Last Updated on 20 April, 2023 by Samuelsson
In today’s world, there is no aspect of our lives that hasn’t been touched by Artificial Intelligence (AI) and Machine Learning (ML). From speech recognition and visual perception to market analysis and trading, machine learning has been used to make things faster and more efficient. But what exactly is machine learning, and how is it used in trading and investments?
In the financial world, machine learning is the subfield of artificial intelligence that creates computer algorithms that can learn and adapt to new data (which can be from the market, news, political events, and social media) without human intervention. Machine learning algorithms employ an enormous amount of structured and unstructured data to make precise predictions based on that data and also execute trades accordingly.
The topic will be discussed under the following subheadings:
- What is machine learning?
- Why does machine learning matter in the finance world?
- How machine learning is used in investment management
- How machine learning is used in trading
- Examples of companies that use machine learning
What is machine learning?
Machine learning is a concept in computing whereby a computer program is made to learn and adapt to new data without human intervention. In financial trading, machine learning is used to keep trading algorithms current regardless of changes in the worldwide economy — a complex algorithm or source code is built into a computer that allows for the machine to identify new data and adjust its model in the light of the new data.
As a subfield of artificial intelligence, machine learning uses superior logic than those of simple computer algorithms to analyze millions of data sets within a short time to improve the outcomes without being explicitly reprogrammed. It uses new data from changes in the market and sociopolitical and economic environment to remodel itself and adapt to new situations — it provides the ability to learn and improve from experience without being programmed. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. That is, the system automatically adjusts its parameters to improve experiences.
Machine learning is now considered a key aspect of several financial services and applications, including quantitative trading, high-frequency trading, investment management, risk evaluation, credit scores calculation, and loans approvals. For any of these functions, computer systems run operations in the background and produce outcomes automatically, according to how it is trained.
Machine learning tends to be more accurate in drawing insights and making predictions when large volumes of data are fed into the system, and the financial services industry tends to encounter enormous volumes of data relating to daily transactions, payments, and customers services. As a result, many leading fintech and financial services companies are now incorporating machine learning into their operations, resulting in a better-streamlined process, reduced risks, and better-optimized portfolios.
Why does machine learning matter in the finance world?
Financial trading deals with huge amounts of data available in different formats from disparate sources, including the market, social media, and news websites. The enormous amount of data, often referred to as big data, is becoming easily accessible due to the progressive use of technology that can scrape and analyze data from the internet.
With the huge insights that can be gained from tapping into big data, the need for advanced computing capabilities required to comb through the wealth of information was obvious. This is why machine learning is being employed by different firms to gather, process, communicate, and share useful information from data sets.
Machine learning is the newest revolution in the field of trading, after online trading and automated execution. With machine learning algorithms for trading, it becomes easy to identify the patterns in the market, assess the investment risks, and analyze the sentiments of the people. Machine learning has also made robo-advisory services possible, which has reduced the costs associated with financial advisory significantly.
Another aspect is in automated chatbots services which make it easier for clients to communicate with their brokers quickly and gain faster access to important information that may aid their decision-making process. These are why financial firms are quickly adopting new machine learning in one way or another, as the applications of these technologies in trading will only continue to prosper in the future.
How machine learning is used in investment management
Machine learning has found a lot of uses in investment and portfolio management, which is why many asset management firms are quickly integrating it into their processes in different ways. Here are some of the uses of machine learning among investment firms:
Investment analysis and research area
Many asset management firms employ machine learning in areas of investment research and analysis. Let’s take, for example, an asset management firm that invests only in mining stocks. The firm may be using a social strategy that is based on scanning the web for all types of news events from businesses, industries, cities, and countries, and this information makes up the data set for their investment analysis.
Gathering such huge information in a short period may be impossible with human efforts, so they can use sophisticated computer algorithms with machine learning capabilities for that. Since they invest only in mining stocks, they can build their model to extract only data about mining companies, regulatory policies on the exploration sector, and political events in select countries with big mining industries.
Investors now leverage robo advisors to create an adaptable portfolio of investments and execute the trade in the different markets of the world. In fact, many investment firms function by robo advisory services alone. Robo advisors are automated computer programs with algorithms that help in creating adaptable portfolios based on the investor’s risk profile and investment objectives. These algorithms enable investors to make accurate decisions in different circumstances using information about their financial profile.
Robo advisors function by first collecting the prospective investor’s information such as financial objectives, timeframe, and risk tolerances and then analyzing this information with the help of its many algorithms including machine learning models to give the best advice to the investor. They are often fully automated, so they also take investment management actions, including rebalancing the portfolio of the customer.
One of the benefits of robo advisors is that they have reduced the cost of financial advisory services since the computer, rather than MBA-holding financial consultants, do the main research and advisory work. Apart from being cost-effective, robo advisors also save time because they are fully automated; they manage the portfolios in the smallest time frame and ensure that trades are executed as early as possible.
You would agree that machine learning is revolutionizing the way we communicate, even in the investing world. For instance, the introduction of numerous useful applications powered by machine learning, such as chatbots, has hastened communication between investors and their brokers or asset managers. Chatbots communicate with the investors and present them with a history of financial statements and other useful information. For example, an investor can ask the chatbot about the broker’s trading offers or payment methods. The chatbots will not only update him about the current prices but will also provide links to more information about the investor’s query.
Some of the valuable broker information you can get from chatbots include real-time quotes, account statements, FAQs (Frequently Answered Questions), and notifications about the steep price movements. These chatbots are faster in response than humans, thereby saving the investors some time. One good thing about chatbots is that they can process and learn from all the past conversations.
How machine learning is used in trading
When it comes to trading proper, there are many ways machine learning is being used by big financial firms to optimize their trading and improve profitability. These are some of them:
Identifying patterns in the data
Many trading firms are using machine learning algorithms to identify market and social patterns that are associated with the most price movements and how the market responds to such patterns. These patterns are ever-changing, and the process of identifying these patterns entails a great deal of time and energy. Moreover, there are often huge amounts of data to analyze, so machine learning algorithms are used to identify the patterns that accurately predict the future picture.
Machine learning algorithms even help to combine fundamental data with technical patterns. With what would have been an enormous analysis now simplified, traders are in a better position to make more accurate trading decisions. The good thing is that the algos are self-learning and adapting to changing market conditions.
One limitation of using machine learning for finding patterns is that many traders in the same market employ it for the same purpose. So, there is so much competition; the patterns identified by one trader are also at the disposal of other traders in the market, rendering them less effective. Thus, it is not enough to use machine learning to find patterns, the trader has to act fast or adapt continuously because the patterns vanish immediately due to the intense competition.
Investors’ sentiment is one of the many factors that affect stock prices. Trends and lack of them are often a result of the sentiments of market participants. As a result, many trading firms now use machine learning to analyze the sentiments of people as a way to predict the prices of stocks. Since people express their views about anything on social media platforms freely, these platforms (Twitter, Facebook, Reddit, etc) are a potent tool for sentiment analysis. Machine learning algorithms can process social media content such as tweets, posts, and comments of people who generally have stakes in the stock market.
This sort of sentiment analysis is carried out by leveraging Natural Language Processing (NLP) — a subfield in machine learning that enables computers to comprehend and analyze human language — to categorize the sentiments of people about a stock into three categories: negative, positive, and neutral. For example, if there is an optimistic feeling about a stock among people, the price is likely to go up. On the other hand, if there is a pessimistic sentiment among people, the stock is likely to decline. This data is then used to train a machine learning model to be able to forecast the stock prices in different scenarios.
Predicting real-world data and assessing risks
In trying to forecast the future value of stocks, traders can use computer programs powered by machine learning to certify the accuracy of their predictions. Machine learning algorithms can account for multiple factors at the same time by leveraging neural networks to detect and analyze the predictors that cause the fluctuations in stock prices.
Since risk assessment is crucial to successful trading, traders also employ machine learning in identifying all risk factors and analyzing the overall risk level. Machine learning algorithms can process huge volumes of data to assess the risks and forecast future changes in the market. This can help traders take proactive actions to mitigate the impacts of the risks.
Algorithms and computer programs make decisions quicker than humans and without the influence of external factors such as emotions, which is why many traders now use trading algorithms — pre-programmed computer systems that follow a clear set of instructions to execute the trades.
Examples of algorithmic trading systems include:
- Strategy implementation algorithms: These systems execute trades by scanning for signals from real-time market data.
- Stealth/gaming trading algos: These systems work by exploiting the price changes caused by large trades or other algorithmic strategies.
- Arbitrage trading systems: They exploit price differences between two exchanges. For example, a stock that is listed on two different exchanges may be trading at different prices. An arbitrage system would buy the stock on the exchange where it is cheaper and sell the stock on the exchange where it is more expensive.
- Trade execution algorithms: These systems work by splitting trades into smaller orders to reduce the impact of huge trade on the stock price. Some commonly used trade execution algorithms include Volume Weighted Average Price (VWAP), Time Weighted Average Price (TWAP), and Percent of Value (PoV).
Trading algorithms have been in use since the late 1980s and 1990s, but they keep developing and getting better with improving technology. Because of its profitability, when algorithmic trading was first introduced in the market, it immediately captured the attention of the traders. But as the competition heightened, the profitability declined significantly. The traditional algorithms, created by programmers and data scientists, depend on “if and then” rules and are unable to upgrade themselves by learning through historical data.
In today’s world, financial trading firms are using machine learning to build algorithms that do not depend on rule-based systems. The algorithms that are powered by machine learning learn new trade patterns automatically without requiring human intervention. That is, machine learning is used to improve the efficiency of algo trading systems. Since machine learning systems can use new data to learn and forecast the forthcoming market picture with terrific accuracy, trading firms use machine learning to optimize the decision-making process and efficiency of their automated trading systems.
High-frequency trading refers to the execution of a large volume of orders within a fraction of a second. Executing numerous orders within a short time is beyond the capacity of humans, as it requires significant time to read the market trend and place bids manually. So, traders use algorithms and computers for automated order executions.
Machine learning systems make such automated trading more efficient. High-frequency trading makes use of many machine learning algorithms and feature creation methodologies. An example is the SVM, which works by creating a line of separation in the data and involves training the models to be able to identify features that reflect an approaching increase or decrease in the bid and market pricing.
Examples of companies that use machine learning
Many big investment firms are rapidly embracing machine learning algorithms for trading. These are some of them:
- Goldman Sachs: Goldman Sachs employed 600 traders at its New York headquarters in 2000, but by 2017, the company had reduced them to only two traders and, instead, employed over 200 computer engineers. The firm obviously automated its trading process and swapped human traders for computers that run complex algorithms and perform huge analyses to predict the most profitable trades.
- Morgan Stanley: This multinational investment bank and financial services company leverages robo advisors powered by machine learning to assist investors in managing their wealth. With machine learning algorithms, the firm helps investors to make better and informed decisions based on real-time data.
- Wealthfront: This is a robo advisor firm. It uses machine learning to offer financial advice to investors at a relatively low cost.
- Tino IQ: This California-based firm uses machine learning algorithms to scan the stocks across the markets to identify patterns with great predictive power. Based on these patterns, they list stocks on their app with buy and sell recommendations.
Obviously, machine learning has revolutionized the trading domain by automating tasks that previously were not possible without human intervention. Traders and firms that lag in the adoption of these tools may find it difficult to compete in today’s financial markets.