Efficient Predictions on Asymmetrical Financial Data Using Ensemble Random Forests

2020 ◽  
pp. 361-372
Author(s):  
Chaitanya Muppala ◽  
Sujatha Dandu ◽  
Anusha Potluri
Keyword(s):  
Random Forests ◽  
Financial Data

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.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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