Convergence Divergence Research Articles

Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset

This research paper presents a deep learning-based predictive model for classifying currency trends using technical indicators. The model is trained on a dataset generated from three technical indicators: relative strength index (RSI), moving average convergence divergence (MACD), and stochastic. The dataset consists of historical currency data along with the corresponding values of the technical indicators. The deep learning model can accurately classify the trends of a given currency based on the importance of these indicators. The model's performance is evaluated using standard metrics, and the results demonstrate its effectiveness in classifying currency trends. The proposed model provides a valuable tool for traders and investors in the foreign exchange market by helping them make informed decisions about the direction of currency prices.

Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1–5 days ahead as the prediction depth, using 1–5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26 % − 27 %; as well outperforms the Buy and Hold strategy by 5 % − 40 %. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54 % − 55 %; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166 %. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing.

A comparative analysis of using Moving Average Convergence Divergence indicator in the forex market with risk-free investment strategies

Technical analysis has been widely studied in academic literature. It so is the Moving average convergence divergence (MACD) indicator in stock and forex markets. The research on MACD indicator mostly shows negative or mixed results when applied to the forex market. However, past studies do not take the fixed risk for every trade, have a very high value of risk employed for every trade that a trader will not mostly undertake, fixed stop loss, or a combination of these. This study analyses MACD indicator’s profitability and a strategy of using MACD indicator with an exponential moving average as a filter for getting into trades, on hourly data for EUR-USD, GBP-USD, USD-JPY from 1st January 2019 to 31st December 2019. It then compares it with risk-free investment strategies using US T-bills for the same period. Fixed and minimal risk of 1% of the initial account balance is employed for every trade. The findings suggest that the strategies fail to give profits more than any of the risk-free strategies for the currencies tested in this paper. In five out of six cases, three currency pairs for MACD strategy and the same three pairs for the MACD with exponential moving average (MACD-EMA) strategy do not generate profit. Only MACD-EMA (where EMA is used as a filter) strategy when applied to EUR-USD gives profit, which is also less than any riskfree investment strategy.

Chaotic Bi-LSTM and attention HLCO predictor-based quantum price level fuzzy logic trading system

There are various indicators, i.e., Relative Strength Index (RSI) [1] [2], Moving Average Convergence Divergence (MACD) [3] [4] [5] [6], Stochastic Oscillator [7] [8] applications, to determine market movements with buying and selling decisions in computational Finance, but they have drawbacks that induced discrepancies to match against the best trading times at fixed order-triggering boundaries and delay problems. For example, RSI [1] [2]’s 70 and 30 overbuy and oversell are fixed boundaries. Orders can only be triggered when RSI’s value exceeds one of these boundaries, its computation only considers past market condition prompting indicators like RSI to trigger orders with delay. In this paper, we proposed a method to reduce these problems with advanced AI technologies to generate indicators’ buy and sell signals in the best trading time. Recurrent Neural Network (RNN) [9] has outstanding performance to learn time-series data automatic with long-time sequences but its ordinary RNN units [10] [11] such as Long-Short-Term-Memory(LSTM)[12] are unable to decipher the relationships between time units called context. Hence, researchers have proposed an algorithm based on RNNs’ Attention Mechanism [13] [14] allowing RNNs to learn information such as chaotic attributes [15] [16] [17] [18] and Quantum properties [19] [20] [21] contained in time sequences. Chaos Theory [15] [16] and Quantum Finance Theory (QFT) [22] are also proposed to simulate these two features (or attributes?). Quantum Price Level (QPL) [22] [23] is one of the well-formed QFT models to simulate all possible vibration levels to locate price. The system used in this paper consists of two components 1) neural network to predict future data and solve indicators lagging problem, and 2) fuzzy logic to solve fixed order-triggering boundaries problem. Its system design has two main parts 1) Chaotic HLCO Predictor consists of LSTM, Lee-Oscillator and attention mechanism to predict the High, Low, Close and Open, 2) QPL-based Fuzzy Logic Trading Strategy to receive the result and trigger trading signals. This new proposed model has obtained significant results in backtesting previous data and outperformed other traditional indicators to facilitate investment decisions when market changes constantly. Codes are available at https://github.com/JarvisLee0423/ Chaotic Quantum Finance AI Predicted Trading System.

Human Resource Management Practices of Foreign Manufacturing Companies in Bangladesh: A Comparative Study

This study aims to investigate and compare human resource management (HRM) practices among the foreign manufacturing subsidiaries operating in Bangladesh. We interviewed 36 managerial employees from three types of companies based on their country of origin: two Japanese, two British and two American. The findings reveal that a blend of HRM practices has been adopted by the foreign subsidiaries, executed by imitating home and host country practices. Results found that Japanese subsidiaries are heavily influenced by their parent company, which affects the core HRM practices of their companies in Bangladesh. On the other hand, the subsidiaries of the United Kingdom and the United States in Bangladesh have taken a different route. They have adapted and adjusted the HRM practices from the host country and implemented them with their internationalisation stages. This study further suggests that the home and host country debate through the framework of national culture may need to be re-examined. This research fills the knowledge gap in the literature on cross-cultural HRM practices, convergence–divergence debate among MNC firms’ HRM practices and foreign subsidiaries’ relationships by analysing the socio-cultural and institutional influences of the host countries.

Use of Moving Average Convergence Divergence for Predicting Price Movements

The main purpose of this study is to carrying out how Moving Average Convergence Divergence (MACD) works for predicting price of securities and to assist investment decisions. It is one of the powerful technical indicators, which is frequently used by technical analysist in stock market. However, most tests fail to verify performance with traditional parameter settings of 12, 26, and 9 days. This technique consists of a combination of close neighbor classification and some well-known tools of technical analysis, namely, stop loss or stop profit. In order to evaluate the potential use of the proposed method in practice, we compare the results obtained with the results obtained by adopting the buy and hold strategy. This paper shows how trade signal generated by this indicator can be used to minimize trading risk in markets. This study also tests which model is able to improve profitability by applying additional criteria to avoid false trade signals. The key performance measure in this technique is profitability. Technical analysis is used to assist in properly timing entry and exit points, when to buy and sell of stocks?

Empirical Research for Investment Model Based on VMD-LSTM

Investment diversification has become an inevitable trend with the development of the world economy. In this work, we first compare the K-Nearest Neighbor model, the Artificial Neural Network model, the grey prediction model, and the LSTM (Long Short-Term Memory Networks) prediction model for a period of data analysis. The experimental results show that LSTM is superior, and thus LSTM model is selected to forecast the long-term prices in this work. Then, we introduce some indicators, such as convergence divergence ratio and risk coefficient to qualitatively analyze the market price. The five-day moving average method is used to formulate the best trading strategy based on the above-introduced indicators. We apply the commonly used regression indicators ( R 2 and R M S E ) to verify the reliability of the prediction model. Then we introduce new strategies to compare the performance of different ones with them. We found that the five-day moving average method achieved 20% higher returns than the other strategies we used for comparison. Considering the fact that transaction costs may change, we perform the polynomial fitting based on existing strategies by changing the commission cost. The results show that a 1% increase in the gold commission will reduce the total return by 24% to 25%, while a 1% increase in the bitcoin commission will only reduce the total return by 7% to 8%.

A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market

In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market.

New Approach for E-Commerce Stock Prices Prediction: Combination of Machine Learning and Technical Analysis

Forecasting stock market is always a challenge task for the investors. This study aimed to develop a new approach for forecasting the price movements of e-commerce stocks. The signals emitted by the technical indicators are used as the features for two machine learning algorithms in predicting the stocks movements. The technical indicators used in this study were Moving Average (MA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Stochastic Oscillator (SO). Meanwhile, the machine learning algorithms used in this study were Random Forest (RF) and K-Neighbor Nearest (KNN). The findings of this study indicated that the inclusion of signals emitted by MA rule with 5-days short MA and 20-days long MA helps to reduce the error values for the prediction model. Besides that, this study also found that the signals from MA, MACD, RSI and SO fit the prediction model well. The investors are recommended to use machine learning algorithms to predict the price movements of e-commerce stocks. Lastly, investors are recommended to consider the signals from these four technical indicators, MA (5-days short MA & 20 long-MA), MACD, RSI and SO as the reference for their investment strategies in e-commerce stocks.

Portfolio construction based on MACD and Beta coefficient

Stock portfolio refers to a method that investors choose and match stocks according to certain laws and principles according to the risk degree and profitability of various stocks, so as to reduce investment risk, which is usually considered as the effective way in the stock market. This paper assesses portfolio construction based on moving average convergence divergence (MACD) and beta coefficient. We classify stock groups by beta coefficient and then use the MACD method to simulate the average return to find whether there is any variation in average return between groups. Moreover, we discuss the hedged circumstance by S&P 500 index. Then we compare the average return and Sharpe ratio among the selected groups in both hedged and unhedged conditions. The results show that the stocks with a lower beta coefficient boost a relatively higher average return. The empirical results in this paper will provide a method of balancing risk and average returns with the considerations of beta coefficient, and it will benefit related investors in financial markets.

Stock Index Prediction with Financial News Sentiments and Technical Indicators

The price of a stock in the market can be influenced by many factors, out of which the sentiment of the investors plays a vital role. Most often, the sentiment of investors depends on the sentiment of the news headlines. Therefore, news headlines also play an important role in the fluctuation of the stock index. This paper uses the combination of Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms for the prediction of news sentiment scores based on national news headlines and financial news data. Technical indicators like Relative Strength Index (RSI), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Stochastic Oscillator along with normal stock indicators like ’Date’, ’Open’, ’Close’, ’High’, ’Low’ and ’Volume’ data can be used to predict the short-term momentum of the stock value. This paper uses the BiGRU algorithm to predict the stock index value (a) with technical indicators only and (b) with technical indicators and news sentiment scores. Keeping all the hyperparameters constant, the BiGRU algorithm provided better prediction results when news sentiment scores were added to the dataset along with technical parameters as an input.

Finding Optimal Parameter Values for the MACD Indicator: Evidence From the Japanese Nikkei 225 Futures Market Using a New Methodology

This paper explores: (1) what parameter values are most often used to optimize the Moving Average Convergence Divergence (MACD) trading system for the Japanese Nikkei 225 futures market; and, (2) the characteristics of good-performing models with the optimized parameter values. To accomplish this purpose, this paper presents a new methodology to find the three optimal parameter values of the MACD trading system; this approach systematically examines specific ranges of optimal parameter values. Evidence from the Japanese futures market demonstrates the validity of this new methodological approach. From this, we find that for the Japanese market the technical trading system is most often optimized by three parameter values within three specific ranges over the last 11 years (2011–2021). These optimal value combinations have a unique characteristic form. These findings give insightful and broader perspectives about the market. This issue, methodology and the results have not been discussed in the existing literature. This paper also considers how the models with optimal parameter values performed during the pandemic period (2020–2021).

Analisis Teknikal Pergerakan Saham PT Bank Jago Tbk dengan Menggunakan Indikator Candlestick dan Moving Average Convergence Divergence

This study aims to determine the movement of shares of PT Bank Jago Tbk for the period January 1 – May 20 2022 using candlestick indicators and Moving Average Convergence Divergence (MACD), this study focuses on the economic aspect. The method used is descriptive method. The type of data used is qualitative data. The source of data used is secondary data obtained from charts on tradingview.com tools. Referring to the results of the analysis that has been carried out, the movement of PT Bank Jago Tbk's shares shows a decline (downtrend) using both candlestick and MACD indicators. This indicates that market participants should take a sell action.

Interdecadal Variability of Spring Eurasian Snowmelt and Its Impact on Eastern China Summer Precipitation

In this study, the interdecadal variability of Eurasian spring snowmelt and its relationship with Eastern China summer precipitation (ECSP) are investigated based on observations and reanalysis data. Results show that the second mode of the empirical orthogonal function (EOF) analysis of the spring snowmelt, featured as a west-east dipole pattern, displays two interdecadal changes near the late-1970s and in the mid-2000s. The increased spring snowmelt over the Western Siberia (WSI) and the opposite situation over the Eastern Europe (EEU) are significantly linked to a meridional quadrupole summertime rainfall pattern on interdecadal time scales, with excessive rainfall over the regions of southern China (SC) and the Huang-Huai River (HHR) and deficient rainfall over the middle and lower reaches of Yangtze River Valley (YRV) and Inner Mongolia-northeastern China (IMNC). Besides, the possible mechanisms are discussed from the perspective of the hydrological effect related to snowmelt-induced soil moisture. Increased spring snowmelt can produce more water inflowing into the soil, leading to anomalous soil moisture, which can persist into summer. Excessive (deficient) snowmelt-related soil moisture anomalies over the WSI (EEU) in summer will change local land thermal conditions and thus generate cyclonic (anticyclonic) circulations with a quasi-barotropic structure. Therefore, the eastward-propagating wave-activity flux (WAF) could be strengthened over the WSI and EEU in the mid- and upper-troposphere and further propagate downstream. Combining the local response of atmospheric circulations to summer surface heating induced by local snowmelt-related SM anomalies over Mongolia, an anomalous anticyclonic (cyclonic) circulation near the Lake Baikal (SC) and the related strong descending (ascending) motion over the IMNC (SC) are founded. Besides, strong positive (negative) upper-level divergence appears over the YRV (HHR), which is the right (left) side of the exit region of upper-level jet. The upper-level divergence could be attributed to the convergence (divergence) of meridional winds on the right side of the anticyclonic circulation, thus the uniform descending (ascending) motion also appears over the YRV (HHR) through the collocation of upper-level convergence (divergence) and the compensatory lower-level circulations. Consequently, a quadrupole pattern of secondary circulation over Eastern China is founded, thereby causing the anomalously distributed ECSP under favorable water vapor conditions.

Informal Bases of Formalisation in Pakistan: Recording the Lived Experiences of Female School Teachers

This paper focuses on exploring the way ingrained cultural wisdom and mechanisms provide the base for actualising official roles in formal organisations. Most of the formal interactions and transactions are made through personalised dealings (Qadeer, 1999). Some senior scholars feel that such cultural wisdom/mechanisms (like VB) are not relevant anymore (Chaudhary, 1999), but the current research and existing practices are giving a different picture of reality (Saher & Mayrhofer, 2014; Saher et al., 2014; Qadeer, 1999). Considering this lacuna in literature and between literature and practice, this paper is going to unravel the process of informalization in formal organisations and will contribute to the ongoing debate on convergence divergence. This paper will also discuss the implications and extend guidance for future research in this field.

The Third Space, mimics and ambivalence of HRM in the Global South: a postcolonial reading

PurposeThis paper aims to accomplish two purposes: firstly, it revisits the “positional identity” – the ambivalent-hybrid disposition – of human resource management (HRM) in the (postcolonial) Global South. Secondly, it seeks to reframe the role of Southern agents of the epistemic community of HRM, particularly human resource (HR) managers, in managing people in the South.Design/methodology/approachThis paper takes inspiration from the postcolonial theory of Homi Bhabha, his notions of hybridity, the Third Space and colonial positionality, to revisit the positional identity of HRM and to reframe the role of HR managers in the South.FindingsIn postcolonial Southern organisations, HR managers play a dual role – as “mimics” and “bastards” of Western discourses of HRM. The dual role tends to put the managers in Southern organisations in a “double–bind”.Research limitations/implicationsThis paper helps in the understanding of the role of HRM as well as HR managers in Southern organisations regarding the (post-)colonial legacy of the South.Originality/valueThis paper provides new insights into the identity of HRM in the Global South beyond the dualistic understanding of HR practices, such as convergence–divergence and the mere form of crossvergence. It argues that hybridisation of HRM in Southern organisations takes place in the form of (post-)colonial hybridity.

The use of accelerometers for the remote detection of mounting in rams and testosterone-treated wethers

It has been estimated that up to a third of all rams have little to no sexual interest in ewes. Despite the prevalence of this issue, it is challenging to monitor the reproductive behaviour of individual sheep in an extensive farming environment. In this study, we developed a single-feature algorithm to detect in-paddock mounting activity from the accelerometer records of rams and androgenised wethers. Tri-axial accelerometers were first deployed on the necks and withers of the rams (n = 15) in a controlled pen test to determine the optimal attachment point for detecting mounting events. A Moving Average Convergence Divergence (MACD) algorithm was applied to accelerometer data with a threshold of 0.3 g. Validation and performance evaluation of the algorithm was based on observed video footage. The MACD was more sensitive and precise when applied to data collected from the withers than the neck (100% vs 89%; 98% vs 86%; respectively). Following the pen test, the MACD was applied to data collected in-paddock from accelerometers attached to the withers of rams (n = 6) and androgenised wethers (n = 8). A MACD + was simultaneously evaluated, which included additional conditions that must be satisfied for the detected peak to be deemed a mounting event. For both algorithms, a series of thresholds were tested. The threshold that returned the highest F1 score (the harmonic mean of precision and sensitivity) for the respective algorithm and male type was selected as the optimal threshold. At their respective optimal thresholds, the MACD + was marginally more precise than the MACD in-paddock for both wethers (91% vs 84%, respectively) and rams (94% vs 84%, respectively). The sensitivity of the MACD in detecting in-paddock mounting behaviour was 91% in rams and 86% in wethers. The MACD + was similarly sensitive at 91% for both rams and wethers. This research demonstrates the success of this algorithm, particularly the MACD+, in detecting mounting activity. Following integration into a commercial sensor and further validation, this algorithm would allow producers to identify rams with low libido and improve reproduction and productivity.

Decoding the Changing Role of the Neo-Liberal State in a Globalising World: Convergence of Public–Private Partnership in Innovative Governance

Ever since the concept of territorial nation-state occupied the centre stage of global politics, the international community has witnessed different political and economic systems being introduced from time-to-time to cater to the needs of the global community. Since the inception of the Westphalia State System (Treaty of Westphalia 1648), nation states have reinvented themselves and re-imagined their political and economic trajectories to adapt themselves to the changing needs of time and circumstances. In the contemporary international political economy, one finds a paradigm shift towards the convergence of interests between private and public, where the states across the globe are striving to achieve common good and promote public interest by creating a synergy with the private stakeholders. This article aims at decoding the circumstances and explaining the rationale behind the changing role of the state, while redefining its economic strategy and priorities in the era of globalisation. It further tries to examine the areas of convergence and issues of divergence in public–private partnership in pursuit of the goals of development and progress. The conceptual and theoretical underpinnings of the article are conceived around the philosophy of liberalism and democracy, on the one hand, and the institutional framework of free market capitalism, on the other, as envisaged in the doctrine of globalisation.

The Complexity of Cryptocurrencies Algorithmic Trading

In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.

Linkage Between Preceding August Asian–Pacific Oscillation and September Precipitation Over Southeast China

Understanding the spatiotemporal variation of autumn precipitation and its relationship with the large-scale circulation is important for planning industrial and agricultural production, economic development, and ecological protection. This study investigated the relationship between the August Asian–Pacific Oscillation (APO) and September precipitation over Southeast China (SC) during the period 1961–2020. Results showed that the August APO can exert considerable control on September precipitation over SC and that a significant positive correlation exists between them. With a strong (weak) August APO, the anomalous southerly (northerly) winds are observed in the north (south) of SC at the upper level, and the deep trough over East Asia and ridge over North Pacific at 500hPa are both reinforced (weakened). This leads to anomalous northward (southward) lower-tropospheric winds over the East Asian coast, accompanied by enhanced convergence (divergence) of warm and cold air masses and anomalous ascent (descent) motion which results in more (less) precipitation over SC. The underlying mechanism can be explained as thermal anomalies induced by the APO that can persist from August to September, which modulates the atmospheric circulation anomalies in September and eventually causes more (less) precipitation over SC. Therefore, in addition to the role of sea surface temperature forcing on precipitation variations, our analyses suggest that the preceding temperature anomalies at the middle and upper troposphere also should be considered as an important precursory factor for the following precipitation over SC.