Recent Articles

Open access

ISSN: 2405-9188

Informed trading and expected returns

Does information asymmetry affect the cross-section of expected stock returns? We explore this question using representative portfolio holdings data from the Shanghai Stock Exchange. We show that institutional...

Pairs trading with time-series deep learning models

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...

Persistent cointegration and regime-sensitive market leadership: Evidence from international tobacco stocks

This paper develops a data-driven framework combining fractional cointegration and structural break detection to examine long-run interdependence and market leadership among international tobacco equities....

GARCH-PDE models for option pricing under stochastic volatility and their finite difference solvers

This paper presents numerical solvers for generative and hybrid option pricing models that unify econometric and diffusion-based approaches. These models are formulated as systems of continuous partial...

Investigating the relationship between processes and profit: A work-based assessment of process used in Australian financial planning firms

The research explores relationship dynamics between process and profit in Australian professional practise. We analyse data collected from 134 financial planning firms located in Southeast Queensland...

Revising data collection methodology - evidence from the Australian financial sector

Time requirements of data collection account for a significant portion of the total time required to provide financial advice. This research applies data collection software to the financial planning...

Cluster-based regression using variational inference and applications in financial forecasting

This paper describes an approach to simultaneously identify clusters and estimate cluster-specific regression parameters from the given data. Such an approach can be useful in learning the relationship...

Deep unsupervised anomaly detection in high-frequency markets

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...

Explicit formulae for the valuation of European options with price impacts

In this work, we examine the consequences of trading a large position in vanilla European options within a multi-period binomial model framework for the underlying asset price, S. Given the significant...

CPC-SAX: Data mining of financial chart patterns with symbolic aggregate approXimation and instance-based multilabel classification

In order to be able to classify financial chart patterns through machine learning, we introduced and applied a novel classification algorithm on time series data of different financial assets through...

Research on credit card default repayment prediction model

This study compares the predictive ability of various machine learning models for credit card default repayment within different prediction frameworks, using data from a commercial bank in China. Firstly,...

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...

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

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