Charles Babbage may not have worked in Wall Street, but surely his ‘Analytical Engine’ devised in 1837 has marked this new eon of analytical science. This revolution continues to prosper in replacing human toil with easily replaceable code embedded machines that lead to ease, efficiency and accuracy in doing business. In 1987, Teknekron Corp. along with Goldman Sachs developed the first computer driven stock trading software named TIBCO.
Since then, automation and digitization have been innovating new ways to perform financial and investment operations. Being not too late now to embrace this change, we can avoid possible surprises in future. Now let me ignite your intuition with few interestingly simple yet revolutionary trends in every financial workspace.
In a world of 24 hours a day, finding time to visit the bank to create savings account or to pull money out of an ATM can be time consuming. Even carrying cash is now considered risky and old fashioned. These changes in culture and preferences have led to innovation in personal finance through online wallets and payment services such as Paypal.com, taking away the need for a bank account.
There is plenty of room to innovate in personal finance industry. These may include few upcoming ideas such as payments through finger-print access, savings using public ledger instruments such as Bitcoin and other crypto currencies. Since every new innovation can bring along various security threats, realizing such changes relies on regulatory approval and continuous innovation in encryption algorithms to protect information from hackers.
It was yesterday when businesses manually did accounting on tally or any other desktop accounting software, now accounting has moved to cloud accounting platforms which can be shared and edited at multiple locations at any given instant. In future we can expect automated accounting, which can load accounts into the cloud automatically using machine learning, where algorithms determine the type of accounts and perform other accounting operations.
Business transactions are simplified through online payment systems and other digitized payment methods. Businesses hire services from payment gateway service providers such as Paypal.com for online checkout or for other merchant transactions. These service providers hire programmers with essential language skills which include web development, cyber security and cloud computing.
Investment banks and high net worth individuals investing in start-up projects is an old tale now, as Crowd Funding has accelerated angel investing through online platforms such as gofundme.com, where large number of people can invest in a single project. In United States, JOBS act was enacted to encourage small businesses in raising capital through equity crowd funding. But much reform needs to be done, with respect to allocation and other issues regarding crowd funding. In most countries, the regulatory authority has not yet come up with a decision to allow this form of angel investing. But we may see them coming forward with regulations to enable crowd funding in future.
Another area where machines have replaced humans is Algorithmic trading, where computer programs perform logical and scenario analysis to perform trade operations in the secondary market though machine learning. These days many small hedge fund firms have started using open source languages such as Python to perform analysis and trade operations.
On the other side of Algorithmic trading, High Frequency Trading ‘HFT’ firms have developed ultra fast trading technologies which is usually housed near the exchanges to avoid ping delays to trade on market making strategies. Though naysayers may cry about Algorithmic trading, regulatory authorities have been encouraging HFT due to liquidity concerns. HFT firms usually hire programmers to device programs that run sophisticated algorithms devised my mathematicians. Few notable languages for coding include SAS, SPSS and Python.
Investment finance has lot to do with data driven decision making, for all investment decisions or business decisions. Be it bond pricing, equity pricing, forecasting, multivariate analysis or any other aspect of financial modelling is done through Microsoft Excel to interpret data and manipulate it. SAS programming language has been enjoying monopoly in data driven decision making, while other programming languages such as R and MATLAB are widely used in academia only. Though there are several languages available for data mining, where you can get lost in, SAS is used widely in the corporate world and R programming language comes next in line.
This blog is written by Ajay Singh (MBA-FA, 2014-2016) student, ICoFP delhi campus.