272 research outputs found

    Analysing behavioural factors that impact financial stock returns. The case of COVID-19 pandemic in the financial markets.

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    This thesis represents a pivotal advancement in the realm of behavioural finance, seamlessly integrating both classical and state-of-the-art models. It navigates the performance and applicability of the Irrational Fractional Brownian Motion (IFBM) model, while also delving into the propagation of investor sentiment, emphasizing the indispensable role of hands-on experiences in understanding, applying, and refining complex financial models. Financial markets, characterized by ’fat tails’ in price change distributions, often challenge traditional models such as the Geometric Brownian Motion (GBM). Addressing this, the research pivots towards the Irrational Fractional Brownian Motion Model (IFBM), a groundbreaking model initially proposed by (Dhesi and Ausloos, 2016) and further enriched in (Dhesi et al., 2019). This model, tailored to encapsulate the ’fat tail’ behaviour in asset returns, serves as the linchpin for the first chapter of this thesis. Under the insightful guidance of Gurjeet Dhesi, a co-author of the IFBM model, we delved into its intricacies and practical applications. The first chapter aspires to evaluate the IFBM’s performance in real-world scenarios, enhancing its methodological robustness. To achieve this, a tailored algorithm was crafted for its rigorous testing, alongside the application of a modified Chi-square test for stability assessment. Furthermore, the deployment of Shannon’s entropy, from an information theory perspective, offers a nuanced understanding of the model. The S&P500 data is wielded as an empirical testing bed, reflecting real-world financial market dynamics. Upon confirming the model’s robustness, the IFBM is then applied to FTSE data during the tumultuous COVID-19 phase. This period, marked by extraordinary market oscillations, serves as an ideal backdrop to assess the IFBM’s capability in tracking extreme market shifts. Transitioning to the second chapter, the focus shifts to the potentially influential realm of investor sentiment, seen as one of the many factors contributing to fat tails’ presence in return distributions. Building on insights from (Baker and Wurgler, 2007), we examine the potential impact of political speeches and daily briefings from 10 Downing Street during the COVID-19 crisis on market sentiment. Recognizing the profound market impact of such communications, the chapter seeks correlations between these briefings and market fluctuations. Employing advanced Natural Language Processing (NLP) techniques, this chapter harnesses the power of the Bidirectional Encoder Representations from Transformers (BERT) algorithm (Devlin et al., 2018) to extract sentiment from governmental communications. By comparing the derived sentiment scores with stock market indices’ performance metrics, potential relationships between public communications and market trajectories are unveiled. This approach represents a melding of traditional finance theory with state-of-the-art machine learning techniques, offering a fresh lens through which the dynamics of market behaviour can be understood in the context of external communications. In conclusion, this thesis provides an intricate examination of the IFBM model’s performance and the influence of investor sentiment, especially under crisis conditions. This exploration not only advances the discourse in behavioural finance but also underscores the pivotal role of sophisticated models in understanding and predicting market trajectories

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    On the Solvability of Inverse Problems Arising From the Two-Layer Lorenz '96 System

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    The two-layer Lorenz ’96 model consists of two linearly coupled systems of ODEs with two distinct time scales. This simple model was designed to reflect the patterns of local instability and growth represented by the interaction of planetary and synoptic dynamics with mesoscale motions and convective clouds. Under the assumption that the large-amplitude variables in the first layer are fully observed, we consider two inverse problems. The first is to estimate the unobserved values of the second layer in the case where the dynamics are known; the second is to solve for both the unobserved small scales and the unknown dynamics governing them. For simplicity, we assume that the dynamics governing the small scales take on a parameterized form with a single unknown parameter. In this case, our goal is to simultaneously estimate that parameter and the unobserved small scales.Our study begins with a verification that the dynamics in the two-layer Lorenz ’96 model are dynamically interesting enough to merit further investigation. We then develop algorithms to solve the two types of inverse problems mentioned above and find theoretical conditions under which those algorithms allow us to estimate the unobserved small scales and, optionally, the unknown parameter. We begin by proving that directly inserting the observations into the model as it is being integrated in time results in synchronization that allows recovery of the unobserved small scales over time. We then make a novel use of derivative information—i.e., the rate at which the observations change over time—to obtain new forms of data assimilation that allow solving the inverse problem faster, under less stringent conditions, and when the parameter governing the small scales is unknown.Throughout we confirm our theoretical results with numerical experiments and remark that solving the inverse problem numerically turns out to be possible even when the system does not satisfy the hypotheses required by our theory

    Supervised Learning in Time-dependent Environments with Performance Guarantees

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    In practical scenarios, it is common to learn from a sequence of related problems (tasks). Such tasks are usually time-dependent in the sense that consecutive tasks are often significantly more similar. Time-dependency is common in multiple applications such as load forecasting, spam main filtering, and face emotion recognition. For instance, in the problem of load forecasting, the consumption patterns in consecutive time periods are significantly more similar since human habits and weather factors change gradually over time. Learning from a sequence tasks holds promise to enable accurate performance even with few samples per task by leveraging information from different tasks. However, harnessing the benefits of learning from a sequence of tasks is challenging since tasks are characterized by different underlying distributions. Most existing techniques are designed for situations where the tasks’ similarities do not depend on their order in the sequence. Existing techniques designed for timedependent tasks adapt to changes between consecutive tasks accounting for a scalar rate of change by using a carefully chosen parameter such as a learning rate or a weight factor. However, the tasks’ changes are commonly multidimensional, i.e., the timedependency often varies across different statistical characteristics describing the tasks. For instance, in the problem of load forecasting, the statistical characteristics related to weather factors often change differently from those related to generation. In this dissertation, we establish methodologies for supervised learning from a sequence of time-dependent tasks that effectively exploit information from all tasks, provide multidimensional adaptation to tasks’ changes, and provide computable tight performance guarantees. We develop methods for supervised learning settings where tasks arrive over time including techniques for supervised classification under concept drift (SCD) and techniques for continual learning (CL). In addition, we present techniques for load forecasting that can adapt to time changes in consumption patterns and assess intrinsic uncertainties in load demand. The numerical results show that the proposed methodologies can significantly improve the performance of existing methods using multiple benchmark datasets. This dissertation makes theoretical contributions leading to efficient algorithms for multiple machine learning scenarios that provide computable performance guarantees and superior performance than state-of-the-art techniques

    Negative binomial mixed model neural network for modeling of pulmonary tuberculosis risk factors in West Java provinces

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    Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts

    Machine learning in portfolio management

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    Financial markets are difficult learning environments. The data generation process is time-varying, returns exhibit heavy tails and signal-to-noise ratio tends to be low. These contribute to the challenge of applying sophisticated, high capacity learning models in financial markets. Driven by recent advances of deep learning in other fields, we focus on applying deep learning in a portfolio management context. This thesis contains three distinct but related contributions to literature. First, we consider the problem of neural network training in a time-varying context. This results in a neural network that can adapt to a data generation process that changes over time. Second, we consider the problem of learning in noisy environments. We propose to regularise the neural network using a supervised autoencoder and show that this improves the generalisation performance of the neural network. Third, we consider the problem of quantifying forecast uncertainty in time-series with volatility clustering. We propose a unified framework for the quantification of forecast uncertainty that results in uncertainty estimates that closely match actual realised forecast errors in cryptocurrencies and U.S. stocks

    Multidimensional Time Series Methods for Economics and Finance

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    Questa tesi mira ad affrontare le questioni inferenziali e interpretative nei modelli ad alta dimensione e multidimensionali nel contesto dell'Economia e della Finanza. La crescente integrazione economica e finanziaria ha reso di fondamentale importanza considerare i Paesi e i Mercati Finanziari come un'unica, grande e interconnessa entitĂ . Le principali sfide indotte da questo quadro riguardano la stima e l'interpretazione di ampi Panel data, in cui le unitĂ  possono essere rappresentate da paesi o attivitĂ  finanziarie, osservate attraverso diversi indicatori nel tempo. Questa tesi propone tecniche di stima Bayesiana per nuovi modelli matriciali e tensoriali e utilizza tecniche della Teoria dei Grafi per facilitare l'interpretazione di network ad alta dimensione. I contributi sono presentati in tre capitoli. Nel Capitolo 2, vengono proposti approcci della Teoria dei Grafi per studiare le strutture e le interazioni in Network direzionali e pesati. Nel Capitolo 3, viene proposto un approccio Bayesiano di variable selection per gestire il problema della sovrapparametrizzazione nei modelli di Autorregressione Matriciale di grandi dimensioni. Nel Capitolo 4, viene esplorata la relazione dinamica tra rendimenti, volatilitĂ  e sentiment nel settore delle criptovalute attraverso un modello Autoregressivo Matriciale, che rappresenta il primo tentativo di considerare i dati sugli asset finanziari come strutture multidimensionali.This thesis aims to address the inferential and interpretational issues in high and multi-dimensional models in the context of Economics and Finance. The growing economic and financial integration has made imperative the need to conceive Countries and Financial Markets as a single, large, interconnected entity. The main challenges induced by this framework concern the estimation and interpretation of large panels, where units can be represented by countries or assets, observed via several indicators across time. This thesis proposes Bayesian estimation techniques for novel matrix and tensor-valued models and employs new methodological tools from Graph Theory to facilitate interpretation of high-dimensional networks. The contributions are presented in three chapters. In Chapter 2, Graph Theory approaches are proposed to study the structures and interactions of weighted directed networks of multivariate time series observations/relationships. In Chapter 3, a Bayesian variable selection approach is proposed to handle the over-parametrization problem in large Matrix Autoregressive models. In Chapter 4, the dynamic relationship among returns, volatility, and sentiment in the cryptocurrency class is explored through a Bayesian Matrix Autoregressive model, which is the first attempt to consider financial asset data as multi-dimensional structures

    Nonparametric data segmentation in multivariate time series via joint characteristic functions

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    Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series termed NP-MOJO. By considering joint characteristic functions between the time series and its lagged values, NP-MOJO is able to detect change points in the marginal distribution, but also those in possibly non-linear serial dependence, all without the need to pre-specify the type of changes. We show the theoretical consistency of NP-MOJO in estimating the total number and the locations of the change points, and demonstrate the good performance of NP-MOJO against a variety of change point scenarios. We further demonstrate its usefulness in applications to seismology and economic time series
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