語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Financial Data Analytics = Theory and Application /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Financial Data Analytics/ edited by Sinem Derindere Köseoğlu.
其他題名:
Theory and Application /
其他作者:
Derindere Köseoğlu, Sinem.
面頁冊數:
XXII, 384 p. 122 illus., 100 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Quantitative Economics. -
電子資源:
https://doi.org/10.1007/978-3-030-83799-0
ISBN:
9783030837990
Financial Data Analytics = Theory and Application /
Financial Data Analytics
Theory and Application /[electronic resource] :edited by Sinem Derindere Köseoğlu. - 1st ed. 2022. - XXII, 384 p. 122 illus., 100 illus. in color.online resource. - Contributions to Finance and Accounting,2730-6046. - Contributions to Finance and Accounting,.
PART 1. INTRODUCTION AND ANALYTICS MODELS -- Retraining and Reskilling Financial Participators in the Digital Age -- Basics of Financial Data Analytics -- Predictive Analytics Techniques: Theory and Applications in Finance -- Prescriptive Analytics Techniques: Theory and Applications in Finance -- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS) -- PART 2. MACHINE LEARNING -- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine -- Pruned Random Forests for Effective and Efficient Financial Data Analytics -- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory -- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications -- PART 3. TECHNOLOGY DRIVEN FINANCE -- Financial Networks: A Review of Models and the Use of Network Similarities -- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets -- Transforming Insurance Business with Data Science -- A General Cyber Hygiene Approach for Financial Analytical Environment.
This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. .
ISBN: 9783030837990
Standard No.: 10.1007/978-3-030-83799-0doiSubjects--Topical Terms:
1366056
Quantitative Economics.
LC Class. No.: HG1-9999
Dewey Class. No.: 332
Financial Data Analytics = Theory and Application /
LDR
:03940nam a22004095i 4500
001
1093287
003
DE-He213
005
20220425093945.0
007
cr nn 008mamaa
008
221228s2022 sz | s |||| 0|eng d
020
$a
9783030837990
$9
978-3-030-83799-0
024
7
$a
10.1007/978-3-030-83799-0
$2
doi
035
$a
978-3-030-83799-0
050
4
$a
HG1-9999
072
7
$a
KFF
$2
bicssc
072
7
$a
BUS039000
$2
bisacsh
072
7
$a
KFF
$2
thema
082
0 4
$a
332
$2
23
245
1 0
$a
Financial Data Analytics
$h
[electronic resource] :
$b
Theory and Application /
$c
edited by Sinem Derindere Köseoğlu.
250
$a
1st ed. 2022.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
XXII, 384 p. 122 illus., 100 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Contributions to Finance and Accounting,
$x
2730-6046
505
0
$a
PART 1. INTRODUCTION AND ANALYTICS MODELS -- Retraining and Reskilling Financial Participators in the Digital Age -- Basics of Financial Data Analytics -- Predictive Analytics Techniques: Theory and Applications in Finance -- Prescriptive Analytics Techniques: Theory and Applications in Finance -- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS) -- PART 2. MACHINE LEARNING -- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine -- Pruned Random Forests for Effective and Efficient Financial Data Analytics -- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory -- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications -- PART 3. TECHNOLOGY DRIVEN FINANCE -- Financial Networks: A Review of Models and the Use of Network Similarities -- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets -- Transforming Insurance Business with Data Science -- A General Cyber Hygiene Approach for Financial Analytical Environment.
520
$a
This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. .
650
2 4
$a
Quantitative Economics.
$3
1366056
650
2 4
$a
Macroeconomics and Monetary Economics.
$3
1365903
650
2 4
$a
Corporate Finance.
$3
1069043
650
1 4
$a
Financial Economics.
$3
669216
650
0
$a
Econometrics.
$3
556981
650
0
$a
Macroeconomics.
$3
554837
650
0
$a
Business enterprises—Finance.
$3
1253877
650
0
$a
Finance.
$3
559073
700
1
$a
Derindere Köseoğlu, Sinem.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1401210
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030837983
776
0 8
$i
Printed edition:
$z
9783030838003
776
0 8
$i
Printed edition:
$z
9783030838010
830
0
$a
Contributions to Finance and Accounting,
$x
2730-6046
$3
1349239
856
4 0
$u
https://doi.org/10.1007/978-3-030-83799-0
912
$a
ZDB-2-ECF
912
$a
ZDB-2-SXEF
950
$a
Economics and Finance (SpringerNature-41170)
950
$a
Economics and Finance (R0) (SpringerNature-43720)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入