|
Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
Solve common and not-so-common financial problems using Python
libraries such as NumPy, SciPy, and pandas Key Features Use
powerful Python libraries such as pandas, NumPy, and SciPy to
analyze your financial data Explore unique recipes for financial
data analysis and processing with Python Estimate popular financial
models such as CAPM and GARCH using a problem-solution approach
Book DescriptionPython is one of the most popular programming
languages used in the financial industry, with a huge set of
accompanying libraries. In this book, you'll cover different ways
of downloading financial data and preparing it for modeling. You'll
calculate popular indicators used in technical analysis, such as
Bollinger Bands, MACD, RSI, and backtest automatic trading
strategies. Next, you'll cover time series analysis and models,
such as exponential smoothing, ARIMA, and GARCH (including
multivariate specifications), before exploring the popular CAPM and
the Fama-French three-factor model. You'll then discover how to
optimize asset allocation and use Monte Carlo simulations for tasks
such as calculating the price of American options and estimating
the Value at Risk (VaR). In later chapters, you'll work through an
entire data science project in the financial domain. You'll also
learn how to solve the credit card fraud and default problems using
advanced classifiers such as random forest, XGBoost, LightGBM, and
stacked models. You'll then be able to tune the hyperparameters of
the models and handle class imbalance. Finally, you'll focus on
learning how to use deep learning (PyTorch) for approaching
financial tasks. By the end of this book, you'll have learned how
to effectively analyze financial data using a recipe-based
approach. What you will learn Download and preprocess financial
data from different sources Backtest the performance of automatic
trading strategies in a real-world setting Estimate financial
econometrics models in Python and interpret their results Use Monte
Carlo simulations for a variety of tasks such as derivatives
valuation and risk assessment Improve the performance of financial
models with the latest Python libraries Apply machine learning and
deep learning techniques to solve different financial problems
Understand the different approaches used to model financial time
series data Who this book is forThis book is for financial
analysts, data analysts, and Python developers who want to learn
how to implement a broad range of tasks in the finance domain. Data
scientists looking to devise intelligent financial strategies to
perform efficient financial analysis will also find this book
useful. Working knowledge of the Python programming language is
mandatory to grasp the concepts covered in the book effectively.
Written by a highly experienced researcher and teachers, this book
provides a much-needed guide to the proper use of Statistical
Package for the Social Sciences (SPSS) software in social research,
particularly where data may not be presented in the most convenient
way. The book focuses on data manipulations and covers the majority
of real-world use of SPSS use. Among the book's many unique
features are its 'syntax diary' method for organization of
manipulations and analyses. Offers both novices and intermediate
users a framework within which they can safely and comfortably work
with SPSS.
Discover the story of your data using the essential elements of
associations and correlations Key Features Get a comprehensive
introduction to associations and correlations Explore multivariate
analysis, understand its limitations, and discover the assumptions
on which it's based Gain insights into the various ways of
preparing your data for analysis and visualization Book
DescriptionAssociations and correlations are ways of describing how
a pair of variables change together as a result of their
connection. By knowing the various available techniques, you can
easily and accurately discover and visualize the relationships in
your data. This book begins by showing you how to classify your
data into the four distinct types that you are likely to have in
your dataset. Then, with easy-to-understand examples, you'll learn
when to use the various univariate and multivariate statistical
tests. You'll also discover what to do when your univariate and
multivariate results do not match. As the book progresses, it
describes why univariate and multivariate techniques should be used
as a tag team, and also introduces you to the techniques of
visualizing the story of your data. By the end of the book, you'll
know exactly how to select the most appropriate univariate and
multivariate tests, and be able to use a single strategic framework
to discover the true story of your data. What you will learn
Identify a dataset that's fit for analysis using its basic features
Understand the importance of associations and correlations Use
multivariate and univariate statistical tests to confirm
relationships Classify data as qualitative or quantitative and then
into the four subtypes Build a visual representation of all the
relationships in the dataset Automate associations and correlations
with CorrelViz Who this book is forThis is a book for beginners -
if you're a novice data analyst or data scientist, then this is a
great place to start. Experienced data analysts might also find
value in this title, as it will recap the basics and strengthen
your understanding of key concepts. This book focuses on
introducing the essential elements of association and correlation
analysis.
Build efficient forecasting models using traditional time series
models and machine learning algorithms. Key Features Perform time
series analysis and forecasting using R packages such as Forecast
and h2o Develop models and find patterns to create visualizations
using the TSstudio and plotly packages Master statistics and
implement time-series methods using examples mentioned Book
DescriptionTime series analysis is the art of extracting meaningful
insights from, and revealing patterns in, time series data using
statistical and data visualization approaches. These insights and
patterns can then be utilized to explore past events and forecast
future values in the series. This book explores the basics of time
series analysis with R and lays the foundations you need to build
forecasting models. You will learn how to preprocess raw time
series data and clean and manipulate data with packages such as
stats, lubridate, xts, and zoo. You will analyze data and extract
meaningful information from it using both descriptive statistics
and rich data visualization tools in R such as the TSstudio,
plotly, and ggplot2 packages. The later section of the book delves
into traditional forecasting models such as time series linear
regression, exponential smoothing (Holt, Holt-Winter, and more) and
Auto-Regressive Integrated Moving Average (ARIMA) models with the
stats and forecast packages. You'll also cover advanced time series
regression models with machine learning algorithms such as Random
Forest and Gradient Boosting Machine using the h2o package. By the
end of this book, you will have the skills needed to explore your
data, identify patterns, and build a forecasting model using
various traditional and machine learning methods. What you will
learn Visualize time series data and derive better insights Explore
auto-correlation and master statistical techniques Use time series
analysis tools from the stats, TSstudio, and forecast packages
Explore and identify seasonal and correlation patterns Work with
different time series formats in R Explore time series models such
as ARIMA, Holt-Winters, and more Evaluate high-performance
forecasting solutions Who this book is forHands-On Time Series
Analysis with R is ideal for data analysts, data scientists, and
all R developers who are looking to perform time series analysis to
predict outcomes effectively. A basic knowledge of statistics is
required; some knowledge in R is expected, but not mandatory.
Many professional, high-quality surveys collect data on people's
behaviour, experiences, lifestyles and attitudes. The data they
produce is more accessible than ever before. This book provides
students with a comprehensive introduction to using this data, as
well as transactional data and big data sources, in their own
research projects. Here you will find all you need to know about
locating, accessing, preparing and analysing secondary data, along
with step-by-step instructions for using IBM SPSS Statistics. You
will learn how to: Create a robust research question and design
that suits secondary analysis Locate, access and explore data
online Understand data documentation Check and 'clean' secondary
data Manage and analyse your data to produce meaningful results
Replicate analyses of data in published articles and books Using
case studies and video animations to illustrate each step of your
research, this book provides you with the quantitative analysis
skills you'll need to pass your course, complete your research
project and compete in the job market. Exercises throughout the
book and on the book's companion website give you an opportunity to
practice, check your understanding and work hands on with real data
as you're learning.
This book introduces econometric analysis of cross section, time
series and panel data with the application of statistical software.
It serves as a basic text for those who wish to learn and apply
econometric analysis in empirical research. The level of
presentation is as simple as possible to make it useful for
undergraduates as well as graduate students. It contains several
examples with real data and Stata programmes and interpretation of
the results. While discussing the statistical tools needed to
understand empirical economic research, the book attempts to
provide a balance between theory and applied research. Various
concepts and techniques of econometric analysis are supported by
carefully developed examples with the use of statistical software
package, Stata 15.1, and assumes that the reader is somewhat
familiar with the Strata software. The topics covered in this book
are divided into four parts. Part I discusses introductory
econometric methods for data analysis that economists and other
social scientists use to estimate the economic and social
relationships, and to test hypotheses about them, using real-world
data. There are five chapters in this part covering the data
management issues, details of linear regression models, the related
problems due to violation of the classical assumptions. Part II
discusses some advanced topics used frequently in empirical
research with cross section data. In its three chapters, this part
includes some specific problems of regression analysis. Part III
deals with time series econometric analysis. It covers intensively
both the univariate and multivariate time series econometric models
and their applications with software programming in six chapters.
Part IV takes care of panel data analysis in four chapters.
Different aspects of fixed effects and random effects are discussed
here. Panel data analysis has been extended by taking dynamic panel
data models which are most suitable for macroeconomic research. The
book is invaluable for students and researchers of social sciences,
business, management, operations research, engineering, and applied
mathematics.
Solve real-world statistical problems using the most popular R
packages and techniques Key Features Learn how to apply statistical
methods to your everyday research with handy recipes Foster your
analytical skills and interpret research across industries and
business verticals Perform t-tests, chi-squared tests, and
regression analysis using modern statistical techniques Book
DescriptionR is a popular programming language for developing
statistical software. This book will be a useful guide to solving
common and not-so-common challenges in statistics. With this book,
you'll be equipped to confidently perform essential statistical
procedures across your organization with the help of cutting-edge
statistical tools. You'll start by implementing data modeling, data
analysis, and machine learning to solve real-world problems. You'll
then understand how to work with nonparametric methods, mixed
effects models, and hidden Markov models. This book contains
recipes that will guide you in performing univariate and
multivariate hypothesis tests, several regression techniques, and
using robust techniques to minimize the impact of outliers in
data.You'll also learn how to use the caret package for performing
machine learning in R. Furthermore, this book will help you
understand how to interpret charts and plots to get insights for
better decision making. By the end of this book, you will be able
to apply your skills to statistical computations using R 3.5. You
will also become well-versed with a wide array of statistical
techniques in R that are extensively used in the data science
industry. What you will learn Become well versed with recipes that
will help you interpret plots with R Formulate advanced statistical
models in R to understand its concepts Perform Bayesian regression
to predict models and input missing data Use time series analysis
for modelling and forecasting temporal data Implement a range of
regression techniques for efficient data modelling Get to grips
with robust statistics and hidden Markov models Explore ANOVA
(Analysis of Variance) and perform hypothesis testing Who this book
is forIf you are a quantitative researcher, statistician, data
analyst, or data scientist looking to tackle various challenges in
statistics, this book is what you need! Proficiency in R
programming and basic knowledge of linear algebra is necessary to
follow along the recipes covered in this book.
|
You may like...
Jonah
Phillip Cary
Paperback
R765
Discovery Miles 7 650
|