The authors observe periodicity in spikes in the number of trades and trading volumes within a trading day, which they attribute to psychological/behavioral attributes of traders. According to the authors, this aspect of trading behavior has not been covered by any earlier research and warrants further in-depth research to investigate implications for investment practitioners.

The authors investigate whether spikes in volume and number of trades are the result of a behavioral bias among traders to target round time numbers or whether there is any advantage associated with executing trades at those particular times (e.g., lower trading costs). They do not find any evidence to support the latter argument because there is no significant change in transaction costs at the times when the spikes are observed.

According to the authors, this aspect of trading behavior is being highlighted for the first time in industry research and warrants further investigation. The authors discover that these intervals may differ from stock to stock. They are observable at round number points in time, such as hourly, half hourly, 15 minutes, 10 minutes, and 5 minutes. Investment practitioners may benefit from improved trading liquidity at such points in time for executing trades in their targeted universe of stocks.

The dataset used by the authors comprises 36 US stocks and 3 exchange-traded funds (ETFs). These stocks were chosen based on the high average daily volume. The period during which observations were made spans a total of 882 trading days from 17 April 2003 to 18 October 2006. The data are sourced from Nanex, which provides real-time option and stock price data to customers. Precise time reporting is a critical aspect for this research because it revolves around the determination of timing intervals between trades, and the Nanex database is suitable for that parameter. Future researchers may consider expanding this research to cover a longer and different span of time to confirm consistency of observations.

The authors examine whether trade clustering becomes more intense at round time intervals by running various regression tests on volume and trade variables, with the most round interval being a whole hour and the least being five minutes, during trading hours from 9:00 a.m. to 4:00 p.m. Measures of transaction costs are also evaluated for these time slots. Although the volume data present the familiar U-shaped pattern with activity declining around lunchtime and then peaking before close, the observed spikes in trading activity at the 10-, 15-, and 30-minute intervals and the whole-hour interval are statistically and economically significant.

The authors have explored an interesting phenomenon in trading patterns. But the attribution of it to a behavioral bias of human beings toward round numbers is speculative. Further research is required to verify this hypothesis and to determine practical conclusions for investment practitioners, especially any implications related to higher trading liquidity at these points in time.