A Review of Artificial Neural Networks Application to Stock Market Predictions

The purpose of this paper is to review artificial neural network applications used in the field of stock price forecasting. The field of stock price forecasting has increasingly grown to be an important subject matter for researchers, everyday investors and practitioners in the finance domain as it aids financial decision making. This study brings to attention some of the neural network applications used in stock price forecasting focusing on application comparisons on different stock market data and the gaps that can be worked on in the foreseeable future. This work makes an introduction of neural network applications to those novels in the field of artificial intelligence. Keywords: Neural Networks, Forecasting Stock Price. Financial Markets, Complexity, Error Measures, Decision Makin

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts

STOCK PRICE TREND PREDICTION USING SUPPORT VECTOR MACHINE AND CORAL REEF OPTIMIZATION ALGORITHM

Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.   &nbsp

Peramalan Indeks Harga Saham Gabungan Indonesia Dengan Menggunakan Metode Artificial Neural Network Algoritma Backpropagation

Nilai Indeks Harga Saham Gabungan merupakan sebuah indeks pasar saham yang digunakan oleh Bursa Efek Indonesia (BEI). Nilai indeks harga saham gabungan merepresentasikan pergerakan seluruh harga saham yang tercatat di BEI. Kegunaan dalam penghitungan indeks harga saham gabungan adalah dapat digunakan sebagai patokan bagi pemerintah untuk mengambil kebijakan dibidang ekonomi. Biasanya indeks harga saham gabungan digunakan sebagai patokan dalam perhitungan pertumbuhan ekonomi sebuah negara dalam bentuk persentase. Setiap transaksi tercatat dengan skala waktu yang kecil, sehingga menyebabkan perubahan yang terjadi pada nilai IHSG sangat cepat dan tidak pasti. Indeks harga saham gabungan sendiri memiliki berbagai ketidakpastian yang dapat mempengaruhi nilai dari indeks harga saham Indonesia. Ketidakpastian tersebut mencakup kondisi internal dan eksternal negara yang dapat meningkatkan atau menurunkan nilai harga saham perusahaan – perusahaan kapitalis yang nantinya akan menurunkan nilai indeks harga saham Indonesia. Salah satu motode dalam peramalan yang populer untuk dapat meramalkan indeks harga saham gabungan Indonesia sendiri adalah dengan menggunakan metode Artificial Neural Network. Sebelum melakukan peramalan, perlu dilakukan pembuatan model yang paling sesuai dengan parameter-parameter yang tersedia sehingga memiliki nilai error yang paling rendah. Model inilah yang akan digunakan untuk melakukan peramalan pada periode selanjutnya dalam pemodelan Artificial Neural Network. Tugas akhir ini memberikan model peramalan indeks harga saham gabungan dengan menggunakan Artificial Neural Network dengan algoritma Backpropagation. Adapun hasil nilai peramalan indeks harga saham gabungan yang didapatkan memiliki model terbaik (16,16,1) dengan nilai MSE 1818.93 dan MAPE 0.613%. Harapannya model terbaik yang dihasilkan dari penelitian ini dapat digunakan untuk membantu pemerintah dalam pembuatan kebijakan yang dapat menigkatkan perekonomian negara di masa yang akan datang. ========================================================================================================== Indonesian Composite Index Index is a stock market index used by the Indonesia Stock Exchange (IDX). The value of the composite share price index represents the movement of all stock prices listed on the Stock Exchange. Usefulness in calculating the composite stock price index can be used as a benchmark for the government to take policy in the field of economy. Usually the composite stock price index is used as a benchmark in calculating a country's economic growth in percentage form. Each transaction is recorded with a small time scale, resulting in changes that occur in the JCI index is very fast and uncertain. The composite share price index itself has various uncertainties that could affect the value of Indonesia stock price index. These uncertainties include the internal and external conditions of the state that can increase or decrease the value of stock prices of capitalist companies that will later lower the Indonesian stock price index. One of the motives in popular forecasting to predict the composite share price index of Indonesia itself is by using Artificial Neural Network method. Before doing the forecasting, it is necessary to make the model that best suits the parameters available so that it has the lowest error value. This model will be used to forecast the next period in Artificial Neural Network modeling. This final project provides forecasting model of composite stock price index by using Artificial Neural Network with Backpropagation algorithm. The results of forecasting value of the composite stock price index obtained has the best model (16,16,1) with MSE 1818.93 and MAPE 0.613%. The hope of the best model generated from this research can be used to assist the government in policy making that can boost the economy of the country in the future

ANALISIS TEKNIKAL DENGAN MENGGUNAKAN MOVING AVERAGE CONVERGENCE-DIVERGENCE DAN RELATIVE STRENGTH INDEX PADA SAHAM PERBANKAN

ABSTRACT Stock investment is an investment that has a high risk. An investor needs to do an investment analysis before deciding to invest. Investment analysis can be carried out using both fundamental and technical approaches. Technical analysis is often an option because it is fast and easy to apply. This study aims to examine the level of differences in the use of technical analysis with the moving average convergence-divergence (MACD) method and the relative strength index (RSI) as a means of making stock investment decisions. The research method used in this research is the descriptive analysis method. This research was conducted on a group of banking stocks that are included in LQ45. The results showed that there was no difference between the price of the buy signal and the sell signal before and after using the MACD and RSI methods. The results also show that there is no difference between the buy signal and the sell signal between MACD and RSI. Therefore, it can be stated that for the same object and period, the MACD and RSI methods produce the same investment decisions (buy signal and sell signal). Keywords: technical analysis, MACD, RSI, buy signal, sell signal   ABSTRAK Investasi saham merupakanjenis investasi yang memiliki resiko tinggi. Seorang investor perlu melakukan analisis investasi sebelum memutuskan untuk berinvestasi. Analisis investasi dapat dilakukan dengan menggunakan pendekatan fundamental dan teknikal. Analisis teknikal seringkali menjadi pilihan karena cepat dan mudah diterapkan. Penelitian ini bertujuan untuk menguji tingkat perbedaan penggunaan analisa teknikal dengan metode moving average convergence-divergence (MACD) dan relative strength index (RSI) sebagai alat pengambilan keputusan investasi saham. Metode penelitian yang digunakan dalam penelitian ini adalah metode analisis deskriptif. Penelitian ini dilakukan pada sekelompok saham perbankan yang termasuk dalam LQ45. Hasil penelitian menunjukkan bahwa tidak ada perbedaan harga antara sinyal beli dan sinyal jual sebelum dan sesudah menggunakan metode MACD maupun RSI. Hasil penelitian juga menunjukkan bahwa tidak ada perbedaan antara sinyal beli dan sinyal jual antara MACD dan RSI. Dengan demikian dapat dikatakan bahwa untuk objek dan periode yang sama, metode MACD dan RSI menghasilkan keputusan investasi yang sama (sinyal beli dan sinyal jual). Kata kunci: analisa teknikal, MACD, RSI, sinyal beli, sinyal jua

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

publishedVersio