What Causes Stock Market Volatility in Pakistan? Evidence from the Field

Ghufran, Bushra; Awan, Hayat Muhammad; Khakwani, Aftab Khan; Qureshi, Muhammad Azeem

doi:10.1155/2016/3698297

Cited by 18 publications

(21 citation statements)

References 22 publications

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“…Namun, investor justru bertindak independen pada saat pasar modal dalam kondisi bullish. Penelitian yang dilakukan oleh (Ghufran, Awan, Khakwani, & Qureshi, 2016) Theory. Seiring dengan meningkatnya keuntungan, investor akan cenderung menuntut tingkat imbal hasil yang semakin tinggi dengan risiko yang semakin tinggi pula.…”

Section: Pendahuluanunclassified

Pengaruh Perilaku Herding dan Volatilitas IHSG terhadap Return Pasar di Bursa Efek Indonesia

Wijaya

Meirisa

2020

JIBM

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This study aims to determine the influence of herding behavior and JCI volatility on market returns at Indonesia Stock Exchange during 2014-2018 period. This research applies quantitative research methods. The sample in this study was taken using a saturated sampling technique. The data in this study uses secondary data collected from various sources, such as the Indonesia Stock Exchange, Yahoo! Finance, Bloomberg and Reuters. This study uses multiple linear regression analysis to process data and test hypotheses. The results of this study found that herding behavior had a positive and significant effect on market returns, while the JCI volatility had a positive and significant effect on market returns. Further researchers are advised to add several other variables that can affect market returns, such as inflation rates, interest rates, and others.Abstrak. Penelitian ini bertujuan untuk mengetahui adanya pengaruh perilaku herding dan volatilitas IHSG terhadap return pasar di Bursa Efek Indonesia selama periode 2014-2018. Penelitian ini menerapkan metode penelitian kuantitatif. Sampel pada penelitian ini diambil menggunakan teknik sampling jenuh. Data dalam penelitian ini menggunakan data sekunder yang dikumpulkan dari berbagai sumber, seperti Bursa Efek Indonesia, Yahoo! Finance, Bloomberg dan Reuters. Penelitian ini menggunakan analisis regresi linier berganda untuk mengolah data dan menguji hipotesis. Hasil dari penelitian ini menemukan bahwa perilaku herding berpengaruh positif dan signifikan terhadap return pasar, sedangkan volatilitas IHSG berpengaruh positif dan signifikan terhadap return pasar. Bagi peneliti selanjutnya disarankan untuk menambahkan beberapa variabel lain yang bisa mempengaruhi return pasar, seperti tingkat inflasi, suku bunga, dan lain-lain.Kata kunci: Perilaku Herding, Volatilitas IHSG, Return Pasar.

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Section: Pendahuluanunclassified

Pengaruh Perilaku Herding dan Volatilitas IHSG terhadap Return Pasar di Bursa Efek Indonesia

Wijaya

Meirisa

2020

JIBM

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“…Political instability, contagion effects from the Asian crisis and herd behavior dominated the market in 1990s. This is in conformity to Ghufran et al (2016). Volatility in the stock prices reduced in 2001-2003 as the SECP initiated reforms to improve the management and operational efficiency of the market.…”

Section: Results and Analysismentioning

confidence: 61%

“…There exists a void between academic research and the conventional process of policymaking on this front. Policymakers merely focus on disciplining the behavior of investors and other market participants to ensure financial stability, while academic research establishes a link between political stability and the performance of various segments of the financial system (Ghufran, Awan, Khakwani, & Qureshi, 2016). More recently, researchers highlighted a flaw in the existing process of macro-prudential policymaking that it ignores political risk as an essential component of systemic financial risk (Danielsson & Macrae, 2016).…”

Section: Introductionmentioning

confidence: 99%

Developing a Financial Stress Index for Pakistan

Sadia¹,

Bhatti²,

Ahmad³

2019

BER

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This paper develops a financial stress index for the Pakistan economy covering the post-reform period. We use time series data for the period 1993M1-2016M12 and employ principal component analysis to aggregate various components of financial markets, real economic activity, and political risk in a single financial stress index. The computed index successfully explains the known periods of financial stress in Pakistan. It stresses upon the political and economic risks as important contributors of financial stress along with financial market factors. This composite index assesses the stability of the financial system, which is a public policy concern in most emerging economies. Thus, we believe that it can serve as an important benchmark to describe the systemic risk in the financial system.

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“…We use Pakistan Stock Exchange as the universe and the sample firms include companies from the manufacturing sector listed on Pakistan Stock Exchange (PSX), previously known as (Karachi Stock Exchange), following (Fama & French, 2015). Pakistan's equity market has attracted massive investments in recent years from all parts of the country, reaffirming the confidence of the people in the bourse's markets (Ghufran, Awan, Khakwani, & Qureshi, 2016). Better infrastructural changes and automation of the primary processes have been introduced and most of the financial sector reforms in Pakistan including the stock market have taken place during the decade of 90's.…”

Section: Methodsmentioning

confidence: 99%

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

Jan

Ayub

2019

Journal of Business Economics and Management

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Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.

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What Causes Stock Market Volatility in Pakistan? Evidence from the Field

Cited by 18 publications

References 22 publications

Pengaruh Perilaku Herding dan Volatilitas IHSG terhadap Return Pasar di Bursa Efek Indonesia

Pengaruh Perilaku Herding dan Volatilitas IHSG terhadap Return Pasar di Bursa Efek Indonesia

Developing a Financial Stress Index for Pakistan

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

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