Prediction of Stock Market Index Using Genetic Algorithm

The generation of profitable trading rules for stock market investments is a difficult task but admired problem. First stage is classifying the prone direction of the price for BSE index (India cements stock price index (ICSPI)) futures with several technical indicators using artificial intelligence techniques. And second stage is mining the trading rules to determined conflict among the outputs of the first stage using the evolve learning. We have found trading rule which would have yield the highest return over a certain time period using historical data. These groundwork results suggest that genetic algorithms are promising model yields highest profit than other comparable models and buy-and-sell strategy. Experimental results of buying and selling of trading rules were outstanding. Key words: Data mining, Trading rule, Genetic algorithm, ANN, ICSPI predictio

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11

Estimation of Default Probabilities with Support Vector Machines

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to estimate default probabilities of German firms. Our analysis is based on the Creditreform database. The results reveal that the most important eight predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Based on the performance measures, the SVM tool can predict a firms default risk and identify the insolvent firm more accurately than the benchmark logit model. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of the SVM parameters. Based on the nonparametric Nadaraya-Watson estimator, the expected returns predicted by the SVM for regression have a significant positive linear relationship with the risk scores obtained for classification. This evidence is stronger than empirical results for the CAPM based on a linear regression and confirms that higher risks need to be compensated by higher potential returns.Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Expected Profitability, CAPM.

Teoría de precios de arbitraje. Evidencia empírica para Colombia a través de redes neuronales

RESUMENEsta investigación utiliza una red neuronal multicapa para relacionar el Índice General de Bolsa de Valores de Colombia (IGBC) con fundamentales macroeconómicos y variables Financieras. Proponemos dos modelos: un modelo APT (fundamentales macroeconómicos) y un modelo APT modificado (fundamentales macroeconómicos + indicador de las bolsas del mundo); de acuerdo a nuestro análisis el APT tradicional se ajusta mejor para predecir el mercado de valores Colombiano. Los resultados confirman que las redes neuronales artificiales (ANN) son más efectivas que los modelos estadísticos tradicionales por su capacidad explicativa y precisión.ABSTRACTThis research uses a multilayer neural network to relate the General Index from Colombia Stock Exchange (IGBC) with macroeconomic fundamentals and fiancial variables. We propose two models: an APT (macroeconomic fundamentals) one and a modified APT (macroeconomic fundamentals + international stock markets indicator); according to our analysis the traditional APT predicts more accurately the behavior of the Colombian stock market. The results confirm that the artificial neural network (ANN) approach is more effective than traditional statistical models given its explanatory power and precision.Teoría de precios de arbitraje, variables macroeconómicas,mercado de valores, redes neuronales artificiales.

Neural Network Forecasting of the Production Level of Chinese Construction Industry

Increased efforts have been devoted over the past several decades to the development and improvement of time series forecasting models. In this paper, we determine whether the forecasting performance of variables under study can be improved using neural network models. Among the best 10 retained networks, an MLP 3- layer network: 1:1-31-1:1 is selected as the ANN model with the minimum RMSE. The performance of the model is evaluated by comparing it with the ARIMA model. The root mean squared forecast error of the best neural network model is 49 per cent lower than the ARIMA model counterpart. It shows that the neural network yields significant forecast improvements. The gains in forecast accuracy seem to originate from the ability of neural networks to capture asymmetric relationships. This methodology has been applied to forecast the Chinese construction industry (CI). Since CI contributes to GDP considerably, it has an important and supportive role in the national economy of China. The empirical results show that the trend of steadily increasing production levels of CI implies a strong potential for future growth

Reflexivity in Financial Markets: A Neuroeconomic Examination of Uncertainty and Cognition in Financial Markets

Financial markets exist to disperse the risks of an unknown future in an economy. But for this process to work in an optimal fashion, investors – and subsequently markets – must have a way to interpret uncertainty. The investor rationality and market efficiency literature utilizes a methodology inadequate to address this fact, so I supplement it with the perspectives of epistemology, economic sociology, neuroscience, cognitive science, and philosophy of mind. This approach suggests that what is commonly viewed as market “inefficiency” is not necessarily caused by investor irrationality, but rather by the inherent nature of the epistemological problem faced by investors. I propose the Reflexive Market Hypothesis to describe how markets, despite their seeming deviations from efficiency, are efficient in the computational sense

Primer on using neural networks for forecasting market variables

Author's OriginalAbility to forecast market variables is critical to analysts, economists and investors. Among other uses, neural networks are gaining in popularity in forecasting market variables. They are used in various disciplines and issues to map complex relationships. We present a primer for using neural networks for forecasting market variables in general, and in particular, forecasting volatility of the S&P 500 Index futures prices. We compare volatility forecasts from neural networks with implied volatility from S&P 500 Index futures options using the Barone-Adesi and Whaley (BAW) model for pricing American options on futures. Forecasts from neural networks outperform implied volatility forecasts. Volatility forecasts from neural networks are not found to be significantly different from realized volatility. Implied volatility forecasts are found to be significantly different from realized volatility in two of three cases. A revised version of this paper has since been published in the Journal of Business Research. Please use this version in your citations.Hamid, S. A. & Iqbal, Zahid. (2004). Using Neural Networks for Forecasting Volatility of S&P 500 Index Futures Prices. Journal of Business Research, 57(10), 1116-1125