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Artificial intelligence (AI) is already impacting the stock market. It is helping to increase the returns of investment portfolios, but it is also introducing new risks. The question is whether AI can replace specialists like financial analysts. AI is an advanced form of computing that can process huge amounts of data.

Econometrics in the 20th century[]

The 20th century was the era of the development of econometrics, a science that combines economic analysis with mathematical modeling. Using mathematical statistics and probability theory, economists sought to describe complex economic processes. The famous statistician from Great Britain, George Box, once noted that forecasting models are never perfect, but can be very useful. After all, any theoretical model is only an approximation to the complex economic reality and has its own limits of accuracy.

Market psychology. The driving forces of the market are the greed and fear of the participants. Their decisions and social behavior confirm this. Information flows directly affect the value of assets. Considering this, it is said about the concept of market efficiency, which evaluates its ability to take into account various data. Three levels of efficiency are distinguished: the first analyzes past data, the second analyzes current publicly available information, and the third analyzes confidential information. Still, even in efficient markets there are deviations due to irrationality of investors' behavior, which leads to errors in calculations.

AI in Financial Analytics. Artificial intelligence is now able to analyze massive amounts of data from social networks and media, which is impossible for a single analyst. This allows us to understand the general opinion of the market that affects stocks. With the increasing number of individual investors influencing the market through forums, even the big players need to adapt to these changes. Thus, there is a new field of analysis that was previously unexplored.

Changes in the Stock Market[]

Due to the withdrawal of foreign investors, market dynamics in Russia have been transformed. Assets are now managed mostly by domestic individuals whose influence is likely to grow. Assessing their influence and coordinated actions is a challenge for future AI research.

Today's AI technologies do not fundamentally change fund trading, serving more as tools for data analysis. While IT professionals are actively embracing AI, experts in finance are also developing their skills. Financiers, known as “quants,” have competencies in math, programming and economics, making them increasingly in demand in the market.

Initially, since the 1970s, the need for quants grew due to the banking sector wanting to expand the derivatives market. Banks sought to create innovative financial instruments such as futures, forwards, swaps and options.

It is the seller's job to carefully assess the risks and guarantee the fulfillment of obligations to customers. The financial crises of the past decades often arose from underestimating the risks associated with derivatives. These events have led to adjustments in risk management practices in the investment industry, which has become the second key function of quants.

Investment Management[]

Investing is not subject to a single technological methodology. The focus is on the investor's unique goals, risk preferences and potential returns. Experienced analysts build investment strategies by minimizing costs and choosing from a variety of assets, from stocks to currencies.

Experts analyze market trends for profitable operations under different market conditions, determine investment timing and manage portfolios. Data-driven algorithms herald the AI era in investments where professional expertise matters.

AI and data for business. The effectiveness of artificial intelligence (AI) is directly dependent on digital transformation and a quality database. Testing models on historical data often promises success that doesn't always materialize in reality as conditions change. Modern machine learning techniques are improving the accuracy of predictions, but AI constantly requires improvement to ensure its reliability.

Artificial intelligence comes with high costs for equipment and training. The effectiveness of such systems is still questionable. For example, generative models like ChatGPT have proven to work, but AI for the investment industry is still in the early stages of testing. Progress in this area remains limited and requires careful research.

Modern artificial intelligence facilitates asset management, but does not surpass the methods of quant analysts. It analyzes big data, identifies trends and important indicators. The full application of AI in the future may strengthen the competencies of analysts who need to constantly evolve in this area. Humans are still in control of machine investment decisions.

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