Stock Market Forecasting Using ant Colony Optimization Based Algorithm

Abstract

Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.

Optimization of Virtual Network Mapping Based on Improved Ant Colony Algorithm in Complex Network Environments

“…e authors also analysed some classical algorithms and concluded that ACO had outperformed all the other algorithms concerning time series and robustness. From the previous studies, it can be observed that Ant Colony Optimization (ACO) provides better performance when compared to other optimization algorithms [5]. In [6], long short term memory (LSTM) neural networks and long short term memory (LSTM) neural networks with embedded layer (ELSTM) are proposed, discussed, and analysed.…”

“…In [8], the authors tried to predict the corporate financial crisis rather than the numerical financial ratios. e authors of [5] have created a hybrid algorithm for stock market prediction. In their model, the ACO algorithm is implemented using the selection-based model.…”

“…(ii) e rule based process would be discharged that once ants have exploited all the attributes. To create rules, the ants are using a probabilistic model E ij as shown in (5) to can choose a parameter value.…”

Forecasting Model of Sustainable Financial Market Trend Based on Neural Network Algorithm

“…Technical analysis indicators, which are often used with the aim of making short-term gains, may be used to manage complicated data with non-linear correlations. Adhering to these recommendations does not need expert financial knowledge or exploring other marketplaces [5]. There are supplemental charts and mathematical functions known as indicators, which are used to offer indications for buying and selling prices.…”

Comportamento de agentes financeiros em um mercado artificial desenvolvido com o algoritmo Particle Swarm Optimization

“…Nesse sentido, algoritmos de inteligência de enxame têm sido adotados na modelagem baseada em agentes, como Particle Swarm Optimization (Hitam et al, 2019), Ant Colony Optimization (Ahmed et al, 2019), Firefly (Tang et al, 2017) e Cuckoo Search (Lajevardy et al, 2017), entre outros. Tais algoritmos são inspirados em padrões complexos observados na natureza para a elaboração de sistemas computacionais (Serapião, 2017).…”