Features of Financial Returns Modelling Price Volatility Forecasting Standard Deviations The Accuracy of Autocorrelation Estimates Testing the Random Walk Hypothesis Forecasting Trends in Prices Evidence Against the Efficiency of Futures Markets Valuing Options Appendix: A Computer Program for Modelling Financial Time Series.

Modelling Financial Time Series

https://link.springer.com/content/pdf/10.1057/jors.1979.126.pdf

Forecasting Economic Time Series

Time series analysis and its applications

This Review presents basic facts regarding the long-run evolution of income and wealth inequality in Europe and the United States. Income and wealth inequality was very high a century ago, particularly in Europe, but dropped dramatically in the first half of the 20th century. Income inequality has surged back in the United States since the 1970s so that the United States is much more unequal than Europe today. We discuss possible interpretations and lessons for the future.

Inequality in the long run

Im echten (analogen) Leben sind wir eher selten offenen Beleidigungen oder Hass ausgesetzt. Im Internet hingegen lesen wir deutlich häufiger wütende, hasserfüllte Kommentare, Snaps, Tweets und Posts gegen uns oder gegen andere. Für diesen Hass im Netz gibt es den Begriff "Hate Speech". Hate Speech ist ein Phänomen dem rund zwei Drittel (67 Prozent) der Onliner schon einmal begegnet sind, wie eine forsa-Umfrage im Auftrag der Landesanstalt für Medien NRW (LfM) zeigt.

What about Hate Speech

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

/pdf/text-mining-in-big-data-analytics-2w6xgx3q9b.pdf

Text Mining in Big Data Analytics

Does inequality really matter in forecasting real housing returns of the United Kingdom

The focus of this paper is to bring to light the vital issue of energy poverty alleviation and how big data could improve the data collection quality and mechanism. It also explains the vicious circle of low productivity, health risk, environmental pollution and energy poverty and presents currently used energy poverty measures and alleviation policies and stresses the associated problems in application due to the underlying dynamics.

Big Data and Energy Poverty Alleviation

Theoretical results and empirical evidences indicate that the Ljung–Box test is sensitive to the number of lags ( H ) involved in the test. In time series literature, different values are suggested for H . This paper is concerned with the selecting optimal number of lags H in Ljung–Box test such that the actual size of the test does not exceed the test’s level while the power of the test does not fall under specific value. A simulation study is employed to investigate the effect of selecting an improper values of H on the actual size and power of the Ljung–Box test. The results confirm that an optimal value of H depends on the time series’ length as well as the test’s level. The comparison results with currently used approaches in the literature confirm that the commonly used techniques are not suggesting a proper value for H .

Selecting optimal lag order in Ljung–Box test

Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this factor in relation to the nonparametric forecasting model of Singular Spectrum Analysis (SSA). In this paper, we focus entirely on the impact of data transformations in the form of standardisation and logarithmic transformations on the forecasting performance of SSA when applied to 100 different datasets with different characteristics. Our findings indicate that data transformations have a significant impact on SSA forecasts at particular sampling frequencies.

Mohammad Reza Yeganegi

Papers

Text Mining in Big Data Analytics

Does inequality really matter in forecasting real housing returns of the United Kingdom

Big Data and Energy Poverty Alleviation

Selecting optimal lag order in Ljung–Box test

The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting