A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To address cyclicality and underperformance, we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We test our system across multiple combinations of smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return oriented portfolios (up to 50$\%$ in excess of market annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60$\%$ in excess of market annually) show model performance improvement with respect to portfolios built using full feature HMMs

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

Výběr a optimalizace portfolia na základě strategie faktorového investování

This thesis's aim is to explore the practical application of factor investment strategies in portfolio construction for individual investors. The traditional portfolio construction method based on historical values is becoming increasingly inadequate in coping with the nowadays complex investment market. Factor investing, an emerging investment concept, aims to capture the performance of underlying fundamental, technical, and systematic risk factors to optimize the portfolio effectively. This thesis discusses the construction of an investment analysis process suitable for individual investors, explaining stock market returns by means of various factors and discriminating factor characteristics through machine learning. It draws on the latest research reports from investment banks on multi-factor model testing. It utilizes quantitative platforms such as Ricequant and Joinquant to maintain the universality and usability of the research environment.Cílem této práce je prozkoumat možnosti praktického využití faktorových investičních strategií při tvorbě portfolia pro individuální investory. Tradiční metoda konstrukce portfolia založená na historických hodnotách se stává stále méně vhodnou pro dnešní komplexní investiční trh. Cílem faktorového investování, nově vznikajícího investičního konceptu, je zachytit výkonnost základních fundamentálních, technických a systematických rizikových faktorů a efektivně optimalizovat portfolio. Tato práce pojednává o konstrukci procesu investiční analýzy vhodné pro individuální investory, vysvětlující výnosy akciového trhu pomocí různých faktorů a rozeznávající charakteristiky faktorů pomocí strojového učení. Vychází z nejnovějších výzkumných zpráv investičních bank o testování vícefaktorových modelů. Využívá kvantitativní platformy, jako jsou Ricequant a Joinquant, aby byla zachována univerzálnost a použitelnost výzkumného prostředí.154 - Katedra financívelmi dobř

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen