7 dataset results for Financial

The StockNet dataset is a comprehensive dataset for stock movement prediction from tweets and historical stock prices. It consists of two-year price movements from 01/01/2014 to 01/01/2016 of 88 stocks, coming from all the 8 stocks in the Conglomerates sector and the top 10 stocks in capital size in each of the other 8 sectors.

29 PAPERS • 1 BENCHMARK

Earnings Call

The Earning Calls dataset consists of processed earning conference calls data (text and audio). It can be used to predict financial risk from both textual and vocal features from conference calls.

8 PAPERS • NO BENCHMARKS YET

EDT

The EDT dataset is designed for corporate event detection and text-based stock prediction (trading strategy) benchmark.

2 PAPERS • NO BENCHMARKS YET

KPI-EDGAR

We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents (the named entity recognition part) and link them to their numerical values (the relation extraction part).

2 PAPERS • 1 BENCHMARK

SupplyGraph (SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks)

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graphlike in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problem using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of fact

1 PAPER • NO BENCHMARKS YET

default of credit card clients Data Set

This research aimed at the case of customers default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural networ

1 PAPER • NO BENCHMARKS YET

MAEC

MAEC (Multimodal Aligned Earnings Conference Call Dataset)

MAEC is a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies.

0 PAPER • NO BENCHMARKS YET

Datasets

7 dataset results for Financial