Stock Market Returns and Direction Prediction: An Empirical Study on Karachi Stock Exchange

There has been much research in the recent past on the predictability of stock return, mainly due to its significance in managing economic gains on a high scale. Our research initiates the forecasting of the Karachi stock return with the help of the Wavelet analysis and Empirical mode decomposition method. This paper attends in large part to investors and traders to deduce a method for predicting the stock market. The collected data ranges from Jan 2009 to Dec 2012. Every training set is selected from January through October and the sets left over are used for testing. What we have discovered is that Empirical Mode decomposition (EMD) method supersedes all other models on the Mean square error and Mean Absolute error criteria. We may also evaluate the performance of these models by changing strategy direction and comparing payoffs to understand which framework performs as a better forecasting model. It is establishes by the results of the study that the same model serves better for forecasting in trading strategy and could rule over other possible models for most periods under consideration. It is our belief that this study will help stock investors to come to quick decisions about optimal buying or selling time in Karachi Stock Exchange Key Words: Forecasting, KSE (Karachi Stock Exchange) 100 Index, Empirical Mode Decomposition, Trading Strateg

Stock Price Prediction using Neural Network with Hybridized Market Indicators

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. The hybridized approach was tested with published stock data and the results obtained showed remarkable improvement over the use of only technical analysis variables. Also, the prediction from hybridized approach was found satisfactorily adequate as a guide for traders and investors in making qualitative decisions

Yapay sinir ağları modelleri ile İMKB - 100 endeksinin günlük ve seanslık getirilerinin tahmin edilmesi

Especially for the last decade, the neural network models have been applied to solve financial problems like portfolio construction and stock market forecasting. Among the alternative neural network models, the multilayer perceptron models are expected to be effective and widely applied in financial forecasting. This study examines the forecasting power multilayer perceptron models for daily and sessional returns of ISE-100 index. The findings imply that the multilayer perceptron models presented promising performance in forecasting the ISE-100 index returns. However, further emphasis should be placed on different input variables and model architectures in order to improve the forecasting performances.Özellikle son on yılda yapay sinir ağları modelleri portföy oluşturma ve hisse senedi piyasası tahminleri gibi finansal problemleri çözmede uygulanmaktadır. Çeşitli yapay sinir ağları modelleri arasında, çok-katmanlı pörseptron modelleri finansal tahmin çalışmaları için yaygın ve etkili bir şekilde kullanılmaktadır. Bu çalışma, çok-katmanlı pörseptron modellerinin İMKB-100 endeksinin günlük ve seanslık getirilerinin tahmin edilmesindeki etkinliğini incelemektedir. Çalışmanın bulgularından yola çıkılarak, çok-katmanlı pörseptron modellerinin İMKB-100 endeks getirisini tahmin etmede umut vaat eden bir performans gösterdiği sonucuna varılabilir. Fakat, yapay sinir ağları modellerinin tahmin güçleri farklı değişkenler ve farklı model yapıları kullanılarak daha da arttırılabilir

Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models

Özellikle son on yılda yapay sinir ağları modelleri portföy oluşturma ve hisse senedi piyasası tahminleri gibi finansal problemleri çözmede uygulanmaktadır. Çeşitli yapay sinir ağları modelleri arasında, çok-katmanlı pörseptron modelleri finansal tahmin çalışmaları için yaygın ve etkili bir şekilde kullanılmaktadır. Bu çalışma, çok-katmanlı pörseptron modellerinin İMKB-100 endeksinin günlük ve seanslık getirilerinin tahmin edilmesindeki etkinliğini incelemektedir. Çalışmanın bulgularından yola çıkılarak, çok-katmanlı pörseptron modellerinin İMKB-100 endeks getirisini tahmin etmede umut vaat eden bir performans gösterdiği sonucuna varılabilir. Fakat, yapay sinir ağları modellerinin tahmin güçleri farklı değişkenler ve farklı model yapıları kullanılarak daha da arttırılabilir.Especially for the last decade, the neural network models have been applied to solve financial problems like portfolio construction and stock market forecasting. Among the alternative neural network models, the multilayer perceptron models are expected to be effective and widely applied in financial forecasting. This study examines the forecasting power multilayer perceptron models for daily and sessional returns of ISE-100 index. The findings imply that the multilayer perceptron models presented promising performance in forecasting the ISE-100 index returns. However, further emphasis should be placed on different input variables and model architectures in order to improve the forecasting performances

Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models

Özellikle son on yılda yapay sinir ağları modelleri portföy oluşturma ve hisse senedi piyasası tahminleri gibi finansal problemleri çözmede uygulanmaktadır. Çeşitli yapay sinir ağları modelleri arasında, çok-katmanlı pörseptron modelleri finansal tahmin çalışmaları için yaygın ve etkili bir şekilde kullanılmaktadır. Bu çalışma, çok-katmanlı pörseptron modellerinin İMKB-100 endeksinin günlük ve seanslık getirilerinin tahmin edilmesindeki etkinliğini incelemektedir. Çalışmanın bulgularından yola çıkılarak, çok-katmanlı pörseptron modellerinin İMKB-100 endeks getirisini tahmin etmede umut vaat eden bir performans gösterdiği sonucuna varılabilir. Fakat, yapay sinir ağları modellerinin tahmin güçleri farklı değişkenler ve farklı model yapıları kullanılarak daha da arttırılabilir.Especially for the last decade, the neural network models have been applied to solve financial problems like portfolio construction and stock market forecasting. Among the alternative neural network models, the multilayer perceptron models are expected to be effective and widely applied in financial forecasting. This study examines the forecasting power multilayer perceptron models for daily and sessional returns of ISE-100 index. The findings imply that the multilayer perceptron models presented promising performance in forecasting the ISE-100 index returns. However, further emphasis should be placed on different input variables and model architectures in order to improve the forecasting performances

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

A Model for Stock Price Prediction Using the Soft Computing Approach

A number of research efforts had been devoted to forecasting stock price based on technical indicators which rely purely on historical stock price data. However, the performances of such technical indicators have not always satisfactory. The fact is, there are other influential factors that can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, investors’ confidence, government policy and political effects, among others. In this study, the effect of using hybrid market indicators such as technical and fundamental parameters as well as experts’ opinions for stock price prediction was examined. Values of variables representing these market hybrid indicators were fed into the artificial neural network (ANN) model for stock price prediction. The empirical results obtained with published stock data show that the proposed model is effective in improving the accuracy of stock price prediction. Also, the performance of the neural network predictive model developed in this study was compared with the conventional Box-Jenkins autoregressive integrated moving average (ARIMA) model which has been widely used for time series forecasting. Our findings revealed that ARIMA models cannot be effectively engaged profitably for stock price prediction. It was also observed that the pattern of ARIMA forecasting models were not satisfactory. The developed stock price predictive model with the ANN-based soft computing approach demonstrated superior performance over the ARIMA models; indeed, the actual and predicted value of the developed stock price predictive model were quite close

Revisión sistemática de literatura: Modelos de pronóstico índice Standard and Poor´s 500 (S and P500)

Resumen: El Standard and Poor´s 500 es el índice bursátil más estudiado en la literatura, ya que representa el sector industrial de Estados Unidos, el objetivo de este trabajo es hacer una Revisión Sistemática de Literatura de gran parte de los artículos que tengan como objetivo la predicción de este índice, que pretende recoger las técnicas, criterios de desempeño, pruebas de validación más utilizados con el fin de agruparlos, clasificarlos y proponer una metodología que permita un avance más rápido en esta área del conocimiento, de igual manera también se pretende establecer cuáles son los modelos de pronóstico que mejores resultados ofrecen. Se encuentra que gran parte de los criterios utilizados para medir modelos de pronóstico no son apropiados para series financieras, también se concluye que es bastante complejo la comparación entre varios autores. / Abstract:The Standard and Poor's 500 index is the most studied in the literature, this is because it represents the U.S. industry, the aim of this paper is to make a Systematic Literature Review of most items that have the objective prediction of this index, which aims to collect technical performance criteria, validation tests commonly used to group them, sort them and propose a methodology to faster progress in this area of knowledge, just as also seeks to establish what the forecast models that offer better results. We find that many of the criteria used to measure forecast models are not appropriate for financial series, also concludes that it is quite complex comparison between various authors.Maestrí

Exchange rate forecasting: an application of radial basis function neural networks

The purpose of this research is to investigate the forecasting performance of Artificial Neural Network models applied to foreign exchange rates. The study concentrates on the behavior of forecasts of exchange rates generated from the radial basis function (RBF) network models where little previous work exists;Exchange rates examined are the German mark/US dollar, Japanese yen/US dollar, and Italian lira/US dollar. One-step-ahead forecasts from univariate and multivariate RBF models are compared with those generated from ARIMA models, random walk forecasts and the forward rates. Interest rates and the money supply (M1) are used as explanatory variables in the multivariate analyses;Out-of-sample evaluation criteria include root mean squared error, correct direction , and speculative direction . Hypothesis tests are used to assess if differences in forecast accuracy from different models are significant and to assess if models can predict the direction of change with statistical significance. The tests employed are the Modified Diebold Marino test [Harvey et al. (1997)], the Pesaran-Timmerman (1992, 1994) non-parametric market timing test, and the chi2 test of independence [see Swanson and White (1997)];The main results of the study indicate that RBF models may be a useful alternative to the other models considered for forecasting exchange rates. In particular, out-of-sample forecasting results indicate that some multivariate RBF models using interest rates as economic variables do have forecasting value for some exchange rates. In the presence of interest rates, the M1 variable does not seem to possess much explanatory power for forecasting the three exchange rates