First Horizon Bank Stock Price History

Getting Historical Price Data for First Horizon Bank (FHN)

If you are looking at First Horizon Bank Stock Price History, you are probably dealing with the ticker FHN, which trades on the NYSE. Regional banks like this one tend to move slower than tech stocks, but that does not mean their price history is straightforward to work with. I have spent too many hours parsing CSV exports from different brokers and realizing they all format dates differently. The basic path is: you need the ticker symbol, the date range you care about, and a source that actually gives you clean OHLC data. Most retail platforms hand you something usable, but it is rarely in a format ready for analysis without some cleanup.

What First Horizon Bank Stock Price History Actually Looks Like

FHN has been publicly traded since its earlier iterations as First Tennessee, going back well before the 2022 acquisition by Truist. That matters because your price history will show a massive gap event around July 2022 when the merger closed. If you pull raw data without adjusting for that split, your charts will look broken and your backtests will be garbage. Here is the thing most people miss: you need adjusted close prices, not raw close prices, if you are doing any kind of return calculation across that merger period. Unadjusted data will make it look like FHN dropped 40 percent in a single day, which is not what happened. It was a corporate action adjustment. Download the adjusted series from whatever source you use, and double check that the adjustment column is actually marked as adjusted. I ran into this head on last year when a client asked me to pull a five year volatility model for FHN. I grabbed the raw closes from a free data API, ran the numbers, and got a standard deviation that looked completely unrealistic. Took me twenty minutes to realize the merger adjustment was not applied. The workaround was switching to a dividend and split adjusted dataset from Yahoo Finance, then cross referencing the adjustment dates against the SEC filings to make sure nothing was missed. That verification step alone saved me from sending wrong numbers back to the client.

Where to Pull the Data

Your options depend on how much work you want to do upfront. Yahoo Finance is the free route. You go to the FHN quote page, click Historical Data, set your date range, and export to CSV. It gives you Date, Open, High, Low, Close, Adj Close, and Volume. The adjustment logic is generally solid for corporate actions like the Truist merger. The catch is rate limits if you script this, and the data can be occasionally inconsistent between sessions. Brokerage platforms like Fidelity, Schwab, or E*TRADE let you download historical data directly from your account. This is convenient if you already trade FHN, but the export formats vary by platform and sometimes omit the adjusted close column entirely. Check before you waste time importing.

Paid APIs like Alpha Vantage, Polygon, or IEX Cloud give you programmatic access with cleaner documentation. For occasional use the free tiers are fine. For anything recurring, the paid plans are worth it because they handle adjustments consistently and do not drop data overnight. SEC EDGAR is the source of truth if you need to verify anything. Financial statements and proxy filings live there. Price data itself is not the primary content, but if a data provider shows something that looks wrong, EDGAR is where you go to confirm the corporate action that caused it.

Common Problems and How to Fix Them

Data gaps are the most frequent issue. Regional banks can have days with no trading if the market is closed or during extreme volatility when trading halts. A single missing row in your CSV will break any time series function in Python or R unless you explicitly handle it. Another problem is the post-merger ticker continuity. After the Truist acquisition completed, FHN shares were exchanged for Truist (T) shares at a fixed ratio. If your date range extends past July 2022, you will see the price drop to zero or disappear entirely from most databases. You need to either truncate your date range or manually splice in the Truist ticker data if you need continuous returns. Dividend adjustments also cause confusion. FHN paid dividends before the merger, and the adjusted close column accounts for these, but if you pull unadjusted data and subtract dividends yourself, you will get double adjustments or none at all depending on your method. Stick to the adjusted close column and do not manually touch the numbers.

I once spent an afternoon debugging a portfolio tracker that kept showing a 22 percent loss on FHN for Q3 2022. The problem was that the broker export had truncated the date at July 15, 2022, missing the merger completion date of July 19. The position appeared to have evaporated rather than converted. The fix was extending the end date and verifying the actual closing price on the conversion date against the SEC press release. Always check the corporate action dates against primary sources when the data looks wrong.

What to Watch Out For

Free data sources are not always complete. Intraday data, for example, is mostly a paid feature across the board. If you need minute level or even daily bars going back ten years, plan to pay for a proper API or download from a platform that lets you pull large date ranges without throttling. Adjusted price data is not the same across providers. Yahoo, Google Finance, and broker exports may differ slightly in their adjustment factors because they use different methodologies for handling splits and dividends. This does not matter for casual checking, but if you are building models that compare against benchmarks, pick one source and stick with it consistently. There is also the issue of illiquidity. FHN is a large cap regional, so it trades frequently, but during periods of market stress or low volume, the spread between bid and ask widens. Close prices on low volume days can be noisy outliers. If you are calculating moving averages or volatility, filtering out days with abnormally low volume helps keep the signal clean.

For anyone who just wants to look at the chart, Yahoo Finance is sufficient and takes about two minutes. For systematic analysis, I recommend a paid API with adjustment guarantees and consistent data hygiene. The cost is usually under fifty dollars a month, and it saves you from the kind of debugging that takes longer than the analysis itself.