Algorithmic Trading- The Future of Stock markets and Traders

Algorithmic trading is a method of executing trades using pre-programmed automated trading instructions. It accounts for variables such as price, timing, and volume. This method uses mathematical models and formulas to automate financial transactions. The automated trading system has the key benefits of high-speed order execution.

It started as early as the 1980s when program trading was introduced. Program trading is a type of securities trading where stock orders are executed using computer programs based on predetermined conditions. It gained traction with more computers and programming systems being involved in the financial markets. This method of trading combines the potential of financial knowledge and computer coding to generate higher market returns. This is super-efficient in finding arbitrage in markets and capitalizing them with huge volumes to gain profits.

Statistics

·       80% of daily volume in the USA is traded using machines

·       92% of forex trades are executed by algorithmic trading (2019)

·       The global Algorithmic Trading market is expected to grow at a CAGR of 10.36% during 2018-2022

·       JP Morgan has allocated a tech budget of $10 Billion for Algorithmic trading

Technology Used

This system deploys cutting-edge technology and builds an alpha-seeking strategy that is heavily dependent on data. Deep research and deep learning on past data are used to find future trends. The historical database is also used for back testing the programmed algorithms and to check the potential of the strategy used. Since it is also configured with the capabilities of Artificial Intelligence (AI), the programs developed by programmers improve themselves using Deep learning. The Systems Architecture (SA) of the machine and the algorithms used are of prime importance. A small programming glitch can crash the entire architecture. The conditions used for automation can track the consensus that is being built among market participants. High speed transactions can execute trades that can capture even the smallest price difference, thus building a huge opportunity for profits. In 2017 it was reported that many firms across the globe have adopted Machine Learning software for trading

Advantages

·       Used primarily by large institutional investors

·       With large order size and volume, the costs are significantly cut down (economies of scale)

·       Faster trade execution

·       Glitches are reduced as trades are automated

·       Since the volume transacted is more, even a small change in price leads to profits

·       Since there is zero emotional quotient, the behavioral finance and bias of human thinking significantly reduces while trading

Disadvantages

·       Algorithmic trading was the main reason behind the Flash crash of 2010

·       Reduces market liquidity

·       Costly to establish, as it involves huge infrastructure

·       Humans are facing stiff competition from computers and are being fast replaced

High-Frequency Trading (HFT)

HFT is an application of Quantitative Finance. When a large Hedge Fund or Trading Bank buys huge volumes of the shares of a particular company, the volume gets executed in more than one exchange purely because of the high volume transacted and one single exchange can’t fulfill the orders. Thus after exhausting one exchange, the order automatically goes to the next exchange to fulfill the order. Like this, the orders move from one exchange to another until the complete order is fulfilled. Firms involved in HFTs place strong servers near the exchanges and receive the buy orders and immediately transmit the signals to the Bank involved in HFT in milliseconds. Before the Hedge Fund can move to the next exchange, the HFT Bank buys the specific shares in the next exchange and profits by a sudden price rise and quickly clears off its position. The entire demonstration happens in a few microseconds and is extremely fast.

Strategies (Not exhaustive)

1. Index fund rebalancing- Exploiting the timing difference when companies are rebalanced in the Index. The companies listed on an Index are rebalanced based on certain criteria, during rebalancing, their values fluctuate.

2. Arbitrage- It is the practice of taking advantage of a price difference between two or more markets

3. Mean reversion- Finding the trading range for stock and taking positions accordingly. We expect the stock price to get to its average price after small fluctuations in price. Using such price differences to profit

4. Pairs Trading- This is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in the relative value of close substitutes.

5. Delta-neutral strategies- It describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security.

Opportunities

We see that there is a growing inclination towards coding and automation. The new-age software developers and programmers are building new tools and new programming languages are getting implemented. New areas such as AI, ML, and deep learning make extensive use of coding and automation. This trend has also entered financial markets, coupled with the growing inclination for everyone to trade in the stock markets, the combination of trading using programming and automation has great rewards. The potentials are well analyzed by big hedge funds and banks which are investing in millions to buy such talents and make a profit. Trading along with coding is a very valuable cross-skill that is in great demand now. The standard returns generated using algorithms are higher than Index fund returns. These are developing alternate investment opportunities, which were relatively unexplored. They also have low correlation of returns with traditional investments.

Industries

We must also understand that the penetration of systems in financial markets has replaced the dynamics of the functioning of the industry. There is an increase in software and computer spending, making the states more capital intensive. There is also a shift in the type of employees working in the finance industry. There is a growing demand for computer programmers than for stock traders. The largest employers of Quantitative finance are huge Hedge funds, Investment banks, Mutual funds, and Pension funds.