Algorithmic Finance: A Companion To Data Science - Image 1

£104.90

World Scientific Publishing Company Algorithmic Finance: A Companion To Data Science

Name: Algorithmic Finance: A Companion To Data Science
Brand: World Scientific Publishing Company
Price: 104.9 GBP
Availability: InStock

Econometrics

Price data last checked 32 day(s) ago - refreshing...

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

This is the most expensive it has ever been. Walk away.

£105 today · previous high £105 · all-time low £105

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 56 days · 56 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 56 days • 1 price levels

Days at Price

Price Analysis

Most common price: £105 (56 days, 100.0%)

Price range: £105 - £105

Price levels: 1 different prices over 56 days

Description

Why is data science a branch of science? Is data science just a catchy rebranding of statistics? Data science provides tools for statistical analysis and machine learning. But, as much as application problems without tools are lame, tools without application problems are vain. Through example after example, this book presents the algorithmic aspects of statistics and show how some of the tools are applied to answer questions of interest to finance. This book champions a fundamental principle of science — objective reproducibility of evidence independently by others. From a companion web site, readers can download many easy-to-understand Python programs and real-world data. Independently, readers can draw for themselves the figures in the book. Even so, readers are encouraged to run the statistical tests described as examples to verify their own results against what the book claims. This book covers some topics that are seldom discussed in other textbooks. They include the methods to adjust for dividend payment and stock splits, how to reproduce a stock market index such as Nikkei 225 index, and so on. By running the Python programs provided, readers can verify their results against the data published by free data resources such as Yahoo! finance. Though practical, this book provides detailed proofs of propositions such as why certain estimators are unbiased, how the ubiquitous normal distribution is derived from the first principles, and so on. This see-for-yourself textbook is essential to anyone who intends to learn the nuts and bots of data science, especially in the application domain of finance. Advanced readers may find the book helpful in its mathematical treatment. Practitioners may find some tips from the book on how an ETF is constructed, as well as some insights on a novel algorithmic framework for pair trading to generate statistical arbitrage.

Product Specifications

Brand: World Scientific Publishing Company
Format: hardcover
ASIN: 9811238308
Category: Books > Subjects > Business, Finance & Law > Economics > Econometrics
Domain: Amazon UK
Release Date: 06 May 2022
Listed Since: 24 March 2021

Barcode

No barcode data available

World Scientific Publishing Company Algorithmic Finance: A Companion To Data Science

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Statistical Analysis of Financial Data: With Examples In R (Chapman & Hall/CRC Texts in Statistical Science)

Data Analytics for Finance Using Python (Advances in Digital Technologies for Smart Applications)

Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance (Wiley Series in Computational Statistics)

Statistics For Business And Financial Economics: Second Edition

Tidy Finance with Python (Chapman & Hall/CRC The Python Series)

Lectures On The Theory And Application Of Modern Finance With R And Chatgpt: 10 (World Scientific Lecture Notes in Finance)

Statistical Arbitrage in Algorithmic Trading: Stationarity, Cointegration, and Mean-Reverting Stochastic Processes With Python (Computational Mathematics Library)

Fuzzy Portfolio Optimization: Advances in Hybrid Multi-criteria Methodologies: 316 (Studies in Fuzziness and Soft Computing, 316)

Machine Learning for Factor Investing: R Version (Chapman and Hall/CRC Financial Mathematics Series)

Wiley Machine Learning and Big Data with kdb+/q Book

Dictionnaire des marchés financiers: Anglais, allemand, espagnol, italien, néerlandais

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis (Chapman & Hall/CRC The R Series)

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis (Chapman & Hall/CRC The R Series)

Forerunners of Modern Financial Economics: A Random Walk in the History of Economic Thought, 1900–1950

Statistics for Economics, Accounting and Business Studies

Springer Statistics and Data Analysis for Financial Engineering

Proceedings of the First International Forum on Financial Mathematics and Financial Technology

Pathwise Estimation and Inference for Diffusion Market Models

Proceedings of the First International Forum on Financial Mathematics and Financial Technology

Introduction to Statistical Methods for Financial Models (Chapman & Hall/CRC Texts in Statistical Science)

Quantitative Portfolio Management: with Applications in Python (Springer Texts in Business and Economics)

Quantitative Portfolio Management: with Applications in Python (Springer Texts in Business and Economics)

Risk Books - The RMB Handbook: Trading, Investing and Hedging

Data Science for Financial Econometrics: 898 (Studies in Computational Intelligence, 898)