need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on … forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price … direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests …

fluctuating components. Our findings show that the stock prices have predictive power for future long-term economic activity in … the panel setting. However, economic activity has more reliable information for stock prices for negative components …

Autoregressive (TMAR) model. The models are applied to the daily IBM stock prices and the monthly Brent crude oil prices. The results …

commodity price changes on China's stock prices are likely to increase. This study uses both auto regressive distributed lag … global commodity prices on China's stock prices. Our findings show that without considering the critical variable of global … commodity prices, there is no cointegration relationship between the RMB exchange rate and China's stock prices, and the …

study investigates the influence of the macroeconomic and firm-specific factors on stock prices of the listed companies …. Data collection consists of 55 listed companies based on the weekly stock prices, from January 2013 till December 2018. The … results indicate that total equity is the only significant element that influences the individual stock prices of the …