An essential introduction to data analytics and Machine Learning
techniques in the business sector In Financial Data Analytics with
Machine Learning, Optimization and Statistics, a team consisting of
a distinguished applied mathematician and statistician, experienced
actuarial professionals and working data analysts delivers an
expertly balanced combination of traditional financial statistics,
effective machine learning tools, and mathematics. The book focuses
on contemporary techniques used for data analytics in the financial
sector and the insurance industry with an emphasis on mathematical
understanding and statistical principles and connects them with
common and practical financial problems. Each chapter is equipped
with derivations and proofs—especially of key results—and
includes several realistic examples which stem from common
financial contexts. The computer algorithms in the book are
implemented using Python and R, two of the most widely used
programming languages for applied science and in academia and
industry, so that readers can implement the relevant models and use
the programs themselves. The book begins with a brief introduction
to basic sampling theory and the fundamentals of simulation
techniques, followed by a comparison between R and Python. It then
discusses statistical diagnosis for financial security data and
introduces some common tools in financial forensics such as
Benford's Law, Zipf's Law, and anomaly detection. The statistical
estimation and Expectation-Maximization (EM) &
Majorization-Minimization (MM) algorithms are also covered. The
book next focuses on univariate and multivariate dynamic volatility
and correlation forecasting, and emphasis is placed on the
celebrated Kelly's formula, followed by a brief introduction to
quantitative risk management and dependence modelling for extremal
events. A practical topic on numerical finance for traditional
option pricing and Greek computations immediately follows as well
as other important topics in financial data-driven aspects, such as
Principal Component Analysis (PCA) and recommender systems with
their applications, as well as advanced regression learners such as
kernel regression and logistic regression, with discussions on
model assessment methods such as simple Receiver Operating
Characteristic (ROC) curves and Area Under Curve (AUC) for typical
classification problems. The book then moves on to other commonly
used machine learning tools like linear classifiers such as
perceptrons and their generalization, the multilayered counterpart
(MLP), Support Vector Machines (SVM), as well as Classification and
Regression Trees (CART) and Random Forests. Subsequent chapters
focus on linear Bayesian learning, including well-received
credibility theory in actuarial science and functional kernel
regression, and non-linear Bayesian learning, such as the Naïve
Bayes classifier and the Comonotone-Independence Bayesian
Classifier (CIBer) recently independently developed by the authors
and used successfully in InsurTech. After an in-depth discussion on
cluster analyses such as K-means clustering and its inversion, the
K-nearest neighbor (KNN) method, the book concludes by introducing
some useful deep neural networks for FinTech, like the potential
use of the Long-Short Term Memory model (LSTM) for stock price
prediction. This book can help readers become well-equipped with
the following skills: To evaluate financial and insurance data
quality, and use the distilled knowledge obtained from the data
after applying data analytic tools to make timely financial
decisions To apply effective data dimension reduction tools to
enhance supervised learning To describe and select suitable data
analytic tools as introduced above for a given dataset depending
upon classification or regression prediction purpose The book
covers the competencies tested by several professional
examinations, such as the Predictive Analytics Exam offered by the
Society of Actuaries, and the Institute and Faculty of Actuaries'
Actuarial Statistics Exam. Besides being an indispensable resource
for senior undergraduate and graduate students taking courses in
financial engineering, statistics, quantitative finance, risk
management, actuarial science, data science, and mathematics for
AI, Financial Data Analytics with Machine Learning, Optimization
and Statistics also belongs in the libraries of aspiring and
practicing quantitative analysts working in commercial and
investment banking.
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