ChatGPT vs Expert Instructor
The rise of artificial intelligence (AI) has sparked numerous discussions about its potential impact on job roles, particularly in the finance industry. At Financial Edge, we were intrigued by this debate and decided to put this to the test. We pitted one of our expert Wall Street training instructors, Phil Sparks, against ChatGPT-3, an advanced AI model, to determine how effectively AI software could value a company. The objective was to compare the capabilities of AI technology with the expertise of a seasoned finance professional for a highly technical task.
Key Learning Points
- ChatGPT is an AI chatbot that can help to answer questions and assist with tasks, offering immediate feedback in the form of human-like conversation.
- There are several limitations with ChatGPT – it can make mistakes, its training data only extends through 2021, and it does not have the ability to analyze live websites or data
- ChatGPT can be a useful tool for investment banking analysts, outlining various valuation methods and steps to perform a discounted cash flow analysis in Excel
- Investment bankers cannot rely on AI chatbots to carry out technical tasks, as these tools cannot tailor their responses to specific circumstances or real-world situations
ChatGPT vs Expert Instructor
Interaction between Trainer and ChatGPT
On the positive side, ChatGPT acknowledges the existence of multiple methods for valuing a company, presenting a helpful list of options. It briefly outlines the valuation process for each method, offering a high-level understanding of value is calculated using each method. It notably includes examples of intrinsic valuation, such as the discounted cash flow (DCF) method, and comparable valuation, both of which are widely recognized and used within the financial industry.
However, there are certain limitations to ChatGPT’s response. It does not provide a detailed breakdown of the steps involved in each valuation method. For instance, it does not delve into the estimation of future free cash flows or provide specific formulas or guidance in calculating these cash flows. Similarly, it omits crucial details like the selection of an appropriate discount rate, such as the weighted average cost of capital (WACC). As a result, for individuals seeking practical application, the information provided is limited.
During the video, Phil points out that the explanation of comparable company analysis lacks a description of the multiples used in the analysis, such as enterprise value (EV) to EBITDA or EV to EBIT multiples. Furthermore, the software neglects to mention precedent transactions as another type of comparable valuation.
While ChatGPT’s response offers a valuable overview, it leaves out critical specifics and practical implementation guidance. It serves as a starting point for understanding valuation methods but falls short of providing comprehensive instructions. Real-world expertise and additional resources remain essential for a thorough and accurate valuation process.
To explore this in further detail, Phil then asks ChatGPT how to build a discounted cash flow model in Excel.
This time, ChatGPT provides a more detailed response, outlining several steps involved in building a discounted cash flow (DCF) model in Excel. It goes further by mentioning specific formulas, including the terminal value and net present value (NPV) formulas, and even highlights the convenience of using built-in Excel functions to simplify the calculations. Overall, the steps appear to be accurate and offer a credible overview of the DCF process.
However, as Phil observes, although we now have a “bare bones” framework of a DCF analysis, the response falls short in addressing the intricacies of performing a comprehensive valuation. For instance, it neglects to mention the crucial assumptions required to forecast and discount future free cash flows, such as the growth rate and the cost of capital. Moreover, while ChatGPT briefly acknowledges that the discount rate can be calculated using the Capital Asset Pricing Model (CAPM), it does not delve into the specific methodology. Furthermore, despite presenting the perpetuity growth method for calculating the terminal value, investment bankers and private equity professionals commonly rely on an exit multiple approach based on comparable company and transaction analysis.
Therefore, while the provided instructions serve as a useful starting point for new analysts, they lack the necessary granularity to make it possible to build a DCF model in Excel. Practical valuation expertise and a deeper understanding of industry practices remain vital to developing robust and accurate financial models.
In conclusion, ChatGPT is undeniably an impressive program that harnesses the vast information available on the internet to provide coherent answers. Its ability to convey information in a digestible format is particularly noteworthy. As a starting point for research, ChatGPT can offer valuable insights and a quick understanding of important aspects and key steps within a given topic. It acts as a signpost for further exploration, providing a solid foundation for deeper investigations.
However, it is crucial to approach ChatGPT with caution and acknowledge its limitations. Firstly, its accuracy is not guaranteed, as indicated by the initial warning provided when using the program. Additionally, in its effort to simplify information, important details or techniques can be omitted. Most significantly, ChatGPT lacks the ability to tailor its responses to specific circumstances or real-world situations. While it can serve up readily available generic information, it cannot replace the expertise and adaptability of a human expert who can analyze and respond to unique contexts. As technology continues to advance, striking a balance between AI capabilities and human insights will be key to maximizing the benefits of both.
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