Quant investing starts with data. Whether analyzing stocks or backtesting strategies, the first thing you encounter is price data. The foundational terms that underpin all subsequent indicators and strategies are OHLCV, returns, and market capitalization.

OHLCV

OHLCV summarizes a day’s price movement in five numbers.

AbbreviationMeaningDescription
O (Open)Opening pricePrice at market open
H (High)High priceHighest price during the day
L (Low)Low priceLowest price during the day
C (Close)Closing pricePrice at market close
V (Volume)Trading volumeNumber of shares traded during the day

Most quant analysis uses the closing price as its basis. The close represents the final consensus price of the day and serves as the default input for return calculations, moving averages, and technical indicators. The opening price may differ from the previous close — this difference is called a gap, caused by overnight news or foreign market movements.

Volume indicates the reliability of a price movement. A price increase on low volume may be a temporary fluctuation driven by few trades. A price increase accompanied by high volume reflects broader market consensus.

OHLCV data can be collected from sources like Yahoo Finance, brokerage APIs, and exchange data systems.

Returns

Returns are the most fundamental measure of investment performance. The same return can be calculated in two ways.

Simple Return

Simple Return = (Today's Close - Yesterday's Close) / Yesterday's Close

This is intuitive. If a stock was $100 yesterday and $105 today, the return is 5%. For a single day, this is accurate.

The problem arises when aggregating returns over multiple periods. Suppose day 1 returns +10% and day 2 returns -10%. Adding them gives 0%, but the actual result differs.

$10,000 × 1.10 = $11,000  (Day 1)
$11,000 × 0.90 = $9,900   (Day 2)

The actual result is -1%. The arithmetic sum of simple returns (+10% + (-10%) = 0%) does not match reality. Log returns solve this problem.

Log Return

Log Return = ln(Today's Close / Yesterday's Close)

This uses the natural logarithm. The key advantage of log returns is that they are additive over time.

Day 1 log return: ln(11,000 / 10,000) = 0.0953
Day 2 log return: ln(9,900 / 11,000)  = -0.1054
Sum: 0.0953 + (-0.1054) = -0.0101

Converting this sum back to an actual return: e^(-0.0101) - 1 ≈ -1.00%, which matches the real result.

Quant analysis favors log returns. They simplify multi-period aggregation and statistical analysis (normal distribution assumptions). However, when daily returns are small (within ±5%), the difference between the two methods is negligible. The difference becomes meaningful during high-volatility periods or when working with long-term cumulative returns.

Market Capitalization

Market Cap = Current Price × Shares Outstanding

Market capitalization represents the total value the market assigns to a company. Stock price alone cannot compare company sizes. A $50 stock can have a larger market cap than a $5,000 stock if it has far more shares outstanding.

Size Classification

Market cap classifies stocks by size. In the Korean market, the Korea Exchange (KRX) categorizes stocks into KOSPI 200, mid-cap, and small-cap groups. Common classification criteria:

CategoryApproximate Criteria
Large-capTop ~100 by market cap (KOSPI 200 constituents)
Mid-capBelow large-cap, top ~300
Small-capBelow mid-cap

In the US market, large-cap generally means market cap above $10 billion, and small-cap below $2 billion.

Role in Screening

In quant strategies, market capitalization serves as the first filter for stock selection. Small-cap stocks often have low trading volume, making actual buying and selling difficult. Their price volatility is high. They may show strong backtest results but prove difficult to replicate in practice. Setting a minimum market cap threshold to exclude small-caps is standard practice.


OHLCV is the basic unit of price data. Returns are the language of performance measurement. Market capitalization is the standard for judging company size. These three concepts form the foundation for understanding valuation, quality, and momentum indicators in subsequent posts.

The next post will cover valuation and quality indicators — the numbers used to judge whether a company is cheap and whether it earns well.

References