I'm interested in researching a topic that involves the integration of AI and Machine Learning, and utilizes data-intensive tasks through the use of Python or R. Specifically, I'm seeking suggestions and guidance on potential research gaps in asset management that could benefit from an AI and Machine Learning approach. Additionally, any relevant literature, academic papers, or reading materials on the subject would be greatly appreciated.
Why not you start with surveys first, what kind of area you wanna put in.. Like factory, education, office, producrion, data analysis, etc...
Then you'll able to think more on topics that relevant to it.. Such as in the production area (factory) : application of AI/ML to transfer the result of production machine to the database syatem and analyse the gap between manual and AI counter... Or tracing the spare parts/goods in some production factories from the raw material/start to the finish good/end part... There will be a lot if we talk about it all.. Just startvwith your research area first i guess...
One potential research gap in asset management that could benefit from an AI and Machine Learning approach is the development of more accurate and effective predictive models for asset performance and risk management. While there has been some research in this area, there is still a need for more sophisticated models that can handle large amounts of data and incorporate multiple sources of information.
Another potential research gap is the use of AI and Machine Learning for portfolio optimization and asset allocation. While there has been some research in this area, there is still a need for more sophisticated models that can incorporate complex financial data, consider multiple constraints and objectives, and provide more accurate and actionable recommendations.
To find relevant literature and academic papers on these topics, you can start by searching academic databases such as Google Scholar or JSTOR for keywords such as "asset management," "AI," "Machine Learning," "predictive models," "portfolio optimization," and "asset allocation." You can also look for academic journals and conferences that focus on these topics, such as the Journal of Financial Data Science or the IEEE Conference on Computational Intelligence in Financial Engineering.
Some recommended reading materials on the subject include:
- "Machine Learning for Asset Managers" by Marcos Lopez de Prado
- "Applied Artificial Intelligence: A Handbook for Business Leaders" by Mariya Yao, Adelyn Zhou, and Marlene Jia
- "Artificial Intelligence in Finance and Investing" by Stefan J. Bosse and Rainer Lenz
Additionally, you may want to consider exploring open-source tools and libraries for AI and Machine Learning in Python or R, such as TensorFlow, PyTorch, or scikit-learn, which can help you implement and test your research ideas.
There are two ways to approach this topic:
1.From a managerial perspective, use existing machine learning approaches to address an asset management issue in your field. Consider asset management for startup tech companies with rapid growth or asset management for small companies in high-risk regions.
2. Search for trending machine learning approaches to address existing asset management issues with improved accuracy, agility, and flexibility. Start by exploring GNN models (GCN, etc) for node and link prediction, which are trend models that are currently being applied in various fields
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