Economic and financial data analysis accounting (financial) statements in the framework of the judicial-accounting examination

"The new source of power is not money in the hands of a few, but information in the hands of many." The aforementioned quote from John Naisbitt seems to be even more relevant in the world of finance at this very moment. Many financial decisions come from watching the information stream, selecting relevant data, analyzing it and acting accordingly. With the increasing global competition, the need for swift data analysis, high accuracy and quality becomes a must. XBRL (Extensible Business Reporting Language)XBRL: http://www.xbrl.org/ standard was proposed to improve efficiency of data exchange in the financial domain. However; it is still struggling with interoperability problems, not to mention comparability of data or multisource data integration. This paper presents the FLORA intelligent platform: an approach for dealing with current financial information shortcomings and achieving more effective way of processing financial data based on the Linked Data principles. The article also explains the process of data extraction and semantic modeling which are the cornerstones of efficient financial data analysis. As a result, the FLORA architecture facilitates effective, data-driven, financial analyses and Web-scale integration between financial applications and platforms.

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Financial data analysis using the informational energy unilateral dependency measure

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280734 ◽

2015 ◽

Cited By ~ 1

Author(s):

Angel Cataron ◽

Razvan Andonie ◽

Yvonne Chueh

Keyword(s):

Data Analysis ◽

Financial Data ◽

Informational Energy ◽

Financial Data Analysis

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A Framework for Development of Recommender System for Financial Data Analysis

International Journal of Information Engineering and Electronic Business ◽

10.5815/ijieeb.2017.05.03 ◽

2017 ◽

Vol 9 (5) ◽

pp. 18-27

Author(s):

Pradeep Kumar M. Kanaujia ◽

◽

Manjusha Pandey ◽

Siddharth Swarup Rautaray

Keyword(s):

Data Analysis ◽

Recommender System ◽

Financial Data ◽

Financial Data Analysis

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Optimization of Quantitative Financial Data Analysis System Based on Deep Learning

Complexity ◽

10.1155/2021/5527615 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Meiyi Liang

Keyword(s):

Deep Learning ◽

Data Analysis ◽

Performance Test ◽

Learning Algorithm ◽

User Interaction ◽

Financial Data ◽

Similar Time ◽

Financial Data Analysis ◽

Analysis System ◽

Data Analysis System

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results.

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A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis

Risks ◽

10.3390/risks8020032 ◽

2020 ◽

Vol 8 (2) ◽

pp. 32 ◽

Cited By ~ 1

Author(s):

José María Sarabia ◽

Faustino Prieto ◽

Vanesa Jordá ◽

Stefan Sperlich

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Stock Returns ◽

Prior Knowledge ◽

Statistical Modeling ◽

Financial Data ◽

Adaptive Inference ◽

Data Adaptive ◽

Financial Data Analysis ◽

Global And Local

This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local estimation and the combining of explicit modeling with purely data adaptive inference. Then, we discuss stepwise approaches with different ways of integrating machine learning. Furthermore, for the modeling of prior knowledge, we introduce classes of distribution families for financial data. The proposed procedures are illustrated with data on stock returns for five companies of the Spanish value-weighted index IBEX35.

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