NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting
Westlake Institute for Advanced Study, Westlake University（西湖大学高等研究院）；School of Engineering, Westlake University（西湖大学工学院）；The Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；Department of Computer Science, University of Warwick（华威大学计算机科学系）
Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, Barry Smyth
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context.
History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting
Fraunhofer IAIS - Department of Media Engineering（弗劳恩霍夫研究所-媒体工程部门）；University of Bonn - Department of Computer Science（波恩大学-计算机科学系）；Lamarr Institute for Machine Learning（拉马尔机器学习研究所）
Tampere University（坦佩雷大学）；Aarhus University（奥胡斯大学）
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network. This is because deep neural network optimized with stochastic gradient descent is sensitive to the input variable range and prone to numerical issues. Different than other types of signals, financial time-series often exhibit unique characteristics such as high volatility, non-stationarity and multi-modality that make them challenging to work with, often requiring expert domain knowledge for devising a suitable processing pi…
University of California, Irvine（加州大学欧文分校）；California State University, Los Angeles（加州州立大学洛杉矶分校）
Stock price forecasting is a highly complex and vitally important field of research. Recent advancements in deep neural network technology allow researchers to develop highly accurate models to predict financial trends. We propose a novel deep learning model called ST-GAN, or Stochastic Time-series Generative Adversarial Network, that analyzes both financial news texts and financial numerical data to predict stock trends. We utilize cutting-edge technology like the Generative Adversarial Network (GAN) to learn the correlations among textual and numerical data over time. We develop a new method…
University of Pavia（帕维亚大学）；Turku Centre for Computer Science（图尔库计算机科学中心）；Hanken School of Economics（汉肯经济学院）；RiskLab Finland（芬兰风险实验室）
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and sy…
Zurich University of Applied Sciences（苏黎世应用科技大学）；University of Twente（特文特大学）
Modeling financial time series is challenging due to their high volatility and unexpected happenings on the market. Most financial models and algorithms trying to fill the lack of historical financial time series struggle to perform and are highly vulnerable to overfitting. As an alternative, we introduce in this paper a deep neural network called the WGAN-GP, a data-driven model that focuses on sample generation. The WGAN-GP consists of a generator and discriminator function which utilize an LSTM architecture. The WGAN-GP is supposed to learn the underlying structure of the input data, which …
Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy while little effort has been spent on the important task of understanding the association between the two data modalities. Performance on the time series hence receives little explanation though human-understandable textual information is available. In this work, we address the problem of given a numerical time series, and a general corpus of t…
Application of time-series quantum generative model to financial data
Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究科）；Department of Physics, Tokyo Institute of Technology（东京工业大学物理系）；Sigma-i Co., Ltd.（Sigma-i有限公司）；Nomura Asset Management Co., Ltd.（野村资产管理有限公司）
