Financial
- The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
- Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.
- The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.
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263 Views
This dataset integrates financial and macroeconomic indicators to support research on stock price prediction and financial forecasting. It includes daily stock data for Malayan Banking Berhad (MBB) (1155.KL) sourced from Yahoo Finance, alongside macroeconomic indicators such as GDP (constant 2015 MYR), GDP growth (YoY %), inflation rate (%), and the Overnight Policy Rate (OPR). The data spans a 20-year period from July 1, 2004, to August 1, 2024, and has been standardized to a daily frequency.
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85 Views
Innostock focuses on stock price movement prediction tasks of newly formed technology companies listed on China's Sci-Tech Innovation Board, aggregating their financial news from various online platforms. It's stock prices were originally collected from CSMAR (https://cn.gtadata.com). To support multimodal input of each stock, we further collect the industrial sector relationships for each stock and build knowledge graphs.
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60 Views
A dataset used in the paper "A Causal Perspective of Stock Prediction Models". The dataset is constructed using the training infromation between 2011 and 2024 via signals provided by GUOTAI JUNAN SECURITIES. Alpha191 is a widely used collection of 191 mathematical formulas, known as "alpha factors," used for quantitative stock analysis. Developed by researchers and practitioners, these factors are designed to capture various statistical properties, behavioral patterns, and market trends from financial data.
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229 Views
This study presents a English-Luganda parallel corpus comprising over 2,000 sentence pairs, focused on financial decision-making and products. The dataset draws from diverse sources, including social media platforms (TikTok comments and Twitter posts from authoritative accounts like Bank of Uganda and Capital Markets Uganda), as well as fintech blogs (Chipper Cash and Xeno). The corpus covers a range of financial topics, including bonds, loans, and unit trust funds, providing a comprehensive resource for financial language processing in both English and Luganda.
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345 Views
This code implements a novel family of generalized exponentiated gradient (EG) updates derived from an Alpha-Beta divergence regularization function. Collectively referred to as EGAB, the proposed updates belong to the category of multiplicative gradient algorithms for positive data and demonstrate considerable flexibility by controlling iteration behavior and performance through three hyperparameters: alpha, beta, and the learning rate eta. To enforce a unit l1 norm constraint for nonnegative weight vectors within generalized EGAB algorithms, we develop two slightly distinct approaches.
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84 Views
This study utilizes the annual loan ledger data obtained from a commercial bank located in Jiangsu Province, China, which is called ChinaZJB. The ChinaZJB dataset consists of 1,329 valid samples of SMEs after merging the non-financial behavioral information and soft information on credit rating with the financial information, loan information, and non-financial basic information found in the annual loan ledger data.
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1033 Views
This dataset contains the SPX call and put option data from 16/09/2022 to 08/09/2023 with different strike prices ranges from 3500 to 4500 with an interval 100. The data for options with different strike prices are listed in different sheets. The price data of call and option options are listed togather in one row, including the date, price, volume and interest rate.
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253 Views
ABSTRACT Analysis of stock prices has been widely studied because of the strong demand among private investors and financial institutions. However, it is difficult to accurately capture the factors that cause fluctuations in stock prices, as they are affected by a variety of factors. Therefore, we used non-harmonic analysis, a frequency technique with at least to more accurately than conventional analysis methods, to visualize the periodicity of the Nasdaq Composite Index stock price from January 4, 2010 to September 8, 2023.
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92 Views