Price Series Data Research Articles

European wood pellet market integration – A study of the residential sector

The integration of European energy markets is a key goal of EU energy policy, and has also been the focal point of many scientific studies in recent years. International markets for coal, oil, natural gas and electricity have previously been investigated in order to determine the extent of the respective markets. This study enhances this field of research to bioenergy markets. Price series data and time series econometrics are used to determine whether residential sector wood pellet markets of Austria, Germany and Sweden are integrated. The results of the econometric tests show that the German and Austrian markets can be considered to be integrated, whereas the Swedish market is separate from the other two countries. Although increased internationalization of wood pellet markets is likely to contribute to European price convergence and market integration, this process is far from completed.

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.

Market Integration in the International Coal Industry: A Cointegration Approach

The purpose of this paper is to test the hypothesis of the existence of a single economic market for the international coal industry, separated for coking and steam coal, and to investigate market integration over time. This has been conducted by applying cointegration and error-correction models on quarterly price series data in Europe and Japan over the time period 1980-2000. Both the coking and the steam coal markets show evidence of global market integration, as demonstrated by the stable long-run cointegrating relationship between the respective price series in different world regions. This supports the hypothesis of a globally integrated market. However, when analyzing market integration over time it is not possible to confirm cointegration in the 1990s for steam coal. Thus, compared to the coking coal market, the steam coal market looks somewhat less global in scope.

The covariance biplot and stock market data: An alternative relative strength chart

Relative strength analysis is an important technique for many investment analysts. In this paper an alternative to the traditional relative strength chart is proposed, based on the covariance biplot. It is argued that this method provides more information than the traditional relative strength analysis in that it provides a visual picture of both the relative riskiness of the individual securities and the degree of co-movement between the various securities, in addition to the change in relative strength over a number of time periods. A methodology is proposed for transforming stock-market data into a form suitable for the covariance biplot routine. This transformation involves both a smoothing and scaling of the original price series data. The methodology is illustrated by application to six of the JSE Actuarial Sector Indices and a step-by-step guide to the interpretation of the covariance biplot is provided.

Forecasting Maine Potato Prices

Forecasting models were developed for annual and monthly average price of Maine potatoes, using Crop Reporting Board data. Several approaches to the forecasting problem were explored, as were a number of statistical aids to model development and evaluation. Attention was focused on providing timely forecasts as data became available.Specifically, three models were developed. The first provided forecasts of the season average price. Attention here was placed on the selection of a subset of influential variables from a set of potentially important ones, as well as the detection of outliers and colinearities. These problems were especially significant given the small number of observations in the annual price series. Production data for five regions were deemed most influential.The other two models were developed to provide forecasts of the monthly average price from November through May. A modified ARIMA (0,1,0) (2,1,0) model was estimated and used to provide forecasts based on the price data series alone. An alternate model, using production levels from four regions, current stocks levels and a monthly trend variable (and, implicitly, lagged price), was also developed.Both monthly models provided current forecasts for each month remaining in the marketing season which were updated as new information became available. The second model, which incorporated more information, was deemed to provide somewhat more accurate forecasts, at all forecast distances, than the simpler ARIMA model, when compared over the 1977–1981 period.