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DISRUPTIVE INNOVATION iNM Volume - 9 equity markets . Likewise , HFT has grown in futures markets — to roughly 80 % of foreign exchange futures volume and two-thirds of both interest rate futures and Treasury 10-year futures volumes .
of a company which is trading at 100 rupees each .
The trader Quotes to buy another 1000 shares at 101 rupee each . The HFT machines record the new price as 101 , the trader then quickly sells his 1000 shares and makes a profit of 1000 which he wouldn ’ t have otherwise . So the HFTs have their own flaws . Recently the revenue of HFTs are very low . But rapid innovations taking place in this sector will turn the situation .
Next evolution of algorithmic trading depends on smarter machines , allowing a broader class of trades to reap the benefits of automation and sophistication . Machine accessible data , big data and Artificial intelligence are shaping the future of algorithmic trading . The agility and the accuracy of the trading will improve .
Growing public discontent with algorithmic trading may lead to regulations on the use of automatic data feeds or smart machines in executing trades , which may lead to reverting some parts of market-making activities to manual processes . The main focus areas for HFTs are price discovery and order execution . These HFT machines do not look for the intrinsic value of the shares , rather they focus more on market information . They assimilate the market informations at a faster pace and execute the orders rapidly . But it is also subjected to flaws , Say a trader has 1000 shares
The development of smarter , faster machines in algorithmic trading will have implications on the market structure in terms of volume , liquidity , volatility and spread .