Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

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.

The objective of the article is to study the influence of political and economic factors on the situation on the world foreign exchange market, substantiate the importance of fundamental analysis in forecasting currency rates. The concept and mechanisms of functioning of the world currency market, its assets and specific features in comparison with other financial platforms are considered in the article. The need for special attention to fundamental analysis during the periods of global political and economic events is grounded. Based on the analysis of the dynamics of the leading world currency pairs in recent years, the period from October 2016 to April 2017 was chosen for an analysis because that period is full of significant events in the world political and economic arena, which are powerful levers of the traders’ behavior, and hence the direction of the dynamics of key currency pairs. These events are as follows:

• presidential elections in the USA;
• two increases of the Federal Reserve rate; and
• the policy of the European Central Bank, aimed at stimulating inflation.

It is substantiated that using the fundamental analysis makes it possible to assess the trend and scale of changes in the rates of the world currencies, and in combination with technical analysis is an integral tool for successful trading and forecasting the dynamics of currency pairs. The possibility of using the mechanisms of functioning of such fundamental analysis tool as artificial neural networks and the ways of their application in solving such problems has been considered.

The profitability of simple technical trading rules remains an interesting topic and has been thoroughly explored in the literature. In this paper, the authors investigate the profitability of two popular moving average (MA) rules in the Bursa Malaysia before, during and after the global financial crisis (GFC) of 2008-2009. Using variable length MA (VMA) and fixed length MA (FMA) technical rules, the authors explore if there were differences in their performance during the different market phases, and if swing traders can gain by trading on the basis of these strategies. When practical trading constraints are considered, the authors find that MA rules performed differently during the three market phases. Over time, the forecasting powers of these rules have diluted and they have performed poorly in the most recent subsample. The findings suggest that the Malaysian stock market is gradually becoming more efficient. This outcome can be attributed to the technological advancements and widespread use of exchange traded funds.