About this Course 18,922 recent views This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques.
In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. This project is for anyone with foundation in programming and machine learning who wants to develop Data science and Machine learning projects but having limited resources on their computer and limited time. You will learn how to use the Google Colaboratory via your web browser to develop a Fake and Real News Detection Data Science Project.
By harnessing troves of historical data, asset and wealth management firms are now exploring AI solutions to improve their investment decisions. Since the market crash in the late 2000s, business regulators and consumers have been more wary about risks seeing as some are still trying to recover from it. Machine learning seeks to provide a paradigm shift in investment management for financial institutions so that they do not find themselves in such a situation again. Over the years, technology has helped companies make sense of the massive amounts of data they possess by analyzing them before reaching final decisions. For example, blockchain allows companies to check the validity of transactions before they are even completed.
We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream. However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies. I will touch upon some key themes in this blog, but will go into more detail in a series to follow.
The human brain is capable of tremendous achievements. But what are its limitations in business transactions, specifically those involving property and real estate investment management? At what point do machine data-based systems make more accurate decisions than intuition? Human intuition certainly has its place. As Deloitte researchers Surabhi Kejriwal and Saurabh Mahajan have noted, "The [real estate investment and management] industry has long thrived on relationships, which is how many investors have traditionally gained access to unique information. Traditionally, most investors have combined this information with their gut instincts to make investment decisions."