Designing an optimum portfolio for allocating suitable weights to its constituent assets so
that the return and risk associated with the portfolio are optimised is a computationally hard …

Abstract Machine learning techniques have recently become the norm for detecting patterns
in financial markets. However, relying solely on machine learning algorithms for decision …

There are a plethora of invented classifiers in Machine learning literature, however, there is
no optimal classifier in terms of accuracy and time taken to build the trained model …

• A robust backtesting method for evaluating the performances of the portfolios is also
designed. The performance results of the portfolios give investors in the stock market a clear …

This chapter delves into three powerful and widely-used technical indicators, Bollinger
Bands, Moving Average Convergence Divergence (MACD), and the Relative Strength Index …

The COVID-19 epidemic has had a profound effect on almost every aspect of daily life,
including the financial sector, education, transportation, health care, and so on. Among …

One of the most important steps when employing machine learning approaches is the
feature engineering process. It plays a key role in the identification of features that can …

This open-source tool, written in Python, referred to as XAI StatArb, implements a machine
learning approach (ML) powered by eXplainable Artificial Intelligence techniques integrated …

Nowadays, statistical arbitrage is one of the most attractive fields of study for researchers,
and its applications are widely used also in the financial industry. In this work, we propose a …

This book brings together real-world cases illustrating how to analyse volatile financial time
series in order to provide a better understanding of their past behavior and robust …