Welcome to the first post of our brand-new series: AI for Financial Analysis! In this journey, we’ll explore how cutting-edge Artificial Intelligence, particularly Large Language Models (LLMs) like Google’s Gemini, can become powerful assistants in your financial analysis toolkit. Gone are the days of exclusively manual data sifting; AI can now help us process information, identify patterns,…
Imagine a future where humanoid robots for home use handle our daily chores, like laundry or even cooking. It’s an exciting idea. Tesla, famous for its electric cars and AI, wants Optimus to be a big part of this future of domestic robots. But getting from a cool prototype to a helpful robot assistant in every house is a tough…
The quest for fully autonomous vehicles, particularly for the potentially lucrative robotaxi market (with a potential increase in Waymo expected revenue growth in the near future), has spurred intense innovation and significant investment.Among the frontrunners, Waymo (Google’s self-driving project) and Tesla have emerged with fundamentally distinct technological philosophies, sparking a crucial debate: which approach – multi-sensor fusion with Lidar, or vision-only AI…
Welcome to the 10th post in our Python for Finance series! We’ve covered significant ground, from ratios to qualitative analysis. Catch up here: Now, we pivot towards valuation, starting with a cornerstone metric for Discounted Cash Flow (DCF) analysis: Free Cash Flow (FCF). FCF represents the cash generated by a company after accounting for all operational needs and…
Hello again, and welcome to the eighth installment of the Python for Finance series! We’ve journeyed from basic ratios to complex data retrieval and even dipped our toes into qualitative analysis by fetching earnings call transcripts and doing basic keyword searches. Catch up on the series here: In Post 8, we saw that keyword counts lack nuance…
Welcome back to the Python for Fundamental Analysis series! We’ve built a strong quantitative toolkit over the last posts: While numbers are essential, they don’t capture management strategy, competitive dynamics, outlook, or the nuances behind performance. For this, we turn to qualitative sources, and one of the richest is the earnings call transcript. These quarterly calls offer direct…
Welcome back to the Python for Fundamental Analysis series! We’ve built a solid foundation by calculating individual metrics and ratios for single companies. Check out the previous posts: Analyzing a company in isolation, however, only tells part of the story. Is a P/E ratio of 25 high or low? Is a Net Profit Margin of 15% good…
Introduction: How Well Are Assets Being Used? Welcome back to the Python for Finance series! We’ve journeyed through valuation, data retrieval, profitability, liquidity, and leverage. If you missed any previous posts, you can catch up here: Now, we shift our focus to Efficiency Ratios, also known as Activity Ratios or Turnover Ratios. These ratios measure how effectively a…
Welcome back to the Python for Finance series! Having explored valuation (Post 1), financial statements (Post 2), profitability (Post 3), and liquidity (Post 4), we now examine another crucial aspect: Leverage. Leverage involves using debt to finance operations and assets. While it can magnify returns, it also increases financial risk, making companies more vulnerable during downturns due to…
Welcome back to the Python for Finance series! In our previous posts, we’ve fetched specific metrics like P/E (Post 1), retrieved full financial statements (Post 2), and visualized profitability trends (Post 3). Now, we shift our focus from profitability to another critical aspect of fundamental analysis: Liquidity. Liquidity refers to a company’s ability to meet its short-term financial…