Exchange rates: the influence of political and economic events. а fundamental analysis approach

The selection of financing is a top priority for businesses, particularly in short- and long-term investment decisions. Mixing debt and equity leads to decisions on the financial structure for businesses. This research analyzes the moderate position of company size and the interest rate in the capital structure over six years (2013–2018) for 29 listed Pakistani enterprises operating in the sugar market. This research employed static panel analysis and dynamic panel analysis on linear and nonlinear regression methods. The capital structure included debt to capital ratio, non-current liabilities, plus current liabilities to capital as a dependent variable. Independent variables were profitability, firm size, tangibility, Non-Debt Tax Shield, liquidity, and macroeconomic variables were exchange rates and interest rates. The investigation reported that profitability, firm size, and Non-Debt Tax Shield were significant and negative, while tangibility and interest rates significantly and positively affected debt to capital ratio. This means the sugar sector has greater financial leverage to manage the funding obligations for the better performance of firms. Therefore, the outcomes revealed that the moderators have an important influence on capital structure.

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

This study aims to examine whether the Autoregressive Integrated Moving Average (ARIMA) model is appropriate to be applied in the Indonesia Stock Exchange, especially for the socially resposible investment stocks. For the ARIMA model combines the autoregressive and moving average method, so it is viewed as a useful tool to predict the stock prices. Those methods are frequently used methods to forecast the stock prices. The data used in this study were daily SRI-KEHATI Index during the period of June 8, 2009 to July 17, 2017. The results showed that the daily SRI-KEHATI Index data were not stationary data, thus this data needed to be transformed. The transformation was done by using the first seasonal differencing transformation process. After being transformed, those data became stationary. Furthermore, this study found that ARIMA (3,1,1) was a model, which might be appropriate and fit with the data condition. This method was also relevant to be applied in the Indonesia Stock Exchange in order to forecast the stock prices.