Artificial Intelligence (AI) is re-shaping the progress of many sectors today; and is perhaps the most important ‘general purpose’ technology of our times. The effects of AI will be continue to expand manifold in the coming decade as the sectors of manufacturing, media and entertainment, retail (especially e-commerce), advertising, finance, healthcare, insurance, education etc- all of which are being impacted by this technology; the usage of which is spreading gradually from the developed nations of the West to India.
One sector that has been majorly impacted is Finance. Let us consider the phenomenon of algorithmic trading for a start- this type of trading uses software and computers to run complex mathematical formulas combined with mathematical models and human oversight for trading in securities, to make decisions to buy or sell financial securities on an exchange.
One sector that has been majorly impacted is Finance. Let us consider the phenomenon of algorithmic trading for a start- this type of trading uses software and computers to run complex mathematical formulas combined with mathematical models and human oversight for trading in securities, to make decisions to buy or sell financial securities on an exchange.
Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second. No human being can replicate this speed; and hence software is often replacing human desk traders.
Conventional (manual) trading models only utilize historical data, are often static, require human intervention, and don’t perform as well when the market changes. Consequently, funds are increasingly migrating towards true artificial intelligence models that can not only analyse large volumes of data, but also continue to improve themselves.
Most AI trading software today can absorb enormous volumes of data to learn about the world and make predictions about the financial market - stocks, bonds, commodities and other financial instruments. To understand global trends, software can consume everything from books, tweets, news reports, financial data, earnings numbers, and international monetary policy to Saturday Night Live sketches. The AI can keep watching this information all the time, never tiring, always learning and perfecting its predictions.
Let us consider the case of perhaps the most well-known of all investment banks, Goldman Sachs. In 2014, Goldman Sachs reportedly invested in and began installing an AI-driven trading platform called Kensho. The usage of this gradually increased- as exemplified by this remarkable statistic: In 2000, Goldman Sach’s U.S. cash equities trading desk in its New York headquarters employed 600 traders buying and selling stock. Today, it has reportedly just two equity traders, with machines doing the rest.
This has essentially been replicated at a macro scale. After a gradual start, by 2010, upwards of 60 percent of all trades were executed by computers, with the figure over 70 percent more recently. In India as well, the percentage of trades executed by computers has crossed the 40 percent mark (As an aside, readers may be interested to read Michael Lewis’ book ‘Flash Boys’ that brought high-frequency, algorithmic trading to the general public’s attention, which spoke about the lives of Wall Street traders and entrepreneurs who helped build the companies that came to define the structure of electronic trading in America. An interesting tenet of his book was that Wall Street firms were engaged in a race to build ever faster computers, which could communicate with exchanges ever more quickly, to gain advantage on competitors – solely through speed, thus completely overpowering those who used more traditional methods to trade on the exchange).
Now a days, even DIY (or do-it-yourself) algorithmic trading has become common- a hedge fund called Quantopian, for instance, crowd source algorithms from amateur programmers who compete to win commissions for writing the most profitable code.
What is also noteworthy is the potential of these algorithms to get better and better; for most softwares today rely on machine learning- programs can improve themselves through an iterative process called deep learning.
Why is algorithmic trading popular? Broadly, it provides the following benefits:
- Trades can be executed at the best possible prices.
- Trade order placement is instant and accurate
- Trades are timed correctly and instantly to avoid significant price changes.
- Reduced transaction costs.
- Simultaneous automated checks on multiple market conditions.
- Reduced risk of manual errors when placing trades.
- Algo-trading can be backtested using available historical and real-time datato see if it is a viable trading strategy.
- Reduced possibility of mistakes by human traders based on emotional and psychological factors.
There are many other benefits of AI based products- some especially relevant for developing nations such as India. In our country, for example, aspects such as the pricing of financial products such as insurance policies, the decision to extend credit facilities (including credit cards) suffer in the absence of information about end users due to the absence of credit records and user profiles. As AI and digital technologies become more widespread, some of these issues could be resolved, at least partially.
AI allows for new ways of pricing financial products accurately to be brought into play. A piece of software called Lenddo, for example, can reportedly look at a potential applicants’ entire digital footprint to determine their creditworthiness. The company claims that it can look at hundreds of factors including social media account use, internet browsing, geolocation data, and other smartphone information.
Their machine learning algorithm turns all this data into a credit score, which banks and other lenders can use towards provision of a credit score; and avoiding the issue of ‘adverse selection’. The technology can therefore help extend credit and insurance to those who were hitherto left out of formal channels.
There are also indirect benefits of a more widespread usage of AI. One of these comes from what has been called ‘robo-journalism’. This technology allows for entire news stories can be written by machines, which can supplement editors and reporters and produce simple factual reports, increase the speed with which they are published, and cover topics currently below the capacity of journalism.
This is especially true when the story deals primarily with facts and data - as do many financial quarterly earnings reports. Associated Press extensively uses AI to churn out financial news related coverage and states that this technology has allowed it to expand its coverage of companies listed on the exchange from just 400 per quarter earlier to 4,000 today.
Greater usage of this technology could help the Indian financial sector, especially the stock exchanges. The Bombay Stock Exchange in India holds the record for the largest number of listed entities in the world however the number of stocks actively traded is low. Should media coverage be expanded to a greater number of entities, it would greatly increase the options to retail investors by broadening the coverage of companies, as people would have information on more stocks and thus feel comfortable to trade in them.
It is clear therefore, that AI and machine learning systems, while not always needing to replace people, can often complement human activities, which can make the work of humans ever more valuable. For example, using ‘robo journalism’ can free up the time of editors to write more pieces dealing with analysis, opinion and stories of human interest and emotion, while leaving the fact-based work to robots.
AI allows for new ways of pricing financial products accurately to be brought into play. A piece of software called Lenddo, for example, can reportedly look at a potential applicants’ entire digital footprint to determine their creditworthiness. The company claims that it can look at hundreds of factors including social media account use, internet browsing, geolocation data, and other smartphone information.
Their machine learning algorithm turns all this data into a credit score, which banks and other lenders can use towards provision of a credit score; and avoiding the issue of ‘adverse selection’. The technology can therefore help extend credit and insurance to those who were hitherto left out of formal channels.
There are also indirect benefits of a more widespread usage of AI. One of these comes from what has been called ‘robo-journalism’. This technology allows for entire news stories can be written by machines, which can supplement editors and reporters and produce simple factual reports, increase the speed with which they are published, and cover topics currently below the capacity of journalism.
This is especially true when the story deals primarily with facts and data - as do many financial quarterly earnings reports. Associated Press extensively uses AI to churn out financial news related coverage and states that this technology has allowed it to expand its coverage of companies listed on the exchange from just 400 per quarter earlier to 4,000 today.
Greater usage of this technology could help the Indian financial sector, especially the stock exchanges. The Bombay Stock Exchange in India holds the record for the largest number of listed entities in the world however the number of stocks actively traded is low. Should media coverage be expanded to a greater number of entities, it would greatly increase the options to retail investors by broadening the coverage of companies, as people would have information on more stocks and thus feel comfortable to trade in them.
It is clear therefore, that AI and machine learning systems, while not always needing to replace people, can often complement human activities, which can make the work of humans ever more valuable. For example, using ‘robo journalism’ can free up the time of editors to write more pieces dealing with analysis, opinion and stories of human interest and emotion, while leaving the fact-based work to robots.
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