Financial data comes in many forms. Stocks, bonds, futures, options, commodities and foreign exchange all trade differently.
2.2.1 Names and tickers
All stocks that are trading in the market are associated with a ticker symbol, serving as an identifier for the specific security. These are often specific to the particular exchange or country of listing. This can often lead to confusion or ambiguity when a company has cross-listings. For instance, the Japanese car manufacturer Toyota is listed as 7203 on the Tokyo Stock Exchange, TYT on the London Stock Exchange and TM on the New York Stock Exchange.
Some identical securities have different names. Depending on the source, searching for data on the S&P 500 (a major American stock market index) requires the label SPX (Bloomberg), ^GSPC (Yahoo Finance), INX (Google Finance), GSPC.INDX (EODHD), and so on.
Firms change names, merge and disappear. Their tickers change too, and exchanges sometimes recycle them.
Always check the vendor’s description and confirm that the ticker matches the series you want.
2.2.2 Permanent asset identifiers — ISIN and PERMNO
Vendors solve this problem with permanent asset identifiers. These labels stay with the asset even when the name, exchange or ticker changes. There are multiple data vendors, each with its own nomenclature and usually inconsistent labelling.
The ISIN is the International Securities Identification Number, an internationally recognised 12-character code unique to each security. Unlike tickers, the ISIN for a given instrument is the same regardless of the market in which it trades, but a depositary receipt, such as Toyota’s NYSE-listed TM, is a separate instrument with its own ISIN.
The PERMNO is the permanent issue identifier of the CRSP dataset.
When downloading data, it is best not to refer to stocks by their ticker symbol but rather by one of the permanent asset identifiers.
2.2.3 Adjusted and unadjusted prices
If you download stock prices, your data will often be unusable because the number of shares outstanding has changed, usually through stock splits, while the recorded prices have not been adjusted.
For example, Amazon announced a 20-for-1 stock split in 2022. This meant that every Amazon stock became 20 stocks, and similarly, the price dropped by a factor of 20, from about $2,400 to $120.
Treating the unadjusted series as returns would record a one-day loss of about 95%, even though the split leaves the investor’s wealth unchanged. It manufactures a return, and a volatility observation, out of nothing.
Use adjusted prices. They remove stock splits and spin-offs and make the series fit for analysis. Some vendors’ adjusted close also reflects dividend reinvestment, a different convention from the split-and-spin-off adjustment used here, so check the vendor’s documentation before comparing series across sources.
Unadjusted data can also contain artificial price jumps from missed corporate actions. Signs include a sudden large price drop that is in fact an unrecorded stock split, a price series that no longer aligns with market indices over the same period, or an unrealistic volatility spike on a specific date.
2.2.4 Asynchronous prices
Problems arise when data comes from different markets and countries due to:
Holidays;
Time zones.
Public holidays, days when the markets are closed, are often different across countries. Examples include national holidays, such as 4 July in the United States, and religious holidays. The exchanges are usually open Monday through Friday, but the Saudi Stock Exchange is open from Sunday through Thursday. Some exchanges close for a lunch break. Some countries have summertime, and others do not, and summertime often happens on different dates in the US and Europe.
Name
Time Zone
Trading Hours
Midday pause
New York Stock Exchange
ET
9:30 a.m. to 4:00 p.m.
No
Shanghai Stock Exchange
China Standard Time
9:30 a.m. to 11:30 a.m., 1:00 p.m. to 3:00 p.m.
11:30 a.m. to 1:00 p.m.
Tokyo Stock Exchange
JST
9:00 a.m. to 3:30 p.m.
11:30 a.m. to 12:30 p.m.
London Stock Exchange
GMT/BST
8:00 a.m. to 4:30 p.m.
12:00 p.m. to 12:02 p.m.
Frankfurt Stock Exchange
CET/CEST
9:00 a.m. to 5:30 p.m.
No
The New York market overlaps with London, and since Tokyo is 9 hours ahead of London (8 hours during British Summer Time), there is no overlap in trading hours with Tokyo.
This means that any research comparing prices across countries at a daily frequency needs to consider these issues. They can be mitigated by using weekly or monthly data.
2.3 Data quality and validation
2.3.1 Missing values and gaps
Financial markets sometimes have trading halts, holidays or technical issues that create gaps in price series. Missing data points can distort volatility calculations and risk metrics. Always check for:
Consecutive missing dates
Unusual gaps in trading volumes
Zero or negative prices (often indicating data errors)
2.3.2 Outliers and data entry errors
Erroneous data points can dramatically affect risk measurements. Common issues include:
Prices quoted in wrong currency units (pence vs pounds)
Decimal point errors (prices off by factors of 10 or 100)
Typographical errors in manual data entry
2.3.3 Basic validation procedures
Before using data for risk calculations:
Plot the price series and look for anomalies.
Check for unusually large returns that may indicate data errors.
Compare data across multiple sources when possible.
Check that adjusted prices align with known stock splits and spin-offs.
Check that zero prices do not coincide with normal trading volumes.
2.4 Data frequency considerations
Tick data captures every trade or quote update, potentially generating millions of observations per day. It is useful for high-frequency trading and market microstructure analysis. Tick data is expensive to store — a single year can run to terabytes.
Minute and hourly data are aggregated from tick data and serve as a practical middle ground, useful for intraday volatility estimation and real-time risk monitoring, with a much smaller data volume than tick data.
Daily data, consisting of end-of-day closing prices, is the most widely used frequency for portfolio risk assessment and regulatory reporting. It is cheap to store and process.
Weekly and monthly data provide further aggregation that is particularly useful for long-term analysis and helps avoid asynchronous trading issues that arise when comparing markets across different time zones and holiday schedules.
Higher frequency data creates three practical problems — storage, cost and processing speed.
Data frequency changes the risks you see. Higher frequency data captures intraday volatility that daily data misses.
2.5 Other data types
Derivatives data adds strikes, expiry dates and contract specifications that equity data does not need. Options data also carries the Greeks — delta, gamma and the other sensitivities — and implied volatility.
Most data vendors provide FX coverage including spot rates, forward rates and cross-currency pairs. FX data is needed to convert foreign positions into the base currency when a portfolio holds international assets.
Alternative data sources have become more common in risk assessment, supplementing traditional financial metrics with information from news sentiment, social media, satellite imagery and ESG ratings. Take satellite images of port congestion or news flow around a troubled bank. Both can signal stress before it shows up in returns. These data types require careful validation and standardisation. Many vendors now offer alternative data products alongside their core financial data services.
Which vendor supplies the data matters less than knowing what the vendor did to it before you saw it. Two series carrying the same ticker over the same dates can differ in their split adjustment, their dividend treatment and their handling of a day the exchange was shut, and each of those differences moves a volatility estimate. Chapter 30 lists the sources used in these notes and what each one covers.
Chapter 11 describes the specific data files used in these notes.