Forecasting Stock Profitability Using Language Models
Session Number
Internship
Advisor(s)
Andrew Kominik
William Blair
Location
Learning Lab
Discipline
Entrepreneurship and Innovation
Start Date
17-4-2024 9:20 AM
End Date
17-4-2024 9:35 AM
Abstract
The success of an investment management firm hinges greatly on its capacity to forecast stock returns, a factor crucial for garnering investor confidence and satisfying customer expectations. One approach to achieving this is through the development of linear regression models using programming languages like Python, leveraging datasets such as those provided by Fred. This methodology exemplifies a proactive strategy aimed at predicting future stock outcomes, thereby enhancing the firm's decision-making process. By harnessing publicly available data sources like FRED, investment firms can construct sophisticated models, including linear regression models, to not only forecast the overall performance of the equity market but also to analyze and predict the performance of specific market segments. This strategic utilization of data-driven methodologies equips investment management companies with valuable insights, empowering them to navigate the dynamic landscape of financial markets with greater precision and confidence.
Forecasting Stock Profitability Using Language Models
Learning Lab
The success of an investment management firm hinges greatly on its capacity to forecast stock returns, a factor crucial for garnering investor confidence and satisfying customer expectations. One approach to achieving this is through the development of linear regression models using programming languages like Python, leveraging datasets such as those provided by Fred. This methodology exemplifies a proactive strategy aimed at predicting future stock outcomes, thereby enhancing the firm's decision-making process. By harnessing publicly available data sources like FRED, investment firms can construct sophisticated models, including linear regression models, to not only forecast the overall performance of the equity market but also to analyze and predict the performance of specific market segments. This strategic utilization of data-driven methodologies equips investment management companies with valuable insights, empowering them to navigate the dynamic landscape of financial markets with greater precision and confidence.