Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment

• URL: http://arxiv.org/abs/2205.01639v1
• Date: Sun, 13 Mar 2022 15:13:36 GMT
• Title: Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment
• Authors: Nicole Koenigstein
• Abstract summary: We propose a new modeling approach for financial time series data, the $alpha_t$-RIM (recurrent independent mechanism) To model the data in such a dynamic manner, the $alpha_t$-RIM utilizes an exponentially smoothed recurrent neural network. The results suggest that the $alpha_t$-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $\alpha_{t}$-RIM (recurrent independent mechanism). This architecture makes use of key-value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the $\alpha_{t}$-RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S\&P 500 universe as well as their news sentiment score. The results suggest that the $\alpha_{t}$-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks such as long short-term memory models.

Related papers

• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Deep incremental learning models for financial temporal tabular datasets with distribution shifts [0.9790236766474201]
  The framework uses a simple basic building block (decision trees) to build self-similar models of any required complexity. We demonstrate our scheme using XGBoost models trained on the Numerai dataset and show that a two layer deep ensemble of XGBoost models over different model snapshots delivers high quality predictions.
  arXiv  Detail & Related papers  (2023-03-14T14:10:37Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• Online learning techniques for prediction of temporal tabular datasets with regime changes [0.0]
  We propose a modular machine learning pipeline for ranking predictions on temporal panel datasets. The modularity of the pipeline allows the use of different models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks. Online learning techniques, which require no retraining of models, can be used post-prediction to enhance the results.
  arXiv  Detail & Related papers  (2022-12-30T17:19:00Z)
• Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling [23.030263841031633]
  We develop a novel Graph-Regularized Regression (GRTR) framework, whereby knowledge about cross-asset relations is incorporated into the model in the form of a graph Laplacian matrix. By virtue of tensor algebra, the proposed framework is shown to be fully interpretable, both coefficient-wise and dimension-wise. The GRTR model is validated in a multi-way financial forecasting setting and is shown to achieve improved performance at reduced computational costs.
  arXiv  Detail & Related papers  (2022-10-26T13:39:08Z)
• A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning [113.75991721607174]
  We introduce an interventional prediction module to estimate the probability of two estimated $hatz_i, hatz_j$ belonging to the same environment. We empirically show that $hatZ$ estimated by our method enjoy less redundant information than previous methods.
  arXiv  Detail & Related papers  (2022-06-09T15:01:36Z)
• Datamodels: Predicting Predictions from Training Data [86.66720175866415]
  We present a conceptual framework, datamodeling, for analyzing the behavior of a model class in terms of the training data. We show that even simple linear datamodels can successfully predict model outputs.
  arXiv  Detail & Related papers  (2022-02-01T18:15:24Z)
• A Scalable Inference Method For Large Dynamic Economic Systems [19.757929782329892]
  We present a novel Variational Bayesian Inference approach to incorporate a time-varying parameter auto-regressive model. Our model is applied to a large blockchain dataset containing prices, transactions of individual actors, analyzing transactional flows and price movements. We further improve the simple state-space modelling by introducing non-linearities in the forward model with the help of machine learning architectures.
  arXiv  Detail & Related papers  (2021-10-27T10:52:17Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models [0.0]
  We present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data.
  arXiv  Detail & Related papers  (2020-04-17T19:41:22Z)

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.