This paper investigates fractional share trading. We develop a latency-based method for identifying a large sample of fractional share trades. We find that high-priced stocks, meme stocks, IPOs, SPACs, and popular retail stocks exhibit considerable numbers of these tiny trades. We surmise that this reflects dollar-based order entry, with many tiny trades being fractional components of larger orders. We show that our fractional trade measure is predictive of future liquidity and volatility, suggesting a new metric to capture the information in retail trades. We identify how data and reporting protocols preclude knowing the extent of fractional share trading, inflate volume data, and provide censured samples of these off-exchange trades.