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Link to original content: https://pubmed.ncbi.nlm.nih.gov/21402941
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. 2011 Mar 29;108(13):5296-301.
doi: 10.1073/pnas.1018462108. Epub 2011 Mar 14.

Synchronicity, instant messaging, and performance among financial traders

Serguei Saavedra  1 , Kathleen Hagerty, Brian Uzzi
Affiliations

Synchronicity, instant messaging, and performance among financial traders

Serguei Saavedra et al. Proc Natl Acad Sci U S A. .

Abstract

Successful animal systems often manage risk through synchronous behavior that spontaneously arises without leadership. In critical human systems facing risk, such as financial markets or military operations, our understanding of the benefits associated with synchronicity is nascent but promising. Building on previous work illuminating commonalities between ecological and human systems, we compare the activity patterns of individual financial traders with the simultaneous activity of other traders--an individual and spontaneous characteristic we call synchronous trading. Additionally, we examine the association of synchronous trading with individual performance and communication patterns. Analyzing empirical data on day traders' second-to-second trading and instant messaging, we find that the higher the traders' synchronous trading is, the less likely they are to lose money at the end of the day. We also find that the daily instant messaging patterns of traders are closely associated with their level of synchronous trading. This result suggests that synchronicity and vanguard technology may help traders cope with risky decisions in complex systems and may furnish unique prospects for achieving collective and individual goals.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Calculating synchronous trading. The synchronous trading sij of a trader i in day j (e.g., the trader whose trades are highlighted in blue) is defined as the degree to which the observed number of other traders trading within the same seconds as trader i (top values) compares to the same value when randomizing just the trades of that particular trader. For advanced and delayed trading, we calculated the number of other traders trading 1 s late and 1 s early, respectively.
Fig. 2.
Fig. 2.
Synchronous trading and uncertainty. (A) The probability density for synchronous trading sij (bottom blue bars), advanced trading formula image (middle green bars), and delayed trading formula image (top orange bars) for all traders across the observation period. The bar size is the sum of the three probability values, and colors correspond to the relative contribution each distribution makes to the total sum. We found that synchronous trading is significantly different from advanced and delayed trading (P < 10−3 using the Kolmogorov–Smirnov test). (B) The positive association (P < 10−4 using Markov randomizations) between the average synchronous trading formula image and level of daily uncertainty in the market, given by the market’s standard volatility index (VIX) (17). The dashed line depicts the relationship estimated via a linear regression.
Fig. 3.
Fig. 3.
Individual performance. (A) The probability density of synchronous trading for traders that make money (left green bars) and for those that do not (right yellow bars). The two distributions are significantly different considering all values (P = 0.004 using the Kolmogorov–Smirnov test), within −2 and 2 exclusively (P = 0.046 using the Kolmogorov–Smirnov test) and outside −2 and 2 (P = 0.038 using the Kolmogorov–Smirnov test). (B) The relationship between synchronous trading sij and the probability of making money pij. The curve depicts the probability of performance (making money) estimated via a logistic regression (Materials and Methods). For any trader under consideration, the probability of making money increases as the synchronous trading increases. The gray region corresponds to the 95% confidence interval.
Fig. 4.
Fig. 4.
Association between IMs and trading. (A) The probability density of observing any trade (black line) and IM (green dashed line) in each hour on average across the observation period. Approximately 95% of trades and IMs are done between 9:30 AM and 4:00 PM, which corresponds to the main operation hours of the NYSE. (B) The empirical relationship (P < 10−10 using Markov randomizations) between the IM–trade coupling θij and synchronous trading sij for all traders across the observation period. The dashed line depicts the association estimated via a linear regression (Materials and Methods).
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