic graphs are widely studied, their focus remains on homogeneous graphs. This paper ...

Financial stress testing is a common method to evaluate the resilience and robustness of financial institutions to adverse scenarios. While prior research has mainly focused on individual firms, few have explored the systemic stability of the entire ...

Recent years have witnessed a growth of research interest in designing powerful graph mining algorithms to discover and characterize the structural pattern of interests from financial transaction networks, motivated by impactful applications including ...

We introduce and examine a default cascade process within stochastic financial networks. This process involves a finite set of agents who hold claims against each other. These agents interact pairwise with their counterparties at random times, which are ...

Mutual funds are interconnected to each other through multiple types of links, including but not limited to co-investment, advisors, firms, and managers. These connections enable information flow among network entities, influence investment decisions, ...

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 ...

One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally, one employs a mix of intuition, theoretical and empirical analysis to ...