Data analysis and interpretation of stock exchange

Because practitioners of the statistical analysis often address particular applied decision problems, methods developments is consequently motivated by the search to a better decision making under uncertainties.

Data analysis and interpretation of stock exchange

All relevant data are within the paper and its Supporting Information files. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model.

Next, 2D 2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness.

The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis PCA and independent component analysis ICA.

Financial Statement Analysis: An Introduction

The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. The stock market is quite attractive if its behavior can be predicted; however, forecasting the stock market index is regarded as a difficult task due to its random walk characteristic.

Data analysis and interpretation of stock exchange

According to the Efficient Market Hypothesis [ 2 ], changes in stock market prices are determined by new information, but because the new information is unpredictable, the stock market price is also unpredictable.

Some researchers argue that the stock market can be predicted over the short term, as reported in studies by Los [ 3 ] and Haugen [ 4 ]. Guo [ 5 ] indicated that the Chinese stock market has been gradually acting as the barometer of the economy since The Shanghai stock market opened inwhich plays an important role in Chinese economic development, so an increasing number of forecasting models are being developed to predict Shanghai stock market trends.

These earlier studies have been reported in Cao et al. Over the past two decades, many models based on soft computing have been proposed [ 11 — 16 ]. In the most existing prediction approaches, there have been numerous studies using RBFNN for stock price prediction.

RBFNN was first used to solve the interpolation problem of fitting a curve exactly through a set of points [ 17 ].

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A large number of successful applications have shown that RBFNN can be useful techniques for stock price forecasting due to their ability to approximate any continuous function with arbitrary precision.

When using RBFNN for stock prices forecasting, the observed original values of prediction variables are usually directly used to build prediction models.

One of the key problems is the inherent noise of original values affecting the prediction performance. Many studies on time series analysis have suggested that raw data preprocessing is useful and necessary for improving system performance and model generalization to unseen data.

For stock market forecasting, as new data is obtained, if the predictive model can be refined to account for it, then the model should be better adapted for the new data, and its predictive accuracy should be improved. Thus, especially for predicting the stock market, with its inherent volatility, the predictive model should be dynamically learned on-line.

In this learning context, the dimensionality of the raw data play an important role in improving the performance and reducing the computational complexity needed to learn the predictive model. In this case, many hybrid system methods were proposed to improve the performance of stock market forecasting systems [ 21 — 23 ].

These existing methods usually contain two stages, the first stage is feature extraction to remove the noise, the second stage is a predictor to forecast the stock price. This indicates that more attention should be paid to the preprocessing methods used in stock market forecasting.

In particular, more effective dimension reduction methods should be introduced to improve the performance of the forecasting model. Common approaches include data normalization, indicator reduction, and PCA [ 24 ], a very popular subspace analysis method which is successfully applied in many domains for dimension reduction.

Tsai [ 27 ] use PCA as a feature selection method of stock prediction. Another well-known approach is ICA. However, this condition is often not satisfied with the stock prediction. In multivariable prediction systems, there is a strong correlation between the variables, and the initial format of the raw data is a tensor.

As feature extraction tools, both PCA and ICA need to transform the tensor into a vector, which contains two drawbacks. One is it requires prohibitive computational complexity, the other is PCA and ICA break the correlation residing in the raw data.

In this work, first, a sliding window and 36 technique variables were used to obtain a multidimensional representation of the forecasting variable.

Second, 2D 2PCA was applied to extract features from the predictor variables. We attach importance to the influence of dimension reduction on the performance of the forecasting system.A hybrid stock trading framework integrating technical analysis with machine learning techniques.

considering real companies of São Paulo Stock Exchange and transaction costs. Table 2 represents the trading signal generated from the trend analysis for the sample data set using the equations. CGG, a geoscience company, provides data imaging, seismic data characterization, geoscience, and petroleum engineering consulting services to the oil and gas exploration and production industry in North America, the Central and South Americas, Europe, Africa, the Middle East, and the Asia Pacific.

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Mar 27,  · Getting and Visualizing Stock Data Getting Data from Yahoo!

Stock Charts Analysis - Volume Price & Volatility

Finance with quantmod. Before we analyze stock data, we need to get it into some workable format.

Stock data can be obtained from Yahoo! Finance, Google Finance, or a number of other sources, and the quantmod package provides easy access to Yahoo!

Market Structure | Research and Analysis

Finance and Google Finance data, along with other sources. About main types of volume analysis: defining money flow, assessing bullish and bearish volume accumulation, spotting volume surges and their interpretation.

Money Flow About flow of the money and the ways of defining whether money are pumped into a stock (index or market) or pulled out - positive and negative (bullish and bearish) money flow.

Data analysis is a process of applying statistical practices to organize, represent, describe, evaluate, and interpret data. What is data? Data is a set of qualitative and quantitative variables.

End of Day Stock Quote Data and Historical Stock Prices