New approaches in modeling and forecasting financial markets: recent progress and future horizons

Financial time series are fundamentally complex, dynamic, noisy, non-linear, non-parametric and chaotic, so forecasting financial markets is one of the most challenging fields in engineering and economics. With the increasing progress of artificial intelligence and the emergence of deep learning methods, the problem of stock market forecasting has been faced with significant developments, especially in the field of prediction models and big data. Four important steps to create a systematic intelligent forecasting model include model inputs, selection of forecasting algorithms and design of the general structure of the forecasting model, using appropriate loss functions to train the algorithm and finally suitable evaluation of the results, according to the desired criteria. In this paper, a comprehensive review of recent approaches to stock market forecasting is provided, focusing on the above steps. The most important achievements of this paper are: 1- A comprehensive review of the problem, including: reviewing the types of model inputs, different prediction structures, training the model and types of loss functions used, and the evaluation metric of the results, in a fully classified and structured way to easily provide the road map and existing challenges for the enthusiasts and also an important research field of each section for the researchers. 2- Complete analysis of each part, specifying the application of each method and discussing their advantages and disadvantages based on the latest developments and providing perspectives on the research boundaries. 3- Determining the ongoing research path, future approaches and open issues for researchers interested in this field.