Navigating the technical analysis in stock markets: Insights from bibliometric and topic modeling approaches

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.

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.