Financial

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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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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This study analyzes the spending of Brazilian municipalities on health using an approach based on computational intelligence. The study was characterized by a quantitative and documentary database, and 117 municipalities with an average population between 2004 and 2019 of more than 100,000 inhabitants were analyzed. The data was obtained from the Brazilian Finance database (Finbra) (National Treasury Secretariat) and processed and adjusted for inflation. The main technique used was cluster analysis via R software, version 3.3.3.

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This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators.

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The dataset tracks the performance of eight stock market indices, from six countries. The indices are: IPC, S\&P 500, DAX, DJIA, FTSE, N225, NDX, and CAC. The time period is from the 1st of June 2006 to the 31st of May 2023.The index and the FX data are sourced from Yahoo Finance, and the rest of the variables are retrieved from the OECD.

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In general,the scale of a network is primarily measured by its number of nodes and edges. But we generated six data tables for each chain, with increasing scale (100, 1000, 5000, 10000, 50000, 100000) and time, based on the number of transitions. Significantly, the fetched data, being in CSV format, cannot be directly used for analysis. Therefore, it was necessary for this study to process the data and employ Pajek-formatted files for graph storage [19]. The subsequent data utilized in this research has been preprocessed into Pajek format.

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This dataset comes from the Wind database. This dataset includes a series of Shanghai Stock Exchange 50 ETF option data due in December 2022. This dataset also includes some economic variables.The data used in this article is the trading data of Shanghai Stock Exchange 50ETF call options. The time of option data is from April 28 to October 26, 2022, sourced from the WIND database. The exercise price range of the selected option is from 2.5 to 3.5 (European call options). In addition, the expiration date of the options in this experiment is December 28, 2022.

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In the Bitcoin transaction autonomous message data set, we collected the transaction autonomy messages in the first 750,360 blocks of Bitcoin. The first message came from block 228,596. The data set spans from about 22:00 on March 29, 2013, to 8:00 on August 21, 2022. We collected 51,463,687 transaction autonomy messages from the Bitcoin system.

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We have collected the transactions in the Bitcoin memory pool from about 17:00 on April 19, 2021 to 21:00 on April 23, 2021 by configuring the Bitcoin full node. This is because the price of bitcoin was high during this period, and frequent transactions caused congestion in the memory pool. The time dependence of Bitcoin transactions is not easy to express in the data on the blockchain.

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Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack
to the recent Poolz Finance arithmetic overflow incident. While historically, the definition of smart contract
vulnerabilities lacked standardization, even with the current advancements in Solidity smart contracts, the
potential for deploying malicious contracts to exploit legitimate ones persists.
The abstract Syntax Tree (AST ), Opcodes, and Control Flow Graph (CFG) are the intermediate representa-

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