Helsinki Stock Exchange at the Beginning of 20th Century: the Experience of Statistical Analysis of Stock Exchange Indexes

Istoriya ◽  
2015 ◽  
Vol 6 (8) ◽  
Author(s):  
Daria Anisimova
Keyword(s):  
Statistical Analysis ◽  
20Th Century ◽  
Stock Exchange ◽  
Helsinki Stock Exchange

scholarly journals Development of Lithuanian physics in the second half of the 20th century: Statistical analysis

2005 ◽  
Vol 45 (6) ◽  
pp. 503-509
Author(s):  
Romualdas Karazija ◽  
Alina Momkauskaitė ◽  
Rasa Kivilšienė
Keyword(s):  
Statistical Analysis ◽  
20Th Century

scholarly journals Volatility of Jakarta Islamic Index

2014 ◽  
Vol 6 (2) ◽  
pp. 207-222
Author(s):  
Hendri Tanjung
Keyword(s):  
Statistical Analysis ◽  
Normal Distribution ◽  
Stock Exchange ◽  
Daily Data ◽  
Using Data ◽  
Simple Statistical Analysis

Volatility of Jakarta Islamic Index. This study investigates the volatility of Jakarta Islamic Index (JII) in Jakarta Stock Exchange. The method that used in this research is used a simple statistical analysis. The normality of JII return is analyzed to answer whether the return of JII follows normal distribution. By using data of Jakarta Islamic Index from 2nd March 2009 to 30th October 2013 (1122 daily data), it is found that the distribution of return of JII is not normal, even the 5 sigma occurred. This means the return of Jakarta Islamic Index is much volatile than the theory predicted. This will make too much gain or loss in one day in the economy  DOI:10.15408/aiq.v6i2.1231

scholarly journals ESG disclosure patterns in the Baltics

2020 ◽  
Author(s):  
Ilze Zumente ◽  
Nataļja Lāce ◽  
Jūlija Bistrova
Keyword(s):  
Content Analysis ◽  
Statistical Analysis ◽  
Stock Exchange ◽  
Financial Disclosure ◽  
Social Dimensions ◽  
Key Characteristics ◽  
Wide Range ◽  
Financial Metrics ◽  
The Baltic ◽  
Disclosure Patterns

The goal of this article is to provide evidence on the volume of ESG disclosures of 34 companies listed on the NASDAQ Baltic stock exchange. It provides a broad view of the non-financial disclosure thoroughness and offers conclusions on the key characteristics of the Baltic listed companies in terms of ESG. By performing content analysis of the publicly available reports based on 106 ESG criteria and statistical analysis of the retrieved data, the disclosure patterns across reporting dimensions, industries, and company characteristics are analyzed. Authors find a wide range (8% to 67%) ESG transparency scores with an average of 41%. On aggregate, governance and social dimensions are reported better (49% and 44%) than environmental (24%). Correlation analysis was performed to test the correlation between ESG and selected financial metrics revealing that the ESG disclosure score correlates with the firm’s market capitalization.

Recidivism Reduction Research

2020 ◽  
pp. 15-23
Keyword(s):  
Statistical Analysis ◽  
20Th Century ◽  
Computer Software ◽  
Control Group ◽  
Probation And Parole ◽  
What Works ◽  
Research Designs ◽  
Complex Statistical Analysis ◽  
And Control ◽  
Second Chance

Since 2009, more than 840 Second Chance Act grant awards have been made to government and nonprofit agencies, and taxpayers have paid nearly 700 million dollars in Second Chance grants. Additionally, $154 million has been spent on probation and parole supervision agencies and staff through the Justice Reinvestment Initiative. Yet, our probation and parole population continue growing! Given the amount of money taxpayers have invested in programs, it seems nothing works. In the 20th century, it was assumed that the use of randomized and control-group research designs and complex statistical analysis and state-of-the-art computer software would be sufficient to find what “works.” But, we have not yet found what “works.” This chapter asks two questions: 1) Is it the case that “nothing works”? or 2) Is it the case that our research methods can't measure what “works”?

News Sentiment Incorporation in Real-Time Trading

2018 ◽  
Vol 26 (4) ◽  
pp. 18-35
Author(s):  
Arodh Lal Karn ◽  
YE Qiang ◽  
Rakshha Kumari Karna ◽  
Xiaolin Wang
Keyword(s):  
Stock Price ◽  
Stock Exchange ◽  
Price Change ◽  
Economic Science ◽  
Change Strategies ◽  
Technical Approach ◽  
For Profit ◽  
Helsinki Stock Exchange ◽  
Event Based ◽  
News Sentiment

This article describes how machines are the new breed of traders as news sentiment arrivals drive the stock price change. Strategies are the technical approach to search for profit from event-based speculations. This paper revisits these topics in a novel way and first uncovers distinctive characteristics of high frequency trading in Helsinki stock exchange insinuating the impression on positive recovers of event trading. Here is a better prediction by the incorporation of news on returns that proposed event trading strategy has significant effects on Finnish stock. This article contributes to the con temporarily embarked, upgrading form of practical paperwork on the take of news events in high economic science.

scholarly journals Analisis Profitabilitas, Struktur Aset, Dan Pertumbuhan Aset Terhadap Struktur Modal Yang Dimoderasi Likuiditas

2020 ◽  
Vol 3 (1) ◽  
pp. 16
Author(s):  
Ade Ningrum Mulyasari ◽  
Subowo Subowo
Keyword(s):  
Statistical Analysis ◽  
Capital Structure ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Negative Influence ◽  
Units Of Analysis ◽  
Negative Effect ◽  
Study Population ◽  
Asset Growth ◽  
The Capital Structure

This study aims to analyze the effect of profitability, asset structure, and asset growth on capital structure with liquidity as moderation. The study population is non-financial companies in the LQ45 index listed on the Indonesia Stock Exchange (IDX) in 2015-2017. The sampling technique is purposive sampling. The study sample are 27 companies for 3 years resulting in 81 units of analysis. The data analysis method used is descriptive statistical analysis and inferential statistical analysis using moderation regression analysis (MRA). The results showed that profitability and asset growth were positive and significant for the capital structure, the asset structure had a significant negative effect on the capital structure. Liquidity also moderates the negative influence of profitability on the capital structure. Liquidity is not able to moderate the influence of asset structure and asset growth on capital structure.

scholarly journals Determinant of Transfer Pricing Decision in Infrastructure, Utility, and Transportation Companies

2020 ◽  
Vol 9 (2) ◽  
pp. 150
Author(s):  
Maharamya Karuna Anggani ◽  
Trisni Suryarini
Keyword(s):  
Statistical Analysis ◽  
Intangible Assets ◽  
Stock Exchange ◽  
Transfer Pricing ◽  
Sample Selection ◽  
Analysis Data ◽  
Tax Planning ◽  
Manufacturing Companies ◽  
Purposive Sampling ◽  
Thin Capitalization

ABSTRAKPenelitian ini bertujuan untuk menganalisis dan mengetahui besarnya pengaruh tax planning, thin capitalization, dan intangible assets terhadap keputusan perusahaan dalam melakukan transfer pricing. Penelitian ini menggunakan 78 perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia (BEI) tahun 2014 hingga 2018 sebagai populasi penelitian. Pemilihan sampel penelitian ini menggunakan metode purposive sampling, dan diperoleh hanya 65 data yang dapat digunakan untuk data analisis. Metode analisis data yang digunakan dalam penelitian ini adalah analisis statistik deskriptif dan analisis statistik inferensial dengan menggunakan uji linear berganda untuk model regresi. Hasil penelitian menunjukkan bahwa thin capitalization dan intangible assets tidak berpengaruh secara signifikan terhadap keputusan perusahaan dalam melakukan transfer pricing. Sedangkan, tax planning berpengaruh secara signifikan terhadap keputusan perusahaan dalam melakukan transfer pricing.  Kata kunci: Transfer Pricing; Tax Planning; Thin Capitalization; Intangible Assets ABSTRACTThis study aims to analyze and determine the affect of tax planning, thin capitalization, and intangible assets on company decisions in transfer pricing. This study uses 78 manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2014 to 2018 as the study population. The sample selection of this study used a purposive sampling method, and the final sample was obtained by 65 data which is can use for data of analysis. Data analysis methods used in this study are descriptive statistical analysis and inferential statistical analysis using multiple linear tests for regression models. The results showed that thin capitalization and intangible assets did not significantly influence the company's decision to transfer pricing. At the same time, tax planning significantly influenced the company's decision to transfer pricing.Keywords: Transfer Pricing; Tax Planning; Thin Capitalization; Intangible Assets 

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