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The volatile behavior of Bitcoin's price, especially during its halving periods, poses considerable obstacles for forecasting and decision-making in cryptocurrency trading. This paper presents a novel method that combines application fuzzy logic with Bollinger Bands to improve trading decision-making in times of high market volatility. This study conducted an experiment utilizing three fuzzy logic controllers and Bollinger Bands (BB) to determine the strength of buy, hold, and sell signals. This dataset includes the initial and final prices that are used to calculate the BB. The raw and computed values serve as the precise input parameters for the Fuzzy Inference System (FIS). The membership functions were categorized into four levels: very low, low, high, and very high, based on the input default settings utilized by traders. Rulesets were created using fuzzy logic to produce signals that indicate the level of strength of a trading advice. This study evaluate the effectiveness of this hybrid method in comparison to the traditional utilization of the Bollinger Band only indicator and Moving Average Convergence Divergence (MACD) indicator, which is widely favored by traders to identify possible market fluctuations. This methodology involves creating a trading simulation that is based on past Bitcoin halving events. The objective is to assess the efficacy of these strategies in managing heightened volatility. The application of fuzzy logic with the Bollinger Bands model yielded a success rate of 92.47% while analyzing 93 daily data points from the previous Bitcoin halving event on May 11, 2020. [ABSTRACT FROM AUTHOR]

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

Technical analysis is widely employed in stock trading, relying on popular indicators such as MACD, DMI, KST etc. to predict stock trends. Despite their common use, these lagging indicators can occasionally generate misleading signals. In the literature, machine learning researchers developed many intelligent strategies for predicting stock trading signals using these indicators as inputs. However, significant differences exist in how these indicators are applied by technical analysts and machine learning experts. Building on this knowledge, this study developed intelligent stock trading signal prediction strategies using MACD, DMI, and KST indicators, and implemented these strategies with LSTM and GRU networks due to their ability to manage long-term dependencies and maintain context. The proposed intelligent trading strategies were assessed using ARR, SR, and win rate metrics, based on historical trading data from 18 stocks—six each from NEPSE, BSE, and NYSE—leading to four key insights. (1) For predicting stock trading signals, a 5-day lookback period is optimal for intelligent strategies based on MACD and DMI, while a 10-day period is best for the KST-based strategy. (2) Intelligent trading strategies implemented with GRU networks demonstrated superior performance compared to those implemented with LSTM. (3) The intelligent trading strategies based on MACD, DMI, and KST indicators outperform their peer classical stock trading methods. (4) Among the three proposed intelligent strategies, the MACD-based approach is found to be the safest and most effective.

When designing a stock portfolio, investors must select stocks with different characteristics and increasing price trends and weigh each capital. Both are fundamental to diversifying loss and profit. Therefore, the mechanisms that accommodate both are needed. Based on this, this research aims to design a stock selection and capital weighing mechanism using the MACD-K-means-Mean-VaR model. The moving average convergence–divergence (MACD) is used to analyze stock buying time, providing trend, momentum, and potential price reversal insights. Then, stocks with increasing price trends are clustered using K-means, a grouping simple pattern data method based on specific characteristics. The best stocks from each cluster are capital weighted using the mean value at risk (mean-VaR), a portfolio optimization model adjusting loss possibility to the investor's acceptance tolerance. The mechanism is then applied to Indonesia's 100 stock index data to analyze variable sensitivities and compare it with another model. The application reveals that all variables significantly impact portfolio return mean and VaR, suggesting the need for clustering and analyzing stock price movements in stock portfolio design. This research academically develops a portfolio design mechanism by clustering stocks and analyzing price movement trends. It enables investors to practically diversify and choose stocks with increasing price trends, reducing losses and increasing profit opportunities. [ABSTRACT FROM AUTHOR]

This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight. [ABSTRACT FROM AUTHOR]

Trend change analysis is a tool that complements the assessment of human body stability and provides information on the number and frequency of postural corrections during an examination. The present research aims to determine the possibility of using this method of analysis to investigate postural stability during tests of standing with open eyes (OE) and closed eyes (CE). A total of 118 participants (67 females, 51males) aged 23 (SD 1.3) were assessed. Tests involved standing on a stabilographic platform for 50 s. Trend change analysis was used to evaluate displacement values of the center of pressure (COP). Values for the COP courses as well as values associated with trend change analysis, such as: TCI, MACD_t and MACD_V were determined. Histograms of distribution were plotted for TCI values. The present study provides information on alterations of the strategy used for maintaining balance, which are associated with the number of postural corrections and COP displacement between corrections for measurements taken during the standing test with CE in relation to OE measurements. The strategy demonstrated an ability to detect a smaller number of quick corrections, an increased number of corrections of longer duration, and the elongation of displacement between subsequent postural corrections. Slight standard deviations in TCI and MACD_t values calculated during trend change analysis, for both the OE and CE conditions, made it possible to classify these values as indexes of postural stability with significant sensitivity to slight changes. [ABSTRACT FROM AUTHOR]

The stock market that has been known volatile is always an attractive target for the researchers to perform research and experiment on. Stock trend prediction is one of the most famous topics that is done as the movement of a stock is full of uncertainty and is affected by many different factors. In this research, the technical indicator of a stocks has been utilized (MA, EMA, RSI and MACD) to get the signal of the upcoming trend of a stock in order to achieve stock trend prediction. Machine learning techniques is also applied to process those stock data and stock indicator. The technique that is proposed to develop the stock prediction model is the Long Short Term Memory Neural Network, also known as LSTM. After the model is developed, it will be used to carry out prediction on stock and compare the actual stock movement with the predicted stock movement to find out its accuracy in making stock trend prediction. Three stocks will be used to validate the performance of the model which are Public Bank, Tenaga, and Apex Healthcare. The results show that the trend of the inspected stocks are successfully predicted using the LSTM model. [ABSTRACT FROM AUTHOR]

In today's complex financial markets, "Algorithmic Trading" has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions. [ABSTRACT FROM AUTHOR]

Purpose – This 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/approach – The 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. Findings – The 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/implications – Limitations 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 implications – The 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/value – This 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.

Copyright of AIN Journal is the property of Arab Institute of Navigation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

The purpose of this study is to undertake technical analysis of selected companies included in the S&P CNX Nifty 50, a leading stock market index in India. We have used the stock price data of twenty leading listed firms in India for a period from January 1, 2012 through December 31, 2017. We have applied Guppy Multiple Moving Average (GMMA), Moving Average Convergence Divergence (MACD), Stochastic Relative Strength Index (Stoch RSI) and Average Directional Index (ADX) to Heikin Ashi charts to back test and provide entry and exit points for the players in the stock market. Analysis of the price information has revealed that the GMMA and ADX are effective indicators for most of the stocks under the study but they give late signals as compared to RSI and MACD. Further, the study has shown that though RSI and MACD give early signals, yet they are risky as the number of false signals generated by them is also found out to be quite high. The study is important as the findings can be used by investors, option traders and portfolio managers to get generate profitable trading signals and obtain good risk to reward ratios.

Scientific topics vary in popularity over time. In this paper, we model the life cycles of 200 trending topics by fitting the Logistic and Gompertz models to their frequency over time in published abstracts. Unlike other work, the topics we use are algorithmically extracted from large datasets of abstracts covering computer science, particle physics, cancer research, and mental health. We find that the Gompertz model produces lower median error, leading us to conclude that it is the more appropriate model. Since the Gompertz model is asymmetric, with a steep rise followed a long tail, this implies that scientific topics follow a similar trajectory. We also explore the case of double-peaking curves and find that in some cases, topics will peak multiple times as interest resurges. Finally, when looking at the different scientific disciplines, we find that the lifespan of topics is longer in some disciplines (e.g. cancer research and mental health) than it is others, which may indicate differences in research process and culture between these disciplines. [ABSTRACT FROM AUTHOR]

The technical approach to investment, essentially a reflection of an idea that prices move in trends which are determined by the changing attitudes of investors towards a variety of economy, monetary, political and psychological forces). The response of stock prices towards the changes in economic variables vary from one to another, hence, it makes trading decision to be very complex. Efficiency refers to the ability to produce an acceptable level of output using cost-minimizing input ratio. Thus, in technical analysis, efficiency refers to the ability of the indicators to indicate a good timing of entry and out of the market with profit. The levels of efficiencies are shown by actual output ratios versus expected output ratios. The higher the actual output ratios against the expected output ratios, the higher the efficiency level of the indicators. This research investigates several technical indicators and found none of the indicators reached the efficiency level. To improve the level, this study applies the Artificial Neural Network model that capable to learn the price and the moving average patterns and suggests a new pattern better than the previous, in term of efficiency level. This research found that the improvements are not just to the efficiency but also increase number of trading as per selected period hence, increase the changes of investor decisions to enter and to exit from the market with possibility of a better profit as compared to traditional technical analysis.

Mobile communications are preparing for the incredible changes from 4 th Generation (4G) to 5 th Generation (5G) in the coming years. In this new generation, co-channel interference is one of the critical challenges to be tackled due to network densification by providing high data rates through several macro and small cells working together, configuring the so-called Heterogeneous Network (HetNet) . The umbrella of 3GPP system (including LTE and its evolution towards 5G) provides the Almost Blank Subframe (ABS) as a scheme of the Enhanced Inter-Cell Interference Coordination (e-ICIC) framework to mitigate interference among macro and small cells. The ABS mutes some of the macro cell transmissions in selected subframes to decrease interference to small cells, thus orthogonalizing macro and small cell transmissions in time-domain. In this paper, we use a moving average convergence/divergence technique based on trading know-how to propose a real time ABS e-ICIC algorithm. Our proof-of-concept simulation results show relative capacity gains of 112% compared to the case without ABS . [ABSTRACT FROM AUTHOR]

Much research has examined performance or market efficiency by using the moving average convergence divergence (MACD) technical analysis tool. However, most tests fail to verify efficiency with the traditional parameter settings of 12, 26, and 9 days. This study confirms that applying the traditional model to Japan's Nikkei 225 futures prices produces negative performance over the period of 2011-2019. Yet, it also finds that the MACD tool can earn significant positive returns when it uses optimized parameter values. This suggests that the Japanese market is not weak-form efficient in the sense that futures prices do not reflect all public information. Hence, the three parameters values of the MACD tool should be optimized for each market and this should take precedence over finding other strategies to reduce false trade signals. This study also tests which models are able to improve profitability by applying additional criteria to avoid false trade signals. From simulations using 19,456 different MACD models, we find that the number of models with improved performance resulting from these strategies is far greater for models with optimized parameter values than for models with non-optimized values. This approach has not been discussed in the existing literature. [ABSTRACT FROM AUTHOR]

Copyright of Economic & Political Thought / Myśl Ekonomiczna & Polityczna is the property of Lazarski University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Trading strategies intended for high frequency trading in Forex markets are executed by cutting-edge automated trading systems. Such systems implement algorithmic trading strategies and are configured with predefined optimized parameters in order to generate entry and exit orders and execute trades on trading platforms. Three high-frequency automated trading systems were developed in the current research, using the MACD (oscillator), the SMA (moving average) and the PIVOT points (price crossover) technical indicators. The systems traded on hourly time frames, employing historical data of closing prices and the parameter optimization for each system was done using the d-Backtest PS method over weekly periods. With this work we intend to extend the methods of parameter selection for automated trading systems in high frequency trading. Through this research and the interpretation and evaluation of its results, we conclude that backtesting parameters' optimization, especially through the d-Backtest PS method, is much more profitable than the default values of the parameters and that the optimization of parameters yields the highest profits through the implementation of restrictive relationships among them. It is also observed that the selection of the most profitable parameters of a trading system can be unrestricted, rendering the validation of the minor divergence occurring among slightly varying prices redundant. Meanwhile, other conclusions that can be drawn are that the most profitable classification system employed by the d-Backtest PS method is calibrated by means of two validation periods and that the most efficient profitability ratio between historical data period and validation period is 6:1 (in- and out-of-the-sample ratio). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Financial Researches & Studies / Finansal Araştirmalar ve Çalişmalar Dergisi is the property of Marmara University, School of Banking & Insurance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to contribute to technical-scientific knowledge about the tools needed to support the decision making in investments in the capital market. We focus our analysis on the following technology companies' performance: Facebook, Apple, Amazon, Netflix and Google (FAANG). The simulation covers 18,389 observations, applying strategies based on the Ichimoku dynamics, technical analysis indicators, MACD and RSI, and the purchase and maintenance strategy (B&H), that gives evidence that the dynamics of the past is the best predictor for the future. We also found theoretical and empirical evidence of the predictive capacity of these investment strategies, and their performance was evaluated by the return provided, through different negotiation methodologies tested. The Ichimoku system trading dynamics offers the best profitability and the lowest concerning the risk, in the short and medium term, evaluated by the return and number of trades with positive returns. [ABSTRACT FROM AUTHOR]

Research topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example "deep learning", "internet of things" and "big data". Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate. [ABSTRACT FROM AUTHOR]

This paper deals with efficiency of the MACD indicator. The aim of the study is to find whether this indicator would be usable for creating a business strategy for active trading at a stock exchange. The research was carried out on the basis of day closing share prices of the company CEZ, traded on the Prague stock exchange, during the ten-year period from 2006 to 2016. The MACD indicator was calculated in three slightly modified methods. The first method was based on the crossing of MACD and signal line, the second method was similar but reliant on oscillation line. The last method was based on the crossing of MACD and oscillation line. All these methods were calculated in three time periods (short-term, medium-term and long-term). None of the MACD methods beat passive strategy “buy-and-hold” in short-term period. However, for the medium-term and long-term periods, in four of six cases the MACD methods were better then the buy-and-hold strategy. Especially the third method shows very promising results. This study could be used as a building block for further research.

The purpose of this study is to determine the effect of MACDIHSG pattern to the MACD Company pattern in the L045 index on the period of February until July 2015 at the Indonesia Stock Exchange. This study also aims to facilitate investors to make investment decisions in the stock market. This study provides benefits to capital market investors, especially for stock investors in the Indonesia Stock Exchange as a mean of enhancing their insights in the development of technical analysis on investing. For the general public as well in order to know that investing in the stock market differs from gambling since there are analyzes that easy and can be applied very simply. In addition, this research aims to enhance the reader's desire to invest in the stock market. The samples were the closing data price of IHSG and shares ofLQ45 in the period of February until July 2015 in the Indonesia Stock Exchange. Based on the hypothesis testing, it can be explained that the MACD Histogram IHSG has a significant effect on 38 issuers listed in LQ45. As for the difference between the MACD Histogram effect against one company with another company that is very small. From the 38 stocks that rank on the top 3 company, the Summarecon Agung Tbk is on the top 1, that amounted to 98.3348%, then Alam Sutera Reality Lestari Tbk amounted to 98.2376%, and Adhi Katya (Persero) Tbk amounted to 98.1320%. The third of these shares have MACD Histogram movement that approaching the MACD Histogram IHSG.

Highlights • The branching ratios were determined to be ε = 0.15 ± 0.02 and α = 0.018 ± 0.004. • Most of the values deduced for ε were in the [0.1, 0.3] range. • Most of the values deduced for α were in the [0.01, 0.03] range. Abstract The branching ratios ε and α in the three-body recombination reaction for O(1S) greenline and O 2 (0,0) atmospheric band airglow chemistry represent the fraction of O 2 that branch into the b 1 ∑ g + and c 1 ∑ u - electronic states, respectively. In the present work, the empirical values of these branching ratios have been deduced using a numerical optimization approach. They were obtained using the optimization scheme known as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with our MACD-00 model and simultaneous volume emission rate (VER) measurements of the O(1S) greenline and O 2 (0,0) atmospheric band emissions. The CMA-ES was employed as the optimization algorithm that would match the O(1S) and O 2 (0,0) VER profiles simulated by the MACD-00 model to observations made by OXYGEN/S35, S310.10, NASA Flight 4.339, ETON flights P229H and P230H, OASIS, SOAP/WINE, MULTIFOT, and WINDII. We found that most of the values deduced for ε were in the [0.1, 0.3] range, while most of the values of α were in the [0.01, 0.03] range. Excluding the outliers, the average branching ratio values involving the production of O 2 (b 1 ∑ g +) and O 2 (c 1 ∑ u -) were determined to be ε = 0.15 ± 0.02 and α = 0.018 ± 0.004, respectively. Overall, the simulations showed good agreement with the observations albeit with some discrepancies in the peak altitudes and shape of the profiles, possibly due to small perturbations in the observed VER profiles that are not considered in our simulations. [ABSTRACT FROM AUTHOR]

Tropical cyclone disasters have a substantial impact on human activity in coastal zones. The traditional methods used to classify wind disasters include the Beaufort Scale, Saffir-Simpson Scale, and Chicago Mercantile Exchange Hurricane Index (CMEHI). This research established a new index, the Haiyang Cyclone Index (HYCI), which considers spatial distributions and sea level anomalies (SLA) using HY-2 satellite data. The HYCI of three mega coastal cities, Qingdao, Shanghai, and Guangzhou, was calculated. A brief analysis was then performed using Moving Average Convergence Divergence (MACD) and Long Short-Term Memory (LSTM). The proposed HYCI can accurately evaluate the impact of cyclone disasters, thereby confirming its financial application potential. • Proposing a new model to assess coastal wind disaster - HYCI Model. • Almost all typhoons are accurately recorded by the MACD indicated by HYCI. • HYCI model can accurately assess the impact of cyclone hazards. • HYCI model has great potential for marine wind hazard assessment and management. [ABSTRACT FROM AUTHOR]