Stock Returns and Inflation: a Bibliometric Analysis

Purpose: The purpose of this research is to review and compare the development of the three major frontier stock markets in Sub-Saharan Africa over time. The study provides some narrative around the historical development of each market as well as a theoretical backdrop for stock market development studies. Theoretical framework: The adaptive market hypothesis was used as the theoretical backdrop for the study. The adaptive market suggests that stock markets develop in an evolutionary manner (similar to natural selection). This evolution of stock market development is influenced by changes in investors’ behavior and regulatory standards. Design/methodology/approach: Data was collected from 1993 to 2020 on the IMF market efficiency score, the value of stocks traded, aggregate market capitalization, and the number of listed companies for the sample markets. The study used descriptive statistics and trend analysis to discuss and compare the stock market development indicators across the different selected frontier markets. Findings: The study discovered an improvement in stock market performance across the sample period. The Johannesburg Stock Exchange was found to be the most developed of the three stock exchanges. The Nigerian Stock Exchange was second while the Nairobi Stock Exchange was third. Some factors that erode the performance of these stock markets were also discussed. The practical and social implications: The sample stock markets are the major frontier markets, and so are often the first stop for foreign investors that want to penetrate the Sub-Saharan African markets. The performance of these markets often determines the level of foreign direct investment (FDI) and foreign portfolio investment (FPI) that flow into Africa. Originality/ Value: Few studies have investigated the performance of stock markets in Sub-Saharan Africa. Also, the few studies that investigate the performance of these markets rarely proceed to discuss the market-wide factors that erode the performance of these markets compared to those of developed economies. Some factors like the size of the economy, low financial literacy, misplaced government policies, poor investment culture, buy-and-hold-tight attitude, and high transaction costs were identified and discussed.

Section: Stock Markets Development In Sub-saharan Africamentioning

confidence: 99%

Historical Development of Frontier Stock Markets in Sub-Saharan Africa

Imhanzenobe¹

2023

“…Modelling these varied behavior patterns in the volatility forecasting process, thus becomes crucial for the investor. Volatility domain has gained more attention, therefore there is a necessity for future research to model predictive accuracy to match the rising volatility and uncertainty environment ((Hoong et al, 2023). Moreover, Sensex being the popular index has always been the consideration for researchers and not much attention has been given for the purpose of those investors who are interested in investing in the size indices.…”

Section: Introductionmentioning

confidence: 99%

Modelling Volatility of Size Indices Through Garch and Tgarch Models: Evidence from India

Sharma¹

2023

Purpose: The objective of this study was to select the best model for modelling the volatility of log return series of four size indices taken from the Indian stock markets. Theoretical framework: Stock markets throughout the world have been designing various kinds of indices depending upon the requirements of the investors. Accordingly, BSE launched some indices, categorized as size indices, depending upon the market capitalization of the stocks listed on their exchange. Such categorization helps the investors in appropriate decision making. Research on these specific indices is scarce and therefore the aim of the present study. Design/methodology/approach: We analyzed the log return series of four indices, namely S&P BSE Sensex, S&P BSE Large cap index, S&P BSE Mid cap Select index and the S&P BSE Small cap index, by fitting the GARCH (1,1) and the TGARCH (1,1) models for the period from 1 Januray 2018 to 31 December 2022. Findings: The research on modelling volatility is voluminous with varied results as per the various assets and datasets utilized for the purpose. Present study confirms the results from previous study that to model the volatility of size indices a GARCH (1,1) model should suffice and there is no betterment achieved by implementing a TAGARCH (1,1) model. Research, Practical & Social implications: We suggest the implementation of a simple GARCH (1,1) model for all those investors who want to invest in Indian size indices in order to model volatility of these indices. Originality/value: Present study is one of its kinds, investigating size indices specifically, through the two of the most popular GARCH-family models in the Indian context.

“…Inflation is related to market mechanisms caused by several factors. (Hoong et al, 2023) explained that Inflation reduces the real value of money, resulting in reduced purchasing power, lower profitability, and decreased real returns on investments, including stocks. As a result, inflation affects the stock market and the economy as a whole.…”

Section: Introductionmentioning

confidence: 99%

Vector Autoregressive (VAR) Method in Analyzing the Effect of Inflation on Food Price Volatility (FPV) in Palopo City, Indonesia

Tenriawaru¹,

Al-Tawaha²,

Rukka³

et al. 2023

Purpose: This study aimed to analyze the effect of Inflation on food prices in Palopo City, where the inflation rate is calculated based on index numbers collected from several commodities traded at each price level in the market, namely the Consumer Price Index (CPI). Theoretical framework: The Vector Autoregressive (VAR) method is simultaneous equation modeling that has several endogenous variables simultaneously, but each endogenous variable is explained by lags of its own value and other endogenous variables in the model. Forecasting using the VAR method is in some cases better than complex simultaneous equations. Design/methodology/approach: The stationarity test in this study uses the Augmented Dickey-Fuller (ADF) method, followed by determining lag optimal Akaike Information Criteria (AIC) and Schwarz information Criterion (SC) analysis. The stability test of the VAR model was carried out through the Johansen Cointegration Test at a 5% significance level which produced six equations with a trace statistically more significant than the critical value. Then an analysis of Impulse Response Functions (IRF) was carried out to see the dynamic response of the CPI variable to food commodity prices. The data used are time series for January 2018 to December 2020. Findings: The inflation response to food price fluctuations in Palopo City resulted in three conditions. The Palopo City CPI responded positively to the price fluctuations of garlic and red chili. In contrast, shallots and chicken meat price fluctuations responded negatively to the Palopo City CPI. The Palopo City CPI did not permanently affect the price fluctuations of cayenne pepper and chicken eggs. Research, Practical & Social implications: The practical implication of this research is that food commodity prices still have an influence on inflation movements. Local governments should cooperate with city governments in the production of food commodities by implementing cropping patterns on these commodities to overcome excess production, and it is necessary to carry out off-season production to achieve harvest deficits so that prices remain stable. Improvements to the trade system also need to be improved in addition to the production system. This is intended so that the distribution of food commodity products can be more efficient by implementing supply chain management. Originality/value: Collecting time series data of the Consumer Price Index (CPI) for 6 commodities, namely shallots, garlic, chili peppers, cayenne pepper, broiler meat, and broiler eggs, in a modern model, namely a multivariate time series model with a high level of predictive accuracy of the model among these variables to predict and analyze the effects among these series and estimate the interactions among these series with the latest data is the value of this research, and then provide recommendations for the government in making economic strategies, especially for those related to inflation and food prices.