14 research outputs found
Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend
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
This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailandβs 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 hybridβs 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
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 β statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like β 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
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
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) β 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
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 Adamβs 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
[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
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΡΠ±ΠΎΡΠ° ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΠΌΠΌΠ°Π½ΡΠΊΠΎΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ
Π€ΠΎΠ½Π΄ΠΎΠ²ΡΠ΅ Π±ΠΈΡΠΆΠΈ β Π½Π΅ΠΎΡΡΠ΅ΠΌΠ»Π΅ΠΌΠ°Ρ ΡΠ°ΡΡΡ ΠΌΠΈΡΠΎΠ²ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ; Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΡ Π΅ΠΆΠ΅Π΄Π½Π΅Π²Π½ΡΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ, ΡΠΎΠ½Π΄ΠΎΠ²ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ ΠΎΡΡΠ°ΠΆΠ°ΡΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΌ ΡΡΠ½ΠΊΠ΅ ΡΠΈΡΠΌ. ΠΠ»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠ³ΠΎ ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ Π² Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ ΡΠ°ΠΊΡΠΎΡΡ, Π½Π°ΠΏΡΡΠΌΡΡ Π²Π»ΠΈΡΡΡΠΈΠ΅ Π½Π° ΠΈΠ½Π΄Π΅ΠΊΡ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ. Π§ΡΠΎΠ±Ρ Π²ΡΡΠ²ΠΈΡΡ, ΠΊΠ°ΠΊΠΈΠ΅ ΡΠ΅ΠΊΡΠΎΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π°Π²ΡΠΎΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠ»ΠΈ ΡΠ΅ΡΡΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΡΠ±ΠΎΡΠ° ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΠ²ΡΠ·ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ 23 ΡΠ΅ΠΊΡΠΎΡΠ°ΠΌΠΈ ΠΈ ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠΌ ΠΠΌΠΌΠ°Π½ΡΠΊΠΎΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ (ASEI100) Π·Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ 2008β2018 Π³Π³. Π ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π±ΡΠ»ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ 10 Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ Π·Π°ΡΠ΅ΠΌ ΠΎΠ½ΠΈ Π±ΡΠ»ΠΈ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½Ρ ΠΈ Π²Π½Π΅ΡΠ΅Π½Ρ Π² ΡΠ°Π±Π»ΠΈΡΡ ΡΠ°ΡΡΠΎΡ. ΠΠ»Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠΈ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°Π»ΠΈΡΡ Π² ΡΠ΅ΡΡ-
ΡΠ΅Ρ
ΠΌΠΎΠ΄Π΅Π»ΡΡ
, Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΡ
Π²Π»ΠΈΡΠ½ΠΈΡ Π½Π° ASEI ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΈ ΠΈ ΠΎΠ±ΡΡΠ½ΡΡ
Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠΈΡ
ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ². Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΡΠ΅ΡΡΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ΅ΠΊΡΠΎΡΠΎΠ², Π½Π΅ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²Π΅Π½Π½ΠΎ Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΠΎΠ±ΡΠΈΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ Π² ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ: Π·Π΄ΡΠ°Π²ΠΎΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅, Π³ΠΎΡΠ½ΠΎΠ΄ΠΎΠ±ΡΠ²Π°ΡΡΠ°Ρ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΡ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²ΠΎ ΠΎΠ΄Π΅ΠΆΠ΄Ρ, ΡΠ΅ΠΊΡΡΠΈΠ»Ρ ΠΈ ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ ΠΈΠ· ΠΊΠΎΠΆΠΈ, Π½Π΅Π΄Π²ΠΈΠΆΠΈΠΌΠΎΡΡΡ, ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠ΅ ΡΡΠ»ΡΠ³ΠΈ, ΡΡΠ°Π½ΡΠΏΠΎΡΡ. ΠΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΠΈΡ
ΡΠ΅ΠΊΡΠΎΡΠΎΠ² ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ ΠΈΠ½Π΄Π΅ΠΊΡΠ° ΠΠΌΠΌΠ°Π½ΡΠΊΠΎΠΉ ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΉ Π±ΠΈΡΠΆΠΈ Π² ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π»ΠΈΠ½Π΅ΠΉΠ½Π°Ρ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΡ Π²ΡΡΠ²ΠΈΠ»Π° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΡΡ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ΅ΡΡΡΡ ΡΠ΅ΠΊΡΠΎΡΠ°ΠΌΠΈ (Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡΠ΅ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅) ΠΈ ASEI (Π·Π°Π²ΠΈΡΠΈΠΌΠ°Ρ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ). ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΠΈΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΡΠ΅ ΡΠ΅ΠΊΡΠΎΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΠΎΡΠ΄Π°Π½ΠΈΠΈ, ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΈΠ½Π²Π΅ΡΡΠΎΡΠ°ΠΌΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΡΡ
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