Why is the price of ticker XXX different?
“How can Google Finance and Yahoo Finance be telling me different prices! What’s going on?!”
“Isn’t the “close price” of a ticker just the last trade at that price, right?”
Theoretically, yes, but there are a number of nuances in the financial system that make this concept not as hard and fast as we would like.
Definition of the Close Price
To keep it simple, we will just focus on the closing price of stocks, but this applies to open, to a lesser extent high and low prices, as well as other metrics.
Logically and theoretically, the last price traded on any given day should be the same as the closing price of a stock. — Ken Clark (Co-manager of $100 million in assets and contributor to Investopedia)
However, there are a few events that blur the line of the definition:
- Last price vs. Close price
- Consolidated quotes vs. exchange specific
- After-hours trading
- Outliers
Last price vs. Close price
How can these even be different?
After a market closes, brokers have some time to close out some of the trades they have on their screen. The close price should be considered the last “on market” trade. However, it can get reported a little differently depending on the timing of the exchange.
Consolidated quotes vs. Exchange specific
When you get a close price on a ticker, who are you getting it from?
If I were to sell Charles Schwab (SCHW) on the NYSE exchange for $44 and $45 on NASDAQ, what is the price of Charles Schwab?
When someone says “that data is wrong” technically, they need that statement to be relative to something. We assume they mean the data is wrong in relation to the general consensus of the exchanges. This relative consensus is known as the consolidated quote. So if the close price of Charles Schwab on the NYSE is $44, and on the NASDAQ it’s $45, the quote that you get when you google the company may just be $44.5.
However, NYSE would tell you the price is $44 and NASDAQ would tell you it’s $45.
This price that you end up at can vary, depending on how many exchanges you want to include in your consolidation, and how far the markets deviated.
After-hours trading
After-hours trading can cause a tickers price to fluctuate as well, and although it shouldn’t be taken into consideration when giving a ticker a true closing price, it will show that the last price (which will be the price that the ticker was last traded at, including after-hours) may differ from the “on market” close price.
Outliers
Fortunately, this one is much more infrequent, but let’s say someone decided to just dump a share at some ridiculous price, or buy a share at some ridiculous price, the exchange my decide that it isn’t the real close price.
Many exchanges, such as NASDAQ, offer an “official close price” which may actually different from the last “on market” trade.
Or even, sometimes, an exchange will report the wrong or duplicate price.
Current price
Luckily, most of the time the consensus among vendors is unanimous, and the market is a bit closer to perfect. However, there are differences in the current price almost every second of the day.
To demonstrate the difference in the current price, I wrote a script that can compare three data vendors. Here is the gist of it below:
(Note: You will need a few API keys to run it.)
At the time that I ran it (around 2:00 PM on a Friday), here is what I got back:
At the same time I checked Google, and here is what Google returned:
How we approach it: Alpha Vantage Quality Data
Due to this being such a prevalent issue, we take extra precautions to make sure that our data is the most accurate reflection of the markets.
- Aggregating data from multiple sources and using our own proprietary methods to cleaning & normalize data to get the most accurate reflection
- Running checks to make sure data makes sense (e.g. a low price is not higher than a high price, etc.)
- Dividend/split based adjustments
- Removing duplicates
Have you seen weird spikes in data? Is there something else we should add here? Want to just jump in the conversation? Let us know!
#data #fintech #datadiscrepencies #stockdata #investing #marketdata
