Exploring applications of topological data analysis in stock index movement prediction
- URL: http://arxiv.org/abs/2411.13881v1
- Date: Thu, 21 Nov 2024 06:41:39 GMT
- Title: Exploring applications of topological data analysis in stock index movement prediction
- Authors: Dazhi Huang, Pengcheng Xu, Xiaocheng Huang, Jiayi Chen,
- Abstract summary: We construct point clouds for stock indices using three different methods.
Four distinct topological features are computed to represent the patterns in the data, and 15 combinations of these features are enumerated and input into six different machine learning models.
We evaluate the predictive performance of various TDA configurations by conducting index movement classification tasks on datasets such as CSI, DAX, HSI and FTSE.
- Score: 5.82416342574148
- License:
- Abstract: Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial impact on prediction results. This paper addresses the classification problem of stock index movement. First, we construct point clouds for stock indices using three different methods. Next, we apply TDA to extract topological structures from the point clouds. Four distinct topological features are computed to represent the patterns in the data, and 15 combinations of these features are enumerated and input into six different machine learning models. We evaluate the predictive performance of various TDA configurations by conducting index movement classification tasks on datasets such as CSI, DAX, HSI and FTSE providing insights into the efficiency of different TDA setups.
Related papers
- AI-Aided Kalman Filters [65.35350122917914]
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing.
Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering.
This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
arXiv Detail & Related papers (2024-10-16T06:47:53Z) - GraphCNNpred: A stock market indices prediction using a Graph based deep learning system [0.0]
We give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of textS&textP 500, NASDAQ, DJI, NYSE, and RUSSEL.
Experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about $4% text to 15%$, in terms of F-measure.
arXiv Detail & Related papers (2024-07-04T09:14:24Z) - Sparse Portfolio Selection via Topological Data Analysis based
Clustering [5.110444063763577]
This paper uses topological data analysis tools and introduces a data-driven clustering-based stock selection strategy tailored for sparse portfolio construction.
Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios.
arXiv Detail & Related papers (2024-01-30T11:42:52Z) - Minimally Supervised Learning using Topological Projections in
Self-Organizing Maps [55.31182147885694]
We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs)
Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU)
Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
arXiv Detail & Related papers (2024-01-12T22:51:48Z) - A topological classifier to characterize brain states: When shape
matters more than variance [0.0]
topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors.
We introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data.
arXiv Detail & Related papers (2023-03-07T20:45:15Z) - NETpred: Network-based modeling and prediction of multiple connected
market indices [8.122270502556372]
We introduce a framework called NETpred that generates a novel graph representing multiple related indices and their stocks.
It then thoroughly selects a diverse set of representative nodes that cover different parts of the state space and whose price movements are accurately predictable.
The resulting model is then used to predict the stock labels which are finally aggregated to infer the labels for all the index nodes in the graph.
arXiv Detail & Related papers (2022-12-02T17:23:09Z) - Search to Pass Messages for Temporal Knowledge Graph Completion [97.40256786473516]
We propose to use neural architecture search (NAS) to design data-specific message passing architecture for temporal knowledge graphs (TKGs) completion.
In particular, we develop a generalized framework to explore topological and temporal information in TKGs.
We adopt a search algorithm, which trains a supernet structure by sampling single path for efficient search with less cost.
arXiv Detail & Related papers (2022-10-30T04:05:06Z) - Towards a Taxonomy of Graph Learning Datasets [10.151886932716518]
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.
Here, we provide a principled approach to taxonomize graph benchmarking datasets by carefully designing a collection of graph perturbations.
Our data-driven taxonomization of graph datasets provides a new understanding of critical dataset characteristics.
arXiv Detail & Related papers (2021-10-27T23:08:01Z) - Towards Open-World Feature Extrapolation: An Inductive Graph Learning
Approach [80.8446673089281]
We propose a new learning paradigm with graph representation and learning.
Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
arXiv Detail & Related papers (2021-10-09T09:02:45Z) - Artificial Text Detection via Examining the Topology of Attention Maps [58.46367297712477]
We propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA)
We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10% on three common datasets.
The probing analysis of the features reveals their sensitivity to the surface and syntactic properties.
arXiv Detail & Related papers (2021-09-10T12:13:45Z) - Topology-based Clusterwise Regression for User Segmentation and Demand
Forecasting [63.78344280962136]
Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level.
This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
arXiv Detail & Related papers (2020-09-08T12:10:10Z)
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.