Convergence Divergence Research Articles

How to Become Rich Using Quantitative Trading?

Bitcoin has become a very common investment product, also known as 'digital gold' because of its global discussion and rising prices. This paper develops a model that can only rely on these data to predict future price fluctuations and plan trading decisions. The trading decision planning model consists of three sub-models that are mutually undertaken. For model 1, in order to judge the price trend reliably, this paper uses the Long Short-Term Memory (LSTM) neural network, which is sensitive to the time series, to learn all the time series price data up to the trading day, and to predict the future price data of them respectively, which is provided to another sub-models for subsequent work. For Model 2, after obtaining the follow-up price data, all the technical indicators needed are calculated in this model, and then the moving average convergence divergence (MACD) rule is used to determine whether the transaction is carried out for the first time, and then the predicted price trend is used for the second judgment. For model 3, the model begins to make decision- making planning. This model mainly solves the bi-objective programming problem with the goal of maximizing returns and minimizing risks. In the range of controllable risks, the model will give the optimal solution of maximizing returns. Finally, using the model, the sensitivity of the model to transaction cost is studied by changing the transaction commission, and it is found that the final investment income is more sensitive to the transaction commission of bitcoin.

Visualizing Convergence Dynamics across Regions and States: h-Convergence

Researchers interested in studying whether convergence dynamics are in place among regions within the same country have adopted both statistical tools and empirical frameworks developed when studying convergence across different economies. We show that this approach is risky, because when an analysis is conducted at the regional level, the absolute and club convergence processes are more likely to co-exist than in the case of world economies. We propose an empirical approach where the two hypotheses are not taken as competing. Our procedure uncovers periods of convergence and periods of divergence for the three samples we studied: Italy observed at both the regional and provincial levels; EU regions; and world economies. We find a process of absolute convergence for Italian regions from 1951 to 1999, and that their convergence process ends in 1971 after a period which we define as clustering convergence. We also find a process of convergence across European regions from 1977 to 1993; that ends in 1985 in favor of a process of clustering and divergence. Finally, our procedure uncovers a process of absolute convergence from 1964 to 1975 and divergence from 1975 to 1999 in the case of world economies.

Technical Indicators of Major Healthcare Companies: A Comprehensive Analysis

This research aims to provide a comprehensive analysis of major healthcare stocks along with the nifty health care index within the healthcare sector. Employing technical indicators such as the Moving Average Convergence Divergence (MACD), Rate of Change (ROC) and Relative Strength Index (RSI) we conduct a detailed trend analysis to identify historical patterns and potential turning points in stock prices. Furthermore, advanced time series forecasting models and predictive analytics are utilized to forecast future share prices for the selected three companies, offering valuable insights for investors and market participants.
 In addition, we investigate the relationship between the stock prices of Apollo Hospital, Fortis Healthcare & Narayana Hruda and the Nifty Healthcare Index. This analysis sheds light on how individual stock prices are influenced by broader sectoral performance. We also explore the impact of MACD, ROC, and RSI technical indicators on the prices of all healthcare stocks through rigorous statistical analysis. Our findings provide healthcare sector stakeholders with a deeper understanding of stock behaviour, potential future movements, correlations with market indices and risk assessment.
 The results of this study enhance the decision-making process for investors and market participants in the healthcare sector. By examining historical trends, forecasting price dynamics, assessing sectoral correlations, evaluating technical indicator effects and risk measurement, this research contributes to a more comprehensive view of these stocks and their positioning within the healthcare industry.

Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model

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.

Learning and processing framework using Fuzzy Deep Neural Network for trading and portfolio rebalancing

Trading strategies are an interesting topic of financial research. Moving Average Convergence Divergence (MACD) indicator is susceptible to performing worse than expected in unstable financial markets. This paper first presents a data-driven Interpretable Fuzzy Deep Neural Network (IFDNN) that provides insight into neural network inferences using fuzzy logic. Fuzzy rules are induced from the inference process of Neural Networks. Next, a learning and processing framework is proposed using IFDNN to detect trend reversals by forecasting look-ahead prices. IFDNN not only learns the drifts and shifts in market patterns, but also provides traders an option to dive into the reasoning behind why Neural Networks predict certain values. Genetic Algorithms are used to optimise trading parameters of the proposed framework. The proposed framework can perform portfolio rebalancing. The effectiveness of the framework is evaluated on three financial market indexes. The whipsaw effects cause frequent entrances and exits from the market. In this paper, a custom percentage oscillator is implemented to avoid this issue. The performances of the proposed framework using f-MACD are compared with those of the vanilla MACD. Two types of Reinforcement Learning models, Advantage Actor Critic and Deep Deterministic Policy Gradient are incorporated into the proposed framework with results compared.

Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq

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.

Beating the market with a simple proposed portfolio and technical trading rules

This study presents evidence that well-known technical trading rules (TTRs) applied to a proposed simple exchange-traded fund (ETF) portfolio provide better risk-adjusted performance than the ‘market’ and top-ranked balanced funds (BFs). We form a portfolio of two well-known ETFs and apply some well-known TTRs like Moving Average (MA), Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) techniques to this simple proposed portfolio from January 31, 2007 to August 31, 2021. We design two strategies: (1) Long/Money and (2) Leverage/Money. We compare the risk-adjusted performance of some well-known trading rules applied to our proposed portfolio with strategy (1), Long/Money, which is a more conservative investment strategy, to the risk-adjusted performance of 14 top-ranked BFs. The best trading rules with strategy (1) applied to our portfolio outperform all the top-ranked BFs for the entire period and over two equal sub-periods. Similarly, the best trading rules applied to our portfolio with strategy (2) outperform the ‘market’, a Buy and Hold (B&H) strategy for the entire period and over two equal sub-periods. This paper contributes to the literature examining the common assumptions of efficient markets and could have important practical implications for many fund managers. In addition, the social impact of our paper could affect thousands of retail traders who are trying to ‘beat’ the market.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.

Why Cross-Country Convergence of Income is Unsustainable: Evidence from Inclusive Wealth in 140 Countries

Recent economic convergence studies show that cross-country income inequalities have declined since the 1990s. However, this study finds that this episode of income convergence is unsustainable in the long run because countries' capacity to earn income diverges. Specifically, the paper analyses the convergence of per-capita Inclusive Wealth, which comprises all capital assets that contribute to the production of goods and services and the well-being of its society. Utilizing a diverse array of techniques to estimate convergence in a sample of 140 countries between 1990 and 2010, the paper demonstrates the simultaneity of unconditional convergence of GDP and unconditional divergence of Inclusive Wealth. Natural-resource-rich countries that lack human capital, in particular, appear unable to match the global per capita Inclusive Wealth growth rate. A trend emerges towards a bimodal Inclusive Wealth distribution with a substantial low-wealth peak. Thus, although swift income convergence appears promising for developing nations, I caution against optimism. When considering a more appropriate measure of future well-being, such as Inclusive Wealth, the economic outlook for many countries is bleaker than recent studies suggest.

Interplay of diet and sympatry in the morphological evolution of noctilionoid bats

AbstractAimThe remarkable ecomorphological diversity of the bat superfamily Noctilionoidea is the result of a diet‐mediated adaptive radiation. Stemming from a putatively insectivorous ancestor, noctilionoid bats diversified to adapt to the widest range of dietary ecologies among mammals. The high concentration of noctilionoid diversity in the Neotropics raises the question as to whether competitive ecological interactions influenced their diversification. However, patterns of morphological diversification along gradients of coexistence in Noctilionoidea are understudied. Here, we assessed the effect of dietary adaptations on patterns of morphological divergence associated with coexistence in noctilionoid bats.LocationThe Americas and the Caribbean.TaxonBat superfamily Noctilionoidea.MethodsUsing a set of five dental morphological traits and a sample of 108 species, we quantified morphological distances between noctilionoid species pairs to reveal patterns of morphological divergence along gradients of range overlap and dietary similarity. We incorporate phylogenetic information to control for the effects of species' relatedness on morphological distances.ResultsOverall, we found a significant association between morphological divergence, coexistence and diet. Decomposing the correlation between morphological divergence and sympatry across dietary groups, our results revealed distinctive patterns consistent with both character convergence and divergence. Specifically, we found evidence of character convergence in herbivorous species and character divergence in omnivorous species.Main ConclusionsOur results indicate that the morphological diversification of noctilionoid bats followed parallel evolutionary trajectories of directional (both divergent and stabilising) and nonselective evolution linked to dietary adaptations.

TECHNICAL ANALYSIS AS THE BASIS FOR DECISION MAKING IN STOCK INVESTMENT IN PT. BANK MESTIKA DHARMA.TBK IN THE INDONESIAN CAPITAL MARKET (DURING THE COVID PANDEMIC JANUARY 2020 - JUNE 2021)

This study uses a quantitative approach to making stock investment decisions using the Moving Average Convergence Divergence indicator and the Stochastic Oscillator indicator by recording sell signals and buy signals and then testing the effectiveness of buy signals and sell signals from the two indicators. This research will be analyzed using the investing.com chart analysis platform. Based on research that has been conducted by researchers, it can be concluded that: 1. From graphic analysis at PT Bank Mestika Dharma Tbk, the Stochastic Oscillator indicator provides a total of 65 signals consisting of 35 buy signals and 30 sell signals while the MACD indicator provides 19 signals consisting of 10 bell signals and 9 sell signals from. 2. The Stochastic Oscillator indicator provides a better level of accuracy with an accuracy rate of 68%, while the MACD indicator has an accuracy rate of 58%. 3. The Stochastic Oscillator indicator has proven to be superior for analyzing stocks with a sideways trend, as experienced by PT Bank Mestika Dharma Tbk during the current economic crisis.

A new index for assessing the coastal wind disasters based on the HY-2 satellite data

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.

Cross‐Country Analysis of HRM Parameters in Emerging Markets: An Assessment of Measurement Invariance

AbstractIn this paper we aim to critically discuss the challenges and benefits of using survey instruments (SIs) by measuring human resource management (HRM) parameters across five emerging markets – Brunei Darussalam, India, Jordan, Kingdom of Saudi Arabia and United Arab Emirates. In so doing, we proceed to an assessment of the measurement invariance of HRM parameters measured by SIs across these five countries. Based on our field experience and empirical results showing lack of measurement invariance across these five national contexts, we provide recommendations for conducting comparative analysis of HRM parameters in emerging markets, ultimately contributing to the HRM convergence–divergence debate in this type of national context.

Trading Strategies Using Technical Indicators: An Active Portfolio Management Approach

The study aims to evaluate the effectiveness of active trading strategies developed using technical indicators against the buy-and-hold passive investment strategy. It uses the simple moving average (SMA), Bollinger band (BB), and moving average convergence divergence (MACD) as technical indicators to generate the buy and sell signals. The study uses the data of treasury bills as a proxy for a risk-free portfolio and the Nifty Total Return Index (TRI) as a proxy for a risky equity portfolio in daily frequency from June 30, 1999 to March 31, 2021. It creates the cumulative wealth index for the sample period using active portfolio management based on the buy and sell signals generated using various technical indicators. The findings suggest that SMA and MACD trading strategies provide better risk-adjusted returns than the buy-and-hold and Bollinger band trading strategies. Also, the shorter days moving average outperforms the longer days moving average trading strategy. The trading strategies perform better during the bear market than the bull market.

The Relationship between VIX and Technical Indicator: The Analysis of Shared-Frailty Model

Abstract This article uses a shared-fragility model for the recurrent spell length for the volatility index (VIX). Ten future VIXs have been chosen. The article examines the impacts of the top five market technical indicators, including the Moving Average Convergence–Divergence (MACD), Relative Strength Index (RSI), Triple Exponential Average (TRIX), Historical Volatility (HV), and Vertical Horizontal Filter (VHF). On the spell time of upward, downward, and overall trends, our findings supported the hypothesis that investors can better understand the pattern and trend of volatility risk by impacting the relevant technical index on various futures VIXs. We examined how technical indicators affect the volatility index. In the Weibull distribution, the upward trend data has the most significant values. The positive values provided by MACD and TRIX imply a strong positive relationship with VIX and a longer survival time. The results also show that the greater the value of historical volatility, the greater the risk to commercial investments. As a result, the possibility of VIX in the trading pattern has a shorter time because the selling signals are coming out. JEL classification numbers: G15. Keywords: VIX Index, Technical Indicator, Shared-frailty Model.

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

Machine learning models are nowadays becoming ubiquitous in algorithmic trading and investment management. These models are mostly used in the pre-trade analysis phase to determine the buy or sell decisions using various machine learning techniques. We aim to implement a machine learning-driven approach using various technical indicators to predict stock market prices and then accordingly make a decision about buying or selling. First, an effective trading strategy is discussed that selects the potentially profitable stocks, and then the technical indicators such as simple moving average (SMA), exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) are calculated for those potentially profitable stocks. Then supervised machine learning algorithms such as multiple linear regression, support vector machine regression, and decision tree regression are applied, where the close price of the stock is predicted using technical indicators for the next day, and based on that buy or sell signals are generated. The model is then tested on 12 different SNP500 stocks, one for every month in 2018, with the mean squared error (MSE) varying between 30.33 and 48.16 and the root MSE varying between 5.51 and 6.93, where the error is calculated on the difference in the number of days when the stock price actually increases and the predicted number of days for various models.

Research on Multistage Dynamic Trading Model Based on Gray Model and Auto-Regressive Integrated Moving Average Model

Quantitative portfolio investment mainly depends on historical data analysis and market trend prediction to make appropriate decisions, which is an important mean to reduce risks and increase returns. Based on summarizing the existing traditional single forecasting models and multiobjective dynamic programming models, this paper puts forward a new quantitative portfolio model to improve the accuracy of asset price forecasting results and the appropriateness of investment trading strategies, to better realize the maximization of investment returns. This model analyzes and forecasts daily price data by establishing a combination forecasting model of the gray GM (1,1) model and the ARIMA time series model and establishes a multiobjective dynamic programming model with moving average convergence divergence (MACD) and Sharpe ratio indicators as risk constraints to formulate appropriate investment trading strategies. The results show that by solving the quantitative portfolio trading model established in this paper and analyzing the sensitivity and robustness of the model, the price of gold and Bitcoin, two volatile assets, can be accurately predicted, and the best investment portfolio trading strategy can be effectively worked out on the premise of considering the risk level.

Missions as boundary objects for transformative change: understanding coordination across policy, research, and stakeholder communities

Abstract Recent times have seen the rediscovery and adaptation of mission-oriented innovation policies (MIPs) for driving transformative change. While such policies seek to mobilise and align stakeholders, little is known about how missions feature in policy coordination processes. We argue that to facilitate the still troublesome operationalisation of MIPs, it is essential to understand missions as ‘boundary objects’ that have some shared meanings among the participants they convene, yet are open enough to be interpreted differently by distinct actors gathering in four interconnected policy arenas—i.e. a strategic, programmatic, implementation, and performance arena. By studying the European Commission’s Horizon Europe missions, we unravel how missions as boundary objects enable and disable the coordination of heterogeneous communities. The resulting analytical perspective highlights three key mechanisms for coordinating mission meanings across communities and arenas: convergence–divergence, passage, and reflexive learning. We conclude with research avenues for studying missions as boundary objects for facilitating concerted action.

Comparative Advantage of Yarn and Weaving Industries: Evidence for Türkiye and Top Exporters

Abstract With the repeal of the MFA agreement, the competitive conditions in yarn and weaving industries have changed. Especially, with the cheap labour of Asian and Far Eastern countries, they have become advantageous in this unrestricted competition environment. Therefore, this study was conducted to help to identify those products where Türkiye and her competitors have comparative advantages. This study mainly focuses on analysing the complementarity and substitutability of selected countries such as Türkiye, India, China and the USA. RTA, RC, RSCA and trade specialisation (TSI) indices were used to evaluate the competitive advantage. The results showed that Türkiye’s competitiveness in cotton yarn and cotton weaving has deteriorated. However, India and China, which are the major countries in the world’s cotton production, have a positive impact on their long-term competitiveness. Moreover, for these countries there is convergence in cotton yarn and divergence in cotton fabric products.

Industry 4.0 and the Changing Employment Relations: A Case of the Indian Information Technology Industry

This article aims to explore the dynamics of employment relations (ER) in the Indian information technology (IT) industry in the context of an ongoing technological revolution. The study utilises the grounded theory approach to draw insights from 32 professionals including project/product managers, senior management representatives and employees from junior to mid-career level currently working in the IT industry. Findings indicate that the following four forces drive the ER in the Indian IT industry: (1) labour laws, (2) compensation and HR, (3) unions and organisations, and (4) health and workplace security. Labour laws need to be updated to suit the requirements of knowledge-based professions. Compensation and HR management styles vary widely due to the disparity and heterogeneity of work. There is scope for a non-politicised union in the industry. The health and security of IT professionals need attention. Findings suggest the changing concepts of workspaces in IT, dilution of HR in IT due to increased outsourcing and the rise of independent workers in future. This article makes theoretical and conceptual contributions to the ER literature. It captures the driving forces of ER in the Indian IT industry. The article also contributes to decent work, convergence–divergence paradigm and outsourcing in Human Resource Management.