scholarly journals The level crossing and inverse statistic analysis of German stock market index (DAX) and daily oil price time series

2012 ◽  
Vol 391 (1-2) ◽  
pp. 209-216 ◽  
Author(s):  
F. Shayeganfar ◽  
M. Hölling ◽  
J. Peinke ◽  
M. Reza Rahimi Tabar
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Oil Price ◽  
Statistic Analysis ◽  
Stock Market Index ◽  
Level Crossing ◽  
Market Index ◽  
German Stock ◽  
German Stock Market

scholarly journals Sticky Stock Market Analysts

2021 ◽  
Vol 14 (12) ◽  
pp. 593
Author(s):  
Ibrahim Filiz ◽  
Jan René Judek ◽  
Marco Lorenz ◽  
Markus Spiwoks
Keyword(s):  
Stock Market ◽  
Investment Strategies ◽  
Stock Market Index ◽  
Market Index ◽  
German Stock ◽  
Long Term Trends ◽  
German Stock Market ◽  
Better Than

Technological progress in recent years has made new methods available for making forecasts in a variety of areas. We examine the success of ex-ante stock market forecasts of three major stock market indices, i.e., the German Stock Market Index (DAX), the Dow Jones Industrial Index (DJI), and the Euro Stoxx 50 (SX5E). We test whether the forecasts prove true when they reach their effective dates and are therefore suitable for active investment strategies. We revive the thoughts of the American sociologist William Fielding Ogburn, who argues that forecasters consistently underestimate the variability of the future. In addition, we draw on some contemporary measures of forecast quality (prediction-realization diagram, test of unbiasedness, and Diebold–Mariano test). We reveal that (a) unusual events are underrepresented in the forecasts, (b) the dispersion of the forecasts lags behind that of the actual events, (c) the slope of the regression lines in the prediction-realization diagram is <1, (d) the forecasts are highly biased, and (e) the quality of the forecasts is not significantly better than that of naïve forecasts. The overall behavior of the forecasters can be described as “sticky” because their forecasts adhere too strongly to long-term trends in the indices and are thus characterized by conservatism.

Local Stock Market Integration with International Gold and Oil Price

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Kanon Kumar Sen ◽  
◽  
Md. Thasinul Abedin ◽  
Ratan Ghosh ◽  
◽  
...  
Keyword(s):  
Stock Market ◽  
Market Integration ◽  
Oil Price ◽  
Policy Implications ◽  
Bounds Testing ◽  
Stock Market Index ◽  
Data Set ◽  
Stock Market Integration ◽  
Market Index ◽  
Short Run

We look for the integration of Bangladesh Stock Market with international gold and oil price using most recent monthly data set from January 2003 to December 2020 (2003m1-2020m12). We employ the bounds-testing approach to cointegration between stock market index (DSEX) and international gold and oil price and eventually find an integration and dynamic significant impact of international gold and oil price on DSEX in the long and short-run. We discuss the important policy implications of the dynamic impact of international gold and oil price on stock market index.

Oil price and stock market index in exporting and importing countries: evidence from MENA

2012 ◽  
Vol 2 (4) ◽  
pp. 363
Author(s):  
Hussein Mohammad Salameh ◽  
Bashar Al Zu' ◽  
N.A. bi ◽  
Khaled Abdelal Al Zubi ◽  
Ihab Khaled Magableh
Keyword(s):  
Stock Market ◽  
Oil Price ◽  
Stock Market Index ◽  
Market Index

scholarly journals Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

2021 ◽  
Vol 5 (3) ◽  
pp. 456-465
Author(s):  
Harya Widiputra ◽  
Adele Mailangkay ◽  
Elliana Gautama
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.  

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