14 research outputs found

    Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

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    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span

    Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

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    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span

    Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database

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    Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like – SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market

    Forecast of Borsa Istanbul Dividend Index (XTMTU) Trend by Using the Method of Artificial Neural Network

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    Use of artificial neural networks (ANNs) in the field of finance contributes to the solution of even the most complex problems by increasing the efficiency and the speed in decision making. Hopeful results have been obtained in the prediction of stock exchanges from the studies conducted by using artificial neural networks (ANNs) just like the other financial forecasts. In this study, we forecast the daily closing values between 03.01.2017 and 07.05.2017 by using the daily closing values of the Borsa Istanbul Dividend Index (XTMTU) betwen 02.03.2014 and 09.03.2017. Our main object in this research is to test the predictabiliy power of artificial neural networks (ANNs) by using the results obtained on the Borsa Istanbul Dividend Index (XTMTU). The results of our study indicate that the artificial neural networks determine the trend of the Borsa Istanbul Dividend Index (XTMTU) far better than many statistical and traditional methods. Keywords: Artificial Neural Networks (ANNs), Multilayered Feedforward Network, Borsa Istanbul Dividend Index (XTMTU), financial analysis

    Klasifikasi Data Microarray dengan Metode Artificial Neural Network dan Genetic Algorithm untuk Kasus Deteksi Kanker

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    Kanker adalah salah satu penyakit yang paling mematikan di dunia. Pada tahun 2012, terdapat 32,6 juta orang yang positif mengidap kanker dan 8,2 juta kematian yang disebabkan oleh kanker. Terdapat banyak cara yang bisa dilakukan untuk mendeteksi kanker sejak dini, salah satu caranya adalah dengan melakukan klasifikasi fitur pada data DNA microarray. Salah satu metode yang digunakan untuk mendeteksi kanker adalah metode Artificial Neural Network (ANN) – Backpropagation dengan bantuan Genetics Algorithm (GA). ANN digunakan sebagai metode klasifikasi untuk memprediksi kanker, sedangkan GA digunakan sebagai metode untuk mereduksi dimensi dari fitur DNA Microarray yang memiliki dimensi yang sangat besar. Pada penelitian ini dilakukan perbandingan antara metode ANN dan metode ANN-GA hybrid. Metode ANN-GA terbukti lebih efektif dari ANN karena dapat menghasilkan nilai akurasi 93.08% dan mereduksi dimensi hingga 51% dengan waktu running time lebih cepat hingga 42.2%. Kata Kunci : Artificial Neural Network (ANN), ANN-GA hybrid, DNA Microarray, Genetics Algorithm (GA

    Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

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    Β© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory

    Hybrid fuzzy neural network to predict price direction in the German DAX-30 index

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    [EN] Intraday trading rules require accurate information about the future short term market evolution. For that reason, next-day market trend prediction has attracted the attention of both academics and practitioners. This interest has increased in recent years, as different methodologies have been applied to this end. Usually, machine learning techniques are used such as artificial neural networks, support vector machines and decision trees. The input variables of most of the studies are traditional technical indicators which are used by professional traders to implement investment strategies. We analyse if these indicators have predictive power on the German DAX-30 stock index by applying a hybrid fuzzy neural network to predict the one-day ahead direction of index. We implement different models depending on whether all the indicators and oscillators are used as inputs, or if a linear combination of them obtained through a factor analysis is used instead. In order to guarantee for the robustness of the results, we train and apply the HyFIS models on randomly selected subsamples 10,000 times. The results show that the reduction of the dimension through the factorial analysis generates more profitable and less risky strategies.GarcΓ­a GarcΓ­a, F.; Guijarro, F.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2018). Hybrid fuzzy neural network to predict price direction in the German DAX-30 index. Technological and Economic Development of Economy. 24(6):2161-2178. https://doi.org/10.3846/tede.2018.6394S2161217824

    ΠŸΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° ΠΎΡ‚Π±ΠΎΡ€Π° ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² для долгосрочного ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π° индСкса Амманской Ρ„ΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡ€ΠΆΠΈ

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    Π€ΠΎΠ½Π΄ΠΎΠ²Ρ‹Π΅ Π±ΠΈΡ€ΠΆΠΈ β€” Π½Π΅ΠΎΡ‚ΡŠΠ΅ΠΌΠ»Π΅ΠΌΠ°Ρ Ρ‡Π°ΡΡ‚ΡŒ ΠΌΠΈΡ€ΠΎΠ²ΠΎΠΉ экономики; благодаря ΠΎΡ‚ΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΡŽ Π΅ΠΆΠ΅Π΄Π½Π΅Π²Π½Ρ‹Ρ… ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ, Ρ„ΠΎΠ½Π΄ΠΎΠ²Ρ‹Π΅ индСксы ΠΎΡ‚Ρ€Π°ΠΆΠ°ΡŽΡ‚ измСнСния ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ прСдставлСнных Π½Π° финансовом Ρ€Ρ‹Π½ΠΊΠ΅ Ρ„ΠΈΡ€ΠΌ. Для построСния ΠΌΠΎΠ΄Π΅Π»ΠΈ прогнозирования Ρ„ΠΎΠ½Π΄ΠΎΠ²ΠΎΠ³ΠΎ индСкса Π˜ΠΎΡ€Π΄Π°Π½ΠΈΠΈ Π² Π΄Π°Π½Π½ΠΎΠΉ ΡΡ‚Π°Ρ‚ΡŒΠ΅ исслСдованы Ρ„Π°ΠΊΡ‚ΠΎΡ€Ρ‹, Π½Π°ΠΏΡ€ΡΠΌΡƒΡŽ Π²Π»ΠΈΡΡŽΡ‰ΠΈΠ΅ Π½Π° индСкс Ρ„ΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡ€ΠΆΠΈ. Π§Ρ‚ΠΎΠ±Ρ‹ Π²Ρ‹ΡΠ²ΠΈΡ‚ΡŒ, ΠΊΠ°ΠΊΠΈΠ΅ сСкторы экономики ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚ наибольшСС влияниС Π½Π° модСль прогнозирования, Π°Π²Ρ‚ΠΎΡ€Ρ‹ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠ»ΠΈ Ρ‡Π΅Ρ‚Ρ‹Ρ€Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° ΠΎΡ‚Π±ΠΎΡ€Π° ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² для изучСния связи ΠΌΠ΅ΠΆΠ΄Ρƒ 23 сСкторами ΠΈ индСксом Амманской Ρ„ΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡ€ΠΆΠΈ (ASEI100) Π·Π° ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ 2008–2018 Π³Π³. Π’ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π±Ρ‹Π»ΠΈ Π²Ρ‹Π΄Π΅Π»Π΅Π½Ρ‹ 10 Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°Ρ‡ΠΈΠΌΡ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π·Π°Ρ‚Π΅ΠΌ ΠΎΠ½ΠΈ Π±Ρ‹Π»ΠΈ ΠΎΠ±ΡŠΠ΅Π΄ΠΈΠ½Π΅Π½Ρ‹ ΠΈ внСсСны Π² Ρ‚Π°Π±Π»ΠΈΡ†Ρƒ частот. Для ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΠΈ достовСрности основных Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ часто Π²ΡΡ‚Ρ€Π΅Ρ‡Π°Π»ΠΈΡΡŒ Π² Ρ‡Π΅Ρ‚Ρ‹- Ρ€Π΅Ρ… модСлях, Π° Ρ‚Π°ΠΊΠΆΠ΅ для ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΈΡ… влияния Π½Π° ASEI использовались ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ рСгрСссии ΠΈ ΠΎΠ±Ρ‹Ρ‡Π½Ρ‹Ρ… Π½Π°ΠΈΠΌΠ΅Π½ΡŒΡˆΠΈΡ… ΠΊΠ²Π°Π΄Ρ€Π°Ρ‚ΠΎΠ². Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдования ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, Ρ‡Ρ‚ΠΎ сущСствуСт ΡˆΠ΅ΡΡ‚ΡŒ основных сСкторов, нСпосрСдствСнно Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° ΠΎΠ±Ρ‰ΠΈΠΉ Ρ„ΠΎΠ½Π΄ΠΎΠ²Ρ‹ΠΉ индСкс Π² Π˜ΠΎΡ€Π΄Π°Π½ΠΈΠΈ: Π·Π΄Ρ€Π°Π²ΠΎΠΎΡ…Ρ€Π°Π½Π΅Π½ΠΈΠ΅, Π³ΠΎΡ€Π½ΠΎΠ΄ΠΎΠ±Ρ‹Π²Π°ΡŽΡ‰Π°Ρ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΡŒ, производство ΠΎΠ΄Π΅ΠΆΠ΄Ρ‹, тСкстиля ΠΈ ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ ΠΈΠ· ΠΊΠΎΠΆΠΈ, Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡ‚ΡŒ, финансовыС услуги, транспорт. ΠŸΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ этих сСкторов ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ для прогнозирования ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ индСкса Амманской Ρ„ΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡ€ΠΆΠΈ Π² Π˜ΠΎΡ€Π΄Π°Π½ΠΈΠΈ. ΠšΡ€ΠΎΠΌΠ΅ Ρ‚ΠΎΠ³ΠΎ, линСйная рСгрСссия выявила статистичСски Π·Π½Π°Ρ‡ΠΈΠΌΡƒΡŽ взаимосвязь ΠΌΠ΅ΠΆΠ΄Ρƒ ΡˆΠ΅ΡΡ‚ΡŒΡŽ сСкторами (нСзависимыС ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Π΅) ΠΈ ASEI (зависимая пСрСмСнная). ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹, ΠΎΠΏΠΈΡΡ‹Π²Π°ΡŽΡ‰ΠΈΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½Ρ‹Π΅ сСкторы экономики Π˜ΠΎΡ€Π΄Π°Π½ΠΈΠΈ, ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ инвСсторами для принятия инвСстиционных Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ
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