Search arXivSearch

arXiv · 1506.07220

Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

Abstract

Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yangtuo Peng, Hui Jiang. 2015-06-24. Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks. https://arxiv.org/abs/1506.07220

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Framework for Discharge Time Prediction of Energy Storage Units Based on Coupled Dynamics and Multi-Factor Aging Models

This paper presents a physically interpretable framework for predicting time to empty (TTE) in portable embedded systems. The framework couples usage-driven load-power decomposition, electrical power-voltage-current closure, a semi-empirical aging model, and SOC-temperature dynamics. Smartphone telemetry is mapped to battery current through an interpretable load model and conversion-efficiency correction. Battery capacity loss is modeled by combining Arrhenius temperature dependence, SEI diffusion behavior, and cycle-related power-law degradation. The coupled dynamic model then predicts TTE under different initial SOC values, ambient temperatures, and usage profiles. Chronological hold-out evaluation on a 6.9-h smartphone discharge session yielded a current RMSE of 0.0095 $\pm$ 0.0006 A, a temperature RMSE of 2.93 $\pm$ 0.24$^\circ$C, and a TTE MAPE of 4.81 $\pm$ 0.61%. Evaluation on NASA cell B0005 produced a capacity-loss RMSE of 0.031 Ah. Baseline, ablation, and counterfactual analyses further illustrate the contributions of thermal and aging corrections and the relative influence of load features. The results demonstrate the feasibility and interpretability of the proposed framework, while broader validation across devices and batteries remains necessary.

cs.CE

Node-Shift-Encoding Genetic Algorithm with fuzzy-enhanced reference tour to solve the bi-objective service-oriented TSP

The Travelling Salesman Problem (TSP) remains a key area of research in combinatorial optimization, with applications in logistics, manufacturing, and service delivery. This paper addresses a bi-objective service-oriented TSP in which the clients' ranks in the delivery path matter. Unlike conventional depot-based TSP formulations, the considered problem does not assume a distinguished depot or a fixed tour origin. To address this setting, we adapt the Miller--Tucker--Zemlin (MTZ)-based formulation and derive an original linearization of the resulting model, enabling its solution with off-the-shelf integer linear programming solvers. This adaptation avoids the rigid tour origin imposed by the conventional MTZ formulation, for which fixing the starting node does not affect the tour cost but can affect the objective in a customer-rank-sensitive TSP. To solve this problem, we present a Node-Shift-Encoding (NSE)-based Genetic Algorithm augmented with fuzzy reasoning to update the reference tour throughout the evolutionary process. Experimental evaluation on TSPLIB benchmarks demonstrates that the proposed method achieves improved performance compared with the classical NSE approach.

cs.CE

Semi-Automated Generation and Hemodynamic Assessment of Surgical Baffle Geometry for Biventricular Repair

Patient-specific computational modeling has emerged as a powerful tool for surgical planning in complex congenital heart disease. One promising application is complex biventricular repair, which often requires construction of a custom intraventricular baffle to establish a physiologic left ventricle-to-aorta outflow pathway. In current practice, baffle geometry is designed and shaped intraoperatively and preoperative planning remains largely manual, limiting the ability to generate anatomically conformal, watertight models suitable for quantitative hemodynamic assessment. In this work, we present a semi-automated computational framework for the design and assessment of patient-specific intraventricular baffles. The method constructs an explicit VSD-to-aorta flow pathway, preserves native right ventricular geometry, and reshapes only the baffle region using section-wise area constraints along a physiologically aligned centerline. The resulting geometry is integrated into a closed, multi-labeled domain for computational fluid dynamics analysis. We retrospectively applied this framework to four patients with double outlet right ventricle (DORV) who previously underwent biventricular repair. For each case, a patient-specific baffle was generated and its hemodynamic performance was evaluated using CFD. Predicted pressure gradients across the reconstructed outflow were within clinically acceptable ranges and comparable to the patients' postoperative echocardiographs. This approach enables quantitative, pre-operative design and evaluation of candidate baffle geometries and provides a reproducible method for generating simulation-ready models. By combining physiologically constrained geometric design with CFD-based assessment, the framework represents a step toward computational, patient-specific decision support for biventricular flow restoration in a complex heterogeneous patient population.

cs.CE