Recent Articles

Open access

ISSN: 2405-9188

Reinforcement prompting for financial synthetic data generation

The emergence of Large Language Models (LLMs) has unlocked unprecedented potential for comprehending and generating human-like text, fueling advances in the finance domain – a tool that can shape investment...

Liquidity risk analysis via drawdown-based measures

Trading volumes are key variables in determining the degree of an asset's liquidity. We examine the volume drawdown process and crash recovery measures in rolling-time windows to assess exposure to...

What drives liquidity in the Chinese credit bond markets?

We study the drivers and pricing of liquidity in the Chinese credit bond markets. We document that the liquidity and liquidity effects priced into yield spreads differ significantly across the four...

China's GDP at risk: The role of housing prices

This paper studies the impact of house prices on the distribution of GDP growth in China (the 5th, median, and 95th percentiles). We show that house price appre-ciation positively affects future GDP...

Machine learning private equity returns

In this paper, we use two machine learning techniques to learn the aggregated return time series of complete private capital fund segments. First, we propose Stochastic Discount Factor (SDF) model combination...

Do commodity prices matter for global systemic risk? Evidence from ML variable selection

We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December...

Tail-driven portfolios: Unveiling financial contagion and enhancing risk management

In financial markets, tail risks, representing the potential for substantial losses, bear significant implications for the formulation of effective risk management strategies. Yet, there exists a notable...

Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model

This study evaluates the effectiveness of the TSMixer neural network model in forecasting stock realized volatility, comparing it with traditional and contemporary benchmark models. Using data from...

NFT price and sales characteristics prediction by transfer learning of visual attributes

Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent...

Technical patterns and news sentiment in stock markets

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...

Detecting Hawala network for money laundering by graph mining

Hawala, a traditional but informal money transfer system, has been prevalent in many parts of the world, such as money laundering. Despite the regulatory actions taken by financial institutions, Hawala...

Interpretable machine learning model for predicting activist investment targets

This research presents a predictive model to identify potential targets of activist investment funds—entities that acquire significant corporate stakes to influence strategic and operational decisions,...

Learning from AI-Finance: A selected synopsis

A general framework for portfolio construction based on generative models of asset returns

In this paper, we present an integrated approach to portfolio construction and optimization, leveraging high-performance computing capabilities. We first explore diverse pairings of generative model...

Fintech, financial inclusion, digital currency, and CBDC

A dynamic partial equilibrium model of capital gains taxation

We analyze a multi-period model of capital gains taxation with endogenous prices. Relative to an economy without taxation, a capital gains tax tends to lower prices and increase returns. Abstracting...

CentralBankRoBERTa: A fine-tuned large language model for central bank communications

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....

The cross-section of Chinese corporate bond returns

We study the relation between bond characteristics and corporate bond returns in China's two distinct and segmented bond markets—the interbank market and the exchange market—with a large cross-sectional...

Machine learning in classifying bitcoin addresses

The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with...

OptionNet: A multiscale residual deep learning model with confidence interval to predict option price

Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult...

Investigating the impact financial content structure has on consumer appreciation: An empirical study of Australian statement of advice documents

This study investigates the impact of financial content structure on consumer appreciation in Australian Statement of Advice (SOA) documents. SOAs are essential for regulatory adherence and consumer...

Stay Informed

Register your interest and receive email alerts tailored to your needs. Sign up below.

Production and hosting by Elsevier on behalf of KeAi Communications Co. Ltd