Videos

CAIA LatAm & CFA Society Colombia Present: Elevate your Career in Finance - Unleashing the Power of Professional Designations

Listen in on this lively conversation about the importance and added value of pursuing professional designations. The panelists discussed employer's hiring preferences, trends, personal experiences, and career advice.

In a world where artificial intelligence is becoming complex and gaining influence, creating and using a machine learning model is not only a technical endeavor but also a personal journey of exploration, challenges, and growth. Tom Pickel shared his journey of building a neural network from the ground up. Tom shared his experience in creating a neural network using Python’s basic data science packages (Numpy and Pandas) for trying to predict movements in the stock market.

Alik Sokolov and Kathryn Wilkens discussed the revolution of natural language processing in recent years, and how it applies to various areas of investment management. Our ability to work with unstructured text data, which is abundant in investment management, has undergone several evolutions from the late 2010's: from sequence-to-sequence models for machine translation, to the advent of transformers and transfer learning, to the recent breakthroughs achieved by Large Language Models popularized by Chat GPT.

Map out your learning journey to obtain the FDP Charter. The FDP team answered questions about the FDP program, where to obtain your curriculum, prep providers, preparation for exam day and focused attention on our testing options.

The secondaries market has experienced significant growth and evolution over the past decade. The market for dedicated co-investment funds has similarly evolved, albeit it on a more limited scale. Both options present investors with significant flexibility and a complex array of considerations as they manage exposure to alternative assets. Watch our panel discussion as we explored current developments in each of these dynamic opportunities.

Russ Goyenko, Associate Professor of Finance at McGill University discussed with Dr. Hossein Kazemi how large language models can, and soon they will produce more accurate analyst forecasts. Using textual information from a complete history of regular quarterly and annual (10-Q and 10-K) filings by U.S. corporations, we train machine learning algorithms and large language models, LLMs, to predict future earnings surprises. First, the length of MD&A section on its own is negatively associated with future earnings surprises and firm returns in the cross-section.

Predicting long-term equity market returns is of great importance for investors to strategically allocate their assets. Harshdeep Ahluwalia, Head of Asset Allocation, Americas for Vanguard Investment Strategy Group is one of the authors invited to discuss the exploration of machine learning methods to forecast US stock returns 10-years ahead and compare the results to the traditional Shiller regression-based forecasts more commonly used in the asset-management industry.

Learn from a group of expert venture lenders and allocators who discussed relevant topics including:

In this presentation, Mike Green, Portfolio Manager and Chief Strategist at Simplify, discussed how passive investing has:

- Increased correlations between securities
- Increased valuations regardless of fundamentals
- Reduced market elasticity raising the risks of extraordinary price movements
- Increased market concentration
- And has reduced the ability for new companies to become public

There's never been a more crucial time to stand out. The transformative effect of data science on the finance industry requires today's finance professionals to understand the application of big data, data mining, workflow automation and machine learning in investment decisions. Learn more about the FDP Community along with the Charter curriculum and a roadmap to prepare for the upcoming FDP exam. This session provided an outline of the curriculum, background requirements, reading materials and study tools to help you prepare.