Neural networks are already helping to build marketing strategies, simplify the collection and processing of data for business. But what about investments? Can a machine replace a financial analyst? We will tell you how artificial intelligence helps to optimize an investment portfolio, predict trends, and reduce risks. And why it is worth double-checking financial algorithms.
Is it possible?
Trading is numbers, analytics, forecasting. Therefore, it is logical that large traders and private investors began to use neural networks for investments. But it should be noted that a neural network cannot yet replace a person. Because:
- it is trained on statistical data - any crisis, instability, new challenges will confuse AI, which will affect the accuracy of forecasts;
- quotes are the result of many events that are either not taken into account or cannot be expressed in a numerical value,
- algorithms give trading signals to buy, sell, but do not justify the decision - for novice investors, this approach increases the risks.
Therefore, you should not rely only on AI. In addition to the opinion of AI, it is necessary to independently evaluate real data, take into account expert opinion, use different platforms, comparing results.
How it can help
You can trust the neural network with routine tasks - searching, processing information, for example:
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quote statistics;
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chart analysis;
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assessment of economic factors affecting the market;
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analytics on securities, issuing companies.
It will do it faster and more accurately than a person. In addition, AI can become a personal assistant - it will tell you about the basic terms, explain what parameters should be taken into account when buying, selling shares, bonds and when it is necessary to review the balance of securities.
Forecasting market trends
Here, AI takes on the most difficult work. It will study:
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historical data;
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news agenda;
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macroeconomic situation;
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user reaction to events.
Based on the data received, the neural network identifies patterns, relationships between key indicators and predicts a trend. For example, if the machine “notices” an increase in the number of company mentions in the news related to expansion or the release of an innovative product, it will assume that the value of this issuer’s securities will grow. And, conversely, if the number of mentions of the company tends to zero or grows, but in a negative context, the AI will have grounds to predict a drop in its quotes at trading.
Optimization of investment portfolios
Services with integrated AI can help with the selection, rebalancing (optimization of the ratio of risky, but high-yield and reliable assets with low volatility) of the investment portfolio. When making forecasts and recommendations, the algorithm takes into account:
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current market conditions;
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risk factors, including macroeconomic and political;
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user preferences in the earning strategy (long-term or with quick profit).
In addition to processing information, the machine can simulate positive and negative market scenarios, predict possible risks, and make recommendations for revising the asset ratio. You can find out how ChatGPT can handle this task right on our website. Just go through a quick registration.
Main types of generative neural networks
Generative models based on AI are:
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Generative-adversarial - consist of a generator and a discriminator. The first produces new data, and the second learns to distinguish real information from fictional. 2. Variational autoencoders – also consist of two components: an encoder that converts input values into a compressed representation and a decoder that brings them to their original form at the output. The inclusion of stochastic processes in the architecture is responsible for variability, obtaining new values that differ from the input ones.
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Autoregressive – to predict the sequence of each next element in the chain, information about the previous ones is used.
The latter are often used in models for natural language processing, text generation and even music. On the site, you can access generative AI – Midjourney, Claude, Perplexity and others, without downloading additional applications.
Tools and platforms for investors
In addition to built-in tools (like the Moscow Exchange, which integrated the service with AI to assess the rating of participants), the following tools are used:
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ChatGPT – compares the financial performance of issuer companies, can highlight and analyze key indicators, is suitable for novice traders, as it gives useful advice, explains the meaning of terms;
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Stock Rover – searches for and systematizes information from stock exchange news, takes into account the performance of market participants, evaluates them according to hundreds of important criteria, generates comparative lists;
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Kavout is a platform for stock analysis that works with stock exchange statistics, can search for, interpret news mentions of issuers, and make forecasts.
On the site, you can access AI models without registering a foreign phone number and bank card. Just choose a tariff and the neural network that you plan to test.
Cases of successful use of neural networks
Here are three examples that confirm that trained neural networks can show good results in forecasting and forming investment portfolios.
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ChatGPT was tasked with creating a portfolio with a limited number of shares of American companies based on the selection principles used by investment funds. The finished portfolio, consisting of 38 securities, grew by almost 5% in a month. At the same time, the shares of the funds that the bot focused on fell in price by 0.8%.
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At Seoul University, the chat bot was given a more difficult task - to select 5 cryptocurrencies, currency pairs from 10,000 variations. Each portfolio created by the AI turned out to be effective, and showed clear signs of using diversification principles in selecting assets.
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The capabilities of the AI Investment Assistant developed by T-Bank are slightly more modest. The algorithm searches for data on request, analyzes economic news, evaluates their impact on the growth and fall of assets. Collect analytics on specified parameters and form a universal investment portfolio.
In general, forecasts for the use of AI in stock trading are positive. This is also evidenced by the growing number of tools with integrated artificial intelligence technology.
Results
AI investment services are really useful for the investor. Provided that the results of its work are re-analyzed, and not perceived as the only correct guide to action.
