Using intelligent optimization methods to improve the group method of data handling in time series prediction

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm

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

STOCK RETURNS PREDICTION BY USING ARTIFICIAL NEURAL NETWORK MODEL FOR PAKISTAN STOCK EXCHANGE

Artificial neural networks are extensively used to predict the financial time series. This study implements the neural network model for predicting the daily returns of the Pakistan Stock Exchange (PSE). Such an application for PSE is very rare. A multi-layer perception network is used for the model used in this study, while the network is trained using the Error Back Propagation algorithm. The results showed that the predictive power of the network was performed by the return of the previous day rather than the input of the first three days. Therefore, this study showed satisfactory results for PSE. In short, artificial intelligence can be used to give a better picture of stock market operators and can be used as an alternative or additional to predict financial variables

Modelling the Volatility of GHC_USD Exchange Rate Using Garch Model

Modelling and forecasting the exchange rate volatility is a crucial area, as it has implications for many issues in the arena of finance and economics.  Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models with their modifications, is used in capturing the volatility of the exchange rates. Simple rate of returns is employed to model the currency exchange rate volatility of Ghana Cedi-United States Dollar. The daily closing exchange rates were used as the daily observations.  The parameters of these models are estimated using the maximum likelihood method. The results indicate that the volatility of the GHC_USD exchange rate is persistent. The asymmetry terms for TARCH are not statistically significant. Also in TARCH case, the coefficient estimate is negative, suggesting that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. This is the opposite to what would have been expected in the case of the application of a GARCH model to a set of stock returns. But arguably, neither the leverage effect or volatility feedback explanations for asymmetries in the context of stocks apply here. Keywords: Exchange rate, volatility, GARCH mode

Market Timing Using Artificial Neural Networks

The emergence of artificial neural networks has given us some of the most impressive technological tools. Inspired by the human brain, these networks consist of interconnected artificial neurons that can detect patterns invisible to the human eye. These qualities have caught the attention of investors seeking ways to beat the market. In this thesis, we explore how artificial neural networks can be used to construct an active trading strategy and evaluate the strategy's performance against two benchmark strategies. Two stock indices were used to train neural networks using the lagged return as input to predict the market state. By using the networks' predictions, an active trading strategy was constructed. To evaluate the network-based strategy, we test if the Sharpe ratio differs significantly from the Sharpe ratio of a simple moving average strategy and the buy and hold strategy. Additionally, we estimate the alpha in the capital asset pricing model. The results show that the network strategy performs similar to the benchmark strategies in terms of Sharpe ratio and fails to generate significant alphas. Overall, our results contribute to the previous literature seeking to apply neural networks to finance and should serve as a reminder of the shortcoming of financial data for machine learning and the importance of statistical testing

Market Timing Using Artificial Neural Networks

The emergence of artificial neural networks has given us some of the most impressive technological tools. Inspired by the human brain, these networks consist of interconnected artificial neurons that can detect patterns invisible to the human eye. These qualities have caught the attention of investors seeking ways to beat the market. In this thesis, we explore how artificial neural networks can be used to construct an active trading strategy and evaluate the strategy's performance against two benchmark strategies. Two stock indices were used to train neural networks using the lagged return as input to predict the market state. By using the networks' predictions, an active trading strategy was constructed. To evaluate the network-based strategy, we test if the Sharpe ratio differs significantly from the Sharpe ratio of a simple moving average strategy and the buy and hold strategy. Additionally, we estimate the alpha in the capital asset pricing model. The results show that the network strategy performs similar to the benchmark strategies in terms of Sharpe ratio and fails to generate significant alphas. Overall, our results contribute to the previous literature seeking to apply neural networks to finance and should serve as a reminder of the shortcoming of financial data for machine learning and the importance of statistical testing

Modelling the volatility of currency exchange rate using GARCH model

This paper attempts to study GARCH models with their modifications, in capturing the volatility of the exchange rates. The parameters of these models are estimated using the maximum likelihood method. The performance of the within-sample estimation is diagnosed using several goodness-of-fit statistics and the accuracy of the out-of-sample and one-step-ahead forecasts is evaluated using mean square error. The results indicate that the volatility of the RM/Sterling exchange rate is persistent. The within sample estimation results support the usefulness of the GARCH models and reject the constant variance model, at least within-sample. The Qstatistic and LM tests suggest that long memory GARCH models should be used instead of the short-term memory and high order ARCH model. The stationary GARCH-M outperforms other GARCH models in out-of-sample and one-step-ahead forecasting. When using random walk model as the naive benchmark, all GARCH models outperform this model in forecasting the volatility of the RM/Sterling exchange rates

Equity price predictions of selected African emerging markets

Abstract : Predicting equity share prices could be useful to various stakeholders. The common methods used to forecast equity share price besides the naïve model are the Autoregressive Conditional Heteroskedasticity (ARCH) and General Autoregressive Conditional Heteroskedasticity (GARCH) models, however, no conclusion has been reached as to which model produces the most accurate predictions. In this research, ARCH and GARCH forecasting models (and their extended variants), as well as the Monte Carlo Simulation, were used to forecast price-weighted equity indices that were constructed from the South African, Nigerian, and Kenyan share markets. These three countries were selected based on their significance in the African continent due to the relative size of their economies and the liquidity of their share markets. The daily closing share prices for companies listed on the FTSE/JSE Top 40 Index, NSE Top 30 Index, and the NrSE Top 20 Index were collected between the 4th of January 2010 and the 30th of June 2015. The companies that were selected from each of these indices to construct the price-weighted indices for each country, were based on criteria to eliminate bias. Different autoregressive models were fitted for the mean equation. The EViews statistical programme was used to analyse the data. The ARCH effects were tested using the ARCH LM test. The ARCH/GARCH family models selected were GARCH (2,1), EGARCH (2,2), and EGARCH (2,1) for Nigeria, Kenya, and South Africa respectively. A Monte Carlo Simulation with 1 200 iterations was also performed to forecast the equity share prices. Post estimation and performance evaluation metrics were performed using the RMSE, MSE, MAD, and MAPE. The results based on the evaluation metrics indicated that the ARCH/GARCH models in-sample forecasts were more accurate than out-of-sample forecasts. The accuracy of the ARCH/GARCH models’ predictions was sounder than that of the Monte Carlo Simulation based on the evaluation metrics. Comparing the forecasting models to the actual graphs, in most cases the ARCH/GARCH models were closer to the actuals than the Monte Carlo II Simulation. The accuracy of the model predictions were also influenced by the sample size, the nature of the data, the leverage effect, and the macro economic conditions. In conclusion, the African equity markets cannot be predicted accurately using the ARCH/GARCH models and the Monte Carlo Simulation. The predictions from the forecasting models are not sufficiently accurate for investors, traders, and company management to use to make informed decisions. However, these predictions are better than the naïve model. The researcher also concluded that the markets are efficient, as the publicly available information cannot be used to gain abnormal returns. This study’s findings are similar to those of previous studies carried out in South Africa and globally.M.Com. (Finance