1,582 research outputs found

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

    Get PDF
    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

    Applying artificial intelligence to determination of legal age of majority from radiographic

    Get PDF
    Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into 'under-18' and 'over-18' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.</p

    Optimizing Asthma Treatment in the Practice Setting: A Quality Improvement Project

    Get PDF
    Technology is rapidly changing the way that clinicians provide care to patients. The use of smart phrases in electronic health records (EHRs) has been shown to improve efficiency amongst clinicians and provide easy access to large amounts of information for clinical decision-making. One area of medicine where treatment algorithms are particularly complex and there has been recent landmark changes to treatment algorithms, is asthma. The 2019 Global Institute for Asthma (GINA) guidelines changed the standard of care for asthma, and the principal investigator (PI) sustained a strong interest in whether the guidelines had been implemented into clinical EHRs effectively. A needs assessment was conducted with clinicians at the clinical site which revealed that there were few useful and current asthma smart phrases existing in the EHR. This technological quality improvement project was created to bridge the gap from science to practice. Based on direct stakeholder feedback, three smart phrases were created, and one existing smart phrase edited, using the Global Institute for Asthma 2022 guidelines. The smart phrases were distributed for review by all clinicians at at the clinical site. After several revisions, the final suite of smart phrases was presented to all stakeholders in a staff meeting. An anonymous survey was employed to measure the efficacy of the intervention, with 100% of participants expressing increased satisfaction with the new asthma smart phrases than with the existing asthma smart phrases

    Carbon Capture Storage Implemented On Flexible Power Plants And Their Grid Impacts

    Get PDF
    The energy grid is under a constant state of evolution from outside factors such as the rise ofrenewable energy in the form of solar and wind generation as well as competing with the changes in weather patterns from climate change leading to significant storms like the arctic event of 2022. Thermal power plants are greatly impacted by these changes. As a significant contributor of CO2 emissions, thermal power plants play an important role in emerging green technology policies to help the efforts of climate change. There is a significant cost associated with this change, however, as thermal plants are some of the cheapest to implement and have provided baseline power supply for decades for both first world countries and especially those still under development. An emerging technology that can bridge the gap between these two ideologies is carbon capture systems, which take the emissions normally emitted to the environment and allow them to be captured for storage later. Carbon Capture Storage (CCS) is not without its tradeoffs though. There is a significant change, of about 15%, in the power generation capability of the plant it is installed on, which can have drastic impacts on a grid already impacted by so many other changes. To combat these difficulties, flexible variations of thermal plants are being brought forth, but the capabilities of these options compared to the traditional is notional. This study used a simulation-based approach to quantify these impacts. The simulations evaluated traditional thermal plants, their flexible counterparts, renewable energy hosting, and the capabilities of both in regard to carbon capture installations. Using results from the developed model, the benefits of pairing flexible plants with dynamic CCS units show that additional renewable energy and load can be hosted, as well as show the limitations that those parameters exhibited

    Анализ временных рядов и прогнозирование цен на золото (XAUUSD) с использованием машинного обучения

    Get PDF
    Каждый день происходит множество розничных и коммерческих банковских сделок с около 11 миллиардами золота. Чтобы получить прибыль на этом волатильном рынке, нам необходимо разработать различные инструменты для прогнозирования и анализа будущих цен, чтобы принимать соответствующие решения. В своем исследовании я использовал исторические данные о золоте, полученные от банковской группы Dukascopy Swiss и использовал инструменты искусственного интеллекта, такие как LSTM и ARIMA, для прогнозирования будущих цен.Every day there are many retail and commercial banking trades around 11B of gold. To make a profit in this violent market we need to develop different tools to predict or analyze future prices to make suitable decisions. In my research, I used the historical data of gold and I obtained this data from Dukascopy Swiss banking group and used AI tools like LSTM and Arima to predict future prices

    Automatic Generation of Personalized Recommendations in eCoaching

    Get PDF
    Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Thermal comfort in indoor sports facilities and the adequacy of demand-controlled ventilation

    Get PDF
    In sports environments, the unstable occupancy density together with the increased heat and moisture emissions from the sports players imposes an extra duty on the ventilation system. This thesis studies the human thermal response and the adequacy of demand-controlled ventilation in indoor sports facilities as part of the LIIKU project, which investigates the indoor air environment therein and the possible effects on the well-being and health of the occupants. Several approaches to assess and predict the thermal sensation of sports players were reviewed, including the Fanger's predicted mean vote model. Amongst those, thermo-physiological comfort models were shown to have strong potential to be applied in sports environments where the human activity level is high and constantly changing. Dynamic thermal sensation (DTS) vote from the IESD-FIALA model was proved to be more reliable and superior to the predicted mean vote in sports-related situations. Ventilation requirements and common practices in indoor sports facilities were investigated, along with the use of demand-controlled ventilation solutions (DCV). The pros and cons of DCV were discussed, particularly the operational challenges. The Latorkartano sports hall in Helsinki was chosen to be studied more closely with an indoor climate and energy simulation in IDA ICE software. In order to predict the thermal sensation of the occupants, the FIALA-IESD model was applied and the DTS was calculated. It was found that reducing the temperature setpoint by 1 to 2oC compared to the current Finnish regulation, which is at 17 to 16oC, could save from 13 to 18% in heating energy of this sports hall without affecting the thermal comfort of the sports players

    3D Visualisation - An Application and Assessment for Computer Network Traffic Analysis

    Full text link
    The intent of this research is to develop and assess the application of 3D data visualisation to the field of computer security. The growth of available data relating to computer networks necessitates a more efficient and effective way of presenting information to analysts in support of decision making and situational awareness. Advances in computer hardware and display software have made more complex and interactive presentation of data in 3D possible. While many attempts at creation of data-rich 3D displays have been made in the field of computer security, they have not become the tool of choice in the industry. There is also a limited amount of published research in the assessment of these tools in comparison to 2D graphical and tabular approaches to displaying the same data. This research was conducted through creation of a novel abstraction framework for visualisation of computer network data, the Visual Interactive Network Analysis Framework (VINAF). This framework was implemented in software and the software prototype was assessed using both a procedural approach applied to a published forensics challenge and also through a human participant based experiment. The key contributions to the fields of computer security and data visualisation made by this research include the creation of a novel abstraction framework for computer network traffic which features several new visualisation approaches. An implementation of this software was developed for the specific cybersecurity related task of computer network traffic analysis and published under an open source license to the cybersecurity community. The research contributes a novel approach to human-based experimentation developed during the COVID-19 pandemic and also implemented a novel procedure-based testing approach to the assessment of the prototype data visualisation tool. Results of the research showed, through procedural experimentation, that the abstraction framework is effective for network forensics tasks and exhibited several advantages when compared to alternate approaches. The user participation experiment indicated that most of the participants deemed the abstraction framework to be effective in several task related to computer network traffic analysis. There was not a strong indication that it would be preferred over existing approaches utilised by the participants, however, it would likely be used to augment existing methods

    Thermo-Electro-Optical Properties of Disordered Nanowire Networks

    Get PDF
    Metallic nanowire networks are promising candidates for next-generation transparent conductors, owing to their exceptional electrical and thermal conductivity, high optical transparency, and mechanical flexibility. A nanowire network is a disordered arrangement of nanowires that exhibits no discernible long-range order or periodicity. Previous studies have placed significant emphasis on the individual analysis of electrical resistance, optical transmission, and thermal conduction in diverse network materials. Nonetheless, insufficient focus has been devoted to comprehending the relationship between the multiple extrinsic and intrinsic variables that characterize a disordered nanowire network (or an ensemble of them) and the trade-offs that arise when investigating the system response trio of namely electrical/ optical/thermal natures. This thesis presents a comprehensive computational study that exclusively employs theoretical and numerical models to examine the thermoelectric and optical characteristics of two types of disordered metallic nanowire networks: (i) junction-based random nanowire networks and (ii) seamless random nanowire networks. The raw materials that compose their nanowires are metals namely, silver, gold, copper, and aluminium and we used a variety of computational tools to obtain prominent physical quantities that infer the network’s performance such as sheet (electrical) resistance, optical transmission, and temperature variation. A range of adjustable parameters, including those pertaining to geometrical structure in device design, have been systematically tuned in order to conduct a figure of merit analysis with respect to thermal and electrical conduction, and optical transmission of the network materials. Moreover, we obtained local current and temperature mappings that detail the conduction mechanisms used by the networks to propagate signals through their disordered skeleton. We verified that, under certain conditions, junction-based and seamless nanowire networks fall into the same temperature distribution mechanisms that can be generally described with Weibull probability density functions. This study offers valuable insights into the electrical/optical/thermal performance of disordered nanowire networks prone to transparent conductor applications

    Quantum critical dynamics in a 5000-qubit programmable spin glass

    Full text link
    Experiments on disordered alloys suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Due to the importance of spin glasses as a paradigmatic computational testbed, reproducing this phenomenon in a programmable system has remained a central challenge in quantum optimization. Here we achieve this goal by realizing quantum critical spin-glass dynamics on thousands of qubits with a superconducting quantum annealer. We first demonstrate quantitative agreement between quantum annealing and time-evolution of the Schr\"odinger equation in small spin glasses. We then measure dynamics in 3D spin glasses on thousands of qubits, where simulation of many-body quantum dynamics is intractable. We extract critical exponents that clearly distinguish quantum annealing from the slower stochastic dynamics of analogous Monte Carlo algorithms, providing both theoretical and experimental support for a scaling advantage in reducing energy as a function of annealing time
    corecore