Let’s picture the stock market 30 years ago:
Let’s picture it now:
Like many other areas now, the investment market has changed immensely with the help of technology. There is a new overlord- investment algorithms, which now complete about 90% of all trade in developed countries.
But, what exactly are these so-called “algos”? What can they do? Why are they super smart?
Buckle up people, we are about to find out!
The Dawn of Algos
Since the financial crisis of 2008, the stock market has changed not just through regulations, new entrants, and shifts of power, but also through the incredible advancement in algorithms which help automatize and improve trading.
Algorithms are a set of instructions, which carry out a certain task. They automate processes and execute trades at a pace which is impossible to match with human traders. The rules are set via a variety of factors: asset type, market, prices, quantity, timing, trends. According to their pre-defined calculation, execution algorithms make decisions and perform trades to maximize profitability.
A key factor of investment algorithms is that they completely rule out the human sentiment. No BS with fear, greed, or psychic predictions. Algorithms do what they need to do to achieve the best results.
Types of Investment Algorithms
Index Fund Rebalancing
Rebalancing is a process where the underlying assets of funds are readjusted according to current market conditions. For example, a pension fund is supposed to be a combination of 50% stocks and 50% bonds. In a few years the value of stocks increases, and now compromises 60% of the portfolio. During rebalancing, some of the stocks are sold, in order to bring back the portfolio to the original 50-50 allocation, and the trader profits. These rebalancing transactions are now automatized by algorithms.
Arbitrage means taking advantage of small market discrepancies for extra profit. For example, the same asset can be traded on different markets at a different price. An arbitrage trader is able to buy the same asset on one exchange at a lower price, and sell it on another for a higher one. Arbitrage algorithms are trained to spot differences and perform transactions instantaneously.
This mathematical method helps calculate the average price of a stock in a certain time period by past indicators, forecasts, and standard deviation. The average price is an indicator to buy when we the asset is under the mean, or sell when the asset jumps higher than the average value. This analytical technique is very common in the stock market. Algorithms help to automatize the process starting from the analytics to the actual transactions.
Market timing algorithms aim to predict the performance of an asset through time. They are complex to construct: the development includes 3 different phases, several datasets, and plenty of tests. The aim is to be able to project the changes in the value of an asset through time with complex analytical methods. Knowing the market outcomes opens a possibility to optimized results and very high profits.
All Hail Investment Algorithms
There are several upsides to algorithmic trading:
- Unambiguous decisions: excludes human emotions with a realistic evaluation supported only by data.
- Precise execution: less human-induced errors.
- Resource efficient: needs less manpower than traditional processes.
- Fast and furious: transactions are performed at a much higher speed thanks to automation.
Algos Be Damned
Let’s see the darker side of investment algorithms:
- Prone to failures– technical issues, as connectivity issues as power issues can highly affect trading and create duplicate or missing orders.
- Programmer dilemma: investment algorithms are as good as the programmers make it, and even well-programmed algorithms can fail in the real market.
- Complex and demanding: to establish functional algorithms we need vast amounts of data, programmer expertise, and computational power.
- Excluding individuals: individuals approaching the market with traditional investment techniques become futile, thus trading power remains in the hands of a few institutions who can allow working with algorithms.