Forex Market Trend Forecasting
What is it about?
This study focuses on successful Forex trading by emphasizing the importance of identifying market trends and utilizing trend analysis for informed decision-making. We have collected low-correlated currency pair datasets to mitigate multicollinearity risk. We have developed a two-stage predictive model that combines regression and classification tasks, using the predicted closing price to determine entry and exit points. The model incorporates Bi-directional long short-term memory (Bi-LSTM) for improved price forecasting and higher highs and lower lows (HHs-HLs and LHs-LLs) to identify trend changes. They proposed an enhanced DeepSense network (DSN) with all member-based optimization (AMBO-DSN) to optimize decision variables of DSN.
Why is it important?
This work outlines a study that focuses on successful Forex trading through the development and comparison of predictive models for identifying market trends and making informed trading decisions. Several key points highlight the importance and contributions of this study: Emphasis on Identifying Market Trends: The study places importance on identifying market trends, recognizing their significance in successful Forex trading. This is crucial as trends can provide valuable insights for making informed decisions. Use of Trend Analysis for Informed Decision-Making: The study advocates the use of trend analysis as a tool for making informed decisions in Forex trading. Trend analysis helps traders understand the direction in which a currency pair is moving, aiding in the determination of entry and exit points. Mitigation of Multicollinearity Risk: The authors address the issue of multicollinearity risk by collecting low-correlated currency pair datasets. Multicollinearity can negatively impact the reliability of predictive models, so mitigating this risk is essential for accurate analysis. Two-Stage Predictive Model: The study proposes a two-stage predictive model that combines regression and classification tasks. This model uses the predicted closing price to determine entry and exit points in Forex trading. Incorporation of Bi-Directional Long Short-Term Memory (Bi-LSTM): The model incorporates Bi-LSTM, a type of recurrent neural network, for improved price forecasting. Bi-LSTM is known for its ability to capture long-term dependencies in sequential data, which is valuable in financial time series analysis. Identification of Trend Changes: The model uses the concept of higher highs and lower lows (HHs-HLs and LHs-LLs) to identify trend changes. This adds a dynamic element to the model, allowing it to adapt to shifting market conditions. Enhanced DeepSense Network (DSN) with All Member-Based Optimization (AMBO-DSN): The authors propose an enhanced DeepSense network with optimization using all member-based optimization (AMBO-DSN). This optimization aims to improve decision variables in the DSN, potentially enhancing the model's performance. Comparison with Various Approaches: The study compares the performance of the developed models with various machine learning, deep learning, and statistical approaches, including SVR, ANN, ARIMA, V-LSTM, and RNN. This comparative analysis helps assess the effectiveness of the proposed models. Optimization Using Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution: The study explores the optimization of the DSN using genetic algorithm, particle swarm optimization, and differential evolution. This optimization process aims to fine-tune the model's parameters for improved performance. Validation through Statistical Analysis: The effectiveness and reliability of the AMBO-DSN approach in forecasting trends for specific currency pairs (USD/EUR, AUD/JPY, and CHF/INR) are validated through statistical analysis. This adds a quantitative measure to the evaluation of the proposed approach. Consideration of Computational Cost: The study takes into account the computational cost associated with the proposed models. This is important for practical implementation, as efficient and computationally feasible models are essential in real-world trading scenarios.