1,636 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

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Data-efficient neural network training with dataset condensation

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    The state of the art in many data driven fields including computer vision and natural language processing typically relies on training larger models on bigger data. It is reported by OpenAI that the computational cost to achieve the state of the art doubles every 3.4 months in the deep learning era. In contrast, the GPU computation power doubles every 21.4 months, which is significantly slower. Thus, advancing deep learning performance by consuming more hardware resources is not sustainable. How to reduce the training cost while preserving the generalization performance is a long standing goal in machine learning. This thesis investigates a largely under-explored while promising solution - dataset condensation which aims to condense a large training set into a small set of informative synthetic samples for training deep models and achieve close performance to models trained on the original dataset. In this thesis, we investigate how to condense image datasets for classification tasks. We propose three methods for image dataset condensation. Our methods can be applied to condense other kinds of datasets for different learning tasks, such as text data, graph data and medical images, and we discuss it in Section 6.1. First, we propose a principled method that formulates the goal of learning a small synthetic set as a gradient matching problem with respect to the gradients of deep neural network weights that are trained on the original and synthetic data. A new gradient/weight matching loss is designed for robust matching of different neural architectures. We evaluate its performance in several image classification benchmarks and explore the usage of our method in continual learning and neural architecture search. In the second work, we propose to further improve the data-efficiency of training neural networks with synthetic data by enabling effective data augmentation. Specifically, we propose Differentiable Siamese Augmentation and learn better synthetic data that can be used more effectively with data augmentation and thus achieve better performance when training networks with data augmentation. Experiments verify that the proposed method obtains substantial gains over the state of the art. While training deep models on the small set of condensed images can be extremely fast, their synthesis remains computationally expensive due to the complex bi-level optimization. Finally, we propose a simple yet effective method that synthesizes condensed images by matching feature distributions of the synthetic and original training images when being embedded by randomly sampled deep networks. Thanks to its efficiency, we apply our method to more realistic and larger datasets with sophisticated neural architectures and obtain a significant performance boost. In summary, this manuscript presents several important contributions that improve data efficiency of training deep neural networks by condensing large datasets into significantly smaller synthetic ones. The innovations focus on principled methods based on gradient matching, higher data-efficiency with differentiable Siamese augmentation, and extremely simple and fast distribution matching without bilevel optimization. The proposed methods are evaluated on popular image classification datasets, namely MNIST, FashionMNIST, SVHN, CIFAR10/100 and TinyImageNet. The code is available at https://github.com/VICO-UoE/DatasetCondensation

    Dataset Condensation with Distribution Matching

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    Metacognitive Decisions on Decision Accuracy: Confidence Judgment and Changes of Mind

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    Even in the absence of external feedback, humans are capable of subjectively estimating the accuracy of their own decisions, resulting in a sense of confidence that a decision is correct. While decision confidence has been proposed to be closely related to other metacognitive judgments, including error awareness (i.e., awareness that a decisions error has occurred) and changes of mind (i.e., reversal of previously made decisions), their relationships so far remain unclear. The current project aimed to investigate how confidence could be related to metacognitive judgments from two perspectives. First, Studies 1 and 2 investigated how confidence and changes of mind were affected by changes in different stimulus properties, particularly absolute evidence strength. In a brightness judgment task, participants were presented with two flickering, grayscale squares and required to select the square that appeared brighter. After each trial, participants reported their subjective accuracy on a rating scale ranging from “surely incorrect” to “surely correct”. Results showed that with stronger absolute evidence (i.e., increased overall luminance across both squares), confidence was increased and the proportion of changes of mind trials was reduced. These consistent changes support the hypothesis that higher confidence could contribute to less frequent changes of mind. Second, Study 3 investigated the relationships between confidence and the event-related potential (ERP) components of the centro-parietal potential (CPP) and the error positivity (Pe), which have been respectively proposed to be indexes of pre- and post-decisional evidence accumulation processes. In the same brightness judgment task, it was found that the relationships between confidence and these two ERP components depended on decision accuracy: Confidence was positively related to CPP amplitudes in correct trials, but negatively related to Pe amplitudes in error trials. These findings suggest that confidence in correct and error decisions involve different pre- and post- decisional processes. Overall, the current findings suggest that (a) confidence could serve as a basis of changes of mind, and (b), confidence in correct and erroneous decisions was differentially related to pre- and post-decisional ERP indexes of evidence accumulation. Taken together, they suggest that confidence might emerge during decision formation and could, with the contribution from post-decisional processes, serve as a basis of changes of mind

    Mining Butterflies in Streaming Graphs

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    This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection. sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals

    Multimodal Dataset Distillation for Image-Text Retrieval

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    Dataset distillation methods offer the promise of reducing a large-scale dataset down to a significantly smaller set of (potentially synthetic) training examples, which preserve sufficient information for training a new model from scratch. So far dataset distillation methods have been developed for image classification. However, with the rise in capabilities of vision-language models, and especially given the scale of datasets necessary to train these models, the time is ripe to expand dataset distillation methods beyond image classification. In this work, we take the first steps towards this goal by expanding on the idea of trajectory matching to create a distillation method for vision-language datasets. The key challenge is that vision-language datasets do not have a set of discrete classes. To overcome this, our proposed multimodal dataset distillation method jointly distill the images and their corresponding language descriptions in a contrastive formulation. Since there are no existing baselines, we compare our approach to three coreset selection methods (strategic subsampling of the training dataset), which we adapt to the vision-language setting. We demonstrate significant improvements on the challenging Flickr30K and COCO retrieval benchmark: the best coreset selection method which selects 1000 image-text pairs for training is able to achieve only 5.6% image-to-text retrieval accuracy (recall@1); in contrast, our dataset distillation approach almost doubles that with just 100 (an order of magnitude fewer) training pairs.Comment: 28 pages, 11 figure
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