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The significant amount of information available in any field
requires a systematic and analytical approach to select the most
critical information and anticipate major events. During the last
decade, the world has witnessed a rapid expansion of applications
of artificial intelligence (AI) and machine learning (ML)
algorithms to an increasingly broad range of financial markets and
problems. Machine learning and AI algorithms facilitate this
process understanding, modelling and forecasting the behaviour of
the most relevant financial variables. The main contribution of
this book is the presentation of new theoretical and applied AI
perspectives to find solutions to unsolved finance questions. This
volume proposes an optimal model for the volatility smile, for
modelling high-frequency liquidity demand and supply and for the
simulation of market microstructure features. Other new AI
developments explored in this book includes building a universal
model for a large number of stocks, developing predictive models
based on the average price of the crowd, forecasting the stock
price using the attention mechanism in a neural network, clustering
multivariate time series into different market states, proposing a
multivariate distance nonlinear causality test and filtering out
false investment strategies with an unsupervised learning
algorithm. Machine Learning and AI in Finance explores the most
recent advances in the application of innovative machine learning
and artificial intelligence models to predict financial time
series, to simulate the structure of the financial markets, to
explore nonlinear causality models, to test investment strategies
and to price financial options. The chapters in this book were
originally published as a special issue of the Quantitative Finance
journal.
Coauthored by one of the creators of the most efficient space
packing solution, the Weaire-Phelan structure, The Pursuit of
Perfect Packing, Second Edition explores a problem of importance in
physics, mathematics, chemistry, biology, and engineering: the
packing of structures. Maintaining its mathematical core, this
edition continues and revises some of the stories from its
predecessor while adding several new examples and applications. The
book focuses on both scientific and everyday problems ranging from
atoms to honeycombs. It describes packing models, such as the
Kepler conjecture, Voronoi decomposition, and Delaunay
decomposition, as well as actual structure models, such as the
Kelvin cell and the Weaire-Phelan structure. The authors discuss
numerous historical aspects and provide biographical details on
influential contributors to the field, including emails from Thomas
Hales and Ken Brakke. With examples from physics, crystallography,
engineering, and biology, this accessible and whimsical book
touches on many aspects of packing objects. It will help you
understand components of packing and aid you in the quest for the
perfect packing solution.
Do you know how banking and money will look like in the new digital
age? This book collects the voices of leading scholars,
entrepreneurs, policy makers and consultants who, through their
expertise and keen analytical skills, are best positioned to
picture from various angles the ongoing technological revolution in
banking and finance. You will learn how lending and borrowing can
exist without banks; how new forms of money can compete to better
serve different society needs; how new technologies are banking the
unbanked communities in the poorest parts of the world, and how
ideas and small projects can be financed by the crowds without the
need to rely upon banks. You will learn how, in the new digital
age, we will interact with new self-organised and autonomous
companies that operate without any human involvement, based on a
set of programmed and incorruptible rules. You will learn that new
business models will emerge thanks to technology-enabled platforms,
upon which one can build new forms of non-hierarchical cooperation
between strangers. And you will also learn that new forms of risks
and threats are emerging that will destabilise our systems and
jeopardise the stability of our financial order.
Do you know how banking and money will look like in the new digital
age? This book collects the voices of leading scholars,
entrepreneurs, policy makers and consultants who, through their
expertise and keen analytical skills, are best positioned to
picture from various angles the ongoing technological revolution in
banking and finance. You will learn how lending and borrowing can
exist without banks; how new forms of money can compete to better
serve different society needs; how new technologies are banking the
unbanked communities in the poorest parts of the world, and how
ideas and small projects can be financed by the crowds without the
need to rely upon banks. You will learn how, in the new digital
age, we will interact with new self-organised and autonomous
companies that operate without any human involvement, based on a
set of programmed and incorruptible rules. You will learn that new
business models will emerge thanks to technology-enabled platforms,
upon which one can build new forms of non-hierarchical cooperation
between strangers. And you will also learn that new forms of risks
and threats are emerging that will destabilise our systems and
jeopardise the stability of our financial order.
The significant amount of information available in any field
requires a systematic and analytical approach to select the most
critical information and anticipate major events. During the last
decade, the world has witnessed a rapid expansion of applications
of artificial intelligence (AI) and machine learning (ML)
algorithms to an increasingly broad range of financial markets and
problems. Machine learning and AI algorithms facilitate this
process understanding, modelling and forecasting the behaviour of
the most relevant financial variables. The main contribution of
this book is the presentation of new theoretical and applied AI
perspectives to find solutions to unsolved finance questions. This
volume proposes an optimal model for the volatility smile, for
modelling high-frequency liquidity demand and supply and for the
simulation of market microstructure features. Other new AI
developments explored in this book includes building a universal
model for a large number of stocks, developing predictive models
based on the average price of the crowd, forecasting the stock
price using the attention mechanism in a neural network, clustering
multivariate time series into different market states, proposing a
multivariate distance nonlinear causality test and filtering out
false investment strategies with an unsupervised learning
algorithm. Machine Learning and AI in Finance explores the most
recent advances in the application of innovative machine learning
and artificial intelligence models to predict financial time
series, to simulate the structure of the financial markets, to
explore nonlinear causality models, to test investment strategies
and to price financial options. The chapters in this book were
originally published as a special issue of the Quantitative Finance
journal.
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