A MoSe nanosheets-based saturable absorber (SA) was fabricated successfully by the liquid-phase exfoliation method. A passively Q-switched crystalline Yb:YAG laser was realized with the MoSe SA inserted inside the cavity. The shortest pulses at a wavelength of 1049 nm with a duration of 250 ns, maximum repetition rate of 181 kHz, and an average output power of 158 mW were emitted, corresponding to a maximum pulse energy of 0.87 μJ. To the best of our knowledge, this is the first experimental demonstration of the nonlinear absorption property of MoSe nanoplatelets in a crystalline Yb-doped solid-state laser, which also proves the great potential of the MoSe SA as an optical modulator in the 1 μm spectral region.
The pricing of convertible bonds (CB) is still a problem that needs to be addressed because it is a kind of hybrid financial instrument. This article proposed a novel method with support vector machine (SVM) integrated to copula function. Unlike existing single-factor or bi-factor pricing models based on corporate value and the underlying stock price, respectively, this model can cope with many limitations on the pricing of CB, such as nonlinearity, the departure from normality, multivariate joint normality distribution, market incompleteness, and so on. And above all, the new model exhibited great flexibility in that copula function can portray dependence structure between the underlying stock price and interest rate and that SVM can further tackle nonlinear relationship among variables. Moreover, the integration of SVM and copula function rendered the sensitivity analysis more convenient and accurate. Empirical analysis showed that the proposed model enhanced generation ability of out-of-sample, with satisfactory robustness and mark increase in pricing accuracy and hedging effectiveness compared with the traditional models.
In this paper, we first construct a three-layer (one hidden layer) multilayer back propagation neural network (BPNN) model to forecast daily closing prices of stocks, but there are considerable errors between the actual values and predicted values. Then, to get better prediction results with higher accuracy, we fit the tendency of the errors by modeling a generalized autoregressive conditional heteroscedasticity (GARCH) model. Since it can better deal with the non-linearity and other characteristics of financial data, so the predictive effect of our method is better than that of the hybrid approach of BPNN and autoregressive integrated moving average (ARIMA) model. Finally, we verify this assertion through experimental results.
Since the stock market is dynamic and nonlinear, we adopt the neural network to forecast the stock price. We construct the single hidden layer prediction model firstly, and analyse the effect of prediction accuracy on neurons amount and epochs. To improve the prediction accuracy and operating rate, we then construct the multiple hidden layers prediction model, and provide some theory guide on setting the number of each hidden layer for neural network with multiple hidden layers. Finally, we make a choice of the number of hidden layers by analysing the effect of stock price prediction, and the empirical results obtained demonstrate that the prediction performance of two hidden layers prediction model is better than that of the single hidden layer prediction model. Additionally, the empirical results obtained also demonstrate that the more epochs of training network, the better the results obtained with using the same number of neurons.
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