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<h2 style="color: #ffffff; background: #d6000d; padding-left: 10px; text-align: left;"><strong>FIN7028 Advanced Financial Data Analytics</strong></h2>
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<h2 class="ig-header-title element_toggler">📌 Introduction</h2>
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<p>Statistics is the science of <strong>uncertainty and variation</strong>. &nbsp;This course is about the statistics used in the Advanced analysis of time-series data used for economic and financial problems. This course aims to teach students to apply time series financial econometrics techniques sensibly in the context of real-world empirical problems.</p>
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<h2 class="ig-header-title element_toggler">📣 Module Coordinator</h2>
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<div class="">Your module coordinator is Barry Quinn (<a class="inline_disabled" href="mailto:b.quinn@qub.ac.uk" target="_blank" rel="noopener">b.quinn@qub.ac.uk</a>).</div>
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<h2 class="ig-header-title element_toggler">🔎 Module Contents</h2>
<p>Aims to deepen participants' understanding of financial predictions and decision-making by exploring the revolutionary impact of combining econometrics, Bayesian methods, and machine learning in financial analytics. &nbsp;This course integrates machine learning, classical financial time series econometrics, and Bayesian methods to tackle complex financial problems characterised by uncertainty and conflicting objectives. It does this by covering:</p>
<p><strong>Financial Markets as Predictive Systems</strong>: It emphasises understanding market prices as reflections of predictions about future asset payoffs and risks, highlighting the predictive nature of financial markets</p>
<p><strong>Classical Econometrics in Finance</strong>: Covers traditional financial time series models such as ARIMA and GARCH, acknowledging their strengths in historical data analysis and noting limitations with complex modern datasets.</p>
<p><strong>Bayesian Methods for Financial Analytics</strong>: Introduces Bayesian methods for their probabilistic approach, enabling the incorporation of prior knowledge and continuous updating with new data, which is crucial for adapting to dynamic financial markets.</p>
<p><strong>Machine Learning's Role:</strong> Explores the role of machine learning in processing large datasets and accurately modelling the complexities of financial markets, complementing traditional econometrics and Bayesian approaches.</p>
<p><strong>Theoretical Foundations</strong>: Discusses machine learning and Bayesian methods' foundational principles, including overparameterisation, regularisation, and probabilistic modelling, to tackle high-dimensional data and uncertainty.</p>
<p><strong>Synergy of Approaches</strong>: Highlights the comprehensive toolkit formed by combining econometrics, Bayesian methods, and machine learning to address the intricacies of financial markets effectively.</p>
<p><strong>Growth Mindset in Learning Financial Analytics</strong>: Advocates for adopting a growth mindset for learning advanced financial data analytics, emphasising embracing challenges, persisting through setbacks, leveraging criticism, and finding lessons in others' success.</p>
<p><strong>Benefits of a Growth Mindset</strong>: Details the advantages of a growth mindset, such as improved performance, resilience, collaboration, adaptability, and a long-term focus on sustainable learning habits.</p>
<p><strong>Developing a Growth Mindset</strong>: Offers practical suggestions to foster a growth mindset, including setting clear goals, tracking progress, seeking new learning opportunities, engaging with peers, and viewing mistakes as learning opportunities.</p>
<p><strong>Empowering Learners</strong>: The course is designed to equip participants with the necessary insights and tools to navigate the sophisticated realm of financial analytics, encouraging a lifelong commitment to learning and development in the field.</p>
<p><a title="Please read carefully" href="https://canvas.qub.ac.uk/courses/25549/pages/please-read-carefully" data-course-type="wikiPages" data-published="true" data-api-endpoint="https://canvas.qub.ac.uk/api/v1/courses/25549/pages/please-read-carefully" data-api-returntype="Page">Follow this link to access all the course materials</a></p>
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<h2 class="ig-header-title element_toggler">⭐ What are my learning outcomes?</h2>
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<li>We are rethinking econometrics as a science of uncertainty and variation.</li>
<li>Understanding and exploiting the stylised facts of financial data to make more intelligent economic predictions.</li>
<li>The appropriate use of dashboard analytics in advanced financial data analytics.</li>
<li>Introduction to open science principles of advanced financial data analytics. (see skills)</li>
<li>Learn to be experimental, iterative, curious, and resilient in analytic practices.</li>
<li>Work independently or in groups towards an empirical goal.</li>
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<h2 class="ig-header-title element_toggler">🏅 How will my skills be enhanced?</h2>
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<p>This module will help you to develop the following skills:</p>
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<li>Introduction to the &ldquo;tidy&rdquo; data principles of Hadley Wickham in Posit.</li>
<li>Introduction to literate programming using the <a class="inline_disabled" href="https://style.tidyverse.org" target="_blank" rel="noopener">&ldquo;tidyverse&rdquo; programming style guide</a>.</li>
<li>Introduction to cloud computing using <a class="instructure_file_link inline_disabled" href="https://rstudio.com/products/team/" target="_blank" rel="noopener">Posit Professional Products</a>.</li>
<li>Introduction to reproducible research using <a class="inline_disabled" href="https://quarto.org" target="_blank" rel="noopener">open-source scientific and technical publishing system</a> and git version control.</li>
<li>Introduction to <a class="inline_disabled" href="https://quarto.org/docs/interactive/" target="_blank" rel="noopener">interactivity</a> in advanced financial data analytics.</li>
<li>The ability to work independently to glean meaning from noisy financial data.</li>
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<h2 class="ig-header-title element_toggler">✅ How will the module be assessed?</h2>
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<p>Assessment of this module will comprise:</p>
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<td style="width: 20%; height: 29px;"><strong>Assessment Type</strong></td>
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<td style="width: 70%; height: 29px;"><strong>Description</strong></td>
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<td style="width: 20%; height: 29px;">Assignment</td>
<td style="width: 10%; height: 29px;">50</td>
<td style="width: 70%; height: 29px;">Critical Essay</td>
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<td style="width: 20%; height: 29px;">Computer Practical</td>
<td style="width: 10%; height: 29px;">50</td>
<td style="width: 70%; height: 29px;">Computer-based exam</td>
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<h2 class="ig-header-title element_toggler">📚 Self-Study</h2>
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<li>How videos are provided for step-by-step guides to analytics task</li>
<li>Interactive analytics tutorials have been created</li>
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<p>Students will use R statistical programming and have access to state-of-the-art cloud computing <a class="inline_disabled" href="https://www.rstudio.com/products/cloud/" target="_blank" rel="noopener">Posit Cloud</a> technologies via the <a class="inline_disabled" title="Link" href="https://www.quinference.com/qrap/" target="_blank" rel="noopener">Q-RaP (Queen's management school Remote Analytics Platform)</a>.&nbsp;</p>
<p><iframe title="YouTube video player" src="https://www.youtube.com/embed/videoseries?list=PL9HYL-VRX0oSn7tBLDiSt4Vnyk3yB6ipA" width="560" height="315" allowfullscreen="allowfullscreen" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" data-mce-fragment="1"></iframe></p>
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<h2 class="ig-header-title element_toggler">📝 Additional Guidance</h2>
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<li>Weekly one-to-one coding clinics</li>
<li>The companion live textbook is written specifically for this course (for in-detail course material)</li>
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