This a group for Quantitative Finance and Machine Learning. The AI impact on Investment Management and Capital Markets has never been profound as it is in the present times. AI has certainly taken the finance world, especially banking and investment services by storm. Artificial Intelligence is a suite that comprises of a set of tools- like machine learning, natural language processing, deep neural networks etc. that are impacting almost every industry, in the most efficient way. At its core, AI is essentially a set of technologies that are meant to augment or perform human tasks, without their intervention. Over a few decades, these technologies have evolved to sense, learn, comprehend, and act. Such a progression now enable systems and software to acquire, identify, recognize, and an analytical database (both structured and unstructured), derive insights, envision the process, and then put them into the real-time use cases. In context to Investment Management and capital market, AI is enabling the machines to do algorithmic trading, qualitative analysis, automate trade execution, and manage risk. What turns AI set a disruption in almost every industry is its decision making ability, which is based on cognitive learning. In contrast to perpetuating up on the programmed responses, AI overcome the limitations, complexities, and challenges by teaching the system to learn through past experiences.
Despite being the hot buzzword these days, machine learning is still fairly misunderstood. It is not artificial intelligence itself, but rather a form of it in which computers fed extremely large data sets are able to learn as changes in that data occur without being explicitly programmed to do so. The data is just one part of the approach, what can be more challenging is making machine learning and data science a core capability among companies so that they instinctively take internal and external data sets and interpret it for patterns, risks, and opportunities. Machine learning is shifting the way finance is looked at into a more Quantitative Finance focus with machine learning at the forefront. It is critical you understand this evolution and below I’ve just put down some discussion topics around machine learning in Quantitative Finance that I hope might be of interest in this meetup group.
· The development and disruption of asset management through machine learning techniques
· Applications of AI and machine learning to finance
· Alternative Data in Quantitative Finance
· Understanding the latest developments in NLP
· Introducing Quantitative methods to fundamental approach in Investment Management
· Cutting edge AI in Quantitative Finance
· Utilizing a blended man and machine approach to risk management
· Aligning your risk modelling, investment strategy and Portfolio Optimization techniques using machine learning
· Machine Learning for security selection and danger of overfitting
· Deploying an end to end AI and ML process. From strategy to execution, where and how can AI be used in your end to end process. Practical insight into the challenges and opportunities of incorporating ML techniques through various stages in the investment process
· How investment management firms can leverage their quant and data science teams to build new business models and add value.
· The outlook for emerging quantitative concepts as a tool to find edge.
· Uncovering and assessing risk factors using machine learning
· The journey of the cloud for investment management
· Quantum computing: Closer than ever. Understanding the landscape, risks and opportunities.
· Opportunities and challenges of machine learning in Quantitative Investment and Wealth Management
· Deep Learning in Finance
· Machine Learning models for corporate bond default, recovery in default and relative value
· Applying machine learning to evaluate systemic risk.
· What is the current state of utilisation of machine learning in Quantitative Finance
· What are the distinct features of machine learning in finance compared to other industries.
· How much value does machine learning add over and above the ‘classical’ techniques such as linear regression, convex optimisation etc.
· High-Performance computing (HPC) as a major enabler of machine learning in Quantitative Finance
· Potential pitfalls in using machine learning in Quantitative Finance
· Insights machine learning can offer into the analysis of financial time series
· The potential of Deep Learning in Quantitative Finance
· How machine learning and HPC will transform Quantitative Finance
· AI driven strategies for investment and risk management
· Machine learning recent trends and applicability to risk and related areas
· Unsupervised anomaly detection in Quantitative Finance