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Data Mining in Finance

London Financial Studies
En London (Inglaterra), New York (Estados Unidos) ySingapore (Singapur) y 1 sede más.
  • London Financial Studies

$50001-60000

This course has really helped me in understanding and learning Data Mining / Machine Learning in Finance - a good mix of code and theory.

LA OPINIÓN DE Anonymous

Información importante

Descripción

The amount of data available to manage risk in a portfolio as well as the information needed to perform a thorough financial analysis of a company grow at an ever increasing speed. Moreover, data can no longer be gathered from one single source of information.

This practical programme covers key techniques including several aspects of supervised and unsupervised machine learning that you can use when mining financial data.

Most exercises and case studies are illustrated in Python, allowing you to learn how to work with this flexible programming language.

Información importante
¿Qué objetivos tiene esta formación?

¿Esta formación es para mí?

Portfolio managers
Risk managers
Professionals looking to introduce data-mining concepts in their day-to-day tasks
IT developers
Statisticians
Quant analysts
Financial Engineers

Requisitos: Basic notions of statistics Good working knowledge of Excel No prior knowledge of Python is required

Instalaciones

Dónde se imparte y en qué fechas

Inicio Ubicación
06 abril 2017
London
34 Curlew Street, se12nd, London, Inglaterra
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30 marzo 2017
New York
New York, Estados Unidos
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15 mayo 2017
Singapore
The Finexis Building, Singapore, Singapur
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06 noviembre 2017
Sydney
New South Wales, Australia
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Opiniones

A

08/04/2015
Lo mejor Decent energetic method for instruction. Altogether different from my exceptionally specialized years in college.

A mejorar Nothing.

Curso realizado: Abril 2015 | Recomendarías este centro? Sí.
H

15/11/2016
Lo mejor Now I amble to use Python and to explore data mining with the techniques I learned through this course. It was a great introduction.

A mejorar Everything OK.

Curso realizado: Noviembre 2016 | Recomendarías este centro? Sí.
A

11/04/2016
Lo mejor This course has really helped me in understanding and learning Data Mining / Machine Learning in Finance - a good mix of code and theory.

A mejorar Nothing bad.

Curso realizado: Abril 2016 | Recomendarías este centro? Sí.

¿Qué aprendes en este curso?

Risk
Data Mining
Finance
Financial
IT risk
Risk manager
Financial Training
Programming
Financial Analysis
Clustering analysis
OLS
Sparsity
Ridge regression
Statisticians
Quant analysts
Finance Data Mining
Data Mining in Finance

Programa académico

Day One

Overview of data mining
Laying out the different components of data mining
  • Association rules
  • Classification vs. regression problems
  • Clustering analysis
Data visualization
  • Overview of third party solutions (Tableau, QlikeTech etc.) for visualization of large sets of data
Regression
  • OLS (ordinary least squares)
  • Ridge regression
  • Sparsity
  • Lasso
Workshop: Working out the optimal hedge of a large real world equity portfolio using futures. The portfolio has a global nature (100+ shares) but only a limited set of futures is available

PCA
  • Principal component analysis of the term structure of interest rates and implied volatilities
  • Principal component regression (PCR)
  • Partial least squares (PLS)
Workshop: Using PCA to reduce dimensionality of a large data set of historical interest rate curves. The complex behaviour of this curve is spread over different maturities and this technique allows a risk manager to have a much better view of the dynamics of interest rate curves

Data classification – regression
Kernel density estimation and classification
  • Kernel density estimation is an unsupervised learning procedure which leads to a simple family of procedures for non parametric classification
Case study: Using kernels to derive probability distributions for financial data

Classification part I
  • Naive Bayes classification: A straightforward and powerful technique to classify data
Case study: Working out a Bayes predictor for a large data set containing different attributes of US banks. The Bayes classifier will be used to separate those banks that are likely to fail from those that are going to remain solvent

Classification part II
  • Linear Discriminant Analysis (LDA)
Day Two

Data classification (cont.)
Classification part III
  • Classification Trees: CART modelling leads to easy to use practical decision trees 
Case study: Concepts such as cost functions, impurity levels, tree pruning and cross validation will be handled in detail
  • Support Vector Machines (SVM)
  • K Nearest Neighbour learning
  • Logistic Regression
Case study: The classification methods (SVM, K Nearest and CART) are going to be put at work on different technical indicators (RSI, MACD etc.) of large sets of real world financial data. This will illustrate how these classifiers can be used to partition stocks in different buckets according to the strength of different attributes in a fast way

Workshop: Data mining tools
An introduction to Python a powerful programming language. The applicability of Python in the domain of data analysis will be illustrated through practical examples with focus on machine learning using the 'scikit learn' package