Statistical Evaluation and Prediction of Financial Time Series Using Hybrid Regression Prediction Models

In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured.

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Financial Time Series Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch136 ◽

2011 ◽

pp. 883-889

Author(s):

Indranil Bose

Keyword(s):

Data Mining ◽

Time Series ◽

Trend Analysis ◽

Prediction Models ◽

Time Series Data ◽

Financial Time Series ◽

Future Trend ◽

Series Data ◽

Time Series Data Mining ◽

Financial Time

Movement of stocks in the financial market is a typical example of financial time series data. It is generally believed that past performance of a stock can indicate its future trend and so stock trend analysis is a popular activity in the financial community. In this chapter, we will explore the unique characteristics of financial time series data mining. Financial time series analysis came into being recently. Though the world’s first stock exchange was established in the 18th century, stock trend analysis began only in the late 20th century. According to Tay et al. (2003) analysis of financial time series has been formally addressed only since 1980s. It is believed that financial time series data can speak for itself. By analyzing the data, one can understand the volatility, seasonal effects, liquidity, and price response and hence predict the movement of a stock. For example, the continuous downward movement of the S&P index during a short period of time allows investors to anticipate that majority of stocks will go down in immediate future. On the other hand, a sharp increase in interest rate makes investors speculate that a decrease in overall bond price will occur. Such conclusions can only be drawn after a detailed analysis of the historic stock data. There are many charts and figures related to stock index movements, change of exchange rates, and variations of bond prices, which can be encountered everyday. An example of such a financial time series data is shown in Figure 1. It is generally believed that through data analysis, analysts can exploit the temporal dependencies both in the deterministic (regression) and the stochastic (error) components of a model and can come up with better prediction models for future stock prices (Congdon, 2003).

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Some Statistical Models vs. Models Based on SC for High Frequency Financial Time Series Applied to Bonds of Commercial Banks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.811.435 ◽

2013 ◽

Vol 811 ◽

pp. 435-440

Author(s):

Dusan Marcek

Keyword(s):

Neural Networks ◽

Time Series ◽

High Frequency ◽

Information Technologies ◽

Prediction Models ◽

Financial Time Series ◽

Significant Risk ◽

Managerial Decision ◽

Neural Network Approach ◽

Financial Time

In neural networks modeling approach, a non-linear model is estimated based on machine learning methods. The study discusses, analytically and numerically demonstrates the quality and interpretability of the obtained prediction accuracy results from prediction models based on advanced statistical methods and models based on neural networks (intelligent methods). Both proposed approaches are applied to the financial time series of s of VUB bond prices. We found that it is possible to achieve significant risk reduction in managerial decision-making by applying intelligent forecasting models based on the latest information technologies. In a comparative study is shown, that both presented modeling approaches are able to model and predict high frequency data with reasonable accuracy, but the neural network approach is more effective.

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A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

Information ◽

10.3390/info10030103 ◽

2019 ◽

Vol 10 (3) ◽

pp. 103 ◽

Cited By ~ 2

Author(s):

Mengxing Huang ◽

Qili Bao ◽

Yu Zhang ◽

Wenlong Feng

Keyword(s):

Time Series ◽

Hybrid Algorithm ◽

Prediction Models ◽

Time Series Data ◽

Stock Exchange ◽

Financial Time Series ◽

Variable Parameter ◽

Series Data ◽

Support Vector ◽

Financial Time

Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.

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