The Impact of Amman Stock Exchange Simulation Room on the Level of the Business College Students at Al-Zaytoonah University of Jordan (Predictive Approach)

Since the mid-sixties of the twentieth century, interest in simulation has increased as an appropriate and effective method in the education process, especially after the advent of computers; The simulation process of concepts, activities and experiments is done through the computer, and it has an important and prominent role in the educational process. In the past, the theoretical aspects of education were the core of the education process for students, and this is certainly not enough to raise the level of students. Furthermore, understanding the handling and investing of securities is a challenging subject that requires students to understand and apply financial theories and models. Hence, this study pursued the influence of the Amman Stock Exchange simulation room on the student level of the College of Business at the Al-Zaytoonah University of Jordan through the predictive approach. The results show that the simulation education system may encourage students and increase their intellectual abilities, attracts the attention of the students as well as gives a great opportunity for students to acquire the skills of dealing with the financial market in a practical and applied manner by using a real trading system based on high technology performance which performed by Amman Stock Market

Predicting the Istanbul Stock Exchange Index Return using Technical Indicators: A Comparative Study

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics

Vectorial model for progressive adaptation for purchase and sale of shares using stock market indicators

Las acciones son consideradas como parte fundamental del mercado de renta variable, ya que sus valores cambian con el tiempo como consecuencia de la oferta y la demanda, y por efecto de la volatilidad de los mercados. Esta volatilidad hace que la negociación de acciones en un mercado bursátil sea una tarea extremadamente difícil. Es por esto que en este artículo se desarrolla y analiza un sistema para la negociación automática de acciones, el cual incorpora una serie de modelos vectoriales por aprendizaje progresivo inspirado en la estructura de una máquina de vector soporte. Para la configuración de la estructura general del modelo, se utilizaron una serie de indicadores bursátiles utilizados por los inversionistas para fijar posiciones de compra y venta, mientras que el aprendizaje el modelo utilizó una estrategia negociación secuencial sobre cinco acciones diferentes inscritas en la bolsa de valores de Colombia, y en donde el aprendizaje estuvo guiado por las posiciones de compra y venta que iban fijando cada uno de los indicadores bursátiles de entrada, Los resultados arrojados por el sistema, mostraron la rentabilidad que el modelo iba logrando en la negociación como consecuencia del avance en el aprendizaje que cada uno de los modelos iba logrando a lo largo de la secuencia de acciones utilizadas para este estudio, haciendo el sistema cada vez más robusto, lo que lo hace ideal para la negociación de acciones basada en indicadores bursátilesThe shares are considered as a fundamental part of the equity market, as their values change over time as a result of offer and demand, and the effect of market volatility. This volatility makes the trading of shares on a stock exchange is an extremely difficult task. That is why in this article develops and analyzes a system for automatic trading shares, which incorporates a series of progressive learning vector models inspired by the structure of a support vector machine. For the configuration of the overall structure of the model, a number of stock market indicators used by investors to establish positions for buying and selling, were used while learning the model used a sequential negotiation strategy on five different shares listed on the stock exchange of Colombia, and where learning was guided by buying and selling positions that were setting each input stock market indicators. Results from the system showed the profitability that the model was achieved in the negotiation as a result of progress in learning that each of the models was achieved along the sequence of actions used for this study, making the system each more robust time, which makes it ideal for trading shares based on stock indexe

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

publishedVersio

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Pairs trading on different portfolios based on machine learning

This article presents an advanced visualization and analytics approach for financial research. Statistical arbitrage, particularly pairs trading strategy, has gained ground in the financial market and machine learning techniques are applied to the finance field. The cointegration approach and long short-term memory (LSTM) were utilized to achieve stock pairs identification and price prediction purposes, respectively, in this project. This article focused on the US stock market, investigating the performance of pairs trading on different types of portfolios (aggressive and defensive portfolio) and compare the accuracy of price prediction based on LSTM. It can be briefly concluded that LSTM offers higher prediction precision on aggressive stocks and implementing pairs trading on the defensive portfolio would gain higher profitability during a specific period between 2016 and 2017. However, predicting tools like LSTM only offer limited advice on stock movement and should be cautiously utilized. We conclude that analytics and visualization can be effective for financial analysis, forecasting and investment strategy

Machine Learning and Portfolio Optimization: an application to Italian FTSE-MIB Stocks

A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation.A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation

Previsão da direção de índices da Bovespa por intermédio de Máquina de Suporte Vetorial

Monografia (graduação)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Departamento de Administração, 2015.Esta pesquisa tem por objetivo analisar a aplicação de Máquinas de Suporte Vetorial com o intuito de prever o movimento de índices de ações da BOVESPA. Os dados da pesquisa abrangem o período de 22/01/2001 até 22/09/2015. Os dados de entrada da máquina são os Log-Retornos dos índices e dois indicadores de análise técnica - Índice de Força Relativa e Médias Móveis Convergentes Divergentes. Esses dados são utilizados para determinar o movimento do índice (subir ou descer) e a probabilidade de ocorrência da previsão. Uma validação cruzada (k-fold) é realizada para a escolha dos melhores parâmetros, onde o melhor desempenho da máquina é uma acurácia de 70% na previsão. ________________________________________________________________________________ ABSTRACTThis research aims to examine the application of Support Vector Machines in order to predict the movement of the Bovespa stock index. This survey data cover the period from 01/22/2001 to 09/22/2015. Machine input data is the log-returns of the indices and two technical analysis indicators - Relative Strength Index and Moving Average Convergence Divergence. These data are used to determine the movement (up or down) of the indices and the probability of the forecast. A cross-validation (k-fold) is performed to choose the best parameters, where the best machine performance in forecasting is a hit ratio of 70%