Online Non-linear Prediction of Financial Time Series Patterns

We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learning, and then both batch and online supervised learning are carried out on Feedforward Neural Networks (FNNs) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with restricted Boltzmann machine pretraining, and variance based initializations. The validity of the FNN backtest results are shown under a rigorous assessment of backtest overfitting using both Combinatorially Symmetrical Cross Validation and Probabilistic and Deflated Sharpe Ratios. Results are further used to develop a view on the phenomenology of financial markets and the value of complex historical data under unstable dynamics

The design considerations and development of a simulator for the backtesting of investment strategies

The skill of accurately predicting the optimal time to buy or sell shares on the stock market is one that has been actively sought by both experienced and novice investors since the advent of the stock exchange in the early 1930s. Since then, the finance industry has employed a plethora of techniques to improve the prediction power of the investor. This thesis is an investigation into one of those techniques and the advancement of this technique through the use of computational power. The technique of portfolio strategy backtesting as a vehicle to achieve improved predictive power is one that has existed within financial services for decades. Portfolio backtesting, as alluded to by its name, is the empirical testing of an investment strategy to determine how the strategy would have performed historically, with a view that past performance may be indicative of future performance

Advancing Systematic and Factor Investing Strategies using Alternative Data and Machine Learning

This thesis advances systematic and factor investing strategies using alternative data and machine learning techniques. The first chapter studies the relevance of high-frequency news data for low-frequency factor investing strategies. We build various news-based equity factors for an investable global equity universe to investigate the factors’ ability to extend the information inherent in standard factor models. Specifically, we document that incorporating news-based equity factors benefits multi-factor equity investments, employing diversified multi-factor equity allocations but also more dynamic factor timing strategies. The second chapter examines dynamic asset allocation strategies that focus on explicit downside risk management. We investigate suitable risk models that best inform tail risk protection strategies. In addition to forecasting portfolio risk based on standalone models such as extreme value theory or copula-GARCH, we propose a novel expected shortfall (ES) and value-at-risk (VaR) forecast combination approach that utilizes a loss function that overcomes the lack of elicitability for ES. This forecast combination method dominates simple and sophisticated standalone models as well as a simple average combination approach in terms of statistical accuracy. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts, given the defensive portfolio insurance mechanics. The third chapter extends the above ES and VaR forecast combination approach using machine learning techniques. Building on a rich predictor set of VaR and ES forecasts from an array of econometric models (including GARCH, CAViaR-EVT, dynamic GAS and realized range models), we leverage shrinkage and neural network models to form combination forecasts. Such machine-learned VaR and ES forecasts outperform a set of competing forecast combination approaches in terms of statistical accuracy as well as economical relevance in dynamic tail risk protection strategies

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Stochastic Mortality Modelling and Management of Longevity Risk with Pricing and Reserving Applications to Annuity Products

Over the past decades, the life insurance sector has been faced with a number of challenges that emerged as a result of the growing longevity and stagnating birth rates for highly developed societies. In the field of actuarial application one could therefore ask for the implications of (long-term) mortality trends and (short-term) population fluctuation on an insurer's pricing and reserving of pension contracts, particularly in interaction with uncertainty in the capital markets. The first part of the thesis focuses on the mathematical description and projection of the mortality of homogeneous populations or insurance cohorts. Besides a survey of the most important representatives, a comprehensive analysis and comparison of stochastic and deterministic mortality forecasting models is carried out. In the second part a full stochastic model approach for two typical old-age provision products is set up and applied in terms of a management of longevity risk. On the one hand, a deferred conventional life annuity is analysed with regard to the combined effects of stochastic mortality and interest rates on different premium principles and risk capital allocation. On the other hand, a modern unit-linked annuity insurance, namely a deferred variable annuity contract, with an additional guaranteed minimum death benefit during the deferment period and a minimum income benefit at retirement is discussed. Mathematically, the guarantees represent options on the greater of the net asset value and a predetermined insurance benefit. For this reason, the existence and uniqueness of an extra fair percentage guarantee charge is proven. Furthermore, a sensitivity analysis of the fair charge and risk neutral option prices concerning different model parameters is considered and several profitability and risk measures are determined