Effects of cyclic adenosine 3,5-monophosphate on photoreceptor disc shedding and retinomotor movement. Inhibition of rod shedding and stimulation of cone elongation.

As a test of the hypothesis that cyclic nucleotides play a role in the regulation of retinomotor movements and disc shedding in the photoreceptor-pigment epithelial complex, we have used an in vitro eyecup preparation that sustains both disc shedding and cone retinomotor movements, Eyecups were prepared in white light from animals in which both shedding and cone movement had been blocked by 4 d of constant-light treatment. In eyecups incubated for 3 h in light, disc shedding was negligible and cones remained in the light-adapted (contracted) position. In eyecups incubated in darkness, however, a massive shedding response (dominated by rod photoreceptors) was induced, and at the same time cone photoreceptors elongated to their dark-adapted position. In eyecups incubated in light dbcAMP promoted cone elongation and thus mimicked darkness; the dbcAMP effect was potentiated by the phosphodiesterase inhibitors papaverine and 3-isobutylmethylxanthine. In eyecups incubated in darkness, on the other hand, both phosphodiesterase inhibitors and dbcAMP reduced the phagosome content of the pigment epithelium. The effects of dbcAMP on the cone elongation and rod shedding appear to be specific in that dbcGMP, adenosine, and adenosine 5-monophosphate had no significant effect. Our results suggest that cAMP plays a role in the regulation of both retinomotor movements and disc shedding

Stock Market Prediction Using Evolutionary Support Vector Machines: An Application To The ASE20 Index

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named evolutionary support vector machine (ESVM) stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a naïve strategy, a buy and hold strategy, a moving average convergence/divergence and an autoregressive moving average model), and a multilayer perceptron neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100 and S&P500 indices

Financial time series prediction using spiking neural networks

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al

Forecasting Government Bond Spreads with Heuristic Models:Evidence from the Eurozone Periphery

This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones

Higher order and recurrent neural architectures for trading the EUR/USD exchange rate

The motivation for this paper is to investigate the use of alternative novel neural network architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate. This is done by benchmarking three different neural network designs representing a Higher Order Neural Network (HONN), a Psi Sigma Network and a Recurrent Network (RNN) with three successful architectures, the traditional Multilayer Perceptron (MLP), the Softmax and the Gaussian Mixture (GM) models. More specifically, the trading performance of the six models is investigated in a forecast and trading simulation competition on the EUR/USD time series over a period of 8 years. These results are also benchmarked with more traditional models such as a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA) and a logistic regression model (LOGIT). As it turns out, the MLP, the HONN, the Psi Sigma and the RNN models all do well and outperform the more traditional models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the GM network produces remarkable results and outperforms all the other network architectures.Quantitative trading strategies, Volatility modelling, Risk management, Options volatility,

The robustness of neural networks for modelling and trading the EUR/USD exchange rate at the ECB fixing

The objective of this study is to investigate the use, the stability and the robustness of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate using the European Central Bank (ECB) fixing series with only autoregressive terms as inputs. This is achieved by benchmarking the forecasting performance of three different NN designs representing a Higher Order Neural Network (HONN), a Recurrent Neural Network (RNN) and the classic Multilayer Perceptron (MLP) with some traditional techniques, either statistical, such as an autoregressive moving average model, or technical, such as a moving average convergence/divergence model, plus a naïve strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period January 1999 – August 2008 using the last 8 months for out-of-sample testing. Our results in terms of their robustness and stability are compared with a previous study by the authors, who apply the same models and follow the same methodology forecasting the same series, using as out-of-sample the period from July 2006 to December 2007. As it turns out, the HONN and MLP networks present a robust performance and do remarkably well in outperforming all other models in a simple trading simulation exercise in both studies. Moreover, when transaction costs are considered and leverage is applied, the same networks continue to outperform all other NN and traditional statistical models in terms of annualised return – a robust and stable result as it is identical to that obtained by the authors in their previous study, examining a different period for

Foreign exchange, fractional cointegration and the implied-realized volatility relation

Almost all relevant literature has characterized implied volatility as a biased predictor of realized volatility. This paper provides new time series techniques to assess the validity of this finding within a foreign exchange market context. We begin with the empirical observation that the fractional order of volatility is often found to have confidence intervals that span the stationary/non-stationary boundary. However, no existing fractional cointegration test has been shown to be robust to both regions. Therefore, a new test for fractional cointegration is developed and shown to be robust to the relevant orders of integration. Secondly, employing a dataset that includes the relatively new Euro markets, it is shown that implied and realized volatility are fractionally cointegrated with a slope coefficient of unity. Moreover, the non-standard asymptotic distribution of estimators when using fractionally integrated data is overcome by employing a bootstrap procedure in the frequency domain. Strikingly, tests then show that implied volatility is an unbiased predictor of realized volatility

Modelling commodity value at risk with higher order neural networks

The motivation for this article is to investigate the use of a promising class of Neural Network (NN) models, Higher Order Neural Networks (HONNs), when applied to the task of forecasting the 1-day ahead Value at Risk (VaR) of the brent oil and gold bullion series with only autoregressive terms as inputs. This is done by benchmarking their results with those of a different NN design, the Multilayer Perceptron (MLP), an Extreme Value Theory (EVT) model along with some traditional techniques, such as an Autoregressive Moving Average Model-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) (1,1) model and the RiskMetrics volatility. In addition to these, we also examine two hybrid NNs-RiskMetrics volatility models. More specifically, the forecasting performance of all models for computing the VaR of the brent oil and the gold bullion is examined over the period 2002 to 2008 using the last year for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms and two loss functions: a violation ratio calculating when the realized return exceeds the forecast VaR and an average squared violation magnitude function, firstly introduced in this article, computing the average magnitude of the violations. As it turns out, the hybrid HONNs-RiskMetrics model does remarkably well and outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations which also have the lowest magnitude on average. The pure HONNs and MLPs along with the hybrid MLP-RiskMetrics model also give satisfactory forecasts in most cases.