Multimedia Library

Large language models such as Bard and ChatGPT are rooted in natural language processing models developed decades earlier. How did we get here? This presentation took us through this fascinating journey. Dr. Hossein Kazemi moderated this presentation with Avi Patel and Raul Salles de Padua who took us on a brief journey through the evolution in NLU and NLP, from word embeddings to the current state of LLMs. We shared how to leverage adoption of LLMs, Generative AI capabilities and better understand the LLM-Ops lifecycle. Finally, we demonstrated the art of the possible with LLMs in action.

Didier Rodrigues Lopes, Founder & CEO of OpenBB discussed his journey from the pain points of doing investment research and how this led him to start building his own investment research platform in Python and raising $8.8M to democratize investment research through open source. He introduced the OpenBB Terminal - the open source investment research platform, and some of its capabilities. In addition, he presented the OpenBB SDK which allows programmatic access to the data from the OpenBB Terminal, allowing quants, analysts and developers to build custom tools and dashboards. Dr. Hossein Kazemi, Senior Advisor for CAIA Association and FDP Institute moderated the session and Cordell Tanny, Founder & CEO of Trend Prophets showed how he uses OpenBB to replicate some advanced Bloomberg charting techniques required for macroeconomic analysis.

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. We mentioned loading data from stock markets, creating the Features and dataset, training networks, k-fold cross validation, choosing the best architectures, backtesting, choosing a trading strategy and more. This webinar presentation is intended for anyone who is interested in learning more about neural networks. No prior experience with neural networks is required, and perhaps it will enable participants to embark on their own journey towards developing their own machine learning models.

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. These changes will have a profound impact on investment management, and we will examine several case studies and applications. We also examined the long-term future of various investment and wealth management roles, and especially the long-term impact on ESG and responsible investing.

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. Second, neither sentiment-based nor classic NLPs approaches are able to ``learn'' from the past managerial discussions to forecast future earnings. Third, only "finance-trained" LLMs have the capacity to "understand'' the contexts of previous discussions to predict both positive and negative earnings surprises, and future firm returns.

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.

Harshdeep discussed with Dr. Kathryn Wilkens, Curriculum Consultant for the FDP Institute, how the authors then implemented the VAR-based two-step approach of Davis et al. (2018) with machine learning techniques and allowed for unspecified nonlinear relationships (a hybrid ML-VAR approach). They found up to 56% improvement in real-time forecast accuracy for 10-year annualized US stock returns.

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

- What are the key features of venture debt from an investor's perspective?
- Why is venture debt now viewed as one of the most attractive and opportunistic investment strategies in the market?
- How does the failure of Silicon Valley Bank create opportunities for non-bank lenders specifically?
- What is the historical risk and return of venture debt?
- How do you mitigate risk in venture debt?
- How are deals sourced, screened, underwritten, and managed to provide optimal risk-adjusted returns?
- What types of investors are involved in venture debt and what is driving demand in each investor segment?
- Where does venture debt fit into an investor's portfolio?
- Why is venture debt very likely to outperform over the next few years?