Abstract
PurposeThis study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.Design/methodology/approachThe study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.FindingsThe paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.Research limitations/implicationsLimitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.Practical implicationsThe findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.Originality/valueThis paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.
Highlights
Stock markets attract the attention of economists, financiers and rulers around the world due to the benefits that their activity provides to the real economy because they serve as an instrument for mobilizing savings toward investment and as a mechanism for allocating resources in the economy (Gupta-Bhattacharya et al, 2014).Within the stock market, investment in equity assets stands out for the possibility of achieving a return higher than most investments in the market while companies are allowed to resort to financing sources with lower cost (Ibrahim, 2011)
This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into technical analysis (TA) investment approaches using moving average convergence/divergence (MACD) in welldeveloped stock markets
The purpose of this study is to determine whether the use of genetic algorithms (GA), applied to the investment strategy of TA through the MACD indicator, allows achieving higher yields to those that would be obtained through the use of the same technical indicator applied under traditional methods and, at the same time, determining if it is possible to exceed the return that would be obtained with the buy and hold (B&H) investment strategy
Summary
Stock markets attract the attention of economists, financiers and rulers around the world due to the benefits that their activity provides to the real economy because they serve as an instrument for mobilizing savings toward investment and as a mechanism for allocating resources in the economy (Gupta-Bhattacharya et al, 2014).Within the stock market, investment in equity assets stands out for the possibility of achieving a return higher than most investments in the market while companies are allowed to resort to financing sources with lower cost (Ibrahim, 2011). Equity assets are characterized by a high level of volatility in their returns which makes them difficult to forecast This is how numerous studies have concluded that the prediction of prices for this type of asset is a very difficult task to achieve due to the characteristics of nonlinearity and non-stationarity of prices (Farias et al, 2017). This means that historical volatility does not have a constant relationship with the so-called implied volatility that tries to determine the variability of the price of the asset in the future (Narwal et al, 2018).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
