scholarly journals MACROECONOMIC VARIABLES AND STOCK MARKET MOVEMENT IN NIGERIA (1988 – 2019)

2021 ◽  
Vol 8 (1) ◽  
pp. 86-95
Author(s):  
Olaolu O. ◽  
◽  
Nwankpa C. ◽  
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Stock Market ◽  
Crude Oil ◽  
Inflation Rate ◽  
Equity Market ◽  
Oil Price ◽  
Portfolio Investment ◽  
Crude Oil Price ◽  
Foreign Portfolio Investment

The goal of this study was to analyse empirically the effect on the stock market movement of five selected macro-economic variables, including the exchange rate, inflation rate, interest rate, crude oil price, and foreign portfolio investment. For the movement of the stock market, stock market capitalizations were used as a reference. Information from the annual time series covering the period between 1988 and 2019 was used. The analysis started with examining stochastic characteristics of each time series by testing their stationarity using Augmented Dickey Fuller (ADF) test. The findings show that only equity market capitalization and crude oil price was found stationary at level, while the other time series were found stationary at first difference. The bounds cointegration test procedure indicates that the variables have long-run equilibrium relationship amongst themselves. Analysis from the study showed that foreign exchange rate, interest rate, inflation rate, crude oil, and foreign portfolio investment are all significant in determining the performance of equity market capitalisation. They were all found to have a significant effect on stock market movement in Nigeria. Based on these findings, the study recommended that there is need to formulate sustainable macro-economic policies to curtail depreciation of the Naira, high inflation, and interest rate while attracting long-term foreign portfolio investors into Nigeria. Aggressive diversification of the economy should be made from its mono-cultural dependence on oil whose price over which Nigeria has no control.

scholarly journals Exploring relationship between crude oil price volatility and stock indices movement using wavelet analysis: evidence from India and China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shekhar Mishra ◽  
Sathya Swaroop Debasish
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Crude Oil ◽  
Equity Market ◽  
Oil Price ◽  
Discrete Wavelet ◽  
Crude Oil Price ◽  
Content Type ◽  
India And China

Purpose This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China. Design/methodology/approach The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables. Findings The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market. Originality/value To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

scholarly journals Impact of Macro - Economic Factors on Indian Stock Market- A Research of BSE Sectoral Indices

2019 ◽  
Vol 8 (2S7) ◽  
pp. 597-602
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Market ◽  
Crude Oil ◽  
Stock Prices ◽  
Gold Price ◽  
Oil Price ◽  
Economic Factors ◽  
Crude Oil Price ◽  
The Impact

Macro-Economic factors plays a major role in decision making. Evaluation of macroeconomic environment is required to examine the behaviour of stock prices, which further influences the investor’s investment behaviour. Even though some macro-economic factors are not directly related to the company or industry, but those factors has an impact on stock prices, further economic activity in the domestic and global level has its own impact on stock market. When economy of the country grows hastily, it leads to faster growth in the industry and vice versa. Financial market plays a central role in the performance of financial system of an economy. Stock market is a market where securities of listed companies are exchanged between different investors, it is very responsive market which, gives a stage to investors to invest their money in various securities. Market indices are the tools to measure the performance of various securities of stock market and Investors make use of those market indices to analyse performance of those industries in which, they prefer to invest. This study takes into account six macro-economic factors (Crude oil Price, Gold Price, Silver Price, Exchange Rate, Inflation and Interest Rate) to study & analyse the impact of these variables on selected sectoral indices at BSE, SENSEX, S&P BSE BANKEX, S&P BSE Oil and Gas, S&P BSE Capital Goods, S&P BSE Consumer Durables, S&P BSE Reality, S&P BSE PSU and S&P BSE Power. The study shows that gold price, exchange rate, consumer price index and interest rate are positively correlated with four indices but crude oil price and silver price have positively correlated with 3 indices. So from the result it is clear that investor need to take of all the variables for their investment decision and the investment banker also take care of these indicators before giving suggestion to their clients

scholarly journals Pengaruh Sistem Perbankan, Makroekonomi, Dinamika Pasar Keuangan terhadap Perkembangan Sukuk Korporasi

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Alyta Shabrina Zusryn ◽  
Rizqi Umar Al Hashfi ◽  
Ananta Hagabean Nasution
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Crude Oil ◽  
Oil Price ◽  
Crude Oil Price ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Interest Rate Spread

Tujuan pada penelitian ini adalah untuk mengetahui faktor-faktor yang berpengaruh terhadap perkembangan sukuk korporasi di Indonesia. Data yang digunakan adalah data bulanan tahun 2013-2016 variabel makroekonomi, crude oil price, kredit perbankan konvensional, interest rate spread, aset perbankan syariah, outstanding obligasi korporasi indonesia, nilai kapitalisasi pasar saham syariah, dan standar deviasi Jakarta Interbank Offered Rate (JIBOR). Pada penelitian ini menggunakan analisis data time series yaitu Autoregressive Distributed Lag (ARDL) yang merupakan pengembangan dan melengkapi kelemahan dari analisis Vector Autoregresive (VAR). Penelitian ini menunjukkan bahwa terdapat pengaruh positif harga minyak mentah dunia, perkembangan obligasi korporasi, dan aset perbankan syariah terhadap perkembangan sukuk korporasi. Selain itu, kapitalisasi pasar saham syariah, interest rate spread, dan volatilitas suku bunga pasar berdampak negatif terhadap perkembangan sukuk korporasi. Hasil tersebut menunjukkan adanya peningkatan permintaan dan penawaran pada sukuk korporasi di Indonesia. Penelitian ini menemukan adanya hubungan komplementer pada obligasi korporasi dan perbankan syariah pada perkembangan sukuk korporasi sehingga diharapkan adanya sinergi dan koordinasi para pemangku kepentingan yaitu pemerintah, pelaku industri, perusahaan dan pihak-pihak terkait

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

The optimal thermal causal path analysis on the relationship between international crude oil price and stock market

Kybernetes ◽  
2018 ◽  
Vol 47 (6) ◽  
pp. 1242-1261 ◽  
Author(s):  
Can Zhong Yao ◽  
Peng Cheng Kuang ◽  
Ji Nan Lin
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Markets ◽  
Oil Prices ◽  
Oil Price ◽  
Chinese Stock Market ◽  
Crude Oil Price ◽  
Content Type ◽  
Crude Oil Prices ◽  
Causal Path

Purpose The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets. Design/methodology/approach The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method. Findings The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices. Originality/value China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

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