With the help of lightning speed internet and vast connectivity, most things can be done in a matter of seconds at our fingertips. We are living in the age of big data, where we rely on heavy information processing techniques and efficient platforms to do our shopping, study and work online.
Just as we require efficiency to perform these everyday tasks, the use of big data and efficient transfer of information in the trading area has been around for decades, with traders developing various new trading strategies every day in the bid to beat the market. The availability of data can be a blessing or a curse, with the ability to utilise it being the determinant of which one it is.
During the pre-big data age when trades are placed through phones, there is a huge time delay between someone placing an order and another person executing it. If there were any fluctuations of price due to trading volume, it will be reported as a lagging value. If the transfer of information could proceed at a higher speed, the participants/traders would be able to take advantage of knowing key details such as price, buy volume and sell volume before others do.
Numerous attempts in the early days were initiated, such as physically constructing cables that cut through mountains/rivers to reduce transmission time for data (from 17 to 13 milliseconds) and utilising fibre optic cables. This also prompted mathematicians, physicists, computer scientists etc around the globe to respond to this race by developing complex methods, eventually leading to the birth of high-frequency trading and a whole new sub-field within the finance industry: financial engineering.
Some would criticise this as transforming the public and democratic financial market into a rigged market where the advantage goes to the technologically advanced party. The substantial change from a trading floor full of brokers shouting and phones slamming to one which is purely determined by a systematic process is certainly one of the most revolutionary changes in the history of finance.
Nowadays, further implementation to algorithmic trading methods to improve efficiency is considered, with artificial intelligence (AI) and machine learning being tested and used in numerous financial institutions around the world. The line between human intuition/decision-making and computer input becomes blurrier as we take a step forward in this ever-expanding maze called the global financial markets.
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