A Stock Market Trading System Using Deep Neural Network

The stock market prediction is a lucrativefield of interest withpromising profit and covered with landmines for the unprecedented. The mar-kets are complex, non-linear and chaotic in nature which poses huge difficultiesto predict the prices accurately. In this paper, a stock trading system utilizingfeed-forward deep neural network (DNN) to forecast index price of Singaporestock market using the FTSE Straits Time Index (STI) in t days ahead is pro-posed and tested through market simulations on historical daily prices. There are40 input nodes of DNN which are the past 10 days’opening, closing, minimumand maximum prices and consist of 3 hidden layers with 10 neurons per layer.The training algorithm used is stochastic gradient descent with back-propagationand is accelerated with multi-core processing. A trading system is proposedwhich utilizes the DNN forecasting results with defined entry and exit rules toenter a trade. DNN performance is evaluated using RMSE and MAPE. Theoverall trading system shows promising results with a profit factor of 18.67,70.83% profitable trades and Sharpe ratio of 5.34 based on market simulation ontest data

REDES NEURONALES EN PREDICCIÓN DE MERCADOS FINANCIEROS: UNA APLICACIÓN EN LA BOLSA MEXICANA DE VALORES (NEURAL NETWORKS IN FINANCIAL MARKET PREDICTION: AN APPLICATION IN THE MEXICAN STOCK EXCHANGE)

ResumenDesde la creación del mercado accionario, conocer las rentabilidades que ofrecen sus activos ha despertado interés tanto de inversionistas como investigadores. Actualmente, se encuentran múltiples aplicaciones documentadas que han intentado predecir acciones, índices u otros activos alrededor del mundo. Sin embargo, las aplicaciones en mercados emergentes y en particular el mexicano, es limitado y poco explorado. Este documento presenta una aplicación que predice las variaciones diarias de una de las empresas participantes en la BMV con un enfoque de análisis híbrido. Utiliza la capacidad de las redes Feed Forward y el algoritmo Backpropagation en problemas de predicción. La selección de variables de entrada a la red se realizó a través del ACP y el estadístico utilizado para medir la precisión de las predicciones es el MSE. Los resultados reflejan una importante contribución a la discusión de la posibilidad o no, de predecir estos activos en el corto plazo.Palabra(s) Clave: ACP, Bolsa Mexicana de Valores, Mercado de valores, Predicción de mercados, RNA. AbstractSince creation of stock market, to know the returns offered by these assets has attracted interest from both investors and researchers. There are multiple documented applications that have tried to predict stocks, indexes or other assets around the world, however, applications in emerging markets as Mexican market, are limited and little explored. This document presents forecast of daily variations of one of companies participating in BMV uses the capacity of Feed Forwards ANNs and Backpropagation algorithm in prediction problems. The selection of input variables to the network was made through the PCA and the MSE statistic was used to measure accuracy of predictions. Results reflect an important contribution to the discussion of the possibility or not of predicting these assets in the short term.Keywords: ACP, Market prediction, Mexican stock exchange, Stock market, RNA

High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

trategy of investment is important tool enabling better investor's decisions in uncertain finance market. Rules of portfolio selection help investors balance accepting some risk for the expectation of higher returns. The aim of the research is to propose strategy of constructing investment portfolios based on the composition of distributions obtained by using high–low data. The ensemble of 176 Evolino recurrent neural networks (RNN) trained in parallel investigated as an artificial intelligence solution, which applied in forecasting of financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to the shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy

Neural Net Stock Trend Predictor

This report analyzes new and existing stock market prediction techniques. Traditional technical analysis was combined with various machine-learning approaches such as artificial neural networks, k-nearest neighbors, and decision trees. Experiments we conducted show that technical analysis together with machine learning can be used to profitably direct an investor’s trading decisions. We are measuring the profitability of experiments by calculating the percentage weekly return for each stock entity under study. Our algorithms and simulations are developed using Python. The technical analysis methodology combined with machine learning algorithms show promising results which we discuss in this report

Artificial neural networks to predict share prices on the Johannesburg stock exchange

The use of historical data to build models for stock market prediction has been extensively researched. Artificial Neural Networks (ANNs) bring new opportunities for predicting stock markets, and is now one of the leading techniques used for time series and specifically stock market prediction. This study explored the application of ANNs to predict share prices in the banking sector of the South African Johannesburg Stock Exchange (JSE). This study used three companies, i.e. Standard Bank, Nedbank and First National Bank, listed on the JSE as case studies for the use of ANNs for predicting the closing share price for the next day, week and month. Historical share price data from the JSE was integrated with datasets of external factors that influence market. The external factors considered in this study include index data from NASDAQ, the JSE top 40 and all share indexes, the exchange rate and the business cycle indicator (BCI) values from the South African Reserve Bank. Comparative analysis were conducted between traditional regression models and ANN models using the lagged share price as input variable. The effect on prediction performance of using external factors as additional input variables was also explored. The ANN models using only the share price was found in general to perform better than both traditional models and ANNs that used the external factors as additional input variables. The average next month prediction model produced a noticeably smaller prediction error compared to the next week, and next day prediction models for all three banks. The results showed that the introduction of external factors as additional input variables did not lead to an improved prediction performance, over models that used only the share price. This study also highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing ANN models for share price prediction in time series data. The results contribute to existing research that indicate that an ANN is more effective than a regression method for predicting banking share prices, and that these predictive models have potential for supporting investment decision making

Determining the impact of window length on time series forecasting using deep learning

Time series forecasting is a method of predicting the future based on previous observations. It depends on the values of the same variable, but at different time periods. To date, various models have been used in stock market time series forecasting, in particular using deep learning models. However, existing implementations of the models did not determine the suitable number of previous observations, that is the window length. Hence, this study investigates the impact of window length of long short-term memory model in forecasting stock market price. The forecasting is performed on S&P500 daily closing price data set. A different window length of 25-day, 50-day, and 100-day were tested on the same model and data set. The result of the experiment shows that different window length produced different forecasting accuracy. In the employed dataset, it is best to utilize 100 as the window length in forecasting the stock market price. Such a finding indicates the importance of determining the suitable window length for the problem in-hand as there is no One-Size-Fits-All model in time series forecasting

STOCK MARKET FORECASTING BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY

This culminating experience project used artificial intelligence (AI) technology to forecast and analyze the stock market and construct complex nonlinear relationships between the input data and the output data. This project used a radial basis function neural network to forecast and analyze the stock market data. Compared the radial basis function neural network performance with the feed-forward neural network and clearly showed the superiority of the radial basis function neural network over the feed-forward neural network in the data processing. The results showed that AI technology could effectively predict stock market performance. Based on the results, the conclusion is that the prediction performance of the RBF neural network is better than that of the multilayer feed-forward neural network. Areas for future research are to explore the use of other AI and other Neural Network Algorithms such as Back Propagation, Convolutional, Kohonen Self Organizing, and Modular to predict stock market performance

A novel hybrid ensemble model to predict FTSE100 index by combining neural network and EEMD

Prediction stock price is considered the most challenging and important financial topic. Thus, its complexity, nonlinearity and much other characteristic, single method could not optimize a good result. Hence, this paper proposes a hybrid ensemble model based on BP neural network and EEMD to predict FTSE100 closing price. In this paper there are five hybrid prediction models, EEMD-NN, EEMD-Bagging-NN, EEMD-Cross validation-NN, EEMD-CV-Bagging-NN and EEMD-NN-Proposed method. Experimental result shows that EEMD-Bagging-NN, EEMD-Cross validation-NN and EEMD-CV-Bagging-NN models performance are a notch above EEMD-NN and significantly higher than the single-NN model. In addition, EEMD-NN-Proposed method prediction performance superiority is demonstrated comparing with the all presented model in this paper, and was feasible and effective in prediction FTSE100 closing price. As a result of the significant performance of the proposed method, the method can be utilized to predict other financial time series data