A News-based Machine Learning Model for Adaptive Asset Pricing

• URL: http://arxiv.org/abs/2106.07103v1
• Date: Sun, 13 Jun 2021 22:38:20 GMT
• Title: A News-based Machine Learning Model for Adaptive Asset Pricing
• Authors: Liao Zhu, Haoxuan Wu, Martin T. Wells
• Abstract summary: The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

Related papers

• AAPM: Large Language Model Agent-based Asset Pricing Models [4.326886488307076]
  We propose a novel asset pricing approach, which fuses qualitative discretionary investment analysis from LLM agents and quantitative manual financial economic factors. The experimental results show that our approach outperforms machine learning-based asset pricing baselines in portfolio optimization and asset pricing errors.
  arXiv  Detail & Related papers  (2024-09-25T18:27:35Z)
• Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis [4.575870619860645]
  We construct a new financial dataset for the claim detection task in the financial domain. We propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function. Here, we observe the dependence of earnings surprise and return on our optimism measure.
  arXiv  Detail & Related papers  (2024-02-18T22:55:26Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• American Option Pricing using Self-Attention GRU and Shapley Value Interpretation [0.0]
  We propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU.
  arXiv  Detail & Related papers  (2023-10-19T06:05:46Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain [42.093876880881886]
  We propose a novel domain specific Financial LANGuage model (FLANG) It uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Our models, code and benchmark data are publicly available on Github and Huggingface.
  arXiv  Detail & Related papers  (2022-10-31T18:35:18Z)
• Federated Learning Aggregation: New Robust Algorithms with Guarantees [63.96013144017572]
  Federated learning has been recently proposed for distributed model training at the edge. This paper presents a complete general mathematical convergence analysis to evaluate aggregation strategies in a federated learning framework. We derive novel aggregation algorithms which are able to modify their model architecture by differentiating client contributions according to the value of their losses.
  arXiv  Detail & Related papers  (2022-05-22T16:37:53Z)
• Machine Learning for Stock Prediction Based on Fundamental Analysis [13.920569652186714]
  We investigate three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.
  arXiv  Detail & Related papers  (2022-01-26T18:48:51Z)
• Deep Learning for Portfolio Optimization [5.833272638548154]
  Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period.
  arXiv  Detail & Related papers  (2020-05-27T21:28:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.