We meet our friends E.T. Jaynes, Aubrey Clayton, Jacob Bernoulli, and Ronald Fisher.

Where we spend way too much (and too little) time talking about too many different things.

What are theories and models and what are some examples? Why should I care?

This is where things really start to go off the rails.

It’s time to leave Silicon Valley Bank (SVB) in the rearview mirror. Today we’re wrapping up the season with a final look at SVB’s stock price, earnings and cash flow versus its peers. Reinforcing our earlier analyses, SVB again appears to be an outlier, with its recent strategy driving stock price growth but then coming unstuck as the macro-economic climate changed. Looking back at the season, Nick wonders if it was possibly the hardest to listen to with talk of graphs and trends, whilst Neeraj has continually found himself lost in financial jargon, struggling to unravel what exactly various financial terms mean.

There’s also changes afoot for the Wild Truth Chase podcast. This goose has rarely sat still and things might be about to get more unpredictable. Listen in to find out more.

This week’s Datalore report

Thumbnail DALL·E prompt: "Design a whimsical cartoon illustration of a dapper goose, dressed in a suit and tie, joyfully ringing a large golden bell to ceremoniously open the stock market for the day, surrounded by excited traders and a bright, bustling financial scene.”

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We’re five episodes deep in our analysis of financial data around the collapse of Silicon Valley Bank (SVB). Over the last week, we’ve been analysing sentiment and cashflow data for banks to help us understand industry trends and the ways in which SVB was unique. The sentiment data, downloaded from the AlphaVantage API, shows some surprising industry trends against a backdrop of economic downturns. As expected, SVB does stand out but not as much as might have been expected. Nick’s analysis of cashflow data again shows SVB to have been an outlier, particularly during between 2021 and 2022.

References

This week’s Datalore report

This weeks’ notebook on Github

Thumbnail DALL·E prompt: "a goose standing next to a river with flowing dollar bills instead of water, cartoon style”Please join us on our website and Discord:

Please join us on our website and Discord:

https://www.wildtruthchase.com

https://discord.gg/gqM54csKSB

What secrets lie in balance sheets? Today, we’re trying to find out. This season on the failure of Silicon Valley Bank (SVB) we’ve bounced between a few different datasets, including macroeconomic data and headline assets and liabilities. This week’s episode takes a more detailed look at the bank’s balance sheet data over the last ten years and tries to place that in the context of the wider banking industry.

Neeraj has spent the week wrangling with balance sheet information to try and make it add up, eventually using data published by the bank itself to break down the contributors to its total assets and liabilities. The results support the accepted conclusion that Silicon Valley Bank held too much of its assets in illiquid long term investments. To try and contextualise these results, Nick has been throwing machine learning at the problem, using dimensionality reduction techniques to try and visualise banks with similar balance sheet breakdowns and how these have varied over time. On the face of it, there are the big banks and then everyone else. But look closer and you can see Silicon Valley Bank on a journey of its own!

References

SVB balance sheet, CAR over time, and t-SNE balance sheet Datalore notebook

https://twitter.com/KobeissiLetter/status/1641474423269081088?t=ZUMQNOlLIgZnkmUG2RjwMw&s=33

This week’s Jupyter notebook on Github

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Please join us on our website and Discord:
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In season five, episode four of a finance-focused podcast, the hosts analyze the balance sheets of Silicon Valley Bank using two approaches: one using AlphaVantage to compare the bank's data with other banks, and the other comparing the bank's capital adequacy ratios with other banks. The analysis reveals a shift in the bank's asset ratio and a low capital adequacy ratio, which could cause issues with converting assets into cash. The podcasters also discuss the industry-wide decrease in capital adequacy ratios for banks due to the pandemic, highlighting several regional banks that may be in trouble. The speakers use machine learning to visualize the balance sheets of different banks and discuss how Silicon Valley Bank stands out on the plot. The conversation concludes with a discussion of future analysis, including looking at macroeconomic indicators and other failed banks. The code used for the analysis is available on GitHub, and the AlphaVantage API can be used to obtain data.

#financepodcast #bankinganalysis #AIforfinance

Timestamps

[00:00:00] "Silicon Valley Bank's Balance Sheets: A Recap"

[00:00:40] "LM Usage: Chat GPT Example and Episode Description Comparisons"

[00:02:21] Falling Number of US Banks: Opening vs. Closing

[00:04:35] Analyzing Silicon Valley Bank's balance sheet changes over the past decade

[00:08:11] "Capital Adequacy Ratio and Troubled Banks"

[00:12:48] "Visualizing High Dimensional Data with Dimensionality Reduction Animation"

[00:16:38] The Financial Dangers of Silicon Valley Bank in 2020

I’ve got competition. Nick wants to replace me with an AI. He says they are faster and better at writing episode descriptions. Well listen here ChatGPT, Bing Chat, Bard and whoever else, challenge accepted!

It’s a two faced episode, starting with large language models (LLMs) and returning to the nominal topic of Silicon Valley Bank. Nick is unsettled. He’s chest deep in an ocean of AI assistants and the water is ever rising. Convinced that things are going to change, he’s suddenly unsure about his place in the world. Neeraj is a luddite, but he’s suspicious of LLMs stealing all our intimate thoughts through conversational interfaces. He believes the mind is the last holdout and when they’re in, it’s over.

Despite their conflicting feelings, Nick and Neeraj have used ChatGPT and Bing Chat to learn more about finance. Analysis of Silicon Valley Banks’ balance sheets show nothing exceptional when compared to other banks but somewhere in the mix is the information that precipitated a bank run. The data shows that bank failures happen in clusters. Is this a one-off or the beginning of a more widespread economic event? Hit play to find out more.

#LLM #GPT #ChatGPT #SVB #Bing #OpenAI

This week’s Datalore Report

This week’s Jupyter Notebook on Github

ChatGPT

Bing AI (click on Chat at the top)

Anime dating sim that can do your taxes

Thumbnail Bing AI prompt: "A cartoon goose digging through stacks of financial documents while sitting behind a desk and wearing an old timey banker's cap that has a transparent green visor"

References

This week’s Datalore Report

This week’s Jupyter Notebook on Github

ChatGPT

Bing AI (click on Chat at the top)

Anime dating sim that can do your taxes

AI-generated summary and timestamps

In episode three of their podcast, Nira Shah and Nick Schaefer analyze Silicon Valley Bank's financial data and discuss the potential impact of AI technology on their work. They also address a question from last week regarding whether depositors or customers should be saved in troubled banks. The hosts also express concerns about the increasing capabilities of large language models, such as Chat GPT, to access and analyze vast amounts of data, and the potential dangers of intimate conversations with these bots. Additionally, they examine the capital adequacy ratio of Silicon Valley Bank and past bank failures during major economic crises. The hosts also touch on the automation of tasks like tax returns and express interest in further exploring financial data in future episodes.

#SVBfinancialanalysis #AIimpactonbanking #ChatGPTdangers

Timestamps

[00:00:01] "The Wild Truth Chase talks Silicon Valley Bank"

[00:00:34] Where to Draw the Line Between Misbehaving Banks and Depositors

00:05:44] The Rapid Advancement of Chat Gbt Technology: A Conversation with Nick Gillespie

[00:11:55] Clues of Trouble at Silicon Valley Bank

[00:18:05] "Bank Failures in Clusters with Macro Events"

[00:18:57] "The History of Bank Failures: A Timeline from the 1930s to Today"

[00:21:06] Learning About Financial Data and the Future of Tax Returns

Continuing our season on Silicon Valley Bank, this week Neeraj and Nick are taking a closer look at the history of the second biggest bank to ever fail in the USA, from its founding by two Bank of America managers through to its untimely demise. The events at SVB remain a hot topic, with medium sized banks encouraging the government to shore up depositor confidence and popular commentators such as Balaji predicting wider contagion.

Our hosts expand on the data they plan on using and the questions it might answer. At the recommendation of ChatGPT (GPT-4!), Nick has started to dig into macro-economic data available in the Federal Reserve Economic Data database (FRED). Neeraj has been focussing on SVB balance sheets over the years. More is on the way, but check out our datalore links for initial analysis.

Balaji Tweet

FRED website

FDIC: Industry Analysis - Bank Data & Statistics

Datalore report with macroeconomic indicator plots

Github copy of Jupyter Notebook with macroeconomic indicators

Bank Term Funding Program (pdf)

Annual reports from SVB

The Cloudcast special episode on SVB

List of largest bank failures in the United States - Wikipedia

Thumbnail DALL·E prompt: "A flock of geese trying to get their money back from a bank, cartoon style"

Please join us on our website and Discord:

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One week ago, Silicon Valley Bank was the place for “proactive guidance for the long run”. It was recognised by Forbes as the 20th best bank in the US and one of Forbes’s Financial All-Stars. Fast forward a week and everything has changed. Closed by California State and its funds now in the hands of the Federal Government, the Silicon Valley Bank story appears to have concluded.

How did one of the most respected banks in America meet with this unexpected demise? This season, Nick and Neeraj are going to dig into the details of this surprising financial disaster to understand what happened and learn more about the banking industry.

First published Datalore notebook

Thumbnail DALL·E prompt: "a goose standing behind a bank teller window"

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