(eToro Blog) As was noted in a previous post, a similar analysis can be done with the eToro data, where the distribution of “closing ratio” was presented, and demonstrated the tendency of traders to close out their position at a profit of 90% or a loss of 50%.
The following figure presents the cumulative total of the number of trades closed. The timescale is at the minute level, giving over 300,000 time intervals. We can clearly see several interesting effects. For example, many traders seem to close their positions out at the end of the day (this actually, can be expected), and at the end of the week (perhaps the result of a desire for a worry-free weekend):
In the next example we see the autocorrelation of the trading probability of the eToro trading community, as a function of the time of day and the day of the week. This was produced using a duration scale rather than a frequency scale (using autocorrelation). It clearly demonstrates the very strong 24 hour and 5-day periodicities:
So, it would appear that eToro traders, at least, prefer to maintain a daily and weekly routine. Moreover, when forced to digress from their habits, they would prefer to wander off as little as possible – showing almost a linear correlation between the magnitude of the deviation from their usual behavior and the likelihood that they would adopt such a behavioral variant. This phenomenon can, of course, be used in a variety of ways.
For example, a trader that wishes to ensure a high degree of liquidity for his future trade could use this information in order to time his activities accordingly. This can also be used by traders who are interested in low trading volumes (sometimes associated with large potential for arbitrage).
Another usage of this information can be by institutional traders and hedge funds – many of which face the occasional need to dump (or purchase) large quantities of stocks or currencies. Due to their fear of “predatory traders” (those that take advantage of such situations by pre-purchasing or pre-buying the same assets) they often divide one big transaction into a large number of smaller ones. The allocation of these smaller transactions during different hours of the day (and days of the week) can be assisted by the information concerning the “typical behavior” of traders, and help these institutes escape being detected by “anomaly detecting algorithms” that monitor the markets.
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