Market capitalization estimates a public company’s current market value: multiply its outstanding shares by the current share price. It measures what investors are willing to pay now, not intrinsic value. section 2.3 section 2.4.4
Use capitalization to compare company size, while treating size as a risk-and-opportunity signal rather than a guarantee.
- Mega-cap: $200 billion or more; large-cap: $10–200 billion; mid-cap: $2–10 billion; small-cap: $250 million–2 billion; micro-cap: below $250 million. section 2.3
- Larger companies may have lower bankruptcy risk, whereas micro-cap companies may offer more room for exceptional growth but expose investors to greater risk. section 2.3 section 4.2.1
- Compare capitalization with intrinsic value cautiously: estimates of intrinsic value remain uncertain, and a market-value gap does not by itself establish a sound investment. section 2.3
Comparing LiDAR candidates
A 2024 screen reported LAZR at $449,100,640, OUST at $308,347,328, AEVA at $175,966,752, and INVZ at $136,608,288. None reached the typical $2 billion mid-cap threshold, so capitalization flagged substantial risk before earnings analysis. The accompanying EPS data showed generally negative earnings, adding another consideration rather than proving an investment decision. section 4.2.1
import yfinance as yf
import pandas as pd
def collect_ratios(tickers: list, ratios: list):
rows = []
for ticker in tickers:
info = yf.Ticker(ticker).info
row = [ticker] + [info.get(ratio, None) for ratio in ratios]
rows.append(row)
return pd.DataFrame(rows, columns=["Ticker"] + ratios)
objects = [“AEVA”, “LAZR”, “INVZ”, “OUST”]
for o in objects:
ticker = yf.Ticker(o)
print(f"ticker {o}: {ticker.info['marketCap']}")
A short yfinance and pandas listing retrieves each ticker’s marketCap value, making the metric directly usable in a candidate-screening workflow. section 4.2.1
