**The most important metric is `close`, the daily closing price.** It is the standard reference price for trend, return and volatility analysis in this dataset of one row per stock per trading day. The other measures are `open`, `high`, `low` and `volume`. I picked `close` by judgement, not by a computed importance score. **Highest closing prices** (top 5 rows in the rendered result) - All five are SK Hynix (000660.KS) in July 2025. - The peak close is **300,000** on 2025-07-14, from an open of 292,500 and an intraday high of 302,500. Volume that day was 2,936,976. - The next closes are 298,500 (07-15), 297,000 (07-10), 296,000 (07-16) and 294,500 (07-11). - This is consistent with the AI-driven surge the dataset is built around, with SK Hynix trading at record levels in mid-July 2025. **Lowest closing prices** (bottom 5 rows) - All five are BrainChip (BRN.AX) in December 2014. - The minimum close is about **0.009265** on 2014-12-05, and it stays at that level on 12-08 through 12-11. - From 12-08 on, open, high, low and close are identical and volume is 0. This looks like a stale price on days with no trading, not real price discovery. **Caveats** - Prices are in each stock's local currency (SK Hynix in Korean won, BrainChip in Australian dollars), so the gap between 300,000 and 0.009 mostly reflects currency and share-price scale. It says nothing about relative company value or performance. - For cross-company comparisons, use returns, percentage changes or a normalised index instead of raw price levels. - The lowest records are also affected by zero-volume days, so they say more about data quality than about market behaviour.