Book Image

The Future of Finance with ChatGPT and Power BI

By : James Bryant, Aloke Mukherjee
2.5 (4)
Book Image

The Future of Finance with ChatGPT and Power BI

2.5 (4)
By: James Bryant, Aloke Mukherjee

Overview of this book

In today's rapidly evolving economic landscape, the combination of finance, analytics, and artificial intelligence (AI) heralds a new era of decision-making. Finance and data analytics along with AI can no longer be seen as separate disciplines and professionals have to be comfortable in both in order to be successful. This book combines finance concepts, visualizations through Power BI and the application of AI and ChatGPT to provide a more holistic perspective. After a brief introduction to finance and Power BI, you will begin with Tesla's data-driven financial tactics before moving to John Deere's AgTech strides, all through the lens of AI. Salesforce's adaptation to the AI revolution offers profound insights, while Moderna's navigation through the biotech frontier during the pandemic showcases the agility of AI-focused companies. Learn from Silicon Valley Bank's demise, and prepare for CrowdStrike's defensive maneuvers against cyber threats. With each chapter, you'll gain mastery over new investing ideas, Power BI tools, and integrate ChatGPT into your workflows. This book is an indispensable ally for anyone looking to thrive in the financial sector. By the end of this book, you'll be able to transform your approach to investing and trading by blending AI-driven analysis, data visualization, and real-world applications.
Table of Contents (13 chapters)
Free Chapter
1
Part 1: From Financial Fundamentals to Frontier Tech: Navigating the New Paradigms of Data, EVs, and AgTech
6
Part 2: Pioneers and Protectors: AI Transformations in Software, Finance, Biotech, and Cybersecurity

Harnessing the social pulse – the Sentinel Strategy for banking trading decisions

This reflects the strategy’s reliance on tracking and analyzing public sentiment to make informed trading decisions.

This trade demonstrates how to utilize the Twitter (now X) API to monitor public sentiment toward banks and convert it into valuable trading signals. We will shift our focus to data collection and pre-processing, incorporating Tweepy to access Twitter’s (now X) API, and TextBlob to quantify sentiment. The next part of our journey revolves around tracking traditional financial indicators using the yfinance module. By the end of this section, you should have a solid understanding of how social media sentiment can be harnessed to make informed trading decisions.

Obtain the Twitter (now X) API (if you don’t have one already)

To obtain Twitter (now X) API credentials, you must first create a Twitter (now X) Developer account and create an application. Here...