In this week’s #tfpforall we try and understand the very basic concepts of Artificial Intelligence in Finance.
How often do we steer away from topics involving technology? If you’re like us, it must be quite often because let’s admit it, these discussions are dry and a bit out of our comfort zone. So today we, at The Financial Pandora, have made an endeavour to simplify the concept. Let’s start with the term you must have frequently heard – “Fintech”.
Fintech
Fintech, integration of FINance and TECHnology, is used to describe the use of technological advancements in the vast field of finance. It is increasingly being used to improve delivery of financial services, be it trading or portfolio management. Fintech has effectively allowed companies and consumers to manage transactions, like every time you recharge your phone using Google Pay or get a cashback on AmazonPay, that’s a part of Fintech.
It is surprising to know that use of technology in finance has been with us since 1860s, with the development of telegraphs, but the current phenomena developed post-2008, after the Global Financial Crisis, and it has been on rise ever since.So let’s dive a little deeper in what fintech really is.
Financial Technology is basically an innovation in the financial transaction activities. It not only includes digital payments (remember “Paytm karo”?) but also complex tools like roboadvisors.
- Roboadvisors are basically digital platforms that collect information from clients about their financial situation, preferences and goals, and help in financial planning of individuals.
Fintech is a constantly changing industry through introduction of evolving products and technologies.
It is, nowadays, being increasingly used in insurance and banking sectors, with some banks introducing virtual assistant,like SIA by SBI Bank or EVA by HDFC bank, which conduct financial transactions and answer FAQs. Stock-trading apps like Robinhood, Zerodha are becoming more popular among investors. It is also being extensively used in crowd-funding like GoFundMe, and in investment management services that allow advisors and managers enter data of clients and using algorithms, optimally allocate assets and generate portfolios.
Wondering how? Let's take an example to understand this thoroughly.
Imagine you are a trader and you spot an arbitrage opportunity.
In finance, arbitrage is the technique of making profits at a low risk by taking advantage of difference in price of one asset across exchanges. Ideally, an identical asset should sell at the same price irrespective of its location if the price is denominated in a common currency. However, this is not always possible due to multiple factors like transaction costs, transportation costs or legal restrictions (like taxes). For instance, there may be a difference in price of furniture ordered from Mumbai and ordering an identical piece of furniture from Delhi due to transportation costs involved in the latter case. In equity markets, these differences in price could be due to difference in exchange fees charged by exchanges or where one exchange has a greater number of buyers and sellers than the other.
We’ll take a simple example where the shares of a company are trading at a lower price on BSE and at a higher price on NSE. Here, to exploit the arbitrage opportunity, you’ll buy the shares at a lower price in BSE and sell at a higher price in NSE. Other traders will spot the opportunity too and place similar trades thereby increasing demand (and thus the share price) on BSE and increasing supply (and thus decreasing the price) on NSE. Since the movement of share prices is in your favour in both the markets, you square off your positions in both the markets (square off: sell the shares on BSE and buy the shares on NSE) thereby making profit at low risk. The risk in an arbitrage transaction is usually low because almost all traders will place similar positions and thereby drive the prices in a direction to minimize the difference in prices across exchanges.
However, an arbitrage is not completely risk free because there is a possibility that the prices fluctuate adversely before you are able to complete all trades (For instance, in this example, what if the prices fall on BSE before you could sell off your shares? You would make less profits, if not a loss). Now, for arbitrage to be successful, it is very important that trades are placed quickly because in an ideal situation, these differences in prices (also known as market imperfections) are corrected due to play of market forces of demand and supply and in more liquid markets (i.e. where there are more number of buyers and sellers) these could be corrected in a matter of seconds(because who wouldn’t want to make profits at lower risk?).
And that’s where computers come to our rescue. Computers can be programmed to place trades as soon as specific conditions are met and these trades are placed in milliseconds or sometimes even microseconds. – andthe process of specifying these instructions can broadly be called Algorithms
However, this example is not exactly use of Artificial Intelligence(AI). If we take this to a level further for instance instead of two exchanges, there are 200 exchanges or instead of you spotting the arbitrage opportunity, the computer does it or instead of you making the allocation of funds to execute the arbitrage opportunity the computer does it. Yes, all of this is possible using AI. Technically, this is known as Algorithmic trading (sometimes also called as automated trading or algo trading).
Algo Trading
Algorithmic trading can be defined as the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader.
Algorithmic trading uses machine learning to analyse data, risks, trends in markets and takes trading decisions based on such analysis. Using AI, millions of trades can be placed per day (known as High Frequency Trading (HFT)). An analogy I had come across aptly described the algorithm as the brain and HFT as the muscles.
To understand what factors can an algorithm consider in its decision-making process, we will continue with the example of arbitrage. One of the factors why an arbitrage opportunity is not always exploited is that the costs (such as exchange fees) of executing trades for the arbitrage can exceed its benefits. To solve this problem, the algorithm of an arbitrage takes all costs into consideration in the decision-making process.
Amazing, isn’t it? Please share your views in the comments section.
To know more about AI in Finance, do read Part-2 of this article. Coming out Tomorrow!
Meanwhile you can checkout our article on Soft Skills in Machine Learning to know whether AI can replace humans.
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