Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index

This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.     

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.     

Photosensitive organic-inorganic hybrid structures based on porous silicon

Abstract

In this study, the photovoltaic organic-inorganic structures were created by deposition of poly(3,4-ethylenedioxythiophene) film doped by poly(styrenesulfonate) and reduced graphene oxide on the porous silicon/silicon substrate. Formation of the hybrid structure was confirmed by means of atomic-force microscopy and Fourier transform infrared spectroscopy. The current-voltage characteristics of the obtained structures were studied. It was found the increase of electrical conductivity and photo-induced signal in organic-inorganic structures. Temporal parameters and spectral characteristics of photoresponse in the 400–1100?nm wavelength range were investigated. The widening of spectral photosensitivity in a short-wavelength range due to light absorption in various layers of the multijunction structure in comparison with single crystal silicon was revealed.     

分数阶Duffing混沌系统的动力性态及其由单一主动控制的混沌同步

随着物理与技术的深入研究,分数阶非线性系统的动力性态及其分数阶混沌系统的同步成为研究的焦点.研究了分数阶Duffing系统的动力性态包括混沌性质,并且由分数阶非线性稳定性准则得到了分数阶非自治系统的混沌同步.特别地,研究了由单一主动控制的分数阶Duffing系统的同步.相应的数值结果演示了方法的有效性.     

Impulsive synchronization of time delay bursting neuron systems with unidirectional coupling

In the article, impulsive synchronization of chaotic bursting in Hindmarsh–Rose neuron systems with time delay via partial state signal is investigated. Based on impulsive control theory of dynamical systems, the sufficient conditions on feedback strength and impulsive interval are established to guarantee the synchronization. Numerical simulations show the effectiveness of the proposed scheme. The obtained results may be helpful to understand dynamical mechanism of signal transduction in real neuronal activity. © 2014 Wiley Periodicals, Inc. Complexity 21: 38–46, 2015     

油田注水系统拓扑布局优化的混合遗传算法

以投资最小为目标函数,建立了注水系统拓扑布局优化数学模型.根据模型特点,将优化问题分为两层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,改进了交叉和变异操作,结合了模拟退火算法,在操作过程中使约束条件得到满足,减少了不可行解的产生,使遗传算法的优化性能得到了提高.优化算例说明了该方法的有效性.     

关于非负下半连续函数最优可测耦合的存在性及其应用

本文证明了两个转移概率关于非负下半连续函数最优可测耦合的存在性定理．作为对这一结果的应用,推广了Ｓｔｒａｓｓｅｎ定理,进而证明了跳过程的随机可比性等价于保序耦合的存在性．     

碾压混凝土坝渗流场与应力场耦合模型研究

针对碾压混凝土坝存在众多施工层面问题,提出了确定影响层厚度以及相应物理力学参数的方法;在此基础上,探讨并建立了碾压混凝土坝渗流场与应力场两场耦合模型,研制了相应的三维有限元程序．研究表明,结合试验和理论分析确定的碾压混凝土坝层面影响层的厚度、弹性模量和泊松比等参数更切合实际;提出的碾压混凝土坝渗流场与应力场两场耦合模型,能较好地体现两场耦合作用,开发的三维有限元模型能较充分反映碾压混凝土坝施工层面的特性．通过研究,为分析施工层面对碾压混凝土坝变化性态影响,以及了解大坝的应力和渗流的相互作用提供了科学依据．     

楔条形阳极探测器位置灵敏的理论计算

通过对楔条形阳极探测器位置灵敏坐标计算公式的推导,证明了楔形和条纹分布面积所对应的级数是等差级数.分析了楔形底宽和条纹宽度等差系数这两个设计参量对探测灵敏度的影响.研究了电极电容的电容性耦合效应对位置灵敏的影响,通过对楔条形阳极探测器坐标计算公式的修正,可在不影响分辨率的前提下有效地消除电容性耦合效应所产生的扭曲变形.     

一种超混沌映射的单双参量自适应同步

利用参量自适应法实现了超混沌系统Kawakami映射的同步.通过严密的数学推导,给出了Kawakami映射在单参量自适应下实现同步的充分条件,并在双参量自适应控制下系统同样实现了同步.数值模拟结果显示,该映射输出具有类似于白噪声的宽谱特性,因此比较适合用于保密通信.