The Effect of Firm Profitability on Expected Stock Return in ASEAN Stock Market

This research discusses and analyzes the company's profitability related to the company's stock return performance Profitability of the firm is related to the firm's performance of stock return. This study uses time-series data with a total sample of 1,010 firms from five countries in ASEAN (Indonesia, Thailand, Malaysia, Philippines, and Vietnam) from January 2010 to December 2019. Fama-French 3 factor model based on two different profitability showed that profitability positively affects the stock return in ASEAN markets. Fama-MacBeth's (1973) regression confirms that firm profitability scaled by operating profit-to-equity or operating profit-to-assets positively influences expected stock returns in the ASEAN market.DOI: 10.26905/jkdp.v25i3.5598

Download Full-text

An empirical testing of comparative efficiency of static and dynamic factor models towards stock returns’ predictability in capital market of Pakistan

Journal of Applied Economics and Business Studies ◽

10.34260/jaebs.427 ◽

2020 ◽

Vol 4 (2) ◽

pp. 141-162

Author(s):

Laila Taskeen Qazi ◽

Atta ur Rahman ◽

Shahid Ali ◽

Sohail Alam

Keyword(s):

Time Series ◽

Stock Returns ◽

Time Series Data ◽

Factor Model ◽

Efficient Market Hypothesis ◽

Factor Models ◽

Series Data ◽

Dynamic Factor ◽

Dynamic Factor Models ◽

Returns Predictability

Efficient Market Hypothesis has its supporters and critics as it has invited significant attention of research scholarship in recent years. The taxonomy and existence of this hypothesis is widely debated in terms of making economic decisions in the capital markets. Stock returns predictability has galvanized researchers to use forecasting models. Literature shows that forecasting is possible yet it debates problems associated with the techniques used for forecasting from the time series data. The study relies on stock returns for 67 randomly selected companies listed on the Pakistan Stock Exchange. The static and the dynamic factor models are compared in terms of forecast efficiency. The study also uses eight macroeconomic variables to forecast stock returns by including gold prices, crude oil prices, market capitalization, PSX- 100 index, PSX-100 index turnover, KIBOR 1-month rates, KIBOR 3 years rates and Rupee to Dollar rates. The results of the hit rates and out-of-sample forecasting technique suggest that dynamic factor model is the best multivariate time series forecasting model in the Pakistani context.

Download Full-text

Did the new Law for State-owned Firms affect those that are publicly traded?

Brazilian Review of Finance ◽

10.12660/rbfin.v18n1.2020.79433 ◽

2020 ◽

Vol 18 (1) ◽

pp. 23

Author(s):

Vitor Kayo De Oliveira ◽

Marcio Holland ◽

Joelson O. Sampaio

Keyword(s):

Time Series ◽

Corporate Governance ◽

Stock Returns ◽

Time Series Data ◽

Series Data ◽

High Dimensional ◽

Publicly Traded

<p>This paper studies the effects of a new law aimed at state-owned enterprises in Brazil. In particular, it analyzes whether this legislation, promoting improved corporate governance, leads to a reduced perception of risks in the management of these companies and, therefore, in the volatility of their stock returns. To do this, the ArCo (Artificial Counterfactual) methodology is applied, using high-dimensional panel time-series data from 2011 to 2018. Our results show that thirteen out of twenty stocks present a reduction in their volatility, six out of twenty stocks have contradictory results and one stock does not present a statistically significant result.</p>

Download Full-text

Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk

Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika ◽

10.31316/j.derivat.v3i1.626 ◽

2020 ◽

Vol 3 (1) ◽

pp. 25-38

Author(s):

Nendra Mursetya Somasih Dwipa

Keyword(s):

Time Series ◽

Stock Returns ◽

Confidence Level ◽

Value At Risk ◽

Time Series Data ◽

Composite Index ◽

Information Criteria ◽

Stock Index ◽

Series Data ◽

Residual Variance

A stock returns data are one of type time series data who has a high volatility and different variance in every point of time. Such data are volatile, seting up a pattern of asymmetrical, having a nonstationary model, and that does not have a constant residual variance (heteroscedasticity). A time series ARCH and GARCH model can explain the heterocedasticity of data, but they are not always able to fully capture the asymmetric property of high frequency. Integrated Generalized Autoregresive Heteroskedascticity (IGARCH) model overcome GARCH weaknesses in capturing unit root. Furthermore IGARCH models were used to estimate the value of VaR as the maximum loss that will be obtained during a certain period at a certain confidence level. The aim of this study was to determine the best forecasting model of Jakarta Composite Index (JSI). The model had used in this study are ARCH, GARCH, and IGARCH. From the case studies were carried out, the result of forecasting volatility of stock index by using IGARCH(1,1) obtained log likelihood values that 3857,979 to the information criteria AIC = -6,3180; BIC = -6,3013; SIC = -6,3180; dan HQIC = -6,3117. Value of VaR movement of the JCI if it becomes greater the investment is Rp.500,000,000.00 with a confidence level of 95% on the date of July 2, 2015 using a model IGARCH (1,1) is Rp7.166.315,00.

Download Full-text

PENGARUH INFORMASI AKUNTANSI TERHADAP RETURN SAHAM (Studi Pada PerusahaanLQ-45 Di Bursa Efek Jakarta)

PERFORMANCE Jurnal Bisnis & Akuntansi ◽

10.24929/feb.v6i2.267 ◽

2016 ◽

Vol 6 (2) ◽

pp. 22

Author(s):

Norsain ,

Keyword(s):

Panel Data ◽

Stock Returns ◽

Stock Return ◽

Time Series Data ◽

Stock Exchange ◽

Secondary Data ◽

Financial Statements ◽

Series Data ◽

Return On Assets ◽

Investment Returns

The use of financial information through the financial statements as a result of an accounting process in the company is an important information in analyzing investment returns in the long term. Through this analysis the investor will be able to assess the ability of a company's profitability, the quality of management performance, as well as future prospects of the company. Data used in this study is panel data, which is a combination of cross section and time series data 45 company financial statements as sample the period 2010 to 2013. The data sources used mainly in this research is secondary data, including data in the form of documents and information relating to the object of a study published by the Indonesia Stock Exchange through the authority of Capital Market information Center accessed from the official website of the Stock Exchange. Once the data is collected, the data were analyzed using Eviews program for this type of panel data. Beginning with the analysis of model selection, and then proceed with the classical assumption. The results of the study variables X1 Price Earning Ratio (PER), no effect on variable Y (stock returns), Variable X2 Price to Book Value (PBV) have a significant effect on the variable Y (stock returns), Variable X3 Return on Assets (ROA) significantly the variable Y (stock return). Simultaneously variable PER, PBV, ROA significant effect on the level of α = 10%.Keywords: PER, PBV, ROA, Stock Return

Download Full-text

The relationship between βeta and stock returns in the JSE securities exchange in South Africa

Corporate Ownership and Control ◽

10.22495/cocv9i1c5art5 ◽

2011 ◽

Vol 9 (1) ◽

pp. 558-566

Author(s):

Raphael Tabani Mpofu

Keyword(s):

South Africa ◽

Time Series ◽

Regression Analysis ◽

Financial Markets ◽

Stock Returns ◽

South African ◽

Time Series Data ◽

Series Data ◽

The South ◽

The Relationship

The purpose of this study was is to examine the relationship between stock βeta and returns in the JSE Securities Exchange. If the model is applicable in its entirety or can explain the beta-stock returns relationship, it raises an important academic question, mainly, how should the South African financial market be viewed by investors and portfolio managers, given the political-social-economical classifications that South Africa finds itself in, sometimes referred to as developing, emerging or underdeveloped? The time-series data used was from Sharenet as well as from the South African Reserve Bank macro-economic time series data. The sample period consisted of 10 years of monthly time series data between January 2001 and December 2010. Regression analysis was applied using the conditional approach. When using the conditional capital asset pricing model (CAPM) and cross-sectional regression analysis, the findings strongly supported the significant relationship between stock excess returns and βeta. However, the results do not provide strong evidence of a CAPM relation between risks and realized return trade-off in the South African financial markets. These results demonstrate that the South African financial markets are complex and financial tools, such as the CAPM can be used to explain complex financial phenomenon as in other developed markets, although complete reliance on the CAPM should be relied upon.

Download Full-text

A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data

PLoS ONE ◽

10.1371/journal.pone.0253307 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253307

Author(s):

Charu Sharma ◽

Niteesh Sahni

Keyword(s):

Time Series ◽

Mutual Information ◽

Stock Returns ◽

High Frequency ◽

Value At Risk ◽

Goodness Of Fit ◽

Time Series Data ◽

Series Data ◽

Joint Probability Distribution ◽

Vine Copula

In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall’s Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns.

Download Full-text

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

Download Full-text

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Download Full-text