Topic
MACD
About: MACD is a research topic. Over the lifetime, 294 publications have been published within this topic receiving 5020 citations.
Papers published on a yearly basis
Papers
TL;DR: In this article, the authors used the Dow Jones Index from 1897 to 1986 to test two of the simplest and most popular trading rules (moving average and trading range break) by utilizing the bootstrap techniques.
Abstract: This paper tests two of the simplest and most popular trading rules—moving average and trading range break—by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, our results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models.
2,491 citations
TL;DR: A hybrid neural network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle volatility forecasts and a neural network fitness function for financial forecasting purposes are introduced.
170 citations
TL;DR: Contrast volume is a key risk factor for CI-AKI and matters the most in the highest-risk patient and the incremental use of contrast beyond the MACD is associated with an increased risk of CI- AKI.
Abstract: Background— Previous work on contrast-induced acute kidney injury (CI-AKI) has identified contrast volume as a risk factor and suggested that there is a maximum allowable contrast dose (MACD) above which the risk of CI-AKI is markedly increased. We hypothesized that there is a relationship between contrast volume and CI-AKI and that there might be reason to track incremental contrast volumes above and below the MACD limit.
Methods and Results— Consecutive patients undergoing percutaneous coronary intervention (PCI) were prospectively enrolled from 2000 to 2008 (n=10 065). Patients on dialysis before PCI were excluded (n=155). MACD was defined as (5 mL×body weight [kg])/baseline serum creatinine [mg/dL]) and divided into categories in which 1.0 reflects the MACD limit: ≤MACD ratios ( MACD (1.0 to 1.5, 1.5 to 2.0, and >2.0). CI-AKI was defined as a ≥0.3 (mg/dL) or ≥50% increase in serum creatinine from baseline or new dialysis. Multivariable regression was conducted to evaluate the effect of exceeding the MACD on CI-AKI. Twenty percent of patients exceeded the MACD. Risk-adjusted CI-AKI increased by an average of 45% for each category exceeding the MACD (odds ratio, 1.45; 95% confidence interval, 1.29 to 1.62) Adjusted odds ratios for each category exceeding the MACD were 1.60 (95% confidence interval, 1.29 to 1.97), 2.02 (95% confidence interval, 1.45 to 2.81), and 2.94 (95% confidence interval, 1.93 to 4.48). CI-AKI for contrast dose
167 citations
TL;DR: Results achieved show that the addition of machine learning techniques to technical analysis strategies improves the trading signals and the competitiveness of the proposed trading rules.
Abstract: Within the area of stock market prediction, forecasting price values or movements is one of the most challenging issue. Because of this, the use of machine learning techniques in combination with technical analysis indicators is receiving more and more attention. In order to tackle this problem, in this paper we propose a hybrid approach to generate trading signals. To do so, our proposal consists of applying a technical indicator combined with a machine learning approach in order to produce a trading decision. The novelty of this approach lies in the simplicity and effectiveness of the hybrid rules as well as its possible extension to other technical indicators. In order to select the most suitable machine learning technique, we tested the performances of Linear Model (LM), Artificial Neural Network (ANN), Random Forests (RF) and Support Vector Regression (SVR). As technical strategies for trading, the Triple Exponential Moving Average (TEMA) and Moving Average Convergence/Divergence (MACD) were considered. We tested the resulting technique on daily trading data from three major indices: Ibex35 (IBEX), DAX and Dow Jones Industrial (DJI). Results achieved show that the addition of machine learning techniques to technical analysis strategies improves the trading signals and the competitiveness of the proposed trading rules.
107 citations
TL;DR: The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators, and clearly beats the remaining approaches during the recent market crash.
Abstract: The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators (EMA, HMA, ROC, RSI, MACD, TSI, OBV). In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies, such as Buy and Hold and a purely random strategy. The time span (2003–2009) employed to test the approach allowed the performance evaluation under distinct market conditions, culminating with the most recent financial crash. The results are promising since the approach clearly beats the remaining approaches during the recent market crash.
104 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 23 |
| 2020 | 20 |
| 2019 | 15 |
| 2018 | 19 |
| 2017 | 29 |
| 2016 | 19 |