scholarly journals Financial Technology and Customer Control over Financial Data in Deposit Taking Savings and Credit Co-Operatives in Baringo County, Kenya

2020 ◽  
Vol 8 (5) ◽  
Author(s):  
Ambrose Jagongo ◽  
Priscah Jelagat Rutto
Keyword(s):  
Financial Data ◽  
Customer Control ◽  
Financial Technology

scholarly journals Long-Run Growth of Financial Data Technology

2020 ◽  
Vol 110 (8) ◽  
pp. 2485-2523
Author(s):  
Maryam Farboodi ◽  
Laura Veldkamp
Keyword(s):  
Data Processing ◽  
Trading Strategies ◽  
Financial Data ◽  
Investment Risk ◽  
Market Outcomes ◽  
Long Run ◽  
Market Inefficiency ◽  
Asset Values ◽  
Future Data ◽  
Financial Technology

“Big data” financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others’ information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others’ demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient. (JEL C55, D83, G12, G14, O33)

scholarly journals Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Sachi Nandan Mohanty ◽  
Paulo Alonso Gaona García ◽  
Vicente García-Díaz
Keyword(s):  
Artificial Intelligence ◽  
Feature Selection ◽  
Financial Crisis ◽  
Internet Of Things ◽  
Simulation Analysis ◽  
Classification Performance ◽  
Financial Data ◽  
Superior Performance ◽  
Learning Machine ◽  
Financial Technology

AbstractFinancial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

Processing of Histopathological Data

1971 ◽  
Vol 10 (03) ◽  
pp. 142-147
Author(s):  
M. RENAUD ◽  
M. AQARQ ◽  
R. GERARD-MARCHANT ◽  
M. WOLFF-TERROINE
Keyword(s):  
Integrated System ◽  
Ease Of Use ◽  
Financial Data ◽  
Identification Number ◽  
Processing Data ◽  
Cancer Hospital

A method is presented for processing data from the histopathological laboratory of a cancer hospital. Emphasis is laid on the ease of use, the connection of medical, administrative and financial data, and the strictness of control of patient’s identification number. The system can be used separately; it is also a module for a large integrated system covering all the activities of the hospital.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

APLIKASI AKUNTANSI PENATAUSAHAAN KEUANGAN DESA MENGGUNAKAN FRAMEWORK CODEIGNITER

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Yati Nurhayati
Keyword(s):  
Programming Language ◽  
Human Error ◽  
Financial Data ◽  
Microsoft Excel ◽  
Financial Administration ◽  
The Village

AbstrakDesa sebagai salah satu bagian dari pemerintahan yang memiliki anggaran besar melalui APBDes untuk peningkatan infrastruktur desa dan kesejahteraan masyarakatnya. Maka desa wajib melakukan pengelolaan keuangan dan pencatatan keuangan (penatausahaan) secara akurat dan detail. Salah satu desa yang memiliki APBDes besar adalah Desa Ciputat. Dalam penatausahaan atau pencatatan keuangan masih dilakukan secara semi manual (Microsoft excel) sehingga banyak kelemahan yaitu besarnya kemungkinan human error dan ketidak seimbangan (balance) keuangan dikarenakan banyaknya format yang harus dibuat dalam beberapa lembar kerja. Sehingga dibutuhkan sebuah aplikasi yang dapat mengelola data keuangan secara cepat dan akurat. Aplikasi ini dirancang menggunakan UML, sedangkan system dikembangkan menggunakan metode RAD. Hasil perancangan diterapkan ke dalam Bahasa pemrograman PHP dan MySQL menggunakan framework CodeIgniter. Hasil akhir dari penelitian ini adalah sebuah aplikasi akuntansi penatausahaan (pencatatan) keuangan di desa dan menghasilkan laporan dalam bentuk Buku Kas Umum dan Buku Pembantu Kegiatan.�Kata Kunci�Desa, Keuangan, Penatausahaan, RAD, PHP, MySQL,CodeIgniter, Akuntansi., Buku Kas Umum, Buku Pembantu Kegiatan�AbstractVillage as a part of government has big estimation through APBDesa for increasing villages infrastructure and prosperity for the citizens. Therefore, the village should do managing financial and registration financial (administration) accurately and detail. One of villages that has a big APBDesa is Ciputat Village. In administration or registration financia, it still conductes semi-manual (Microsoft excel) so there are many weakness such as big posibility of human error and un-balance of financial caused many kind of format that should be produced in several sheets. So, it needs an application that can manage financial data accurately and fast. This application is designed by using UML, while the system is developed by using RAD methodology. And results are implemented into PHP Programming Language and MySQL by using framework CodeIgniter. Last Results of this research is an accounting application of Financial Administration (registration) in village and produce report in General Ledger form and subsidiary ledger form.Keywords� Village, Financial, Administration, RAD, PHP, MySQL, CodeIgniter, Accounting., General Ledger, Subsidiary Ledger

Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

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