A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange

This paper aims to compare the volatility forecasting performance of linear and nonlinear models for ISE-30 future index which is traded in Turkish Derivatives Exchangefor the period between 04.02.2005-17.06.2011. As a result of analyses, we conclude that ANN model has better forecasting performance than traditional ARCH-GARCH models. This result is important in many fields of finance such as investment decisions, asset pricing, portfolio allocation and risk managemen

Financial time series forecasting using artificial neural networks

This study builds an artificial neural network framework with the use of stacked autoencoders (SAE) to extract deep denoised features, and long short-term memory (LSTM) to generate forecasts for the next-day adjusted closing price of S&P500. Data for seven different stock indices, technical indicators, and macroeconomic variables is used to train three different models: a 'price model' which predicts the next-day price, a 'change model' which predicts the relative change in price, and a ’binary model’ which predicts the probability of a price increase. The models were judged based on predictive accuracy and profitability. Results show the models either fail to generalize well or fall prey to a vicious minimum approximating a naive predictor. Furthermore, the models appear particularly poor at predicting breaks in the series, likely due to their infrequency. This might provide evidence supporting the efficient market hypothesis.Este estudo constrói modelos de redes neuronais artificiais com o uso de "stacked autoencoders" (SAE) para extrair variáveis latentes sem ruído e "long short-term memory" (LSTM) para gerar previsões para o "next-day adjusted closing price" do S&P500. Dados para sete índices de ações diferentes, indicadores técnicos e variáveis macroeconómicas são usados para treinar três modelos diferentes: um 'modelo de preço' que prevê o preçoo do dia seguinte, um 'modelo de mudança que prevê a mudança relativa no preçoo e um 'modelo binário' que prevê a probabilidade de um aumento de preço. Os modelos foram avaliados com base na sua precisão preditiva e lucratividade. Os resultados mostram que os modelos falham em generalizar bem ou caem num mínimo vicioso que se aproxima de um "naive predictor". Além disso, os modelos parecem particularmente fracos a prever quebras na série, provavelmente devido à sua infrequência. Isto pode fornecer evidências que apoiam a hipótese do mercado eficiente

On stock return prediction with LSTM networks

Artificial neural networks are, again, on the rise. The decreasing costs of computing power and the availability of big data together with advancements of neural network theory have made this possible. In this thesis, LSTM (long short-term memory) recurrent neural networks are used in order to perform financial time series forecasting on return data of three stock indices. The indices are S&P 500 in the US, Bovespa 50 in Brazil and OMX 30 in Sweden. The results show that the outputs of the LSTM networks are very similar to those of a conventional time series model, namely an ARMA(1,1)-GJRGARCH(1,1), when a regression approach is taken. However, they outperform the time series model with regards to direction of change classification. The thesis shows significant results for direction of change classification for the small Swedish market, and insignificant results for the large US market and the emerging Brazilian market. When a trading strategy is implemented based on the direction of change, a deep LSTM network vastly outperforms the time series model

Do artificial neural networks provide improved volatility forecasts:Evidence from Asian markets

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.</p

Do artificial neural networks provide improved volatility forecasts:Evidence from Asian markets

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.</p

A hybrid of bekk garch with neural network for modeling and forecasting time series

Gold prices change rapidly from time to time. The change is not only in the mean, but also in the variability of the series. The Malaysian Kijang Emas (MKE) is the official national bullion gold coin of Malaysia and it is high in demand. The purchase and resale prices of MKE are determined by the prevailing international gold market price. However, the value of Ringgit Malaysia (RM) that is used to purchase MKE is affected by United States (U.S.) dollar. Thus, the purpose of this study is to develop the best model for forecasting international gold prices, U.S. dollar index and MKE prices by investigating their co-movement. In an attempt to find the best model, fifteen years of data for MKE prices, international gold prices in U.S. dollar and U.S. dollar index were used. This study initially considered three standard methods namely bivariate generalized autoregressive conditional heteroskedasticity (GARCH), trivariate GARCH and multilayer feed-forward neural network (MFFNN). Bivariate and trivariate GARCH are from Baba-Engle-Kraft-Kroner (BEKK) GARCH. The current study further hybridized these methods to improve forecasting accuracy. Bivariate and trivariate GARCH were used to examine the relationship between gold prices and U.S. dollar. The trivariate GARCH was modified to develop GARCH-in-mean model due to the existence risk that was expected in the data. Analysis was done by using E-Views software. However, analysis using MFFNN model and hybridized models were carried out using MATLAB software. Analyses of performances were evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). The MAPE for all in and out sample forecasts were less than 1%. The lowest values of MAPE were 0.8% for gold prices and 0.2% for U.S. dollar index. These low values were produced by using trivariate GARCH-in-mean model that was developed by the current study either as a single or hybdridized model with MFFNN. MSE recorded the values when trivariate GARCH-in-mean model was hybridized with MFFNN using 15 hidden nodes

Neural Network Prediction of Math and Reading Proficiency as Reported in the Educational Longitudinal Study 2002 Based on Non-Curricular Variables

Predicting student achievement is often the goal of many studies, and a frequently employed tool for constructing predictive models is multiple linear regression. This research sought to compare the performance of a three-layer back propagation neural network to that of traditional multiple linear regression in predicting math and reading proficiency from 103 non-curricular variables collected in the National Center for Educational Statistics\u27 2002 Educational Longitudinal Study. The neural network model was implemented using the Java programming language and the coefficients for the regression equations were produced by SPSS. The results showed that, for this data set, neither model provided an advantage over the other in terms prediction accuracy when presented with error-free cases. When synthetic noise was introduced into the data, however, the neural network model showed a greater resistance to degradation. The fact that the neural network model performed as well as, and in some cases better than, regression suggests that further study of neural network modeling is warranted to better understand the most effective ways to harness this flexible modeling technology

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

Background: Grain commodities are important to people's daily lives and their fluctuations can cause instability for households. Accurate prediction of grain prices can improve food and social security. Methods & Materials: This study proposes a hybrid Long Short-Term Memory (LSTM)-Convolutional Neural Network (CNN) model to forecast weekly oat, corn, soybean and wheat prices in the United States market. The LSTM-CNN is a multivariate model that uses weather data, macroeconomic data, commodities grain prices and snow factors, including Snow Water Equivalent (SWE), snowfall and snow depth, to make multistep ahead forecasts. Results: Of all the features, the snow factor is used for the first time for commodity price forecasting. We used the LSTM-CNN model to evaluate the 5, 10, 15 and 20 weeks ahead forecasting and this hybrid model had the lowest Mean Squared Error (MSE) at 5, 10 and 15 weeks ahead of prediction. In addition, Shapley values were calculated to analyse the feature contribution of the LSTM-CNN model when forecasting the testing set. Based on the feature contribution, SWE ranked third, fifth and seventh in feature importance in the 5-week ahead forecast for corn, oats and wheat, respectively, and 7–8 places higher than total precipitation, indicating the potential use of SWE in grain price forecasting. Conclusion: The hybrid multivariate LSTM-CNN model outperformed other models and the newly involved climate data, SWE, showed the research potential of using snow as an input variable to predict grain prices over a multistep ahead time horizon

Implementing machine learning in the stock picking process of Nova students portfolio

In a time when algorithmic trading accounts for over 50% of US equities’ traded volume, this work project proposes a holistic approach to the implementation of Machine Learning in the Stock Picking process of the Nova Students Portfolio. The presented algorithms can help investors in the identification of the features that drive stock returns and results show that our predictive algorithm provides an edge in the selection of outperforming stocks. An investor using our method from 2006 to 2019 would have achieved an annualized return of 4.8% in excess of the S&P 500 and an Info Sharpe gain of 0.2