• 7 October 2020

Machine Learning is becoming embedded in finance

Machine Learning is becoming embedded in finance

Machine Learning is becoming embedded in finance 984 555 FinScience

Machine learning for Algorithmic Trading

Machine learning is an emerging technology in finance. Developments in artificial intelligence enabled the spread of systems that can learn from experience through a process called “deep learning”. Deep learning consists in algorithms that process data on different layers of abstraction, to transform step by step the unlabeled input in a more classified output. Traders are now adopting deep learning to stock trading, to try to beat the market with useful additional information.

Algorithms of machine learning are able to conduct trades autonomously. This particular application of artificial intelligence is called algorithmic trading. This trading approach has been developed to take advantage of the calculation speed of modern computers, that far exceed human capabilities. 

The evolution of algorithmic trading began with the introduction of computers in trading during the 1970s. Back then, computers were still very expensive an only few institutions could afford them. In the 1980s many stock exchanges started to accept electronic trading and now, since computers are less expensive and can provide incredible calculation ability, the majority of trades are executed by algorithms.

In the past years, many individual traders started to perform algorithmic trading by their own. This was possible thanks to the development of technologies that are now more easily accessible.

What is Algorithmic trading?

Algorithmic trading consists of executing trading orders following algorithm previously programmed by traders. Basically, the software places trades following parameters, such as price, timing and volume established before by programmers. This method uses advanced mathematical models and formulas to make autonomous decisions regarding trades on the stock market. The systems adopt complex algorithms to analyze the markets and spot emerging trends in a fraction of a second. 

Applications of algorithmic trading

Algorithmic trading is largely used to manage index funds. To achieve the goal to track a specific financial index (i.e. S&P 500), those funds have to be periodically rebalanced and algorithms perform this task perfectly.

Pairs trading is another investment strategy performed by algorithms. It enables traders to profit regardless to market movements, taking advantage of the correlation between two securities.

Algorithmic trading is used to profit on price differences between stocks of the same company traded in different markets, with a strategy called arbitrage

Mean reversion is a mathematical methodology applied to finance that, through the use of algorithms, tries to identify the average price of a stock to sell it when it is above and buy it when it is below.

The extremization of algorithmic trading is the high frequency trading (HFT). This investment strategy consists in executing hundreds of thousands of trades per day. By essentially anticipating market trends, institutions that implement high-frequency trading benefit from minute price differences that, on large amounts of trades, can result in great profit.

What are the strengths of the algorithmic trading?

Institutional investors and banks take advantage of algorithmic trading to save in terms of costs. Since the strategy adopted in algorithmic trading usually consists in making large orders, cost related to trading are minimized. Moreover, this method allows for faster and easier execution of orders, which convert in greater profits for investors. Traders can buy and sell high volumes of stocks in a short period of time, thus benefiting from small changes in prices.

Algorithmic trading is also used by players to create a large amount of liquidity in a very short time.

The ability to learn from historical data and build experience makes this software capable of taking decisions to react quickly to market changes. 

Another crucial aspect of this investment approach is the total absence of emotionality. Humans are subject to impulses and sentiments, while machines follow procedures and algorithms created to be as rational as possible. Putting aside emotions is essential to obtain the best investments results, therefore, many investors are adopting algorithmic trading.

What are the threats of algorithmic trading?

Algorithmic trading works only if the orders that the algorithm decides to trade is a percentage relatively small compared to the total volume traded in the market. High speed and quantity of orders execution can create problems if the volume exchanged in the market is not sufficiently great.

Another drawback is about liquidity. If the algorithm decides to invest a large amount of capital on a trade, that liquidity will disappear and will not be available for other opportunities until the algorithms decide to leave totally or partially the position. 

Those disadvantages can be avoided by constantly supervising the progress of the programs and stopping the algorithm when something unwanted happens.

What are FinScience’s solutions?

For starters, having understood the cruciality of machine learning algorithms in the financial world, FinScience has developed several proprietary machine learning algorithms that have been used in multiple application fields, such as for the creation of a platform that collects and utilizes alternative data to keep updated the investor and let him take the right decision once he has all the information needed, assessing credit and risk scores, and ultimately to be used as predictive models or portfolio strategies regarding quantamental investing. Referring to the last solution, it is a new methodology which combines a quantitative (based on measurable and objective data) approach to investing with a fundamental one (based on subjective knowledge of the companies). The model relies on financial and alternative data to predict stock market performance and to build innovative investments strategies.

This new approach is driven by the innovations of the past years. The Cloud now give incredible computational possibilities at low cost, the alternative data available are almost endless and the improvement of machine learning have been remarkable. Those innovations made possible the development of the Quantamental investing, a revolution in the field of financial investing.