The paper analyzes MACD using operator theory.
problem Understanding the mathematical foundation of MACD.
method Developed a functional-analytic framework interpreting MACD as a phase-corrected, smoothed derivative operator.
result MACD is structurally equivalent to a band-pass filter and can be expressed as a finite difference of delayed and doubly averaged signals.
Study improves MACD trading strategy with volume and price adjustments.
problem Signal lag and false signals in traditional MACD trading rules.
method Develops VP-MACD framework with sensitivity calibration.
result Proposed framework outperforms baseline MACD in profitability and risk-adjusted return.
Improved MACD trading strategies with other indicators for better performance.
problem Evaluating the effectiveness of MACD-based trading strategies in the US stock market.
method Backtested various MACD-based trading strategies on US stock indices using Python.
result Win-rate of MACD strategies improved with other momentum indicators, leading to a new VPVMA indicator.
The paper optimizes portfolios using MACD signals derived from price history.
problem Optimizing risky asset portfolios with latent mean-reverting and momentum factors.
method Derives optimal strategies based on MACD signals from EMA processes.
result Establishes admissibility and verification of optimal strategies.
Explains the difference between EMA and moving EMA, focusing on market trend indicators.
problem Understanding the difference between exponential moving average and moving exponential average.
method Explains the mathematical tools and definitions of trend indicators.
result Discusses the properties of the MACD indicator and its use in market trend analysis.
The paper analyzes portfolio management in the Heston model, proposing new strategies.
problem Investment performance influenced by asset diversity and cash inclusion.
method Monte Carlo simulations in the Heston model, MACD and RSI technical analysis.
result New portfolio management strategies based on MACD and RSI.
The purpose of this research is to apply technical analysis of Sutte Indicator in stock trading which will assist in the investment decision making process i.e. buying or selling shares. This research takes data of "A" on the Indonesia Stock Exchange(IDX or BEI) 29 November 2006 until 20 September 2016 period. To see t…
Machine learning models outperform traditional technical analysis in Bitcoin trading.
problem Maximizing profits in the Bitcoin market using trading signals.
method Comparison of machine learning models (LightGBM, LSTM) and technical analysis strategies (EMA, MACD+ADX).
result LSTM model achieved a 65.23% cumulative return over a year, significantly outperforming other strategies.
Stock prediction has always been attractive area for researchers and investors since the financial gains can be substantial. However, stock prediction can be a challenging task since stocks are influenced by a multitude of factors whose influence vary rapidly through time. This paper proposes a novel approach (Word2Vec…
A3T-GCN model forecasts FTSE100 stock prices using technical indicators and financial ratios.
problem Forecasting closing stock prices of FTSE100 constituents.
method Hybrid A3T-GCN architecture using technical indicators, financial ratios, and sector correlations.
result A3T-GCN model improves prediction accuracy with annualized log-returns and shorter sequence lengths.
The study uses stock market indicators to forecast COVID-19 cases.
problem Forecasting the spread of COVID-19 for resource allocation.
method Reinterpreting daily cases as candlesticks and applying stock market indicators.
result The stock market indicators show statistical significance in predicting COVID-19 cases.
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
problem Traditional trading strategies fail during high volatility and regime shifts.
method Combines trend-following, mean-reversion, sentiment analysis, machine learning, and market regime filtering.
result Hybrid model achieved 135.49% return on investment over 24 months.
TINs use neural networks to interpret technical indicators for trading.
problem Lack of interpretable neural architectures for technical indicators in trading.
method Introduced TINs, a neural architecture that reformulates technical indicators into trainable modules.
result Improved risk-adjusted performance compared to traditional indicator-based strategies.