Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.     

Monetary policy effectiveness and stock market cycles in ASEAN-5

This article examines the asymmetric effects of monetary policy on real output in bull and bear phases of stock market in five ASEAN economies (Malaysia, Singapore, Indonesia, the Philippines and Thailand) using the recently developed pooled mean group (PMG) technique. Stock market cycles are identified by employing Markov switching models and the rule-based nonparametric approach. Estimating the models using monthly data from 1991:1 to 2011:12, the results show that monetary policy (measured by short-term interest rate) has a negative and statistically significant long-run effect on real output in bull and bear market periods while the effects are stronger in bear periods than bulls. In the short run, there is no statistically significant relationship between monetary policy and real output. These results are consistent with finance constraints (capital market imperfection) models that predict that monetary policy is more effective during bear periods than bulls.