Machine Learning In Portfolio And Risk Management
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Author | : Timothy Tao Hin Law |
Publisher | : |
Total Pages | : 0 |
Release | : 2019 |
Genre | : |
ISBN | : |
This thesis investigates the applications of machine learning in Financial Portfolio and Risk Management. The focus is to customize machine learning algorithms to accommodate the intuitions or practical needs in the domain. Empirical experiments are carried out to examine the proposed customizations. An extensive breadth of machine learning topics are discussed, explored, and extended. The experiments in this thesis represent the customization of algorithms in three aspects of any portfolio and risk management system: 1. A generic prediction framework that automates predictions to provide insights for future expectations. 2. A risk-aware agent that controls the balance between actively shifting a portfolio and the transaction costs involved. 3. A robust dynamic portfolio selection algorithm that continually diversifies to track switching regimes. Experiment 1: Practical Bayesian support vector regression for Financial Time Series Prediction The first experiment outlines a generic prediction framework that takes advantage of the powerful support vector regression. This framework introduces a faster and easier parameter selection process to determine the model that generates predictions and the corresponding uncertainty estimates. It is shown that the generalization performance of this parameter selection process can reach or sometimes surpass the computationally expensive cross-validation procedure. In addition, an ad-hoc adaptive calibration process is described to enable practical use of the prediction uncertainty estimates to assess the quality of predictions, which is also interpreted as a potential indicator of market condition changes. Experiment 2: Risk-Aware Reinforcement Learning Algorithm to Improve a Portfolio The model-free Monte Carlo control reinforcement learning algorithm is extended, by making use of its episodic nature, to allow consideration of "risk" when training the algorithm. The risk-aware reinforcement learning algorithm introduced allows the user to intuitively and flexibly incorporate any form(s) of risk consideration desired. A procedure is then suggested to filter out potentially unstable policies. The risk-aware mechanism is examined, and its abilities to control "risk" are demonstrated in empirical experiments. In addition, it is recommended to diversity out-of-sample by simultaneously following multiple policies with high in-sample Sharpe ratio. Experiment 3: Expert Advice Algorithms for Dynamic Portfolio Selection The connections between online machine learning and the sequential investment problem are explored in this experiment, and the Smart Switching Portfolio (SSP) Algorithm is proposed. It continually diversifies wealth to assets based on their previous performances to track switching regimes. A newly introduced scaling parameter illustrates the linkage between the learning rate and the action of leveraging. Moreover, the algorithm is theoretically generalized to select assets from a dynamic pool of investible assets. The behavior of the SSP Algorithm is examined. The effect of the new parameter under different volatility levels is also assessed. The proposed algorithm is shown to be the most robust. It outperforms some well-known algorithms, and is particularly impressive as transaction cost increases. A few ad-hoc methods are proposed to potentially enhance the algorithm further.
Author | : Theo Lynn |
Publisher | : Springer |
Total Pages | : 194 |
Release | : 2018-12-06 |
Genre | : Business & Economics |
ISBN | : 3030023303 |
This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.
Author | : Söhnke M. Bartram |
Publisher | : CFA Institute Research Foundation |
Total Pages | : 95 |
Release | : 2020-08-28 |
Genre | : Business & Economics |
ISBN | : 195292703X |
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Author | : Marcos M. López de Prado |
Publisher | : Cambridge University Press |
Total Pages | : 152 |
Release | : 2020-04-22 |
Genre | : Business & Economics |
ISBN | : 1108879721 |
Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.
Author | : El Bachir Boukherouaa |
Publisher | : International Monetary Fund |
Total Pages | : 35 |
Release | : 2021-10-22 |
Genre | : Business & Economics |
ISBN | : 1589063953 |
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Author | : Christian L. Dunis |
Publisher | : Springer |
Total Pages | : 349 |
Release | : 2016-11-21 |
Genre | : Business & Economics |
ISBN | : 1137488808 |
As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.
Author | : Tshepo Chris Nokeri |
Publisher | : Apress |
Total Pages | : 182 |
Release | : 2021-05-27 |
Genre | : Computers |
ISBN | : 9781484271094 |
Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems. What You Will Learn Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management Know the concepts of feature engineering, data visualization, and hyperparameter optimization Design, build, and test supervised and unsupervised ML and DL models Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk Who This Book Is For Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)
Author | : Mohammad Zoynul Abedin |
Publisher | : Routledge |
Total Pages | : 275 |
Release | : 2021-06-20 |
Genre | : Business & Economics |
ISBN | : 1000394123 |
This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Author | : Emmanuel Jurczenko |
Publisher | : John Wiley & Sons |
Total Pages | : 460 |
Release | : 2020-10-06 |
Genre | : Business & Economics |
ISBN | : 1786305445 |
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Author | : Qingquan Tony Zhang |
Publisher | : Springer Nature |
Total Pages | : 340 |
Release | : 2022-10-31 |
Genre | : Business & Economics |
ISBN | : 3031116127 |
This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.