scholarly journals United Kingdom “top 5” pop music lyrics

2017 ◽  
Vol 46 (5) ◽  
pp. 638-661 ◽  
Author(s):  
Adrian C North ◽  
Amanda E Krause ◽  
Robert Kane ◽  
Lorraine Sheridan
Keyword(s):  
United Kingdom ◽  
Gross Domestic Product ◽  
Interpersonal Relationships ◽  
Stock Exchange ◽  
Environmental Security ◽  
Pop Music ◽  
Security Hypothesis ◽  
Closing Price ◽  
Music Lyrics ◽  
Computerised Analysis

The present research conducted a computerised analysis of the content of all lyrics from the United Kingdom’s weekly top 5 singles sales charts (Study 1, 1962–2011), and considered their macroeconomic correlates (Study 2, 1960–2011). Study 1 showed that coverage of interpersonal relationships consistently reflected a self-centred and unsophisticated approach; coverage of violence featured predominantly anti-authoritarian denial rather than overt depictions; and more recent lyrics were more stimulating. Study 2 showed no evidence that variations in lyrical optimism predicted future variations in economic optimism and subsequently Gross Domestic Product; but, consistent with the environmental security hypothesis, economic turbulence (defined as volatility in the closing price of the London Stock Exchange) was associated with the later popularity of lyrics concerning certainty and succour. These findings are discussed in terms of the advantages and limitations of computerised coding of lyrics.

Effect Of Leverage, Inflation And GDP To Share Price Pt. Astra Otoparts Tbk.

ProBank ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 17-21
Author(s):  
Heriyanta Budi Utama ◽  
Florianus Dimas Gunurdya Putra Wardana
Keyword(s):  
Gross Domestic Product ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Share Price ◽  
Domestic Product ◽  
Coefficient Of Determination ◽  
Test Results ◽  
Purposive Sampling

The purpose of this study was to obtain empirical evidence about the effect of leverage, inflation and Gross Domestic Product (GDP) of the share price at PT. Astra Autopart, Tbk. companies in Indonesia Stock Exchange in 2011-2015. The sampling technique in this study using a purposive sampling. With the technique of purposive  sampling, all the members of the research samples by criteria. Samples that meet the criteria are used research data. Then followed the classic assumption test and test hypotheses by linear regression. The results of this study demonstrate the regression results in regression equation that Y = 2605,424 + 1561,550 X1 + 2,338 X2 + 38,994X3. T test results showed that the leverage anda GDP (Gross Domestic Product) is positive and significant effect on stock prices, while inflation is not positive and significant effect on stock prices. F test results showed that jointly leverage variables, inflation and GDP variables affecting the stock price significantly. The test results R2 (coefficient of determination) found that the variable leverage, inflation and GDP able to explain 35,4% of the stock price variable, while the remaining 64,6% is explained by other variables.Keywords: leverage, inflation, GDP, and the share priceThe purpose of this study was to obtain empirical evidence about the effect of leverage, inflation and Gross Domestic Product (GDP) of the share price at PT. Astra Autopart, Tbk. companies in Indonesia Stock Exchange in 2011-2015.The sampling technique in this study using a purposive sampling. With the technique of purposive  sampling, all the members of the research samples by criteria. Samples that meet the criteria are used research data. Then followed the classic assumption test and test hypotheses by linear regression.The results of this study demonstrate the regression results in regression equation that Y = 2605,424 + 1561,550 X1 + 2,338 X2 + 38,994X3. T test results showed that the leverage anda GDP (Gross Domestic Product) is positive and significant effect on stock prices, while inflation is not positive and significant effect on stock prices. F test results showed that jointly leverage variables, inflation and GDP variables affecting the stock price significantly. The test results R2 (coefficient of determination) found that the variable leverage, inflation and GDP able to explain 35,4% of the stock price variable, while the remaining 64,6% is explained by other variables.Keywords: leverage, inflation, GDP, and the share price

scholarly journals PENGARUH KURS, GROSS DOMESTIC PRODUCT DAN SOLVABILITAS TERHADAP RETURN SAHAM INVESTOR PADA PERUSAHAAN PERTAMBANGAN

2019 ◽  
Vol 8 (10) ◽  
pp. 6262
Author(s):  
Martina Carissa Dewi ◽  
Luh Gede Sri Artini
Keyword(s):  
Exchange Rate ◽  
Gross Domestic Product ◽  
Exchange Rates ◽  
Stock Returns ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Domestic Product ◽  
Mining Company ◽  
Macroeconomic Factors ◽  
Regression Test

The level of return obtained by investors is influenced by microeconomic and macroeconomic factors. This study aims to obtain empirical evidence regarding the effect of exchange rates, Gross Domestic Product and solvency on stock returns. This research was conducted at the mining company in the coal sub-sector on the Indonesia Stock Exchange. All the coal mining sub-sector companies listed on the Stock Exchange for the period 2014-2017 used as the population. The method of determining the sample used is using a saturated sampling technique. Multiple linear regression test used as the data analysis on this research. Based on the results of the analysis of this study it was found that the exchange rate and GDP had a negative and significant effect on stock returns. The solvency proxied by DER has a positive and significant effect on stock returns. Keywords: Exchange Rate, Gross Domestic Product, Solvability and Return.

scholarly journals Where Are the Best European Road Runners and What Are the Country Variables Related to It?

2021 ◽  
Vol 13 (14) ◽  
pp. 7781
Author(s):  
Mabliny Thuany ◽  
Sara Pereira ◽  
Lee Hill ◽  
Jean Carlos Santos ◽  
Thomas Rosemann ◽  
...  
Keyword(s):  
Logistic Regression ◽  
United Kingdom ◽  
Gross Domestic Product ◽  
Population Size ◽  
Binary Logistic Regression ◽  
Demographic Characteristics ◽  
Domestic Product ◽  
The United Kingdom ◽  
Relevant Role

Background: The environment can play a relevant role in performance in runners. This study aimed to verify the distribution of the best European road runners across the continent, and to investigate variables related to country representatives in the European Senior outdoor top list 2019. Methods: The sample comprised 563 European runners, aged 18–48 years, ranked in the European Senior outdoor top list 2019 for distances of 10–42 km. Country-related variables were gross domestic product (GDP), competition place, population size, and sports investment. The countries were categorized as “top ten countries” or “other countries”. Binary logistic regression was used for analysis. Results: The United Kingdom showed the highest prevalence of runners in the ranking (men—17.6%; women—23.0%), followed by Spain (male ranking—12.1%) and Germany (female ranking—8.6%). For men, sports investment (OR = 1.13; CI95% = 1.03–1.28) and country GDP (OR = 0.96; CI95% = 0.93–0.98) showed an association with the chances of the athlete to reach the Top 10 ranking, while among women, the only variable significantly related was the competition venue (OR = 3.97; CI95% = 1.40–11.23). Conclusion: As in other sports considered “non-expensive”, the economic and demographic characteristics of the place where athletes train can provide advantages in performance.

Simulation of Stock Prediction System using Artificial Neural Networks

2016 ◽  
Vol 3 (3) ◽  
pp. 25-44 ◽  
Author(s):  
Omisore Olatunji Mumini ◽  
Fayemiwo Michael Adebisi ◽  
Ofoegbu Osita Edward ◽  
Adeniyi Shukurat Abidemi
Keyword(s):  
Test Data ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Stock Exchange ◽  
Training Data ◽  
Prediction System ◽  
Stock Trading ◽  
Closing Price ◽  
Non Linear ◽  
Predicted Values

Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

Simulation of Stock Prediction System using Artificial Neural Networks

2020 ◽  
pp. 511-530
Author(s):  
Omisore Olatunji Mumini ◽  
Fayemiwo Michael Adebisi ◽  
Ofoegbu Osita Edward ◽  
Adeniyi Shukurat Abidemi
Keyword(s):  
Neural Networks ◽  
Test Data ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Stock Exchange ◽  
Training Data ◽  
Prediction System ◽  
Closing Price ◽  
Non Linear ◽  
Predicted Values

Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

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