چكيده
Financial markets are the most important part of the financial system so that the financial resources through which they circulate. For the economic and financial system of any country, the existence of dynamic financial markets with appropriate depth will bring many benefits. Success in financial market trading requires that market trends in the future be properly predicted so that investors can be immune to potential risks and traders have the opportunity to make a profit through trading. Over the years, classical methods have been used to predict financial markets, but with the development of artificial intelligence and meta-innovative methods, artificial neural networks, and fuzzy artificial neural networks, more and more applications have emerged in the discussion of financial market forecasting, especially stock price index. The present study uses intelligent algorithms such as BPNN, ANFIS, and clustering as well as deep learning algorithms (LSTM) and considering data mining applications, seeks to provide a flexible and more accurate forecasting model in financial markets. It has adaptability in all three modes of market trend, acceptable accuracy, and performance. In this study, first, two combined forecasting methods including CL-BPNN and Cl-ANFIS were proposed to predict the daily direction of the S&P 500 index. Price data, MACD, and data related to search and user attention are considered as model input. The results showed that the CL-BPNN and CL-ANFIS approaches perform better than the PPNN and ANFIS models and can predict the direction of the next day's opening price of the S&P 500 index with 88.75% and 91.26% accuracy, respectively. The LSTM model was then used to accurately predict the BTC / USD price. The MACD indicator was added as the fifth feature of the input data to increase the performance of the model. Also, the size of the model input matrix, which includes price data and the value of the MACD indicator for the past days, was analyzed. In this model, in addition to the accuracy of the model in predicting the direction, its efficiency and profitability in real-world Tradings were also analyzed. The results showed that the model has acceptable predictive accuracy and appropriate profitability and can be a reliable choice for an investor.
