Portfolio Management: The Holistic Data Lifecycle

21 pagesMachine learning provides many benefits to Portfolio Managers in analysing data and has the potential to provide much more. A concern with the approach to Machine Learning in Portfolio Management is that is caught between two domains: finance and information systems. In reality, to ensure its success, having these two separate and distinct domains are problematic. What is required is a holistic view, facilitating discussions, with data being the unifying concept and the one that is key to success. The data value map is a lens that allows all involved, in the use or adoption of Machine Learning in Portfolio Management, to form a shared understanding of the lifecycle of the data involved. Rather than being seen as a financial concept or a technical concept, this view of the data lifecycle provides a platform for all involved to determine what is required, and to identify and deal with any potential pitfalls along the way. A holistic view, and shared understanding, are required for the success of Machine Learning in Portfolio Management. Research on the intersection between Machine Learning and Portfolio Management is currently lacking. A focus on the different parts of the data lifecycle provides an opportunity for further research

Applications of machine learning in finance: analysis of international portfolio flows using regime-switching models

Recent advances in machine learning are finding commercial applications across many sectors, not least the financial industry. This thesis explores applications of machine learning in quantitative finance through two approaches. The current state of the art is evaluated through an extensive review of recent quantitative finance literature. Themes and technologies are identified and classified, and the key use cases highlighted from the emerging literature. Machine learning is found to enable deeper analysis of financial data and the modelling of complex nonlinear relationships within data. The ability to incorporate alternative data in the investment process is also enabled. Innovations in backtesting and performance metrics are also made possible through the application of machine learning. Demonstrating a practical application of machine learning in quantitative finance, regime-switching models are applied to analyse and extract information from international portfolio flows. Regime-switching models capture properties of international portfolio flows previously found in the literature, such as persistence in flows compared to returns, and a relationship between flows and returns. Structural breaks and persistent regime shifts in investor behaviour are identified by the models. Regime-switching models infer regimes in the data which exhibit unique characteristic flows and returns. To determine whether the information extracted could aid in the investment process, a portfolio of global assets was constructed, with positions determined using a flowbased regime-switching model. The portfolio outperforms two benchmarks, a buy & hold strategy and the MSCI World Index in walk-forward out-of-sample tests using daily and weekly data

Machine learning for financial applications: self-organising maps, hierarchical clustering and dynamic time-warping for portfolio constructive

This study investigates how modern machine learning (ML) techniques can be used to advance the field of quantitative investing. A broad literature review evaluated the common applications for ML in finance, and what ML algorithms are being used. The results show ML is commonly applied to the areas of Return Forecasting, Portfolio Construction, Ethics, Fraud Detection Decision Making Language Processing and Sentiment Analysis. Neural Network technology and support vector machine are identified as popular ML algorithms. A second review was carried out, focusing in the area of ML for quantitative finance in recent years finds three primary areas; Return forecasting, Portfolio construction and Risk management. A practical ML experiment carried out as a proof of concept of ML for financial applications. This experiment was informed by the results of the broad and more focused literature searches. Two forms of ML techniques are used to analyse market return data and equity flow data (provided by State Street Global Markets) and create a portfolio from insights derived from the ML technology. The ML technologies employed are those of Self-Organising Maps and Hierarchical Clustering. The portfolios created were tested in terms of risk, profitability and stability. Stable regimes and profitable portfolios are created. Results show that portfolios obtained by analysing equity flow data consistently outperform those created by analysing return data

A governance framework for algorithmic accountability and transparency

Algorithmic systems are increasingly being used as part of decision-making processes in both the public and private sectors, with potentially significant consequences for individuals, organisations and societies as a whole. Algorithmic systems in this context refer to the combination of algorithms, data and the interface process that together determine the outcomes that affect end users. Many types of decisions can be made faster and more efficiently using algorithms. A significant factor in the adoption of algorithmic systems for decision-making is their capacity to process large amounts of varied data sets (i.e. big data), which can be paired with machine learning methods in order to infer statistical models directly from the data. The same properties of scale, complexity and autonomous model inference however are linked to increasing concerns that many of these systems are opaque to the people affected by their use and lack clear explanations for the decisions they make. This lack of transparency risks undermining meaningful scrutiny and accountability, which is a significant concern when these systems are applied as part of decision-making processes that can have a considerable impact on people's human rights (e.g. critical safety decisions in autonomous vehicles; allocation of health and social service resources, etc.). This study develops policy options for the governance of algorithmic transparency and accountability, based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems. Based on a review and analysis of existing proposals for governance of algorithmic systems, a set of four policy options are proposed, each of which addresses a different aspect of algorithmic transparency and accountability: 1. awareness raising: education, watchdogs and whistleblowers; 2. accountability in public-sector use of algorithmic decision-making; 3. regulatory oversight and legal liability; and 4. global coordination for algorithmic governance

Distinguishing between conduct disorder with high versus low levels of callous-unemotional traits

The aim of this thesis was to investigate differences between conduct disorder with high versus low levels of callous-unemotional traits. Differences in parenting, grey matter volume and facial emotion recognition ability were investigated using univariate and machine learning methods. In Chapter 3, youths with conduct disorder experienced more negative and less positive parenting than typically developing youths. The high callous-unemotional group also experienced less positive parenting than the low callous-unemotional group. All groups were classified with above-chance accuracy. In Chapter 4, when controlling for ADHD, youths with conduct disorder exhibited reduced grey matter volume in the insulae relative to typically developing youths. Youths with conduct disorder and high callous-unemotional traits exhibited additional reductions in the left orbitofrontal cortex. All groups were classified with above-chance accuracies. In Chapter 5, youths with conduct disorder – regardless of callous-unemotional traits – were poorer at recognising emotions than typically developing youths. Youths with conduct disorder were classified against typically developing youths at above-chance levels, but the classifier did not exceed chance when distinguishing between high and low callous-unemotional groups. Together, these findings indicate both similarities and differences in conduct disorder with high versus low levels of callous-unemotional traits, consistent with different developmental pathways to similar outcomes

Energy and Service Life Management Strategy for a Two-Drive Multi-Speed Electric Vehicle

Regulations of zero emission passenger cars appear on the horizon, and battery electric vehicles (BEV) are the main solution from the current market. It has been a focus of both academia and industry to extend their range. One of the main approaches is to reduce their energy consumption. Recent studies have shown that the two-drive topology and the multi-speed topology help to do so. It is natural to combine both concepts and to design a two-drive multi-speed topology for BEVs. Due to its more than one degree of freedom, an online energy management strategy (EMS) controlling torque set points of both electric motors and target gear positions is necessary to exploit its potential for reducing total energy consumption in real-world applications. There are numerous studies on EMSs for BEVs and hybrid electric vehicles. The overwhelming majority of them shared the same assumption: shift processes are neglectable. Based on the shift duration statistics, the shift processes of the most common transmissions in today’s market are too long to be ignored for an EMS with an operation frequency of at least 1 Hz. How to develop an EMS that considers shift processes? Suppose that an EMS is developed. It controls the powertrain in favour of low energy consumption, and the parts and the components are loaded accordingly. Some parts might fatigue and fail much faster than others, not because of poor construction dimensioning, but because of excessive use. What can an EMS do to prevent such an extreme scenario? Furthermore, is there a general way to design EMSs for multi-drive BEVs? This thesis is initiated by developing an online EMS for a two-drive multi-speed BEV called “Speed4E”, and tends to address the questions raised earlier. A predictive EMS in a Model Predictive Control framework is developed. A hybrid system considering the shift processes is proposed. Based on it and the Hybrid Minimum Principle, a solver and its algorithms are developed. The Principle is chosen for its accuracy and low time complexity, the two most important attributes of an online EMS. Minimizing the instantaneous Hamiltonian in the Principle is mathematically analysed. Several Lemmas that reduce the time complexity considerably are produced. Compared to an EMS that minimizes instantaneous energy consumption and ignores shift processes, the predictive EMS reduces the energy consumption in the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) by 0.26 % and the shift count by 63.41 %. The hybrid system, the predictive EMS and the mathematical analysis are, as far as the author knows, first of their kinds. A novel multi-criteria operation strategy (MCOS) considering powertrain service life is proposed. Thanks to the hybrid system, the influence of the shift processes on fatigue is included. The MCOS extends the powertrain service life by several times but sacrifices the energy consumption. A general multi-drive (at least two) multi-speed electric powertrain is proposed. Its hybrid system is formulated. The Principle is applied to produce the optimality condition. It is showcased, how to modify certain sets and sample space in the formulation to have the general model and problem represent certain electric powertrains. A unified framework to design EMS for the general multi-drive electric powertrain is proposed, where the algorithms developed for the predictive EMS can be applied