An Exploration of Dynamical Relationships between Macroeconomic Variables and Stock Prices in Korea

Stability and sustainability of the biggest banks in any country are extremely important. When big banks become unstable and vulnerable, they typically stop lending. The resulting credit squeeze pushes the economy into recession or a slow growth path. The present study examines the financial stability and sustainability of the 30 large banks operating in the six Gulf Cooperation Council countries. These banks represent 70% of the GCC banking market. Monte Carlo simulation was attempted assuming that key drivers can vary randomly by twenty percent on either side of the current values. The conclusions are drawn based on 300 simulation trails of the five-year forecast balance and income statement of each bank. Year 2020 is not favorable for the GCC countries because of the COVID-19 pandemic and low oil prices, though the future years may be better. The study identifies several banks, which may become financially unsustainable because the simulations indicate the possibility of negative profitability, unacceptably low capital ratios and potential for heavy credit losses during periods of economic turbulence, which is the current situation due to the COVID-19 pandemic. Through simulation the paper is able to throw light on which factors lead to bank instability and weakness.

Section: Introductionmentioning

confidence: 99%

Financial Stability of GCC Banks in the COVID-19 Crisis: A Simulation Approach

Al-Kharusi¹,

Murthy²

2020

“…The empirical findings showed that six factors i.e., dividend policy, management quality, financial position, liquidity saving, asset size, and the nature of firm work have a positive relationship with the market stock prices. Moreover, Lee & Brahmasrene (2018) investigated dynamic relationships between selected macroeconomic variables and stock prices in the Korea Stock Exchange. The data is restricted to the period for which monthly data was available from January 1986 to October 2016 (370 observations) and was retrieved from the Economic Statistics System database sponsored by the Bank of Korea.…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of Share Prices of Listed Companies Operating in the Steel Industry: An Empirical Case from Vietnam

Nguyen¹,

Nguyen²,

Tsai³

et al. 2020

In accordance with huge demand for capital to meet the expansion of steel production, there are more and more steel companies who have officially listed their stocks in HOSE and HNX. One of the key issues in successful initial public offerings and seasonal offerings for these companies is how to make stocks of steel companies become more attractive in the eyes of investors. The purpose of this research is to analyze the determinants of share prices of listed steel companies in Vietnam. This study utilized macroeconomic variables, ratios and indicators representing characteristics of steel industry collected from Quarter 1/2006 to Quarter 4/2019 in association with the panel data and the feasible generalized least square (FGLS) model to evaluate the degree of these factors on the share prices. The results of the research show that ROE, Cons_rate, and CO2_rate are three main factors affecting the share prices of listed steel companies. Among which, ROE and Cons_rate have a positive effect, while CO2_rate has a negative effect on the share prices of listed steel companies. It also confirms the relationship between the environmental factor, construction industry factor and the stock prices. This lays foundations for recommendations for the future policies towards environmental protection and sustainable development.

“…Although the number of studies conducted in emerging markets is a lot fewer than the developed ones, since the beginning of 2000s, a significant increase has been observed in the number of studies made on the emerging market indexes (Al-hnaity & Abbod, 2016;Banik et al, 2014;Chang et al, 2012;Andre & Tulio, 2007;Chen et al, 2003;Chatterjee, 2000;Lee & Brahmasrene, 2018;Camba, 2020;Kakinuma, 2020;Sayed, 2014;Nguyen & Nguyen, 2019). Furthermore, some researchers hybridized several intelligence techniques to predict the direction of daily price changes of stock markets.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model Approach to the Complexity of Stock Trading Decisions in Turkey

Cavdar¹,

Aydin²

2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.