Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management
In the era of rapid globalization and digitalization, accurate identification of similar stocks
has become increasingly challenging due to the non-stationary nature of financial markets …
has become increasingly challenging due to the non-stationary nature of financial markets …
Multi-attention recommender system for non-fungible tokens
Recommender systems have become essential tools for enhancing user experiences across
various domains. While extensive research has been conducted on recommender systems …
various domains. While extensive research has been conducted on recommender systems …
Detecting financial market manipulation with statistical physics tools
We take inspiration from statistical physics to develop a novel conceptual framework for the
analysis of financial markets. We model the order book dynamics as a motion of particles …
analysis of financial markets. We model the order book dynamics as a motion of particles …
Enhancing mean–variance portfolio optimization through GANs-based anomaly detection
Mean–variance optimization, introduced by Markowitz, is a foundational theory and
methodology in finance and optimization, significantly influencing investment management …
methodology in finance and optimization, significantly influencing investment management …
Stop-loss adjusted labels for machine learning-based trading of risky assets
Since the rise of ML/AI, many researchers and practitioners have been trying to predict future
stock price movements. In actual implementations, however, stop-loss is widely adopted to …
stock price movements. In actual implementations, however, stop-loss is widely adopted to …
Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering
In this paper, we propose a novel approach to improving forecasts of stock market indexes
by considering common stock prices as hierarchical time series, combining clustering with …
by considering common stock prices as hierarchical time series, combining clustering with …
Household financial health: a machine learning approach for data-driven diagnosis and prescription
Household finances are being threatened by unprecedented social and economic
upheavals, including an aging society and slow economic growth. Numerous researchers …
upheavals, including an aging society and slow economic growth. Numerous researchers …
A GANs-Based Approach for Stock Price Anomaly Detection and Investment Risk Management
S Kim, J Hong, Y Lee - Proceedings of the Fourth ACM International …, 2023 - dl.acm.org
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 …
data-driven approach. In investment management, it is important to detect and avoid market …
LLMs Analyzing the Analysts: Do BERT and GPT Extract More Value from Financial Analyst Reports?
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 …
models and GPT-3.5, in the sentiment analysis of Korean financial analyst reports. Due to …
Dynamic Asset Allocation Using Machine Learning: Seeing the Forest for the Trees.
C Mueller-Glissmann, A Ferrario - Journal of Portfolio …, 2024 - search.ebscohost.com
High inflation and aggressive monetary policy tightening in 2022 triggered one of the largest
return drawdowns for a US 60/40 portfolio in the last 100 years. In this article, the authors …
return drawdowns for a US 60/40 portfolio in the last 100 years. In this article, the authors …