The Research of Morphological Characteristics in Time Series of Stock Prices Based on CBR

2013 ◽  
Author(s):  
Yuan Xiao ◽  
Wen-Gang Che ◽  
Zhong Wang ◽  
Chi-Chang Yang
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Morphological Characteristics

Determinants of Common Stock Prices: A Time Series Analysis

1977 ◽  
Vol 32 (2) ◽  
pp. 417-425 ◽  
Author(s):  
Marshall R. Blume ◽  
John Kraft ◽  
Arthur Kraft
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Common Stock ◽  
Series Analysis

scholarly journals Characterizing sand ripples at equilibrium phases

2013 ◽  
Vol 61 (4) ◽  
pp. 293-298 ◽  
Author(s):  
Jie Qin ◽  
Deyu Zhong ◽  
Guangqian Wang
Keyword(s):  
Time Series ◽  
Random Fields ◽  
Morphological Characteristics ◽  
Structure Functions ◽  
Second Order ◽  
Order Structure ◽  
Two Dimensional ◽  
Sand Ripples ◽  
Digital Elevation ◽  
Equilibrium Phases

Abstract Morphological characteristics of ripples are analyzed considering bed surfaces as two dimensional random fields of bed elevations. Two equilibrium phases are analyzed with respect to successive development of ripples based on digital elevation models. The key findings relate to the shape of the two dimensional second-order structure functions and multiscaling behavior revealed by higher-order structure functions. Our results suggest that (1) the two dimensional second-order structure functions can be used to differentiate the two equilibrium phases of ripples; and (2) in contrast to the elevational time series of ripples that exhibit significant multiscaling behavior, the DEMs of ripples at both equilibrium phases do not exhibit multiscaling behavior.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

scholarly journals Relationship Between Deferred Tax Category and Stock Prices in Pharmacy

2018 ◽  
Vol 66 (6) ◽  
pp. 1469-1475
Author(s):  
Petr Habanec
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Exchange ◽  
Negative Relationship ◽  
Accounting Standards ◽  
Earning Management ◽  
International Accounting ◽  
International Accounting Standards ◽  
Deferred Tax ◽  
The Relationship

The paper deals with relationship between stock prices and deferred tax category. Joos, Pratt and Young provided evidence that book‑tax differences are correlated with earning management. In this paper is confirmed negative relationship between stock prices and deferred tax. The relationship is assessed on sample of companies making business in pharmacy (CZNACE‑C‑21). The relationship between deferred tax category and stock prices is assessed on a sample of companies in the time series from 2005 to 2015. Sample consists of companies listed on Frankfurt stock exchange and reporting in accordance with international accounting standards IAS/IFRS. The stock prices dataset is based on Morningstar database. The results are compared with the results of author ’s previous study concerning the deferred tax materiality.

scholarly journals BLACK SWAN GLOBAL MARKET: ANALYSIS OF THE EFFECT OF THE COVID-19 DEATH RATE ON THE VOLATILITY OF EUROPEAN FOOTBALL CLUB STOCK PRICES (CASE STUDY OF JUVENTUS F.C., MANCHESTER UNITED, AJAX AMSTERDAM AND BORUSSIA DORTMUND)

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Franco Benony Limba ◽  
Jacobus Cliff Diky Rijoly ◽  
Margreath I Tarangi
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Global Market ◽  
Bad News ◽  
Series Data ◽  
Black Swan ◽  
Financial Industry ◽  
Market Effect ◽  
European Football

Abstract: The Covid-19 pandemic that hit the world also directly affected financial markets and global stock markets; this condition in economic terminology is known as the Black Swann Global Market Effect. Black Swan Global Market Effect is also experienced by sports industries in the financial industry, the football industry. The purpose of this paper is to see whether there is an influence between the Covid-19 pandemic conditions on the share value of several major European football clubs, namely Ajax Amsterdam, Borussia Dortmund, Juventus F.C., and Manchester United, as a result of the Black Swan Global Market Effect. The data used in this paper is time-series data from March 2020 to August 2020. Meanwhile, to answer the black swan effect phenomenon, the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) method is used. The results showed that stocks that were the object of research (Ajax, Borussia Dortmund, Juventus, and Machester United) showed a large response to bad News (an increase in deaths due to covid-19). Abstrak:Pandemic covid-19 yang mengantam dunia juga secara langsung mempengaruhi pasar keuangan serta pasar saham global, kondisi ini dalam terminology ekonomi dikenal sebagai Black Swann Global Markert Effect. Black Swan Global Market Effect hal ini juga dialami industry-industri olahraga yang berada dalam industry keuangan tersebut salah satunya industry sepakbola.Tujuan penulisan ini adalah untuk melihat apakah terdapat pengaruh antara kondisi pandemic covid-19 terhadap nilai saham beberapa klub sepakbola besar eropa yaitu Ajax Amsterdam, Borussia Dortmund, Juventus FC, dan Manchester United sebagai akibat dari Black Swan Global Market Effect.Data yang digunakan dalam penulisan ini adalah data time series dari bulan maret 2020 hingga agustus 2020. Sementara untuk menjawab fenomoena black swan effect ini digunakan metode Threshold Generalized Autoregressive Conditional Heteroskedacity (TGARCH). Hasil Penelitian menunjukkan bahwa, saham-saham yang menjadi objek penelitian (Ajax, Borussia Dortmund, Juventus, dan Machester United) menunjukan respons yang besar terhadap bad news (peningkatan jumlah kematian akibat covid-19). Black Swan Global Market, Pandemi Covid-19, TGARCH Models

scholarly journals Forecasting leading industry stock prices based on a hybrid time-series forecast model

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0209922 ◽  
Author(s):  
Ming-Chi Tsai ◽  
Ching-Hsue Cheng ◽  
Meei-Ing Tsai ◽  
Huei-Yuan Shiu
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Forecast Model ◽  
Time Series Forecast ◽  
Leading Industry

scholarly journals A Time Series Analysis of the Nexus Between Macroeconomic Fundamentals and Stock Prices in Nigeria

2018 ◽  
Vol 13 (2) ◽  
pp. 69-91
Author(s):  
Amassoma Ditimi ◽  
Bolarinwa Ifeoluwa
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Macroeconomic Factors ◽  
Series Analysis ◽  
Integration Test ◽  
Macroeconomic Fundamentals ◽  
The Government

AbstractSince macroeconomic fundamentals have been found to play a vital role for changes in the economy of a country. Consequently, the onus is on the appropriate regulatory authorities to take measures in making amendments in these policies to put the economy on the right development track. The aim of this study is to use time series analysis to empirically showcase the nexus between macroeconomic fundamentals and stock prices in Nigeria. The method used for this study was the Co-integration test and the EGARCH technique to estimate the possible influence of the selected macroeconomic fundamentals on stock prices. Volatility was captured by using quarterly data and estimated using GARCH (1,1) respectively. The study found there is a positive relationship between macroeconomic factors and stock prices in Nigeria. Therefore, the study recommends that the Federal authority should put in place policy measures that will enable the exchange rate to be relatively stabilized. This is because empirical evidence from studies has shown that exchange rate affects stock market prices. In addition, the government authority should ensure an enabling environment that would build the mindset of institutional investors in the Nigerian stock market due to the existence of information asymmetry problems among potential investors.

scholarly journals From time series analysis to a modified ordinary differential equation

2018 ◽  
Vol 12 (2) ◽  
pp. 85-90
Author(s):  
Meiyu Xue ◽  
Choi-Hong Lai
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Time Series ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Time Frame ◽  
Equation Approach ◽  
Advantages And Disadvantages ◽  
Series Analysis ◽  
Bank Stock

In understanding Big Data, people are interested to obtain the trend and dynamics of a given set of temporal data, which in turn can be used to predict possible futures. This paper examines a time series analysis method and an ordinary differential equation approach in modeling the price movements of petroleum price and of three different bank stock prices over a time frame of three years. Computational tests consist of a range of data fitting models in order to understand the advantages and disadvantages of these two approaches. A modified ordinary differential equation model, with different forms of polynomials and periodic functions, is proposed. Numerical tests demonstrated the advantage of the modified ordinary differential equation approach. Computational properties of the modified ordinary differential equation are studied.

A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index

2019 ◽  
Vol 18 (06) ◽  
pp. 1967-1987
Author(s):  
Tai-Liang Chen ◽  
Ching-Hsue Cheng ◽  
Jing-Wei Liu
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Stock Prices ◽  
Performance Indicator ◽  
Time Series Model ◽  
Stock Index ◽  
Forecasting Model ◽  
Critical Factors ◽  
Causality Test ◽  
Proposed Model

Stock forecasting technology is always a popular research topic because accurate forecasts allow profitable investments and social change. We postulate, based on past research, three major drawbacks for using time series in forecasting stock prices as follows: (1) a simple time-series model provides insufficient explanations for inner and external interactions of the stock market; (2) the variables of a time series behave in strict stationarity, but economic time-series are usually in a nonlinear or nonstationary state and (3) the forecasting factors of multivariable time-series are selected based on researcher’s knowledge, and such a method is a “subjective” way to construct a forecasting model. Therefore, this paper proposes a causal time-series model to select forecasting factors and builds a machine learning forecast model. The “Granger causality test” is utilized first in the proposed model to select the critical factors from technical indicators and market indexes; next, a “multilayer perceptron regression (MLPR)” is employed to construct a forecasting model. This paper collected financial data over a 13-year period (from 2003 to 2015) of the Taiwan stock index (TAIEX) as experimental datasets. Furthermore, the root mean square error (RMSE) was used as a performance indicator, and we use five forecasting models as comparison models. The results reveal that the proposed model outperforms the comparison models in forecasting accuracy and performs well for three key indicators. LAG1, S&P500 and DJIA, are critical factors in all 11 of our time sliding windows (T1–T11). We offer these results to investors to aid in their decision-making processes.

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