Design and application of Artificial Neural Networks for predicting the values of indexes on the Bulgarian Stock market
05 Jun 2013-pp 1-6
TL;DR: Conclusions made by multiple authors that Artificial Neural Networks do have the capability to forecast the stock markets studied and can improve the robustness according to the network structure are put to the test in this paper by constructing and applying three different models that will be tested in the environment of the Bulgarian capital market.
Abstract: The Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. They are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, Artificial Neural Networks are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their importance in this field is growing. In this paper our aim will be to analyze different neural networks for financial time series forecasting. Specifically their ability to predict future values of The Bulgarian Stock exchange - Sofia and the respective representative indexes. In order to yield better results Artificial Neural Networks need to have an optimal architecture and be trained in a suitable way. This will be the main challenge for the authors of this paper. Conclusions made by multiple authors that Artificial Neural Networks do have the capability to forecast the stock markets studied and, if properly trained, can improve the robustness according to the network structure are put to the test in this paper by constructing and applying three different models that will be tested in the environment of the Bulgarian capital market.
Citations
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TL;DR: An overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others, are given.
Abstract: We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.
399 citations
Posted Content•
TL;DR: Different normalization methods are used on time series data before feeding the data into the DNN model and the impact of each normalization technique on DNN to forecast the time series is found.
Abstract: For the last few years it has been observed that the Deep Neural Networks (DNNs) has achieved an excellent success in image classification, speech recognition. But DNNs are suffer great deal of challenges for time series forecasting because most of the time series data are nonlinear in nature and highly dynamic in behaviour. The time series forecasting has a great impact on our socio-economic environment. Hence, to deal with these challenges its need to be redefined the DNN model and keeping this in mind, data pre-processing, network architecture and network parameters are need to be consider before feeding the data into DNN models. Data normalization is the basic data pre-processing technique form which learning is to be done. The effectiveness of time series forecasting is heavily depend on the data normalization technique. In this paper, different normalization methods are used on time series data before feeding the data into the DNN model and we try to find out the impact of each normalization technique on DNN to forecast the time series. Here the Deep Recurrent Neural Network (DRNN) is used to predict the closing index of Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE) by using BSE and NYSE time series data.
45 citations
Cites background from "Design and application of Artificia..."
...The ANN mimic the process of human’s brain and solve the nonlinear problems, that’s why it widely used for predicting and calculating the complicated task....
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...The main objective of ANN[5] is to develop a system that can perform various computational tasks faster than the traditional systems....
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...Artificial Neural Network (ANN) is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks....
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...Nowadays, several Artificial Neural Network (ANN) models such as Multilayer Perceptron (MLP) neural network, Back Propagation (BP)[6][7] neural networks are used to predict the stock market price....
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...Originally, the concept of deep learning was developed from ANN research....
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TL;DR: It is found that informed traders’ activities contain informational content and may provide actual investors with information that is useful for stock price prediction.
Abstract: This study investigates the predictive power of informed traders’ activities in stock price movements by employing neural networks. Specifically, we examine whether informed investors’ trading activities can predict drastic changes in stock prices in the subsequent 5-day period. Our empirical results show that the probability of the model being correct can be as high as 74%. In addition, the simulated trading strategies based on our trained model lead to significantly positive risk-adjusted returns and show strong performance measures. Overall, we find that informed traders’ activities contain informational content and may provide actual investors with information that is useful for stock price prediction.
7 citations
TL;DR: Modified BP algorithm was proposed in this study to improve the learning speed of RBFN using discretized data and the experimental results indicate that the proposed method performs better in error rate convergence and correct classification compared to the result with continuous dataset.
Abstract: Radial Basis Function Network (RBFN) is a class of Artificial Neural Network (ANN) that was used in many classification problems in science and engineering. Backpropagation (BP) algorithm is a learning algorithm that was widely used in ANN. However, BP has major disadvantages of slow error rate convergence and always easily stuck at the local minima. Hence, Modified BP algorithm was proposed in this study to improve the learning speed of RBFN using discretized data. C programming language was used to develop the program for the proposed method. Performance measurement of the method was conducted and the experimental results indicate that our proposed method performs better in error rate convergence and correct classification compared to the result with continuous dataset. T-test statistical analysis was used to check the significance of the results and most were found to be satisfactorily significant. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7032
7 citations
Cites background from "Design and application of Artificia..."
...A study by [7] reported that at present ANN have numerous real life applications in machine learning and other fields....
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TL;DR: In this article, the authors present a detailed survey of the state-of-the-art methods for stock market prediction, including data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions.
Abstract: Stock market prediction is a challenging task as it requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends. The challenge is further exacerbated due to the high volatility of stock price trends. However, a detailed overview that discusses the overall context of stock prediction is elusive in literature. To address this research gap, this paper presents a detailed survey. All key terms and phases of generic stock prediction methodology along with challenges, are described. A detailed literature review that covers data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions is presented for news sensitive stock prediction. This work investigates the significance of using structured text features rather than unstructured and shallow text features. It also discusses the use of opinion extraction techniques. In addition, it emphasizes the use of domain knowledge with both approaches of textual feature extraction. Furthermore, it highlights the significance of deep neural network based prediction techniques to capture the hidden relationship between textual and numerical data. This survey is significant and novel as it elaborates a comprehensive framework for stock market prediction and highlights the strengths and weaknesses of existing approaches. It presents a wide range of open issues and research directions that are beneficial for the research community.
7 citations
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Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
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Abstract: From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
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Posted Content•
TL;DR: In this paper, an analysis of the predictability of the returns reveals that emerging market returns are more likely than developed countries to be influenced by local information and that low correlations with developed countries' equity markets significantly reduces the unconditional portfolio risk of a world investor.
Abstract: The emergence of new equity markets in Europe, Latin America, Asia, the Mideast and Africa provides a new menu of opportunities for investors. These markets exhibit high expected returns as well as high volatility. Importantly, the low correlations with developed countries' equity markets significantly reduces the unconditional portfolio risk of a world investor. However, standard global asset pricing models, which assume complete integration of capital markets, fail to explain the cross-section of average returns in emerging countries. An analysis of the predictability of the returns reveals that emerging market returns are more likely than developed countries to be influenced by local information.
1,296 citations
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