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Financial Analytics

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Essentials of Business Analytics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 264))

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Abstract

Data analytics in finance is a part of quantitative finance. Quantitative finance primarily consists of three sectors in finance—asset management, banking, and insurance. Across these three sectors, there are four tightly connected functions in which quantitative finance is used—valuation, risk management, portfolio management, and performance analysis. Data analytics in finance supports these four sequential building blocks of quantitative finance, especially the first three—valuation, risk management, and portfolio management.

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Author information

Authors and Affiliations

  1. Indian School of Business, Hyderabad, Telangana, India

    Krishnamurthy Vaidyanathan

Authors
  1. Krishnamurthy Vaidyanathan
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Correspondence to Krishnamurthy Vaidyanathan .

Editor information

Editors and Affiliations

  1. Applied Statistics and Computing Lab, Indian School of Business, Hyderabad, Telangana, India

    Bhimasankaram Pochiraju

  2. Gies College of Business, University of Illinois at Urbana Champaign, Champaign, IL, USA

    Sridhar Seshadri

1 Electronic Supplementary Material

Supplementary Data 1

Financial Analytics Steps 1, 2 and 3.xlsx (XLSX 362 kb)

Supplementary Data 2

Financial Analytics Steps 4 and 5.xlsx (XLSX 752 kb)

Supplementary Data 3

Financial Analytics Step 6.xlsx (XLSX 123 kb)

Supplementary Data 4

Financial Analytics Step 7.xlsx (XLSX 44 kb)

Supplementary Data 5

Financial Analytics Step 8.xlsx (XLSX 10 kb)

Supplementary Data 6

nifty50.txt (Ex. 20.1) (TXT 14 kb)

Supplementary Data 7

ptsr.txt (TXT 14 kb)

Supplementary Data 8

randomgaussian.txt (Ex. 20.2) (TXT 19 kb)

Supplementary Data 9

randomgaussiancurrency.txt (Ex. 20.4) (TXT 19 kb)

Supplementary Data 10

tsr.txt (TXT 952 kb)

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Vaidyanathan, K. (2019). Financial Analytics. In: Pochiraju, B., Seshadri, S. (eds) Essentials of Business Analytics. International Series in Operations Research & Management Science, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-68837-4_20

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