Search

Algorithmic trading is one important financial area of interest to both academic and industrial researchers. With the development of machine learning and deep learning, all kinds of models and techniques are utilized in algorithmic trading. This paper proposes a novel framework for enhancing stock technical analysis strategies by survival analysis. The main idea is to integrate an existing trading strategy with a survival model and make them complementary to each other. By means of survival analysis, the original trading strategy can be extended to introduce an investment target, which is treated as the event of interest. On the other hand, the original trading signal provides survival analysis with a simple and clear starting time point of observation. The trained survival models are used to filter out false trading signals to improve the strategy performance. Under the framework, we propose different filtering methods, utilize different deep survival models, and compare their performance from both trading and model perspectives. We perform extensive and strict backtesting on the daily trading data of 380 plus stocks. The experimental results show that the framework can well improve the performance of technical analysis strategies in different market situations. [ABSTRACT FROM AUTHOR]

This study examines the application of machine learning methods to improve the accuracy of stock price prediction by integrating technical analysis. In this study, we evaluate machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Neural Networks, and Logistic Regression in the context of stock price prediction by utilizing technical indicators such as Moving Average, Relative Strength Index (RSI), and MACD. We use historical stock price datasets from the Indonesian capital market over a five-year period to train and test the models. The results show that the integration of technical analysis with machine learning methods can significantly improve prediction accuracy compared to using technical analysis or machine learning separately. The Neural Networks model performed the best in terms of prediction accuracy, with an improvement of 15% compared to the traditional method. These findings have important implications for investors and financial professionals in data-driven decision making. This study contributes to the development of more effective stock price prediction methods by combining analytical and technological approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unique moving average (MA) band parameter optimization algorithm. By comparing its performance with standard fixed-band trade rules, we show that adjustable band parameter MA rules provide relatively superior returns. We find the better performance of adjustable band trade rules in cases of high long-period moving average rules and in periods of general price decline. We provide evidence that average optimal adjustable bands have remained fairly constant in the DJIA for several decades, and that both standard MA technical analysis (TA) and band-optimization TA perform well in periods of price decline. [ABSTRACT FROM AUTHOR]

This study explores the effectiveness of technical and fundamental analysis in predicting and trading the returns of 12 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dash, Cardano, Avalanche, Binance Coin, Dogecoin, Polkadot, Litecoin, Terra and Solana. A universe of 7846 technical rules, five log moving average‐based ratios and 59 fundamental factors are used to test predictability and profitability through the Lucky Factors methodology and Superior Predictive Ability test. We observe predictability for a small set of technical and fundamental rules, while only the short‐term log moving average‐based ratio and Hashrate Index demonstrate genuine in‐sample and out‐of‐sample profitability. Our findings question the value of both technical and fundamental analysis on cryptocurrencies. [ABSTRACT FROM AUTHOR]

Popular methods to value Bitcoin include the stock-to-flow model, Metcalfe's Law, technical analysis, and sentiment-related measures. Within this paper, I test whether such models and variables are predictive of Bitcoin's returns. I find that the stock-to-flow model predictions and Metcalfe's Law help to explain Bitcoin's returns in-sample but have limited to no ability to predict Bitcoin's returns out-of-sample. In contrast, Bitcoin market sentiment and technical analysis measures are generally unrelated to Bitcoin's returns in-sample and are poor predictors of Bitcoin's returns out-of-sample. Despite the poor performance of Bitcoin return predictors within out-of-sample regressions, I demonstrate that a very successful out-of-sample Bitcoin tactical allocation or "market timing" strategy is formed via blending out-of-sample univariate model predictions. This OOS-blended model trading strategy, which algorithmically allocates between Bitcoin and cash (USD), significantly outperforms buying-and-holding or "HODL"ing Bitcoin, boosting CAPM alpha by almost 1300 basis points while also increasing portfolio Sharpe Ratio and Sortino Ratio and dramatically reducing portfolio maximum drawdown relative to buying-and-holding Bitcoin. [ABSTRACT FROM AUTHOR]

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps. The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers. The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events. There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias. Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives. This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

Technical analysis is generally regarded as an ineffective investment strategy. However, with the advent of machine learning in finance, it has been suggested that technical indicators can play a role as features when trying to predict asset returns. One direct application of this approach is portfolio selection and optimization. Technical indicators as predictors represent an attractive choice, as they can easily be obtained. However, although some studies addressed this topic, the literature on this subject is still not well developed. In this study, we apply tree-based methods that use technical indicators as predictors for daily stock returns. We describe the procedures employed for the tuning of the models and we then develop some portfolio strategies that build on the predictions provided by such models. Finally, we conduct a detailed empirical analysis to gauge the profitability of the approach considered in this paper. We find that our machine learning model shows predictive power and that its performance greatly increases when feature selection is performed. While the resulting investing strategies do not consistently beat simpler alternatives after accounting for transaction costs, our results look promising and provide new insights on the use of technical indicators as stock return predictors. [ABSTRACT FROM AUTHOR]

Motivated by the implication of return extrapolation models that a joint consideration of past price changes and firm fundamentals could efficiently identify stock mispricing, we propose an integrated approach that combines fundamental and technical information. This integrated approach generates substantial economic gains, which are comparable to those of strategies double‐sorted on characteristics related to high turnover and trading costs and state‐of‐the‐art machine learning strategies in existing studies. The performance net of transaction costs is still attractive. Simple transaction cost mitigation approaches could further enhance the performance of the integrated approach by reducing portfolio turnover. Consistent with behavioural models, limits to arbitrage and information asymmetry play a significant role in explaining the super performance of this integrated approach. [ABSTRACT FROM AUTHOR]

Stock market prediction is a debatable area of research since many decades and various studies have been done to predict the returns and movement of stock prices which will help the investors to take long or short positions in their portfolio. The main objective of the study is to understand the efficacy of fundamental, technical indicators and combined (technical and fundamental) in prediction of CNX Nifty 50 Index employing Linear regression, Random forests, Decision trees, Support Vector regression and Feed forward neural networks. Required data for analysis are collected from NSE website and time frame of the data collected is from 01-Jan-2018 to 31-Dec- 2021. It was found from the analysis that feed neural networks outperform other models and linear model also provide equally good results with technical indicators and hybrid indicators. Even though technical analysis and combined approach have good performance metrics, combined approach has lower error metrics such as MAD and SSR than technical analysis. [ABSTRACT FROM AUTHOR]

Using methods that are based on "artificial intelligence (AI)", this work makes an effort to solve the challenge of predicting the "stock market". In order to create models that can accurately anticipate the "stock market", the major foundations that may be utilised are "fundamental analysis" and technical analysis. By utilising regression "machine learning algorithms", technical analysts are able to make predictions regarding the final positions of stocks at the conclusion of the trading day. This is accomplished by analysing past prices & patterns. The categorisation of sentiment in the news and social media is accomplished via the use of "machine learning classification algorithms" within the realm of basic analysis. In contrast to "fundamental analysis," which evaluates tweets that are available to the general public and include information on the "stock market," technical analysis makes use of "Yahoo Finance's price history data." A "fundamental analysis" is conducted with the purpose of determining the influence that sentiment has on forecasts regarding the "stock market." It would be premature to assert that "artificial intelligence" can transcend the financial markets, bearing in mind the studies that reveal a reasonable level of performance. This is because the current stage of technical advancement in the field of artificial intelligence is not yet complete. [ABSTRACT FROM AUTHOR]

Technical analyses are popular in investment practice in the Chinese stock market. However, with the in-depth study of technical indicators, trend strategies constructed based on a single window period have been proven to be highly volatile in terms of returns. And also, there is a wide range of academic views on the implementation of trend strategies to achieve excess returns in the Chinese market. Based on these, this paper constructs a trend factor using a moving average model, employs Chinese A-share data from 2010-2023 to verify the effectiveness of the trend factor for single-factor stock selection in the Chinese stock market through empirical analyses, respectively, and combines the trend factor with a traditional asset pricing model to validate the feasibility of the improved Fama-French five-factor model stock selection strategy by adding the trend factor and the pricing ability of the model, and the robustness of the findings is confirmed by robustness tests. The conclusions of the study provide targeted recommendations for individual and institutional investors and have certain theoretical and practical significance. [ABSTRACT FROM AUTHOR]

Surprisingly, single bamboo drifting (SBD), which originated from indigenous boating in southwestern China, has evolved into a new and captivating sport. This research focuses on the competitive aspect of SBD, examining the oar-rowing strategies and techniques through which top athletes achieve success. The study aims to provide evidence-based training advice for participants by analysing the performance of five athletes selected based on their results in a 60-meter straight track event. Research methods include literature review, observation, video analysis, and statistical mathematics. The Dartfish sports technique video analysis system is employed to scrutinize precise movements and compare them to evaluate pole-rowing techniques. Findings indicate that athletes' positioning on the bamboo, the angle of pole entry into the water, rowing power and speed management, and bamboo direction control are crucial technical aspects affecting competitive outcomes. Correct posture and efficient pole-rowing techniques minimize water drag and enhance bamboo velocity. Effective force management ensures bamboo stability and rowing efficiency. Yawing phenomena during competition disrupt rowing rhythm and stability, hindering performance. The study conducts a comparative analysis of the pole-rowing tactics of three athletes, identifying differences in movement intricacies and effectiveness throughout the rowing cycle. The findings provide a scientific foundation for future training, offering specific recommendations such as using advanced technology for technical monitoring, enhancing physical training for better rowing efficiency, and practicing direction control under various conditions. These guidelines aim to help athletes optimize their techniques and improve competition performance. This research not only provides theoretical support for technical training in SBD but also offers practical instructions for the sport's scientific advancement and broader adoption. [ABSTRACT FROM AUTHOR]

• In-depth scientific study of a rare painted shroud from a female Egyptian mummy. • Mummy dates to the 1st – 2nd century C.E. and was likely traced back to Upper Egypt. • Color palette consists of a few mineral pigments and plant-derived dyes. • Organic materials include animal fat, plant lipid, Pinaceae resin, gum, and beeswax. • Shroud and inner bandages were made of linen, now severely degraded. This article describes a multi-analytical technical study of a rare painted shroud still wrapped around a female Egyptian mummy (MCABo EG 1974) in the collection of the Museo Civico Archeologico of Bologna, Italy. Long stored in the museum's warehouses, these mummified human remains were recently rediscovered within the Bologna Mummy Project (BOmp), an interdisciplinary endeavor promoted by the Museo Civico Archeologico and the Institute for Mummy Studies of Eurac Research, Bolzano, Italy. Dating to the Roman period (1st-2nd century C.E.), as confirmed by radiocarbon dating, this mummy displays a unique series of technical features, including the exceptional finding of a colorful painted shroud still preserved in its original location around the wrapped body, to which it is secured with textile straps and resin. In this context, scientific analysis aimed to deepen our current knowledge of the artistic practices of Roman Egypt through an in-depth study of the painting technique, context of production, and possible provenance of the mummy, while promoting a science-informed, enhanced approach to the preservation of the mummified human remains. This research relied on an integrated analytical protocol based on the combined use of imaging and mapping techniques, non-invasive point analysis, and micro-invasive investigation of minute samples, each carried out at the different partnering institutions. Computed tomography (CT) highlighted varying radio-densities for some of the flesh tones and red decorations. Visible photography and multiband imaging provided information on the nature and distribution of various materials on the surface. Fiber optics reflectance spectroscopy (FORS), X-ray fluorescence (XRF) spectroscopy, and Raman spectroscopy contributed to the characterization of the shroud's color palette along with optical microscopy (OM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), as well as high-performance liquid chromatography coupled with diode-array detection and mass spectrometry (HPLC/DAD and HPLC/MS). Materials detected include a limited number of mineral pigments and plant-derived dyes such as red lead, red ocher, madder, an unknown yellow dye, Egyptian blue and green, and a carbon-based black. Through access to MOLAB equipment and expertise, macro-XRF (MA-XRF), as well as combined X-ray diffraction (XRD) spot analysis and mapping, enhanced pigment identification. A combination of transmission Fourier-transform infrared (FTIR) spectroscopy with gas chromatography / mass spectrometry (GC/MS) was used to identify the paint binders and any additional organic substances possibly involved in embalming practices and ritual traditions, including animal fat, plant lipid, Pinaceae resin, gum, and beeswax. Mineralogical data gathered by XRD on surface deposits and soil residues collected from within the shroud's inner folds was crucial to put forward hypotheses, in support of the stylistic study of the shroud, concerning a possible provenance from Upper Egypt, most likely West Thebes. Both the shroud and inner bandages were found to be made with linen using OM. After undergoing scientific analysis and conservation treatment, the mummy was displayed in the "Mummies. Unwrapping the past" exhibition and was featured at the 10th World Congress on Mummy Studies, both held in Bolzano in the fall of 2022. [ABSTRACT FROM AUTHOR]

Lero Fish Landing Base (FLB) in South Sulawesi, Indonesia, is expected to function as a support for the capture fisheries sub-sector and the realization of fisheries production centers on an efficient economic scale. This study aimed to determine the conditions and examine the use of facilities and infrastructure. Addtionally, it sought to determine management strategies to improve the welfare of the community, especially those engaged in fisheries. This research was conducted from May-November 2023 at Lero FLB. The research methods included observation and interviews. Samples were determined by purposive sampling, and the number of respondents was 48 people. The data were analyzed descriptively, using utilization rate analysis and SWOT analysis. The results show that the condition of Lero FLB facilities and infrastructure is in good to very good condition, and it is necessary to manage its utilization and human resource development. Facility utilization is still below 100%, it can be stated that the level of facility utilization has not reached optimal. Based on the relative index of production value, a value of > 1 was obtained indicating that the production of fishery products landed has experienced a good trade quality for the last three years, namely from 2020- 2022. The strategy to improve management and increase fisheries production is the S-O (Strengths-Opportunities) strategy. This involves enhancing the management and utilization of the existing facilities to capture the greatest opportunities. The government, fishermen, and all users of the facilities should collaborate to manage and utilize the available facilities at Lero FLB effectively [ABSTRACT FROM AUTHOR]

Analysing data from high-level matches soft tennis from elite players is something that coaches and athletes can use to improve their training and competition. The study aimed to compare the match workload action areas and analyse techniques between male and female soft tennis players in high-level competitions from soft tennis in 10 final matches. Data analysis of match workload, techniques, and action areas occur in matches performed using the Dartfish software. The data is analysed statistically, compared between males and females for match workload, and presented as percentages for each technique and action area. The results of the analysis showed that Rally time shots, Rally total time Work Density, and Percentage rally during the match of male players were significantly higher than those of female players, p ≤ .05. The Flat technique was used more at the competition (Male; Forehand 38.77% Backhand 39.19% and Female player, Forehand and Backhand are equal at 36.91%) with the correlation between both techniques being r = .0974. Slice and Lob techniques 3.00-10.08%, and Volley and Smash techniques used the least during matches. The playing area of all players will be at the Mid left, up to 31.99-41.10%. Fore right is the area with the least play, 1.97-3.21%. Data from the study shows that male players have a higher match workload than women. However, there are no differences in the use of techniques in the competition, including the playing action area. This result will help coaches and players practice and develop their abilities at a higher level. [ABSTRACT FROM AUTHOR]

Accuracy and speed are critical factors that influence tennis performance. Various studies have examined speed, but less attention has been given to understanding the probabilistic structure of errors (i.e., accuracy). Tennis, a racquet sport requiring high physical fitness during competition, emphasizes the importance of both ball accuracy and speed for success. This is especially true for young male competitive tennis players. This study aimed to analyze the ball accuracy and ball speed of running groundstrokes on a clay court. Sixteen males at national-level junior tennis were assessed (age: 15.50 ± 0.63 years; height: 178.44 ± 8.08 cm; body weight: 67.25 ± 7.72 kg; body mass index: 21.12 ± 1.93 kg/m2). All participants measured running groundstroke performances by hitting a ball to a target square and hitting it as fast as possible by using a ball machine that fed 10 balls at a velocity of 60 km/h, with a 2-minute break between trials. The running groundstroke variables measured included Running Forehand Down the Line (RFDL), Running Forehand Cross-Court (RFCC), Running Backhand Down the Line (RBDL), and Running Backhand Cross-Court (RBCC). The results found that the forehand technique was a more accurate skill than the backhand, with higher averages than other skills (RFCC; 1.81±0.544 RFDL; 0.37±0.500, RBDL; 0.25±0.447, RBCC; 0.62±0.719) p = 0.011. The forehand technique also showed a higher average speed, particularly in RFCC (p = 0.001). The Pearson correlation of forehand techniques in RFCC and RFDL was moderately related, r = .697, with statistical significance at p ≥ 0.05. These results suggest that the differences in groundstroke running each technique are different. Thus, coaches should adopt specific training programs focusing on ball accuracy and ball speed to enhance the performance of youth tennis players on clay courts. [ABSTRACT FROM AUTHOR]

In basketball, shooting stands out as a vital technique directly affecting the game results. Identifying shooting mechanics early in a basketball player's career can significantly develop shooting performance in subsequent years. This study aimed to explore shooting percentages based on angular positions of joint parts in the shooting techniques of youth basketball players. Fifteen male participants (average age: 14.1±0.7 years, height: 180.7±7.9 cm, body weight: 65.4±10.0 kg, sports experience: 4.7±0.4 years) voluntarily participated in the study. They were divided into high shooting rate (n: 7) and low shooting rate (n: 8) groups. Both groups attempted 60 shots from the free throw line (20 shots), right forward position (20 shots), and left forward position (20 shots). Joint angle values during the initial and final shooting phases were compared between groups using Independent t-tests. Significant differences were found between groups in the shoulder part during the beginning phase and the wrist part during the final shooting phase (p<0.05). These findings underscore the potential for targeted enhancement of shooting mechanics among young basketball players through posture analysis (joint angle parts) using both performance assessments and electronic software tools. [ABSTRACT FROM AUTHOR]

This document outlines the analysis, design, implementation, and benchmarking of various neural network architectures in a short-term frequency prediction system for the FOREX market. Our objective is to emulate the decision-making process of a human expert (technical analyst) through a system that swiftly adapts to market condition changes, thereby optimizing short-term trading strategies. We have designed and implemented a series of LSTM neural network architectures that take exchange rate values as input to generate short-term market trend forecasts. Additionally, we developed a custom ANN architecture based on simulators for technical analysis indicators. We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost. [ABSTRACT FROM AUTHOR]

The Relative Strength Index (RSI) is a widely used technical indicator in stock trading because of its simple application. However, its application combined with fundamental analysis in Kuala Lumpur Stock Exchange (KLSE) is yet to be explored extensively. Hence, this study aimed to investigate the effectiveness of RSI as a predictive tool in the KLSE market. First, 39 stocks that met the set of criteria guided by fundamental soundness were identified. Trading signals are then initiated based on the RSI indicators, which are superimposed on the charts of the identified stocks. The return on investment (ROI) was then estimated and benchmarked against KLCI performance over a period of five years starting from 21st July 21, 2018. It was found that the average ROI was 3.46% against KLCI's - 24.37% over the same period of 5 years, measured between 21st July 21, 2018, 21st July 21, 2023. The average ROI improves as the number of trade iterations increases to five. The peak average ROI is approximately 22%. Technology is the top performer, recording an ROI of more than 40%. In conclusion, the combination of RSI and fundamental analysis yielded a mixed set of results. Although it outperforms KLCI, the annualised ROI is only slightly above 4%. Nevertheless, the results of this study provide valuable insights into the application of the RSI as a trading signal generator in the KLSE market. [ABSTRACT FROM AUTHOR]

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. First, we consider the conventional GARCH-based volatility models. Next, we extend the corresponding GARCH-based forecasting and calculate a specific probability associated with the predicted volatility levels. As the probability evaluation is based on a stochastic model, we develop an advanced data-driven estimation of this probability. The novel statistical estimation we propose uses real market data. The obtained analytical results for the statistical probability of the levels are also discussed in the framework of the integrated volatility concept. The possible application of the established probability estimation approach to the volatility clustering problem is also mentioned. Our paper includes a concrete implementation of the proposed volatility prediction tool and considers a novel trading and volatility estimation module for crypto markets recently developed by the 1ex Trading Board group in collaboration with GoldenGate Venture. We also briefly discuss the possible application of a model combined with the data-driven volatility prediction methodology to financial risk management. [ABSTRACT FROM AUTHOR]

Technical analysis in the foreign exchange (Forex) market has yielded mixed results, particularly regarding its effectiveness over different holding periods in swing trading. This study addresses this gap by evaluating 497 technical trading rules across 10 currencies over 22 years (January 2000 to December 2022). Focusing on swing trading windows of 1-7 days, the research introduces the concept of an ‘optimal holding period,’ examining how price movements align with trading signals at varying time lags post-signal. The results demonstrate that technical trading rules significantly predict price movements in both developed and emerging market currencies, with emerging markets showing higher levels of predictability. Notably, simple moving average (SMA) indicators perform most effectively for emerging market currencies, while oscillator-based strategies prove more successful for developed markets. These findings have practical implications for Forex traders employing short-term strategies, providing actionable insights for optimizing trade timing. Additionally, the study opens new avenues for future research on the role of technical analysis in enhancing trading performance in global currency markets.

Background: With the increase in basketball competition intensity, video image analysis-based methods have been increasingly applied to improve the technical level of basketball players. Methods: Taking the example of one-handed set shoulder shooting (the basketball player spreads legs apart at shoulder width, holds the ball over the right shoulder, extends the right hand along with lower body movement, and releases the ball softly using fingers), this study compared the technical differences between 20 level 2 basketball players (level 2 group) and 20 basketball specialized students (specialized group) by capturing video images using a high-speed camera and conducting independent sample t-tests. Results: The level 2 group had a significantly higher shooting percentage than the specialized group (p=0.005). Additionally, the level 2 group had shorter dribbling time and longer shooting time compared to the specialized group (p=0.032; p=0.021). The amplitude of variation in the knee, hip, and elbow joints was significantly greater than that of the specialized group (p=0.008/0.016/0.031). The flexion angles of the index finger and middle finger after striking in the level 2 group were significantly higher than those of the specialized group (p=0.021/0.036). The level 2 group also had a smaller striking angle and a larger striking height and speed than the specialized group (p=0.001; p=0.005/0.006). Conclusion: Athletes of different levels show differences in phase duration and joint angles when performing one-handed set shoulder shooting, providing a basis for athletes' training.

Abstract This paper examines the potential profit of bull flag trading rules in the Shanghai Stock Exchange Composite Index (SSE) using a template matching technique based on price pattern recognition. This paper fills a gap in the literature by applying a template matching technique for the recognition of bull flag patterns in the Shanghai Stock Exchange Composite Index (SSE) during the period of 1991–2021. To the best of our knowledge, no previous study has applied bull flag trading rules to the Chinese stock market. Our results indicate that bull flag trading rules can correctly predict the price movement direction of the index most of the time, achieving significantly positive excess profits. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance. This research may have relevant practical implications for investors who opt for this indicator in their asset allocation decisions.

The nonlinear nature of financial data series and the intricate incorporation of data into market prices necessitate a comprehensive exploration of key research findings, prevailing trends, intense debates, and subfields in the market behavior realm. Studies exploring the way in which technical analysis can exploit the deviation from market efficiency in stock markets, based on new prediction techniques (machine learning, deep learning, and artificial intelligence), are lacking. This study presents a comprehensive bibliometric assessment of market behavior using the Scopus database from 1972 to 2022. A thorough assessment process, which included keywords, filters, and data cleaning, was employed to narrow down the literature from 30,551 to 8,289 relevant papers. The research framework delineates seven primary themes that underpin this study: market efficiency, behavioral finance, technical analysis, volatility, fractals, asset pricing, and price discovery. For practitioners, investors, and policymakers, our study presents evidence regarding emerging themes, such as technical analysis, adaptive market hypothesis, and machine learning, which diverges from the findings of the proponents of equilibrium models based on investors’ rationality. Moreover, an in-depth inquiry into the role of technical analysis in shaping portfolio investment presents a promising future research avenue.

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration. [ABSTRACT FROM AUTHOR]

Computer aided environment design technology, also known as Computer aided environment (CAE) technology, is a technology that utilizes computers for engineering design, analysis, and optimization. Simulation data refers to the data generated by simulating the real environment or system in a computer, with its core characteristics of simulation and predictability, used for performance evaluation. CAE technology simulates the state and behavior of buildings, facilities, or environments, evaluating the effectiveness of design solutions and predicting performance through simulation data. This article aims to conduct a technical analysis and practical case studies to evaluate the effectiveness, draw conclusions and suggestions from the technical analysis, in order to promote the development of computer-aided environmental design technology and improve the efficiency and quality of environmental design. Therefore, this article uses CAE technology to simulate the thermal performance, structural response, and fluid dynamics changes of buildings. This article simulates the temperature environment of rooms in buildings and the pedestrian density and traffic flow in traffic through experimental simulation and data comparison. It is found that the peak pedestrian density occurs at 16 and 17 minutes, and the lowest value is at 45 minutes. There is a certain similarity between the data results and the real data. [ABSTRACT FROM AUTHOR]

This paper examines the potential profit of bull flag trading rules in the Shanghai Stock Exchange Composite Index (SSE) using a template matching technique based on price pattern recognition. This paper fills a gap in the literature by applying a template matching technique for the recognition of bull flag patterns in the Shanghai Stock Exchange Composite Index (SSE) during the period of 1991–2021. To the best of our knowledge, no previous study has applied bull flag trading rules to the Chinese stock market. Our results indicate that bull flag trading rules can correctly predict the price movement direction of the index most of the time, achieving significantly positive excess profits. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance. This research may have relevant practical implications for investors who opt for this indicator in their asset allocation decisions. [ABSTRACT FROM AUTHOR]

The aim of the research is to find out the relationship between fundamental analysis and technical analysis towards stock price. Fundamental analysis is interpreted by debt to equity ratio, earnings per share, current ratio, and return on assets. Technical analysis is interpreted by volume of trade and The Composite Stock Price Index. Furthermore, by applying dummy variable, dividend policy is applied as moderation variable to test the relationship between fundamental analysis towards the stock price as well as technical analysis towards the stock price. The population of the research is banking companies listed in IDX in 2019-2022. Technical analysis applied double regression analysis and moderated regression analysis (MRA) using SPSS 26. The finding of the research stated that technical analysis and fundamental analysis influence the stock price. Company policy to share dividend for the stock holders was proven to influence the relationship between those variables. [ABSTRACT FROM AUTHOR]

Small-scale photovoltaic (PV) power systems have been proven to be successful in generating electricity, conserving fossil fuels, and reducing greenhouse gas emissions in the residential sector, which is one of the largest consumers of energy. In Algeria, to reduce energy consumption in this sector, the authorities are considering implementing a policy that would encourage grid-connected residential PV systems. This paper presents a techno-economic assessment of grid-connected residential PV systems in four climate zones in Algeria. This work was performed using HOMER software for two different PV system configurations, grid/PV and grid/PV/battery. The technical performances of the considered systems were evaluated through the assessment of the self-consumption and self-sufficiency, while the net present value (NPV), internal rate of return (IRR), profitability index (PI), and discounted payback period (DPBP) were used to determine their feasibility. A sensitivity analysis was carried out to evaluate the effects of feed-in tariff (FiT), battery costs, and PV array capacity on the profitability of the systems. The results revealed that the grid/PV systems are technically and economically feasible in all of the four climate zones. For the grid/PV/battery systems, the grant of battery costs and the development of a regional FiT system are recommended. This article provides a tool for policymakers to assess the technical and financial performance of residential solar PV systems to develop adequate policy supports and tariff structures for Algeria. [ABSTRACT FROM AUTHOR]

This study analyzes the pigments and binders used in the painted wooden structure of DaZhong Gate in the Confucius Temple in Qufu, Shandong Province, China. Five samples were collected from the building and analyzed using techniques such as polarized light microscopy (PLM), energy-dispersive X-ray spectroscopy (EDX), micro-Raman spectroscopy (m-RS), and Fourier-transform infrared spectroscopy (FT-IR). The findings reveal that the red, yellow, green, and blue pigments are identified as lead red, lead chromate yellow, emerald green, and ultramarine, respectively. The white pigment is determined to be a combination of chalk and lead white or anglesite. Considering the production period of the yellow and green pigments, it is inferred that architectural paintings underwent restoration or repainting during the late Qing Dynasty. The analysis of the binder in the pigment using pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) reveals that the binder employed is a protein-based glue. Additionally, the detected presence of Heat-bodied tung oil suggests a potential connection to traditional Chinese painting techniques on wooden surfaces. This discovery not only contributes to the historical research of the Confucius Temple but also provides crucial data for the conservation and restoration efforts of this culturally significant heritage site. [ABSTRACT FROM AUTHOR]

This study introduces the application of the Diffusion Variational Autoencoder (D-VAE), a deep learning technique, for predicting stock prices in the Indonesian stock market. With the challenges presented by market volatility and complex data distributions, D-VAE is explored for its capability to encapsulate uncertainty and model complex distributions. This study is significant as it explores the potential of D-VAE in the context of the Indonesian stock market, which has not been widely studied before. Historical stock data from Yahoo Finance was collected over one year and preprocessed for training and validation of the model. The model is trained with an architecture designed to allow tuning of the latent space, utilising ReLU and linear activation functions for the encoder and decoder. The model's performance is evaluated using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared metrics, yielding results that highlight the model's capability to enhance the accuracy of stock price predictions. By leveraging machine learning techniques in stock price prediction models, this study underscores the significant contribution such approaches can make to informed and successful investment decisions underpinned by robust data. [ABSTRACT FROM AUTHOR]

It is quite remarkable how solo bamboo drifting (originated from indigenous boating in southwestern China) has transformed into a captivating and innovative sport when compared to more conventional forms of competition. This study focuses on the competitive element of solo drifting on bamboo, which involves direct contact with the terrain. Instead, the focus is on studying a variety of oar-rowing strategies and understanding how successful athletes achieve victory in competitions. The primary objective of the research is to offer training advice that is supported by empirical evidence for the participants/athletes involved in the sport. The investigation involves five athletes who were selected for the case studies based on their performance in the 60-metre straight track event. In this context, the study tools used include literature analysis, observation, video playback, and statistical mathematics. The Dartfish sports technique video analysis system, a specialised app, enables you to scrutinise movements closely and compare them to your athletes to evaluate their pole-rowing techniques. The results suggest that the athletes' technique plays a vital role in determining the competitive outcomes in solo bamboo drifting. Factors such as their position on the bamboo, the angle at which the pole enters the water, the management of rowing power and speed, and the control of the bamboo's direction all contribute to the overall performance. By maintaining proper posture and employing effective pole-rowing techniques, you can greatly reduce water resistance and increase the speed of the bamboo's forward movement. Efficiently managing the force applied during rowing is crucial for maintaining the stability of the bamboo and maximising the overall effectiveness of the rowing process. Furthermore, the competitors' yawing phenomena during the competition have a negative impact on rowing efficiency, disrupting the rhythm and stability of the rowing motions. This study employs comparative analysis to investigate the pole-rowing tactics of three athletes, emphasising differences in their movement intricacies and effectiveness during the rowing cycle. The results of this study provide a solid basis for future training efforts. This paper provides a comprehensive set of training suggestions derived from the research findings. These recommendations involve incorporating cutting-edge technology for technical monitoring and analysis, improving physical training to optimise rowing efficiency, and honing direction control in different competition conditions. The purpose of these guidelines is to help athletes enhance their pole-rowing techniques and elevate their performance in competitions. This research not only provides theoretical justification for the technical training of solo bamboo drifting but also offers practical instructions for the scientific advancement and widespread adoption of the sport. [ABSTRACT FROM AUTHOR]

More than 90% of grasslands in China are in a state of degradation or ongoing degradation. Some difficulties have posed on the sustainable supply and feed of animal husbandry. At the same time, the ever-increasing degree of ecological environment damage has seriously threatened sustainable animal husbandry and ecological environment. Therefore, it is crucial to improve the degraded grasslands at present. However, there is a great research gap among scientific theory, practice, technology, and machinery of grassland improvement in China. The overall promotion can be limited in the grassland improvement. In this review, 24 common technologies were summarized and then classified for grassland improvement. The characteristics and applicable conditions were given for the different technologies. A systematic analysis was implemented on the grassland improvement techniques. A classification framework was then established for a comprehensive process pipeline of grassland improvement. Four types of degraded grassland improvement were proposed, including less damage and improvement (improvement type I), the optimal growth environment of forage (improvement type II), the promoting development and improvement of forage (improvement type III), and the changing biological community and improvement (improvement type IV). The improvement techniques, processes, and machinery were analyzed within the framework of the four types of grassland improvement. The research and development direction of machinery and equipment were proposed after that. Four aspects were further determined in grassland improvement, including the intensive protection of grassland, the mechanized process of degraded grasslands, the new types of improvement machinery, as well as decision-making on grassland protection and improvement. The synergistic effect of these four aspects can bring positive promotion to the improvement of degraded grasslands. Since the current situation of grassland degradation cannot be fully curbed, the following work should be focused on the future grassland protection and mechanized improvement of degraded grasslands. 1) To strengthen grassland protection, the prevention of grassland degradation in the path of combining grass and land. 2) In grassland improvement, the existing no-till live broadcast can be optimized to develop compound machinery. 3) In grassland mechanization, the fundamental research should be strengthened for the technical improvement machinery, in order to fully leverage the technological advantages. 4) Grassland protection and improvement policies support and technical guidance to grassroots should be strengthened to enhance their enthusiasm for grassland protection and improvement. The technology schemes of degraded grassland improvement, machinery selection, and relevant policies can provide a strong reference for the research and development of grassland improvement machinery and equipment. [ABSTRACT FROM AUTHOR]

Stock investment is now a popular choice for many individuals and business entities. To optimize profits in investing, a deep understanding of price movements, timing, and accurate predictions in trading is required. The Long Short-Term Memory (LSTM) algorithm, a type of neural network suitable for time series data such as stock prices, can recognize complex temporal patterns in financial data. This algorithm has the potential to help investors and financial analysts predict BBRI stock prices more accurately. The purpose of this research is to predict the closing price of BBRI stock using the LSTM algorithm. This system can also conduct technical analysis with various indicators to understand the characteristics of the financial market. The research data includes BBRI stock prices from January 2006 to the present, with closing prices as the main variable. The research results show good model performance with a Mean Squared Error (MSE) of 0.000279, Mean Absolute Error (MAE) of 0.0133, and Root Mean Squared Error (RMSE) of 0.0167 on the training data. This reflects the model's level of accuracy against the training data. Although there is a slight increase in the validation data, these values remain within an acceptable level, indicating the model's ability to recognize data patterns that have not been seen before. [ABSTRACT FROM AUTHOR]

The issue of successful investment has been addressed since the beginning of the financial markets. For investors and the academic community, there is a strong motivation to test the reliability of models for predicting the development of prices on these markets. Our goal is to determine which models are optimal for short-term predictions during periods of high market volatility, and what their reliability is. A number of studies indicate that methods of technical analysis are a suitable approach for their ability to be as effective as modern methods, user-friendliness, and their ability to be applied by basic software. The price prediction reliability for the first two weeks of March 2022 is tested on the case of copper based on three conventional forecasting strategies: a) linear regression, b) naive forecasting, and c) exponential smoothing, with the frequencies of data being the daily prices of the preceding month, the daily prices of the preceding three months, and the annual average monthly prices of the preceding 13 months. The results show that linear regression is the most suitable model with the frequency distribution of daily historical prices for the period of three months (MAPE = 4.16%) and average monthly data from the preceding year (MAPE = 4.37%). Predictions based on the daily frequency of data from the period of one previous month achieved the worst predictive results for all the models. The outcomes of this study, reflecting conditions of high market uncertainty, can help investors minimise risks and maximise returns. They demonstrate which method of technical analysis is suitable in terms of predictive power and the choice of data frequency under high market volatility. [ABSTRACT FROM AUTHOR]

A trading system in any stock market is built on long-term, intermediate-term, and short-term indicators. Some 'lagging' indicators, such as the simple and exponential moving averages, can be used to determine the direction of a medium- to long-term trend. Some 'leading' oscillators, on the other hand, can tell a trader whether or not a trend is losing momentum. This paper examines how well moving average envelopes and Bollinger Bands measure stock price volatility, and how useful these technical analysis tools are for short-term horizons. The paper then attempts to evaluate the speed of these indicators in order to explain the sensitivity and response time of data collected from a secondary survey in the Indian capital market. The article concludes that moving average envelopes outperform Bollinger Bands in real trading settings, since technical trading rules are generally designed for short-term investments. Bollinger Bands can detect abrupt price fluctuations, however they are not more effective than moving average envelopes to measure profitability. [ABSTRACT FROM AUTHOR]