How Statistically Significant is the DMCA Coefficient?

2021 ◽  
Author(s):  
Everaldo Freitas Guedes
Keyword(s):  
Cross Correlation ◽  
Moving Average ◽  
Statistical Significance ◽  
Statistical Test ◽  
Behavioral Pattern ◽  
Critical Values ◽  
Window Length ◽  
Climatological Data ◽  
Confidence Levels ◽  
Research Show

In this paper, we proposed a statistical test for the Detrending Moving-Average Cross-Correlation Coefficient ([Formula: see text]). With this methodology, it is possible to evaluate the statistical significance of [Formula: see text] for different confidence levels. The test was applied to financial market and climatological data. Findings on this research show that rejection or non-rejection of the null hypothesis of [Formula: see text] depends on the size [Formula: see text] of the series and the moving average window length [Formula: see text] evaluated. Our findings also show a behavioral pattern in the critical values of [Formula: see text]. Fixing the size of the series [Formula: see text], as the size of the moving average window length [Formula: see text] increases, the critical values tend to increase.

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

2014 ◽  
Vol 14 (2) ◽  
pp. 60
Author(s):  
Greis S Lilipaly ◽  
Djoni Hatidja ◽  
John S Kekenusa
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Research Use ◽  
Autoregressive Integrated Moving Average ◽  
Minimum Data ◽  
Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham  harian  maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

scholarly journals Alpha Is Not the False Alarm Rate: An Activity to Dispel a Common Statistical Misconception

2018 ◽  
Vol 46 (1) ◽  
pp. 72-79 ◽  
Author(s):  
W. Burt Thompson
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Research Finding ◽  
Statistical Power ◽  
Prior Probability ◽  
Statistical Significance ◽  
Statistical Test ◽  
Test Results ◽  
Web App ◽  
New Research

When a psychologist announces a new research finding, it is often based on a rejected null hypothesis. However, if that hypothesis is true, the claim is a false alarm. Many students mistakenly believe that the probability of committing a false alarm equals alpha, the criterion for statistical significance, which is typically set at 5%. Instructors should take specific steps to dispel this belief because it leads students to misinterpret statistical test results and it reinforces the more general misconception that results can be interpreted in isolation, without reference to theory or prior research. In the present study, students worked with a web app that shows how the false alarm rate is a function of the prior probability of an effect, statistical power, and alpha. Quiz scores suggest the activity helps correct the misconception, which can improve how students conduct and interpret research.

A new computer program for estimating the statistical significance of cross-dating positions for "floating" tree-ring series

1992 ◽  
Vol 22 (9) ◽  
pp. 1215-1221 ◽  
Author(s):  
David K. Yamaguchi ◽  
George L. Allen
Keyword(s):  
Tree Ring ◽  
Moving Average ◽  
Statistical Significance ◽  
Control Program ◽  
Fortran Program ◽  
Data Quality Control ◽  
Quality Control Program ◽  
Significance Level ◽  
False Rings ◽  
Cross Dating

CORREL is a FORTRAN program that employs cross correlation to (i) determine potential cross-dating (matching) positions for "floating" (undated) ring series; (ii) detect missing or false rings; and (iii) estimate the statistical significance of potential dating positions. To work properly, CORREL input data must be detrended and modeled using the autoregressive moving average procedure. To guard against spurious dating, the output's best date should be checked for dating consistency. The significance level of the best date is obtained by adjusting its single-dating-trial significance for multiplicity (repeated dating trials). Ideally, COREL should be used with the detrending tree-ring programs ARSTAN or INDEX, and with the data quality-control program COFECHA.

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

2014 ◽  
Vol 29 (01) ◽  
pp. 1450236 ◽  
Author(s):  
Guangxi Cao ◽  
Yan Han
Keyword(s):  
Correlation Coefficient ◽  
Stock Markets ◽  
Cross Correlation ◽  
Sunshine Duration ◽  
Statistical Test ◽  
Nonlinear Method ◽  
Weather Variables ◽  
Chinese Stock Markets ◽  
The Cross ◽  
The Relationship

Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ DCCA (n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ DCCA (n) by improving the traditional ρ DCCA (n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ DCCA (n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole.

Statistical test for Multiple Detrended Cross-Correlation Coefficient

2021 ◽  
Vol 562 ◽  
pp. 125285 ◽  
Author(s):  
A.M. da Silva Filho ◽  
G.F. Zebende ◽  
A.P.N. de Castro ◽  
E.F. Guedes
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Statistical Test

scholarly journals Seasonality of Influenza‐Like‐Illness and Acute Cardiovascular Events Are Related Regardless of Vaccine Effectiveness

2020 ◽  
Vol 9 (20) ◽  
Author(s):  
Erin R. Kulick ◽  
Michelle Canning ◽  
Neal S. Parikh ◽  
Mitchell S. V. Elkind ◽  
Amelia K. Boehme
Keyword(s):  
New York ◽  
Time Series ◽  
Influenza Vaccination ◽  
Correlation Functions ◽  
Cardiovascular Events ◽  
Cross Correlation ◽  
Moving Average ◽  
International Classification Of Diseases ◽  
Time Series Regression ◽  
Influenza Like Illness

Background Influenza has been identified as a trigger for stroke and myocardial infarction (MI) with prior studies demonstrating that influenza vaccination may decrease risk of stroke and MI. Methods and Results We used data from the New York Department of Health Statewide Planning and Research Cooperative System to evaluate whether annual variability in influenza vaccination effectiveness (VE) would be associated with cardiovascular events. Daily and monthly counts of outpatient and inpatient visits for influenza‐like illness (ILI), stroke, and MI were identified using International Classification of Diseases, Ninth Revision ( ICD‐9 ) codes; VE data for each year are publicly available. We identified pertinent lags between ILI, stroke, and MI using prewhitening cross‐correlation functions and applied them to autoregressive integrated moving average time series regression models. Time series forecasting systems assessed correlations among ILI, stroke, and MI, and the effect of VE on these relationships. Cross‐correlation functions indicated stroke events increased 1 month after increases in ILI rates; MIs increased immediately. Accounting for seasonality and lag, peaks in ILI rates were significantly related to peaks in stroke ( P =0.04) and MI ( P =0.01). Time forecasting analyses indicated no relationship between VE and cardiovascular events. Conclusions We identified that seasonality of cardiovascular events may be associated with seasonality in ILI, though VE did not modify this relationship.

scholarly journals The Effect of Guided Inquiry Laboratory Activity with Video Embedded on Students’ Understanding and Motivation in Learning Light and Optics

2019 ◽  
Vol 2 (3) ◽  
pp. 79-84 ◽  
Author(s):  
Tina Afriani ◽  
Rika Rafikah Agustin ◽  
Eliyawati Eliyawati
Keyword(s):  
Junior High School ◽  
Sampling Technique ◽  
Statistical Test ◽  
Guided Inquiry ◽  
Laboratory Activity ◽  
Significant Difference ◽  
Before And After ◽  
Post Test ◽  
Convenience Sampling ◽  
Research Show

This research aims to investigate the effect of guided inquiry laboratory activity with video embedded on students’ understanding and students’ motivation in learning lights and optics topic. The method used in this research was pre-experiment. The sampling technique used in this research was convenience sampling, and the samples were taken from grade 8 in one of junior high school in Bandung. The sample was 20 students. The class implemented guided inquiry laboratory activity with video embedded in learning light and optics. The students’ understanding was measured using test given at pretest and post-test while students’ motivation was calculated using software ministeps (RASCH Model). The t-test paired sample also was performed on the average level of 95% to identify the significant difference of students’ understanding before and after the implementation of guided inquiry laboratory activity with video embedded. The results of this research show that the use of guided inquiry laboratory with video integrated gives an improvement of students understanding. Even though the value of n-gain is 0,264 (categorized as low level), the statistical test shows that there is a significant difference between students understanding before and after the implementation of guided inquiry laboratory activity with video embedded. There are 15 students from 20 students who are motivated in learning light and optics by using guided inquiry laboratory activity with video embedded. Students are motivated by the implementation of guided inquiry laboratory activity with video embedded.

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