Liquidity has a powerful impact on price and the valuation of Equities. Stocks with little liquidity are used to earning higher returns than stocks with high liquidity. In this algorithm, we present the effect of liquidity on returns for the lowest capitalization quartile from the largest 1,500 stocks.


In coarse universe selection, we filter stocks whose price is higher than $5. ADRs, ETFs, and closed-end funds are all excluded with the HasFundamentalData property of the CoarseFundamental objects.

In fine universe selection, in the first step, we exclude stocks with the market cap less than ten million. To evaluate the liquidity of stocks, we choose the annual turnover which is the number of shares traded divided by the stock’s outstanding shares. The main advantage of turnover against volume is its market capitalization-neutrality, as either small-cap or large-cap stocks can have low or high turnover rates. Although turnover is capitalization neutral, the liquidity effect is the strongest among small-cap stocks. Therefore, stocks are then divided into quartiles based on their market capitalization. Stocks from the lowest market-cap quartile are again divided into 5% and 95% quantiles based on their turnover. To calculate the turnover, we request the historical daily volume for the last one year and compute the mean volume. The turnover is the average annual volume divided by SharesOutstanding in CompanyProfile. Stocks in the 5% quantile and in the top 95% percentile are saved in self.long and self.short lists, respectively.

In OnData, the algorithm goes long on stocks in the lowest turnover list and short on stocks in the highest turnover list. Stocks not in those two lists are liquidated. The portfolio is rebalanced once a year and stocks are weighted equally.


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