This paper provides a historical perspective for China’s economic reform. It first summarizes nine stylized facts of China’s gradual reform, and then explains what forces have driven China into gradualism and how China’s gradual reform has become a success. The paper shows that the transition from a centrally-planned system to a market economy is an evolutionary process with interactions among ...

The ability of the echo state network to learn chaotic time series makes it an interesting tool for financial forecasting where data is very nonlinear and complex. In this study I initially examine the Mackey-Glass system to determine how different global parameters can optimize training in an echo state network. In order to simultaneously optimize multiple parameters I conduct a grid search to...

This study implements a chaos-based model to predict the foreign exchange rates. In the first stage, the delay coordinate embedding is used to reconstruct the unobserved phase space (or state space) of the exchange rate dynamics. The phase space exhibits the inherent essential characteristic of the exchange rate and is suitable for financial modeling and forecasting. In the second stage, kernel...

1 S. Singh and E. Stuart. A Pattern Matching Tool for Forecasting, Proc. 14th International Conference on Pattern Recognition (ICPR'98), Brisbane, IEEE Press, vol. 1, pp. 103-105 (August 16-20, 1998) . ABSTRACT In this paper we describe a pattern recognition based tool for forecasting. We compare the results of forecasting with this tool against the Exponential smoothing method on Santa Fe seri...

This paper proposes a new approach for estimating and forecasting the moments and probability density function of daily financial returns from intraday data. This is achieved through a new application of the distributional scaling laws for the class of multifractal processes. Density forecasts from the new multifractal approach are typically found to provide substantial improvements in predicti...

It is proposed to have study on a business forecasting based on Neural Networks. The neural networks exhibit mapping capability, they can map input pattern to their associated output patterns. Neural network architectures can be trained with known examples of a problem before they are tested for their inference capacity on unknown instances of the problem. The present paper focuses on Neural Ne...

Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We describe the Fa system where users and applications can pose declarative forecasting queries—both onetime queries and continuous queries—and get forecasts in real-time along with accuracy estimates. Fa supports efficie...

This paper examines the predictability of real estate asset returns using a number of time series techniques. A vector autoregressive model, which incorporates financial spreads, is able to improve upon the out of sample forecasting performance of univariate time series models at a short forecasting horizon. However, as the forecasting horizon increases, the explanatory power of such models is ...

Yingfu Xie. Maximum Likelihood Estimation and Forecasting for GARCH, Markov Switching, and Locally Stationary Wavelet Processes. Doctoral Thesis. ISSN 1652-6880, ISBN 978-91-85913-06-0. Financial time series are frequently met both in daily life and the scientific world. It is clearly of importance to study the financial time series, to understand the mechanism giving rise to the data, and/or p...

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this arti...