Last edited by Mat
Saturday, May 2, 2020 | History

4 edition of One-step prediction of financial time series found in the catalog.

One-step prediction of financial time series

Srichander Ramaswamy

One-step prediction of financial time series

by Srichander Ramaswamy

  • 262 Want to read
  • 14 Currently reading

Published by Bank for International Settlements, Monetary and Economic Dept. in Basle, Switzerland .
Written in English

    Subjects:
  • Time-series analysis.,
  • Finance -- Mathematical models.,
  • Business forecasting -- Mathematical models.,
  • Prediction theory -- Mathematical models.,
  • Rate of return -- Mathematical models.

  • Edition Notes

    Statementby Srichander Ramaswamy.
    SeriesBIS working papers,, no. 57, BIS working papers (Online) ;, no. 57.
    ContributionsBank for International Settlements. Monetary and Economic Dept.
    Classifications
    LC ClassificationsHG3879
    The Physical Object
    FormatElectronic resource
    ID Numbers
    Open LibraryOL3285852M
    LC Control Number2003616614

      Financial time series analysis and their forecasting have an history of remarkable contributions. It is then quite hard for the beginner to get oriented and capitalize from reading such scientific literature as it requires a solid understanding of basic statistics, a detailed study of the ground basis of time series analysis tools and the knowledge [ ]Related PostOutlier detection and. This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering.

    Predictions in Financial Time Series Data by Dr. Allan Steel One-step ahead forecasts were generated from the time series models which were then consumed in trading al-gorithms. In general the time series models had limited predictive capabilities on the nancial markets tested and pro ts from the corresponding trading systems were mod-File Size: 1MB. A neural network is a computer program that can recognise patterns in data, learn from this and (in the case of time series data) make forecasts of future patterns. There are now over 20 commercially available neural network programs designed for use on financial markets and there have been some notable reports of their successful application.

    Analysis of Financial Time Series, Third Edition is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level. It also serves as an indispensible reference for researchers and practitioners working in business and finance/5(33). Time series appear in many forms in a variety of fields. Domains such as medicine, speech, and finance have applications that involve the study of temporal data []. This thesis applies temporal data mining techniques in the area of financial time series prediction. Temporal data mining is a sub-field of data mining that focuses primarily onFile Size: 1MB.


Share this book
You might also like
Cancer-cell organelles

Cancer-cell organelles

A to Z Mysteries Volume 3

A to Z Mysteries Volume 3

Trinity college

Trinity college

Straty archiwów i bibliotek warszawskich w zakresie rekopiśmiennych źródee historycznych.

Straty archiwów i bibliotek warszawskich w zakresie rekopiśmiennych źródee historycznych.

The Simple Art of Murder

The Simple Art of Murder

Epistemology modalized

Epistemology modalized

A practical guide to recovery-oriented practice

A practical guide to recovery-oriented practice

Art and architecture in Europe

Art and architecture in Europe

The exotic affair

The exotic affair

Applied engineering mechanics

Applied engineering mechanics

ICSC directory of products & services, 1991.

ICSC directory of products & services, 1991.

Before the Wall Fell

Before the Wall Fell

Autobiography of Tom Sterling

Autobiography of Tom Sterling

Life-span developmental psychology

Life-span developmental psychology

Frmd-Foil-Warrior The-11x14

Frmd-Foil-Warrior The-11x14

The excellency of the knowledge of Christ crucified

The excellency of the knowledge of Christ crucified

One-step prediction of financial time series by Srichander Ramaswamy Download PDF EPUB FB2

One-step prediction of financial time series. Basle: Bank for International Settlements, Monetary and Economic Dept., [] (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Srichander Ramaswamy; Bank for International Settlements.

Monetary and Economic Department. This paper examines the one-step prediction of financial time series from a binary decision theory perspective. Under the assumption that the decision statistic of the binary hypothesis testing problem is a Gaussian random variable, bounds for the forecasting efficiency of the hypothesis testing problem are derived.

When the criterion for forecasting performance is the total return over. This work presents a framework based on a self-learning genetic algorithm for discovering prediction patterns in financial time series. By modifying a complex mathematical algorithm for evolutionary optimization in a manner more suitable for financial time series, specifics typical to asset trading were taken into account and were reflected in the solution set.1/5(1).

This paper examines the one-step prediction of financial time series from a binary decision theory perspective. Under the assumption that the decision statistic of the binary hypothesis testing problem is a Gaussian random variable, bounds for the forecasting efficiency of the hypothesis testing problem are derived.

ONE-STEP PREDICTION OF FINANCIAL TIME SERIES by Srichander Ramaswamy* July Abstract This paper examines the one-step prediction of financial time series from a binary decision theory perspective.

Under the assumption that the decision statistic of the binary hypothesis testing problem is a Gaussian random variable, bounds for theCited by: 4. This paper examines the one-step prediction of financial time series from a binary decision theory perspective.

Under the assumption that the decision statistic of the binary hypothesis testing problem is a Gaussian random variable, bounds for the forecasting efficiency of the hypothesis testing problem are by: 4. Financial Time Series and Their Characteristics 1 Asset Returns, 2 Distributional Properties of Returns, 7 Review of Statistical Distributions and Their Moments, 7 Distributions of Returns, 13 Multivariate Returns, 16 Likelihood Function of Returns, 17 Empirical Properties of Returns, 17 Processes Considered, 20File Size: 4MB.

Financial Time Series and Their Characteristics 1 Asset Returns, 2 Distributional Properties of Returns, 6 Processes Considered, 17 2. Linear Time Series Analysis and Its Applications 22 Stationarity, 23 Correlation and Autocorrelation Function, 23 White Noise and Linear Time Series, 26 Simple Autoregressive.

There are some good, free, online resources: The Little Book of R for Time Series, by Avril Coghlan (also available in print, reasonably cheap) - I haven't read through this all, but it looks like it's well written, has some good examples, and starts basically from scratch (ie.

easy to get into).; Chap Statistics with R, by Vincent Zoonekynd - Decent intro, but probably slightly more. In the last paper FINANCIAL SERIES PREDICTION USING ATTENTION LSTM authors compare various deep learning models for financial time series prediction.

They compared multilayer perceptron (MLP), one-dimensional convolutional neural networks (1D CNN), stacked long short-term memory (stacked LSTM), attention networks, and weighted attention networks.

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.

The author begins with basic characteristics of financial time series data before Reviews: 1. However, the prediction is done only for 1 step — the series is constructed by adding the correct value to the series at each point once it is known for the next day prediction, and even.

A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series data. RNNs process a time series step-by-step, maintaining an internal state summarizing the information they've seen so far.

For more details, read the RNN tutorial. In this tutorial, you will use a specialized RNN layer called Long Short Term Memory. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): International Settlements, and from time to time by other economists, and are published by the Bank.

The papers are on subjects of topical interest and are technical in character. In this work we present a data-driven end-to-end Deep Learning approach for time series prediction, applied to financial time series. A Deep Learning scheme is derived to predict the temporal trends of stocks and ETFs in NYSE or NASDAQ.

Our approach is based on a neural network (NN) that is applied to raw financial data inputs, and is trained to predict the temporal trends of stocks and ETFs Cited by: 2. Time Series Analysis: With Applications in R (Springer Texts in Statistics) by Jonathan D.

Cryer and Kung-Sik Chan | out of 5 stars Analysis of Financial Time Series, Third Edition is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level.

It also serves as an indispensible reference for researchers and practitioners working in business and finance/5(34).

I think the mainstay textbook on this (for economists anyway) is James Hamilton's Time Series Analysis [1]. If this is your passion, do get it.

However, it's long and very dry and for a first-timer, not great to read at all. If you're just inter. Business Analysis of Financial Time Series.

Instructor: Ruey S. Tsay. @ Phone: Fax: (Please put my name on the cover page) Office HPC: Lecture: Bus Wednesday AM to AM at Class R Harper Center Bus Tuesday PM to PM at RoomGleacher. The one-step-ahead prediction of the financial time series requires not only the latest data, but also the previous data.

Benefit of the self feedback mechanism of the hidden layer, the RNN model has an advantage in deal with long-term dependence problems, but there are difficulties in practical application [26].Cited by:. Business Analysis of Financial Time Series. Instructor: Ruey S. Tsay. @ Phone: Fax: (Please put my name on the cover page) Office HPC: Lecture: Bus Friday AM to AM at Class R Harper Center Bus Saturday PM to PM at NBCNBC Tower.Selecting a time series forecasting model is just the beginning.

Using the chosen model in practice can pose challenges, including data transformations and storing the model parameters on disk. In this tutorial, you will discover how to finalize a time series forecasting model and use it to make predictions in Python.

After completing this tutorial, you will know: How to finalize a model.Time series modeling and forecasting has fundamental importance to various practical domains.

Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series Cited by: