Pairs trading with time-series deep learning models
December 2025
Pairs trading is a well-studied statistical arbitrage strategy including the identification of asset pairs exhibiting correlated changes in their historical prices. This statistical arbitrage strategy...
The use of predictive analytics in finance
November 2022
Statistical and computational methods are being increasingly integrated into Decision Support Systems to aid management and help with strategic decisions. Researchers need to fully understand the use...
Audit data analytics, machine learning, and full population testing
November 2022
Emerging technologies like data analytics and machine learning are impacting the accounting profession. In particular, significant changes are anticipated in audit and assurance procedures because of...
End-to-end large portfolio optimization for variance minimization with neural networks through covariance cleaning
December 2026
We develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform historical returns and marginal volatilities and how to regularise...
Finding money launderers using heterogeneous graph neural networks
December 2025
The finance industry depends on effective anti-money laundering (AML) systems to ensure compliance and maintain operational efficiency. However, existing AML systems, which are predominantly rule-based,...
Selecting appropriate methodological framework for time series data analysis
June 2018
Economists face method selection problem while working with time series data. As time series data may possess specific properties such as trend and structural break, common methods used to analyze other...
Credit scoring methods: Latest trends and points to consider
November 2022
Credit risk is the most significant risk by impact for any bank and financial institution. Accurate credit risk assessment affects an organisation's balance sheet and income statement, since credit...
Explainable ensemble machine learning for financial transaction fraud detection: Insights from XGBoost and deep neural networks
December 2026
The rapid digitalization of financial services has enhanced transaction speed and accessibility but also amplified exposure to fraud activities that undermine institutional integrity and consumer trust....
Making it into a successful series A funding: An analysis of Crunchbase and LinkedIn data
November 2023
Startups are a key force driving economic development, and the success of these high-risk ventures can bring huge profits to venture capital firms. The ability to predict the success of startups is...
The applications of big data in the insurance industry: A bibliometric and systematic review of relevant literature
November 2023
The insurance industry has changed rapidly over the last few decades. One factor in this change is the continuous growth of massive amounts of data that need to be processed properly to be optimally...
Short-term bitcoin market prediction via machine learning
November 2021
We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform...
Machine learning for cryptocurrency market prediction and trading
November 2022
We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest...
Technical patterns and news sentiment in stock markets
December 2024
This paper explores the effectiveness of technical patterns in predicting asset prices and market movements, emphasizing the role of news sentiment. We employ an image recognition method to detect technical...
Characterization of S&P 500 companies by sector using artificial intelligence: Statistical evidence and machine learning application
December 2026
This article explores the extent to which established sector classifications continue to provide meaningful insights into the financial underpinnings of firms. Utilizing a contemporary cross-sectional...
CentralBankRoBERTa: A fine-tuned large language model for central bank communications
November 2023
Central bank communications are an important tool for guiding the economy and fulfilling monetary policy goals. Natural language processing (NLP) algorithms have been used to analyze central bank communications....
Financial inclusion, technologies, and worldwide economic development: A spatial Durbin model approach
December 2025
Using panel data from 144 countries, this study constructed an inclusive financial evaluation index and depicted the inclusive finance development worldwide under digital empowerment through classification....
A meta reinforcement learning approach to goals-based wealth management
December 2026
Applying concepts related to zero-shot meta-learning and pre-training of foundation models, we develop a meta reinforcement learning approach (denoted MetaRL) that is pre-trained on thousands of goals-based...
Reinforcement learning for automated market making in cryptocurrency perpetual futures: Integrating funding rate dynamics with historical BTCUSDT evidence
December 2026
The emergence of cryptocurrency perpetual futures has created unique opportunities for automated market making, yet existing approaches often ignore the funding rate mechanism that transfers payments...
Deep unsupervised anomaly detection in high-frequency markets
December 2024
Inspired by recent advances in the deep learning literature, this article introduces a novel hybrid anomaly detection framework specifically designed for limit order book (LOB) data. A modified Transformer...
Machine learning portfolio allocation
November 2022
We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected...
An overview on data representation learning: From traditional feature learning to recent deep learning
December 2016
Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, either linear or nonlinear, either supervised or unsupervised, either...
Symbolic Modeling for financial asset pricing
December 2025
Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression,...
Enhancing bookkeeper decision support through graph representation learning for bank reconciliation
December 2025
The emergence of cloud-based bookkeeping platforms has made it possible to streamline decision-making in tedious accounting tasks, such as bank reconciliation. Bank reconciliation involves tracing the...
RMSE-triggered rebalancing for deep learning-guided portfolio optimization
December 2026
This paper addresses the gap between improved forecasting accuracy from machine learning and deep learning models and their limited translation into realized portfolio performance under transaction-cost...
Fintech, financial inclusion, digital currency, and CBDC
November 2023
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