Use Of Technical Analysis Research Articles

Peran Analisis Teknikal dan Manajemen Risiko Syariah dalam Upaya Menghindari Spekulasi Investasi Emas

This study aims to determine whether technical analysis and Islamic risk management can avoid speculation in gold investment. This study uses a descriptive qualitative method with a phenomenological approach. Methods of data collection using interviews, and documentation. Data collection is carried out to the regional leadership board of the Association of Digital Investment Trading Practitioners and expert participants. The results showed that the use of technical analysis and sharia risk management can prevent investors from speculating when investing in gold, in the form of price reading, price feasibility, risk control, capital adequacy and understanding of sharia principles in investment.

Multi-level deep Q-networks for Bitcoin trading strategies

The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

Can technical indicators based on underlying assets help to predict implied volatility index

AbstractGiven the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well‐known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID‐19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out‐of‐sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV‐related portfolios.

Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach

The Philippine stock market, just like most of its neighbors in the region, was seriously impacted by the global pandemic COVID-19. Investors remain hopeful while continuing to seek great ones in the damaged market. This paper developed a methodology for portfolio selection and optimization with the use of technical analysis, machine learning techniques, and portfolio optimization model. The combined methods of technical analysis, K-means clustering algorithm, and mean-variance portfolio optimization model will result in the development of the proposed TAKMV method. The study aims to integrate these three important analyses to identify portfolio investments. This paper uses the average annual risk and annual rate of return data for the years 2018 and 2020 to form the clusters and assessed the stocks that correspond to the investor's technical strategy such as Moving Average Convergence/Divergence (MACD) and Hybrid MACD with Arnaud Legoux Moving Average (ALMA). This paper solved the risk minimization problem on selected shares of the companies, based on the mean-variance portfolio optimization model. There are 230 and 239 companies for 2018 and 2020, respectively, listed in Philippine Stock Market, and all simulations were performed in MATLAB environment platform. Results showed that MACD strategy dominates the MACD-ALMA strategy in terms of the number of assets with a positive annual rate of return. The MACD works efficiently in the pre-COVID-19 condition while MACD-ALMA works efficiently during-COVID-19 condition, regardless of the number of assets with a positive annual rate of return. The results also show that the maximum expected portfolio return (RP) can be achieved using the MACD and MACD-ALMA in the pre-and during-COVID-19 conditions, respectively. The MACD-ALMA shows an advantage during high-risk market conditions and can also provide maximum RP. The performance of the TAKMV method was validated by applying its results and comparing it to the next year's historical price. The 2018 results were compared to 2019 data and the 2020 results were compared to 2021 data. For consistency, the comparison was applied to the same company per portfolio. Simulation results show that the MACD strategy is more effective compared to MACD-ALMA.

Predicting stock prices with LSTM: A hybrid machine learning model for financial forecasting

This article discusses the challenges of accurately predicting the direction of the stock market and proposes a new approach using machine learning and manual forecasting. The article explores the use of technical analysis and machine learning to predict current stock market indices’ values by training on historical data. The authors demonstrate how these methods can be used to influence investor judgments at different levels of consideration, including unrestricted, near, medium, high, and volumic. The article also explores the use of social media platforms like Twitter and the correlation between stock prices and local weather patterns to improve forecasting accuracy. The authors present their research in three phases, demonstrating the potential of machine learning and technical analysis to provide accurate and reliable predictions for investors seeking to protect themselves from market volatility.

Development of Modern Approaches to Exchange-Traded Financial Asset Evaluation

Estimation of exchange-traded financial asset fair value is one of the most important tasks in modern financial science. However, the importance of methods for prediction of future prices is constantly changing. The study provides a comparative analysis of fundamental and technical analysis methods, reveals their advantages and disadvantages. Such characteristics of fundamental analysis as profit, income, profitability and the level of debt burden are considered in detail, the degree of their influence on the price of securities is analyzed. After the study, conclusions were drawn about the increasingly narrow use of technical analysis during short-term and investment problem solution. A prediction is given for a further decrease of fundamental analysis importance as the main method of assessment. To obtain these conclusions, we used statistical data reflecting the dynamics of exchange-traded assets when companies entered IPOs. The data obtained indicate a clear correlation between the innovation component of a business, market share and its price. At the same time, the company profit has finally ceased to be the main characteristic of the business during its assessment. Additionally, they studied the relationship between the fairness of exchange-traded asset evaluation and quantitative easing (QE) programs. Conclusions are made about the need to increase the forecast prices in the case of a soft monetary policy application.

Testing the Efficiency of the Indian Stock Market during COVID-19

Purpose: The aim of this paper is to assess the market efficiency of Indian Stock Markets during the COVID 19 Pandemic. More specifically it tests for the validity of Weak Form Efficiency. Market efficiency is fairly important for various market players as it is an indicator on which they base their investment decision. Design/methodology/approach: Daily data of 10 indices has been gathered for a period of 15 months from March, 2020 to May, 2021. A variety of tests namely the Runs test, Autocorrelation Functions, Correlograms and Box Pierce test have been used to evaluate efficiency levels. Findings: This paper concludes that the Indian Stock Markets are not weak form efficient for the period under consideration, indicating that stock prices do not reflect all possible information and are mispriced. This allows for the use of technical analysis, trading rules and Fundamental analysis to generate abnormal returns. Research Limitations/Implications: The current study is based on particular time frame. Originality/Value: This research is original in contribution to existing literature.

How and why do countries make Universal Health Care policies? Interplay of country and global factors.

BackgroundAn examination of country policy making tends to reveal more complex processes that reflect domestic as well as external pressures and influences. The paper examines the interplay of external and internal, as well as other, factors in universal health care (UHC) decision-making for a select number of countries spanning the income range from low to high income.MethodsAfter developing a conceptual framework to help identify variables to explore in answering our study questions, we reviewed literature on health policies and policy making, especially around the time of the adoption of relevant policies for a number of UHC reform countries, followed by a narrative review of countries for more in-depth study. For more quantitative data, we consulted databases maintained by international institutions.ResultsWe found that, for low-income countries (LICs)/lower-middle-income countries (LMICs), the external environment helps set the policy agenda that drives national priorities and resource allocation decisions, while national actors take the actual decisions consistent with the interests of their constituencies and their goals. The upper-middle-income countries (UMICs) and high-income countries (HICs) in the study were less influenced by externally driven agendas and more by their own internal dynamics. For LICs/LMICs, a country’s income level as well as growth record did not appear to play any overt role at the start of the reform, whereas the UMIC/HIC countries were generally at a higher economic stage with steady growth when they initiated the reforms. The use of technical analysis and evidence to guide the UHC reform decisions was much more pronounced in the UMICs/HIC. The findings on alignment of the UHC program to national health priorities were more mixed. On sustainability, the UMICs/HIC were much more likely than LICs/LMICs to phase in their reforms, whether in terms of the geographical extension of coverage, the population groups to be covered or the expansion of the benefit package in the course of time.ConclusionsThe near-systematic use of scientific evidence by the UMICs/HIC to inform decisions on the path to UHC in contrast to the LICs/LMICs leads to the conclusion that some LICs/LMICs may have made less than optimal resource allocation decisions based on scanty evidence and factors not conducive to sustainability of their UHC efforts.

Exchange rate forecasting using economic models and technical trading rules

The use of technical analysis by practitioners in the foreign exchange market contrasts with the ongoing debate among academics on the poor predictive ability of macroeconomic variables. This paper compares these two methods by constructing pools of economic models and technical trading rules and evaluates their in-sample and out-of-sample performance both locally and globally. Results suggest the presence of local forecastability that is overlooked when relying on global measures of predictability. The local predictability is captured using a rolling model selection approach to generate aggregate forecasts across separate pools of economic models and technical trading rules as well as both combined. The out-of-sample results for our aggregate forecasts using pools of economic models fail to beat the random walk as do pools of technical trading models. However combining the two pools of models results in forecasts that beat the random walk for four out of the six sample currencies. This result suggests that exchange rate forecasts can be improved by pooling both sets of models.

The Profitability of Technical Analysis during Financial Bubbles

This article examines the use of technical analysis, namely, a moving-average technique, to improve upon a buy-and-hold investment strategy during financial bubbles. Econometric techniques can identify financial bubbles in real time, and these techniques successfully identify commonly acknowledged bubbles in the US stock market. The authors find that technical analysis significantly improves investment returns over a buy-and-hold strategy during three of the five financial bubbles since 1928 and performs identically in the other two. The authors also demonstrate similar results using three international markets. TOPICS:Developed markets, financial crises and financial market history, portfolio theory, technical analysis Key Findings • Technical trading techniques may be effective during periods of market instability, such as a financial bubble. • Econometric techniques can identify stock market financial bubbles in real time. • The combination of technical trading and real-time identification of financial bubbles can be used to significantly outperform a buy-and-hold strategy.

Application of Cloud Model in Qualitative Forecasting for Stock Market Trends.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

The Use of Technical Analysis in the US, European and Asian Stock Markets

Technical analysis is a widely used tool in making investment decisions. Nowadays it becomes very popular in the context of big data analysis and artificial intelligence framework. Although the analysis of the results of indicators in certain markets often becomes the axis of technical analysis research, it is difficult to find articles aimed at applying and comparing this analysis in different markets. This paper attempts to answer the question of whether technical analysis indicators work in the same or different ways in the US, European, and Asian stock markets. For this purpose, 8 indicators are calculated, and their results are compared in three selected markets. The correlation between the indicators themselves in individual markets is also determined. It has been observed that the performance of technical analysis is similar in different markets so this type of analysis can be used in artificial intelligence framework.

Myopic Loss Aversion dan Literasi Keuangan Pada Pengambilan Keputusan Investor Pemula Indonesia

Hambatan seseorang bergabung dalam pasar modal adalah hambatan psikologis, yaitu perilaku risk averse. Perilaku risk averse akan membatasi pelaku pasar modal untuk mengambil keputusan, sehingga perlu diantisipasi melalui peningkatan literasi keuangan. Literasi keuangan yang rendah akan meningkatkan frekuensi evaluasi, sehingga akan meningkatkan perilaku risk averse dan menurunkan return. Permasalahan ini dapat dicegah dengan meningkatkan literasi keuangan sehingga pelaku pasar modal akan berperilaku risk seeker dan meningkatkan return. Maka penelitian ini bertujuan untuk menguji faktor-faktor yang berpengaruh terhadap return investor. Analisis regresi linear berganda menggunakan SPSS 20 untuk melihat pengaruh literasi keuangan, frekuensi evaluasi, maxhold, durasi investasi, penggunaan analisis teknikal, dan penggunaan analisis fundamental terhadap peningkatan return. Hasil yang diperoleh menunjukkan bahwa literasi keuangan dan durasi investasi berpengaruh signifikan positif terhadap return investor, penggunaan analisis teknikal berpengaruh signifikan negatif terhadap return investor, sedangkan belum cukup bukti untuk menunjukkan bahwa frekuensi evaluasi, maxhold, dan penggunaan analisis fundamental berpengaruh terhadap return investor pada tingkat kepercayaan 90 persen.

Um algoritmo de negociação automatizado baseado em uma análise gráfica, pode apresentar um bom resultado?

Investimentos baseados na análise técnica vêm sendo utilizados com maior frequência para examinar o desempenho estratégico das negociações automatizadas por meio de um robô (algoritmo) de investimentos, em particular, usando os indicadores Parabolic SAR e Fibonacci. Este trabalho utilizou a avaliação de cenários para posteriormente comparar seus resultados em relação à estratégia de buy and hold. Os cenários se diferenciam em relação à utilização de fatores de risco, timeframes e níveis de preço. Backtests foram realizados por um período compreendido entre Janeiro de 20015 e Abril de 2017 para comparar as estratégias. Como resultado, foi percebido que a utilização da análise técnica por meio da negociação automatizada pode resultar em lucros superiores à buy and hold. Entretanto, tal forma de negociação apresenta um alto nível de volatilidade.

Behaviour of Stock Returns in the KLSE: A Test of the Random Walk Hypothesis

A sample of 224 companies listed in the Kuala Lumpur Stock Exchange was taken for the period 1991-96. The serial correlations tests of varying lags and the runs tests were employed to test for the random walk theory. The bulk of the results tilts towards the rejection of non-randomness, lending weight to the argument that the stock market has no memory, and casting doubt upon the usefulness of technical analysis.

Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques

Stock market movements are affected by numerous factors making it one of the most challenging problems for forecasting. This article attempts to predict the direction of movement of stock and stock indices. The study uses three classifiers - Artificial Neural Network, Random Forest and Support Vector Machine with four different representation of inputs. First representation uses raw data (open, high, low, close and volume), The second uses ten features in the form of technical indicators generated by use of technical analysis. The third and fourth portrayal presents two different ways of converting the indicator data into discrete trend data. Experimental results suggest that for raw data support vector machine provides the best results. For other representations, there is no clear winner regarding models applied, but portrayal of data by the proposed approach gave best overall results for all the models and financial series. Consistency of the results highlight the importance of feature generation and right representation of dataset to machine learning techniques.

The use of technical analysis, source of information and emotion and its influence on investment decisions

We contribute to corporate social responsibility research by examining the influence of intrinsic Islamic religiosity, operationalized by Islamic Worldview, on investment decisions related to a firm faced with alleged environmental degradation in Pakistan. The findings based on a sample of 176 junior business executives establish that investors with higher scores on Islamic Worldview are less likely to invest in a profitable firm that is alleged of environmental degradation than those with lower scores. Guided by their intrinsic religiosity, investors make decisions different from those of the supposedly economically rational person.

Ensuring Trading Strategies Profitability with Technical Analysis Tools and Computer Technology

The paper is devoted to the study of the tools of technical analysis using computer technology. It analyzes the current conditions and the essence of the stock market, the basis of its functioning. The advantages of technical analysis are presented including accessibility and relative simplicity of its use. It analyzes the main indicators of the world stock market development and modern constant signs and tendencies of the process. It is revealed that the improvement of the technical analysis and increase of the forecast accuracy is a complex process. The prospects for the effective use of technical analysis in trading strategies for promoting analysis by using historical data of price changes for the asset under study by using computer software are determined. Back testing calculations are presented making clear the fact that the financial result would be given by a trading strategy based on the historical profitability of the asset.

Economic benefits of technical analysis in portfolio management: Evidence from global stock markets

AbstractProducing good economic value in trading strategies for investors based on technical analysis is an issue of major interest in the academe and in practice. This study considers 9,555 trading rules and examines the usability of technical analysis. The double‐or‐out (DO) and the optimal‐portfolio (OP) strategies are used to investigate how investors construct their ass et allocation. The sample for empirical study is comprised of 20 major stock indexes from global markets as risky assets from 1998 to 2013. The DO strategy on average produces higher terminal wealth rather than does the buy‐and‐hold (BH) strategy, but the average utility (AU) of the former is worse than the latter. Nevertheless, using the OP strategy not only increases the terminal wealth of investors but also generates higher utility. Given a starting investment of one dollar and considering the best 100 trading rules, the DO and OP strategies result in average terminal wealth of 17.6 dollars and 5.9 dollars, respectively. In addition, in terms of AU, both of them are better than the BH strategy. These pieces of evidence demonstrate that investors who use an appropriate strategy of technical analysis in ass et allocation can produce good economic value, a finding that supports the continued use of technical analysis in practice.

Use of Technical Analysis Tools in Nepalese Stock Market

This paper has attempted to find the interest of Nepalese investors, brokers and depository participants on the use the technical tools for the analysis of the stock market. The use of technical analysis in context to Nepal shows that the participants in the Nepalese stock market are highly interested on the use of new Hi-Lo price while making their investment decisions. Another interest was seen for trade volume indicators. The Nepalese stock market participants are not seen interested in using the resistance and support level followed by the pattern i.e. candlestick charts while analyzing the stock market trend.