79 research outputs found
Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network
In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model
Theoretical and experimental evidence on stock market volatilities: a two-phase flow model
The volume–volatility relationship usually ignores possible effects
of stock shares. This article proposes a two-phase flow model
assuming that capital and stock flows determine stock price and
return volatility. Computational simulations suggest that monodirectional capital or stock flows and collective flows exert different
effects on stock return volatilities. Considering the impact of stock
flows, the positive relationship between capital and return volatility is no longer guaranteed. The inflow of capital and the outflow
of stock increase stock price similarly; but exhibit completely different effects on stock return volatilities. A persistent stock inflow
(outflow) reduces (intensifies) return volatilities, whereas a monodirectional persistent capital outflow has no such effect. When
capital and stock flows’ velocities satisfy critical values determined
by the initial state of the market, the market enlargement accompanied with increasing stock and capital shows no impact on
market stability because of stable return volatilities. Otherwise,
stock flows drive return volatilities with stronger effects than capital flows. Further experimental studies that simulate the real
stock market through a trading system provide strong evidence
supporting the two-phase flow model. Given similar driving forces
of capital and stock flows, the interaction of them should be considered in constructing investment strategies and setting polici
Empirical properties of inter-cancellation durations in the Chinese stock market
Order cancellation process plays a crucial role in the dynamics of price
formation in order-driven stock markets and is important in the construction
and validation of computational finance models. Based on the order flow data of
18 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate
the empirical statistical properties of inter-cancellation durations in units
of events defined as the waiting times between two consecutive cancellations.
The inter-cancellation durations for both buy and sell orders of all the stocks
favor a -exponential distribution when the maximum likelihood estimation
method is adopted; In contrast, both cancelled buy orders of 6 stocks and
cancelled sell orders of 3 stocks prefer Weibull distribution when the
nonlinear least-square estimation is used. Applying detrended fluctuation
analysis (DFA), centered detrending moving average (CDMA) and multifractal
detrended fluctuation analysis (MF-DFA) methods, we unveil that the
inter-cancellation duration time series process long memory and multifractal
nature for both buy and sell cancellations of all the stocks. Our findings show
that order cancellation processes exhibit long-range correlated bursty
behaviors and are thus not Poissonian.Comment: 14 pages, 7 figures and 5 table
Recommended System for Optimizing Battery Energy Management with Floating Car Data
Atualmente, os veículos pesados que transportam mercadoria sensível à temperatura utilizam sistemas de refrigeração ruidosos e com elevado consumo de combustível. Para combater estas desvantagens, está a ser instalado um sistema capaz de recuperar e produzir energia elétrica durante as travagens e a partir de painéis fotovoltaicos. Esta energia é armazenada num conjunto de baterias para, posteriormente, alimentar o sistema frigorífico em modo elétrico. Adicionalmente, estão a ser recolhidos dados em tempo real sobre o comportamento do veículo e do sistema.Tendo em conta que toda a energia disponível durante a condução está condicionada por diversas variáveis de operação, é fulcral extrair conhecimento a partir da análise dos dados recolhidos, identificando padrões que possam otimizar a produção e gestão da energia preditivamente. Este processo de extração de conhecimento inclui seleção e avaliação dos dados a recolher, construção do modelo preditivo do sistema e estudo da sua aplicação. Assim sendo, num dado momento, tendo em conta não só as métricas recolhidas da viagem atual, mas também de dados históricos de um dado percurso, será possível ao sistema de gestão de energia instalado no camião decidir qual a melhor ação a tomar de forma a otimizar a energia produzida sem causar stress ao sistema.Nowadays, heavy vehicles that transport temperature-sensitive goods, generally use a fuel-needy dedicated diesel engine. Towards solving this problem, an energy management system (EMS) capable of producing energy on-board of the vehicle is being developed. This recovery is possible due to the regenerative braking (RB) functionality, which consists in converting kinetic energy to electrical energy during a slowdown. The recovered energy is then stored in a set of batteries that supplies the refrigeration system when needed, allowing it to run in electrical mode. Using data retrieved from the vehicle's operation and this management system, an opportunity towards intelligently using the regenerative braking functionality emerges. By introducing an intelligence layer on the energy management system, a decision on applying the RB functionality could be made based on the trip's energetic potential. This decision will optimize the battery usage and reduce the load and wear on the EMS components.In order to calculate the energetic potential of a certain route, an estimation of the road is needed. This document presents context information and different approaches towards this end. In the modeling approach recommended and implemented, a route is divided in several spatial segments and each segment is categorized among three pre-defined classes. A classification model is used to predict traffic historical data as input. By using this modeling approach based on travel times, information on traffic flow and intersection queues are incorporated and by calculating the most likely sequence of states, a estimation of the road ahead is made.Using the information of the modeled path, when the RB systems detects a situation where the functionality can be applied, a decision will be made by weighting the energetic potential of the path ahead and the energy need. When the algorithm sees fit, a higher torque may be applied to the generator, which will result in a larger quantity of energy recovered. Since this causes stress to the system, this functionality needs a robust intelligence layer
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