STOCK MARKET ANOMALIES: A CASE OF CALENDAR EFFECTS ON THE MALAYSIAN STOCK MARKET

2021 ◽  
Vol 6 (1) ◽  
pp. 772
Author(s):  
Nurul Sima Mohamad Shariff ◽  
Nur Aisyah Yusof
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Composite Index ◽  
Investment Strategy ◽  
Series Data ◽  
Kuala Lumpur ◽  
Market Anomalies ◽  
Calendar Effects ◽  
Year Effect

The existence of market anomalies for the return reveals the inefficiency in the market that could affect investor investment strategy, portfolio selection, and profit management. It is due to the unpredictable movement of the stock market return that will affect the decision of investors later. As such, this study intends to investigate day of the week effect, a month of the year effect, and a quarter of the year effect on the Malaysian Stock Exchange, namely the Kuala Lumpur Composite Index (KLCI) on data from 2nd January of 2015 until 31st December 2018. Based on the findings from Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model analysis, it is found that the daily effect on returns was insignificant. Possible reasons for the insignificant return could be due to the lack of time-series data. However, the significant monthly effect on returns of May, November, and December while the quarterly effect on the returns is found significant in the first quarter. This study also concludes that volatility shock is persistent in the returns for all those three market anomalies.

scholarly journals Portfolio Learning Based on Deep Learning

2020 ◽  
Vol 12 (11) ◽  
pp. 202
Author(s):  
Wei Pan ◽  
Jide Li ◽  
Xiaoqiang Li
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Composite Index ◽  
Series Data ◽  
High Dimensional ◽  
Chinese Stock Market ◽  
High Return ◽  
Stock Information

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

scholarly journals The dynamic relationship between stock returns and trading volumes in Nepalese stock market

2020 ◽  
Vol 23 (2) ◽  
pp. 161-172
Author(s):  
Prem Lal Adhikari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Time Series Data ◽  
Stock Exchange ◽  
Causal Effect ◽  
Empirical Relationship ◽  
Insurance Companies ◽  
Series Data ◽  
Dynamic Relationship

 In finance, the relationship between stock returns and trading volume has been the subject of extensive research over the past years. The main motivation for these studies is the central role that trading volume plays in the pricing of financial assets when new information comes in. As being interrelated and interdependent subjects, a study regarding the trading volume and stock returns seem to be vital. It is a well-researched area in developed markets. However, very few pieces of literature are available regarding the Nepalese stock market that explores the association between trading volume and stock return. Realizing this fact, this paper aims to examine the empirical relationship between trading volume and stock returns in the Nepalese stock market using time series data. The study sample is comprised of 49 stocks traded on the Nepal Stock Exchange (NEPSE) from mid-July 2011 to mid-July 2018. This study examines the Granger Causality relationship between stock returns and trading volume using the bivariate VAR model used by de Medeiros and Van Doornik (2008). The study found that the overall Nepalese stock market does not have a causal relationship between trading volume and return on the stock. In the case of sector-wise study, there is a unidirectional causality running from trading volume to stock returns in commercial banks and stock returns to trading volume in finance companies, hydropower companies, and insurance companies. There is no indication of any causal effect in the development bank, hotel, and other sectors. This study also finds that there is no evidence of bidirectional causality relationships in any sector of the Nepalese stock market.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

scholarly journals Factors Affecting Kuala Lumpur Composite Index (KLCI) Stock Market Return in Malaysia

2016 ◽  
Vol 12 (1) ◽  
pp. 122 ◽  
Author(s):  
Uma Murthy ◽  
Paul Anthony ◽  
Rubana Vighnesvaran
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Market ◽  
Money Supply ◽  
Composite Index ◽  
Oil Price ◽  
Series Data ◽  
Kuala Lumpur ◽  
Market Return ◽  
Stock Market Return

This paper studies the relationship between Kuala Lumpur Composite Index Stock Market Return with four macroeconomic determinants, namely interest rate, exchange rate, money supply and oil price from January 1997 to December 2015 on a monthly basis with a total of 228 observations. However, most of the studies are carried out in developed countries and large economic nations instead of in emerging markets such as Malaysia. Thus, this study aims to extend the existing studies to include the impact of several macroeconomics determinants namely interest rate, exchange rate, money supply and oil price on KLCI stock market return. This paper employed Multiple Linear Regression to examine the statistical relationship and to test the hypotheses. The data was analyzed using Statistical Package for Social Science, SPSS. For diagnostic checking, there is existence of autocorrelation problem which is typically found in time-series data.  Results indicated that there is negative relationship between exchange rate and stock market return and positive relationship between money supply and stock market return. Interest rate and oil price are found to have insignificant relationship with stock market return.

scholarly journals Anticipated Volatility, Unanticipated Volatility Shocks, Excess Monthly Returns and Stock Market Behaviour of Pakistan

2021 ◽  
Vol 7 (1) ◽  
pp. 77-91
Author(s):  
Muhammad Ramzan Sheikh ◽  
Sahrish Zameer ◽  
Sulaman Hafeez Siddiqui
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Portfolio Choice ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Financial Assets ◽  
Exchange Market ◽  
Risk Return

An investor considers various factors to choose the financial assets. The portfolio theory suggests that risk, return, taxes, information and liquidity are vital factors in portfolio choice. The study is based on risk premium, uncertainty, shocks and volatility of Pakistan stock exchange market. The study has used monthly time series data of returns of ten sectors of Pakistan stock market ranging from 2006 to 2014 to measure the anticipated and unanticipated factors of risk, return and uncertainty. Using CAPM, it is pointed out that volatility factor is present and high in overall stock market and the level of volatility in different sectors of the market moves in the same direction which suggest that speculative activities are widely spread in every sector and in overall market as well.

scholarly journals Gold and Government Bonds as Safe-Haven Assets Against Stock Market Turbulence in China

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402199065
Author(s):  
Wei Huang ◽  
Meng-Shiuh Chang
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Investment Strategy ◽  
Chinese Stock Market ◽  
Safe Haven ◽  
Copula Models ◽  
Government Bonds ◽  
Market Turbulence ◽  
Vine Copula

We examine whether gold and China’s government bonds are safe-haven assets against the turbulence of the Shanghai Stock Exchange Composite Index by employing vine copula models during the 2003 to 2015 period. We find that either bonds or gold can be a weak safe haven but only gold can be a strong safe haven. Our simultaneous analysis advises against a joint safe-haven strategy of gold and bonds, given the high- to low-tail correlation. This result highlights an investment strategy of using a single safe-haven asset against the Chinese stock market turbulences.

scholarly journals The Determinant of Indonesian Stock Returns’ Volatility: Evidence from Islamic and Conventional Stock Market

2021 ◽  
Vol 6 (3) ◽  
pp. 277-296
Author(s):  
Septiana Indarwati ◽  
Agus Widarjono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Money Supply ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Distributed Lag ◽  
Macroeconomic Indicators

Islamic stock market is apparently different from the conventional stock market due to the prohibition of unlawful goods and excessive risk-taking behavior. This study explores the extent to which the Indonesian Islamic and conventional stock returns' volatility responds to the macroeconomic indicators. This study employs Jakarta Islamic Index (JII) and Indonesian Stock Exchange (IDX) and uses monthly time-series data covering 2001: M1 - 2019: M12. The volatility of stock returns is measured using Generalized Autoregressive Conditional Heteroskedasticity (GARCH). By employing the Autoregressive Distributed Lag Model (ARDL), the results validate the evidence of the long-run relationship between the stock market's volatility and macroeconomic variables. A rising in money supply and an economic upturn reduce the volatility of conventional stock returns but only an expansionary money supply diminishes the volatility of Islamic stock returns. Conversely, high inflation and sharp depreciation of the Rupiah boost the stock returns' volatility. The results further show an interesting finding that the Islamic stock market's volatility is more responsive to changes in macroeconomic indicators than the volatility of their counterpart conventional stock market. Policymakers should take strict rules during the worst economic conditions to minimize the negative impact of the instability of macroeconomic variables.

scholarly journals Pengaruh Variabel Makroekonomi Terhadap Indeks Harga Saham Gabungan (Studi pada Indeks Harga Saham Gabungan di Bursa Efek Indonesia Periode 2014-2018)

2019 ◽  
Vol 8 (2) ◽  
pp. 75
Author(s):  
Rina R. Mamahit ◽  
Tinneke M. Tumbel ◽  
Joanne V. Mangindaan
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Composite Index ◽  
Multiple Linear Regression Analysis ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Negative Effect ◽  
The Exchange Rate

This research aims to determine whether the macroeconomic variables i.e., the exchange rate, inflation and BI rate simultaneously and partially influence Indonesia Composite Index at The Indonesia Stock Exchange (IDX). The approach in this study is a quantitative method, using multiple linear regression analysis. The data used are time series data from January 2014 until December 2018. The result indicates that exchange rates, inflation and BI together have a significant impact to Indonesia Composite Index. Individually, only the BI rate variable has a significant effect and has a negative effect to Indonesia Composite Index. The exchange rate and inflation had no significant effect to Indonesia Composite Index.

scholarly journals Daily Stock Movements Through the Lens of Event and News: Evidence from the Pakistan Stock Exchange

2021 ◽  
Author(s):  
Shahid Raza ◽  
Baiqing Sun ◽  
Pwint Kay Khine
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
News Daily ◽  
Stock Market Index ◽  
Daily Newspaper ◽  
Monthly Basis ◽  
Market Index ◽  
The Impact

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

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