I do have a rule of thumb. 2 * LoopStdDev(“(Hi(Ctr)-Low(Ctr))/Close(Ctr)”,100) * ([average trade size]/MedianDailyTot(126))^0.5+[commissions & taxes divided by trade amount] < 0.05
This is for stocks without spread data. If I have spread data, I lower the 2 at the beginning of the formula to 1 and add 0.5*LoopMedian(“Spread(Ctr)/Close(Ctr)”,35)
I’ll explain each component now.
The basic formula for the transaction cost of a stock is x * volatility * square root of shares you trade divided by total shares traded + y * spread.
Through gathering lots of data on my own trades, I’m estimating x = 0.5 and y = 0.25. If I don’t have the spread cost, I’ll double the other part of the formula to make up for that. (That doesn’t mean that if you use spread alone, it’s 0.25. It’s actually more like 0.5. But if you use it together with the other part of the formula it’s closer to 0.25.)
I measure volatility in daily trading as the standard deviation of the high-low difference divided by the closing price.
I measure the total shares traded by using the median, as SpacemanJones suggests.
0.05 is my approximate excess return per stock before slippage. If the round-trip transaction costs (twice the single transaction cost) are greater than my approximate excess return per stock, then it’s obviously not worthwhile buying the stock.
I also scale my buys by the transaction cost. If a stock has a high transaction cost as measured by this formula, I’ll buy much less of it.