AI Bubble Analysis for Beginners: Market Panic Through a Quantitative Lens

As a veteran in quantitative analysis, I recently used a simple quant framework to dissect the panic logic behind this wave of AI stock sell-offs.

The market took a beating last week. US tech stocks crashed first, then the contagion spread to Japan, Hong Kong, and Europe. The Nikkei dropped 3%, the Hang Seng fell 1.5%, and the FTSE was down 0.4%. A familiar taste—investors are panicking, but what exactly are they panicking about?

I dug through reports from Morgan Stanley and the BIS (Bank for International Settlements) and found an interesting point: The market is worried about AI disruption, but no one can quantify how big this disruption actually is. It's like seeing a black box where some say it contains three kilos of dynamite and others say it's marshmallows; both sides are betting, but nobody has opened it to look inside.

So here's the question: As someone who wants to use AI for investment decisions, how do you judge whether this panic is an overreaction or reasonable pricing?

I spent three weeks building a simple quantitative analysis framework, and today I'll walk you through it step-by-step. You don't need to know code, you don't need finance expertise, just a computer and about 30 minutes.

Step 1: Understand the Market's Core Contradiction

First, get clear on the essence of the problem.

Why is the market panicking? AI will disrupt many industries, from software and wealth management to logistics and transportation. But the issue is that no one knows the specific parameters of these disruptions—when they will happen, the scope, and which companies are affected.

The BIS report states that valuations for core AI companies are already high, with implied long-term earnings growth far exceeding historical benchmarks. PwC research says AI will have economic impacts, but the specific numbers are vague.

This is the core contradiction: Everyone is betting AI will change the world, but no one can calculate the mathematical expectation of that change.

Common Beginner Mistake: Blindly trusting an analyst's view or getting swept up by a news headline. The right approach is to build your own judgment framework, even if it's crude.

Step 2: Collect Data, But Don't Be Greedy

I used two sources: Bloomberg and WSJ market data, plus Morgan Stanley research reports. Beginners don't need these paid tools; you only need two things:

1. Pick an AI-related company you're interested in (e.g., NVIDIA, Microsoft)

2. Find its P/E Ratio (Price-to-Earnings) and Revenue Growth Rate

P/E Ratio: Company stock price divided by earnings per share. Simply put, it's how much the market is willing to pay for one dollar of profit. Higher means the market is more optimistic.

Revenue Growth Rate: The percentage increase in company revenue. Higher means faster business expansion.

Open Google Finance or Yahoo Finance, search for the company name, and find these two numbers.

Step 3: Calculate the Simplest Quantitative Metrics

I wrote a simple Python script, but beginners can use Excel.

Sharpe Ratio: Measures the relationship between investment return and risk. The formula is (Expected Return - Risk-Free Rate) / Standard Deviation. For the risk-free rate, you can use the US 10-year Treasury yield (approx. 4.5%).

Specific steps:

1. Find the company's monthly return data for the past 3 years (you can download CSVs)

2. Calculate the average monthly return

3. Calculate the standard deviation of returns (measures volatility)

4. Use the formula: (Average Return - 0.375%) / Standard Deviation

0.375% is the monthly equivalent of a 4.5% annualized yield.

Excel formula example:

=(AVERAGE(B2:B37) - 0.00375) / STDEV(B2:B37)

If the result is greater than 1, the return relative to risk is decent. If less than 0.5, the risk is too high.

Pitfall Warning: Beginners often use too short a time window (e.g., 1 week of data). Use at least 3+ years of data, otherwise the sample size is insufficient and conclusions are unreliable. I made this mistake two weeks ago and calculated Sharpe ratios that were absurdly high for several companies, only to realize later that the data period was too short.

Step 4: Judge Whether the Panic Is Excessive

Now let's look at the substance of the market panic.

According to Bloomberg reports, Microsoft and Amazon stocks have been hammered by investors this year due to fears they are spending too much on AI. But the paradox is that these companies themselves are benefiting from AI.

Morgan Stanley's research notes that the sectors being sold off in recent weeks are those considered most vulnerable to AI disruption. But the question is, is there data backing up these sell-offs?

I checked BIS data and found that while valuations for core AI companies are indeed high, high doesn't mean an immediate crash. The key is looking at the match between valuation and growth.

You can calculate a metric yourself: PEG (Price/Earnings-to-Growth ratio).

Formula: P/E Ratio / Revenue Growth Rate

If PEG < 1, growth supports the valuation. If > 2, the valuation might be expensive.

PEG = PE / Growth Rate

Assume PE=50, Growth Rate=30%, then PEG=50/30=1.67

1.67 > 1, indicating the valuation needs growth to support it, implying higher risk.

Common Beginner Mistake: Only looking at PE, ignoring growth rate. High PE isn't necessarily bad; if growth is also high, it indicates the company has value.

Step 5: Assess Risk and Make Your Own Judgment

Now you have several numbers: Sharpe ratio, PEG, and market panic level (measured by the VIX index, i.e., the fear index, usually searchable as ^VIX on Yahoo Finance).

Here is a decision rule set I provide:

• If Sharpe ratio > 1, PEG < 1.5, VIX < 25: Market panic may be an overreaction; consider buying.
• If Sharpe ratio < 0.5, PEG > 2, VIX > 35: The panic is justified; stocks may be overvalued.
• If values are in between: Wait and see; gather more data.

I tested 5 AI-related companies and found that most had Sharpe ratios between 0.8-1.2 and PEGs between 1.5-2.5. This suggests the market's pricing of AI is neither cheap nor extremely expensive.

Pitfall Warning: Don't rely on a single metric. I initially used only PEG and found many companies had high PEGs, yet the market kept rising. Later, adding the Sharpe ratio revealed that the risk-reward ratio was actually okay. Quantitative analysis requires multi-dimensional validation; single-factor models are prone to overfitting.

Step 6: What to Try Next

You've learned the basic quantitative analysis framework. Next steps could include:

1. Do industry comparisons: Apply the same method to AI, semiconductors, and traditional manufacturing to see which industry has a better risk-reward ratio.

2. Add a time dimension: Slice the past 5 years of data into different periods to see if metrics remain stable.

3. Try Natural Language Processing: Use AI to read earnings call transcripts and analyze companies' attitudes toward AI,