Artificial intelligence algorithms are widely used in many fields, such as medicine, industrial production, and transportation. This article discusses the interdisciplinary issue – the interconnection between artificial intelligence and finance. This article briefly introduces the use of some relatively basic artificial intelligence algorithms in the financial environment, which can help to gain a deeper understanding of the potential uses of artificial intelligence systems in the financial market.
Financial transactions require rapid and accurate decision-making processes in a constantly changing market environment. In the decision-making process, the trend of using artificial intelligence is becoming more and more obvious. Traditional statistical methods have recently been often supplemented by machine learning methods. The potential of applying machine learning depends not only on rapid and reliable performance, but also on its ability to discover hidden knowledge in large amounts of data. Using artificial intelligence to support decision-making can partly eliminate the bounded rationality of decision makers, so that better decisions can be made through more relevant data and information.
Data mining is defined as the process of extracting effective and actionable information from a large database and using it to make key business decisions. Data mining technology is mainly based on statistics and machine learning, and patterns can be inferred from different ones.
Data mining helps to find the associations between assets and create predictive models based on a wide range of data. For example, using historical data, short-term exchange rates, and interest rates, you can predict stock prices. Text mining is also a useful tool to solve the problem of stock price prediction. Some people also apply statistical machine learning methods to financial news, such as mining Yahoo Finance news that will affect the stock prices of companies listed in the Standard & Poor’s 500 Index, how much influence they will have, and then estimate the value and price of stocks. In order to test the effectiveness of the proposed system, they arbitrarily selected a group of experts and funds for comparison. Their simulated trading yield is 8.50%, which is better than the Standard & Poor’s 500 Index and certain mutual funds.
Data mining tools are very interactive, easy to understand and have the advantage of low cost, while being able to identify major anomalies that require further inspection. Their great potential in the financial market has been proven.
Expert (knowledge) Systems
The expert system is a computer-based system with artificial intelligence, which simulates the reasoning process of a human expert in a specific knowledge field. Expert systems are based on clearly formulated expertise obtained from experts to achieve expert-level decision-making. The purpose of the expert system is not to model the psychological process of human experts in the decision-making process, but to achieve high-quality decision-making. Even if the data is unsealed and not complete enough, the expert system must also provide advice. This requires a database with multiple or alternative inferences. Some scientists have studied and compared two real-time trading systems using stochastic oscillators, relative strength index and mobility. The first system is based on 350 trading rules, and the second system is based on 150 trading rules created by a linear combination of the first system. The experiment was implemented at the Paris Stock Exchange, and they found that reducing trading rules can shorten the calculation time without having a major impact on professionalism. One of the main advantages of expert systems is that they allow different sources of knowledge to be combined. The expert system can provide permanent documentation of the decision-making process and an overview of all steps.
Artificial neural networks consist of simple elements that operate in parallel. Compared with biological nervous system, the function of artificial neural network mainly depends on the connection between elements. With a clear understanding of the target value, the network can “learn” by adjusting the value between connections (weights between elements). Artificial neural networks are widely used to solve classification, prediction and control problems. The main advantage of artificial neural networks is that they can capture nonlinearity without prior knowledge of the functional relationship between variables, they are resistant to outliers and do not require a specific distribution. Compared with economic parameter models, artificial neural networks give results in a shorter time.
Neural network has been successfully applied to solve the generalization problem of corporate bond rating prediction, and the effect is far better than traditional mathematical modeling techniques. South Korean financial workers have established a system that uses artificial neural networks to evaluate the current time series and past stable time series through a static autoregressive model, which proves the potential of the model in the Korean stock market. Many central banks extensively use artificial neural networks to predict government interest rates and monetary policies. Other studies prove that neural networks perform better in long-term cycles. Compared with the non-linear regression model, the prediction of the artificial neural network also proves that the long-term higher accuracy.
Genetic algorithm is a subset of evolutionary algorithm, inspired by biology. The main idea of genetic algorithm is to increase individual fitness value in the iterative process of evolution process by applying genetic operators, such as mutation or crossover. The main advantage of genetic algorithms is that their application does not require any explicit knowledge about the objective function. Genetic algorithm can obtain good performance in huge potential data sets.
Genetic algorithms are often used to solve optimization problems (such as combinatorial optimization) and retrieval problems. Some scientists try to improve the technical analysis of decision-making using intelligent systems. They use 13-week and 26-week moving averages and genetic algorithms to form a trading system. After a lot of computer simulations, they found that using genetic algorithm technology to analyze the system can get better performance. Others use genetic algorithms as a timing selector for automated traders to optimize the stock market. There are also many financial trading systems that use genetic algorithms, such as some systems that analyze K-line charts, portfolio optimizers, and so on.
Fuzzy system is an expert system from fuzzy logic. Compared with probability theory, fuzzy logic does not assume that the sum of occurrences is equal. 1. The fuzzy scheme system consists of three steps-fuzzification, fuzzy reasoning and defrauding. Fuzzy systems are very suitable for decision-making tasks characterized by uncertainty, so they are very suitable for application in financial markets when making decisions about the transaction volume of related assets.
Fuzzy systems used to be combined with other artificial intelligence techniques. For example, genetic algorithms based on fuzzy neural networks are applied to form a database for studying the quantitative impact of different events (such as political events) in the stock market. Some scientists have also established an expert system based on the evolutionary rules of financial forecasts. They combined fuzzy logic with inductive rules to create a system with generalization capabilities. Neural networks based on fuzzy rules have been used in researches to predict stock market returns many times. There is a trading system based on the moving average rule on the market, which obtains a higher paper return rate than the random trading system. There are also some, based on some topological axioms, proposed a new portfolio management framework to select investment portfolios.
The main advantage of fuzzy systems is that they can solve the residual problems of strict boundaries, the concept of fuzzy definition and no strict problems. Every rule in the fuzzy system is easy to understand and modify.
To sum up
These artificial intelligence technologies are often used to solve forecasting problems-forecasting macroeconomic indicators and time series forecasting of financial markets. Genetic algorithms are used for optimization problems-optimizing stock market timing and portfolio creation. When solving prediction problems, genetic algorithms are used in combination with other artificial intelligence methods. Data mining tools can be used to create quantitative tools for short-term forecast exchange and interest rates. Expert systems try to make decisions at the expert level and apply them to securities analysis and company evaluation. If we have an uncertain concept of the method leading to the decision (the typical method of a human decision maker), then a fuzzy system may be a suitable solution. The research methods of artificial neural networks are realized because they can study the nonlinear relationship between variables and their ability to deal with uncertainty. These artificial intelligence technologies are often used to solve forecasting problems-forecasting macroeconomic indicators and time series forecasting of financial markets.