Machine Learning Project in Finance The main goal for our Master Project is to implement different machine learning algorithms (including reinforcement learning and deep learning) in finance. We are mainly interested in testing the performance of these algorithms (with comparison to available approaches) in:1.     Risk Assessment - assessing and managing risks in investment portfolios2.     Portfolio Optimization - using algorithms to construct diversified portfolios that maximize returns while managing risksSome of the Learning Objectives of the project could be:-        Use of ML, historical data and probability statistics to model potential scenarios and make predictive analyses-        Consider fundamental ML techniques such as regression, classification, clustering, dimensionality reduction, and reinforcement learning-        Evaluate the performance of advanced machine learning algorithms in identifying patterns in market data to make more accurate predictions about asset performance-        Predict future volatility levels and other risk assessment measures in financial markets (using models such as GARCH and LSTM)-        Evaluate the effectiveness of portfolio optimization methods in dynamically adjusting asset allocations based on forecasts of returns and risk-        Compare machine learning techniques and financial modeling tools for quantitative analysis in the context of asset allocation and market forecasting-        Compare the limitations and benefits of existing techniques and ML models

We believe we can help companies elevate their qualitative solutions and make better decisions. By aligning our academic pursuits with real-world challenges, our thesis is designed to offer companies innovative approaches and practical insights into enhancing their processes and operations. We aim to deliver tangible benefits, such as improved risk assessment accuracy and more refined portfolio optimization strategies.Our collaboration doesn't only revolve around the promise of groundbreaking research; it's about translating these findings into practical tools that can augment decision-making processes. By leveraging the predictive power of historical data, probability statistics, and cutting-edge machine learning methodologies, our project aims to create more informed analyses of potential market scenarios.  We aim to bring forward innovative solutions and intelligent strategies that minimize risks while maximizing returnsIn essence, our collaboration offers not only academic rigor but a value proposition.

Our shared interest in this topic arose from our parallel academic paths, having been through DTU's finance study program. Although my degree is in finance, my colleague Dušana is studying financial engineering at EPFL in Switzerland as part of an exchange program. Working together on multiple courses, most notably Financial Engineering, revealed a good working relationship and a mutual interest in the thesis subject. Together, we hope to take advantage of this particular area as a way to deepen our understanding of the complex worlds of technology and finance, as well as to further develop our skills and knowledge.

Our ability to combine technical expertise and commercial savvy to create a cohesive and adaptable team is what makes us appealing. We have successfully collaborated on five different courses together, demonstrating our ability to blend the perspectives of both fields nicely. Our strength comes from combining technical and business expertise, but it also comes from our ambition, willingness to learn and improve and our dedication to what we work on at each point in time. This dedication, along with our collective expertise, puts us in a position to provide a unique viewpoint and value that businesses would find difficult to turn down.