Abstract
In recent years, securities trading algorithms have been developed, and they have allowed automated high-frequency trading (HFT) without human intervention. HFT has changed the face of trading: it is widely considered to have a marked impact on its quality, and it can be established that it is responsible for a notable share of its quantity, including on the Tel Aviv Stock Exchange (TASE). HFT reflects various trading strategies, and this paper identifies the main ones based on intraday data on securities trading. Its findings indicate that the various strategies have different connections with trading-quality indicators: while HFT that functions as a market maker reduces transaction costs and volatility, and improve the price discovery process, other strategies are not characterized by the same connections and at times even negatively impact the quality of trading. It was also found that HFT that functions as a market maker significantly decreases its activity on noisy days. This phenomenon indicates that HFT probably creates phantom liquidity, which enhances the systemic risk in the secondary market. These findings cast doubt on the advantages of the algorithmic trading tools functioning with the various strategies, and particularly question the advantages of HFT that does not function as a market maker.