The subject of the present paper represents the result of a research that uses artificial intelligence, through the artificial neural networks, in order to simulate the Romanian Economic Sentiment Indicator (ESI-mentioned by The Economist and provided by the European CommissionEconomic and Financial Affairs website). The reason for the research is to determine a better method to forecast the Romania ESI considering its nonlinear behavior. For the simulation, a feed forward artificial neural network (ANN) was used. For this type of ANN, the best training algorithm is the back propagation algorithm. Training condition were set to a smaller than 5% error between the real data and the simulated data. The research is then extended to new input data (not available at the time of ANN training) used for comparison and forecasting of the real trends with the simulated ones. Even with new data, the use of the ANN determined forecasting results smaller than 5% (between -4.92; 5.16%). Also the ANN simulation offers an image about how indicators influence the ESI. In conclusion, the use of the ANN is considered a success and the authors determine the possibility that ANN research application be extended to other countries ESI or even to the European zone

As the transformative power of AI crosses all economic and social sectors, the use of it as a modern technique for the simulation and/or forecast of various indicators must be viewed as a tool for sustainable development. The present paper reveals the results of research on modeling and simulating the influences of four economic indicators (the production in industry, the intramural research and development expenditure, the turnover and volume of sales and employment) on the evolution of European Economic Sentiment using artificial intelligence. The main goal of the research was to build, train and validate an artificial neural network that is able to forecast the following year’s value of economic sentiment using the present values of the other indicators. Research on predicting European Economic Sentiment Indicator (ESI) using artificial neural networks is a starting point, with work on this subject almost inexistent, the reason being mainly that ESI is a composite of five sectoral confidence indicators and is not thought to be an emotional response to the interaction of the entrepreneurial population with different economic indicators. The authors investigated, without involving a direct mathematical interaction among the indicators involved, predicting ESI based on a cognitive response. Considering the aim of the research, the method used was simulation with an artificial neural network and a feedforward network (structure 4-9-6-1) and a backward propagation instruction algorithm was built. The data used are euro area values (for 19 countries only—EA19) recorded between 1999 and 2016, with Eurostat as the European Commission’s statistical data website. To validate the results, the authors imposed the following targets: the result of the neural network training error is less than 5% and the prediction verification error is less than 10%. The research outcomes resulted in a training error (after 30,878 iterations) of less than 0.099% and a predictive check error of 2.02%, which resulted in the conclusion of accurate training and an efficient prediction. AI and artificial neural networks, are modeling and simulation methods that can yield results of nonlinear problems that cover, for example, human decisions based on human cognitive processes as a result of previous experiences. ANN copies the structure and functioning of the biological brain, having the advantage through learning and coaching processes (biological cognitive), to copy/predict the results of the thinking process and, thus, the process of choice by the biological brain. The importance of the present paper and its results stems from the authors’ desire to use and popularize modern methods of predicting the different macroeconomic indices that influence the behavior of entrepreneurs and therefore the decisions of these entrepreneurs based on cognitive response more than considering linear mathematical functions that cannot correctly understand and anticipate financial crises or economic convulsions. Using methods such as AI, we can anticipate micro- and macroeconomic developments, and therefore react in the direction of diminishing their negative effects for companies as well as the national economy or European economy.

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The present paper shows the discussion and results of the research that simulated the fluctuation of the US Consumer Credit (CONS) using Artificial Neural Network (ANN). The research had several objectives, like: building, training and using an ANN as a possible tool for decision making, through the simulation of the US Consumer Credit. The condition for a successful training of the ANN was established as a smaller difference than 1.5% between the real data and the simulated data. A feed forward artificial neural network and a back propagation algorithm were used for the training and preparation of future use of the ANN. For the training result, two testing sessions were used. For the use of ANN in CONS forecasting, the research was extended with the simulation of CONS trend using trained ANN and a new set of consecutive values for each of the input data. Also, the new simulations determined a hierarchy of the inputs that were considered for the simulations of the CONS. In the conclusion, the researchers consider the ANN training and testing a success due to the values obtained: a difference of [-0.69; 0.32] % between the real and simulated CONS values. The trend simulation also shows the training success with accuracy smaller than 1.5%. The authors consider that the research can be extended to other countries or by adding others indicators.