The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. With the high productivity in the machine learning area applied to the prediction of financial market prices, objective methods are required for a consistent analysis of the most relevant bibliography on the subject. This article proposes the use of bibliographic survey techniques that highlight the most important texts for an area of research. Specifically, these techniques are applied to the literature about machine learning for predicting financial market values, resulting in a bibliographical review of the most important studies about this topic. Fifty-seven texts were reviewed, and a classification was proposed for markets, assets, methods, and variables. Among the main results, of particular note is the greater number of studies that use data from the North American market. The most commonly used models for prediction involve support vector machines (SVMs) and neural networks. It was concluded that the research theme is still relevant and that the use of data from developing markets is a research opportunity.

Literature review: Machine learning techniques applied to financial market prediction

In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles. 

Machine learning techniques and data for stock market forecasting: A literature review

The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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Neural Network-based System for Automatic Passport Stamp Classification

Attentive gated graph sequence neural network-based time-series information fusion for financial trading

In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment.

Financial information prediction and information sharing supervision based on trend assessment and neural network

A return scaling cross-correlation function of exponential parameter is introduced in the present work, and a stochastic time strength neural network model is developed to predict the return scaling cross-correlations between two real stock market indexes, Shanghai Composite Index and Shenzhen Component Index. In the proposed model, the stochastic time strength function gives a weight for each historical data and makes the model have the effect of random movement. The empirical research is performed in testing the model forecasting effect of long-term cross-correlation relationships by training short-term cross-correlations, and a corresponding comparison analysis is made to the backpropagation neural network model. The empirical results show that the proposed neural network is advantageous in increasing the forecasting precision.

Haiyan Mo

Papers

Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics