Technical Analysis: Does Recent Market Data Substantiate the Efficient Market Hypothesis?

The efficient modeling of execution price path of an asset to be traded is an important aspect of the optimal trading problem. In this paper an execution price path based on the second order autoregressive process is proposed. The proposed price path is a generalization of the existing first order autoregressive price path in literature. Using dynamic programming method the analytical closed form solution of unconstrained optimal trading problem under the second order autoregressive process is derived. However in order to incorporate non-negativity constraints in the problem formulation, the optimal static trading problems under second order autoregressive price process are formulated. For a risk neutral investor, the optimal static trading problem of minimizing expected execution cost subject to non-negativity constraints is formulated as a quadratic programming problem. Whereas, for a risk averse investor the variance of execution cost is considered as a measure for the timing risk, and the mean–variance problem is formulated. Moreover, the optimal static trading problem subject to stochastic dominance constraints with mean–variance static trading strategy as the reference strategy is studied. Using Static approximation method the algorithm to solve proposed optimal static trading problems is presented. With numerical illustrations conducted on simulated data and the real market data, the significance of second order autoregressive price path, and the optimal static trading problems is presented.

Stock market price prediction involves forecasting future stock values using techniques like technical analysis, fundamental analysis, and machine learning. Technical analysis looks at historical price movements, while fundamental analysis evaluates a company's financial health. Machine learning models use large datasets to identify patterns. This paper introduces a vital approach to time series analysis and forecasting, emphasizing its widespread practical applications. Time series data consists of an ordered sequence of data points recorded at equal time intervals. The stock market, one of the most complex financial systems, experiences significant fluctuations in stock prices over time. Stock market forecasting aims to uncover market trends and help investors maximize returns while minimizing risks. Given the market's sensitivity to rapid changes, this research focuses on developing innovative approaches to forecast stocks for high profits. Specifically, this paper analyzes the time-series data of the Indian stock market and builds a statistical model to predict future stock prices efficiently

The Role of Technical Analysis in Retail Investor Trading

The paper presents an indicator created to reflect the long-term stability in the trend of real growth of the global economy, based on the current market data through a basket of currencies with a government bond yields based premium. The results suggest that the indicator is adequate to be considered as a benchmark for widespread indexation, with a potential hedging for diverse types of contracts and assets against price level volatility and currency risk on an international scale.

This paper presents an updated critical review about several attempts to contribute methionine (Met) to the world market with an emphasis on fermentation processes, especially from natural biological sources. Analytical methods for the determination of methionine are reviewed as well as applications in feed, food, pharmacy, and medicine. Fermentation studies published within the last five decades are elucidated critically, mainly with respect to the sulfur balance, substrate yield, and the analytical validity. From all the published fermentation data, it can be concluded that up to now no more than 5 g/L methionine are achievable without using genetically modified organisms (GMOs). The highest L-methionine concentration from natural sources reached so far amounts to 35 g/L and is published as a patent using a GMO of Escherichia coli. The review closes with a comprehensive overview of the role and activities of global methionine manufacturers. Some current market data is also presented.

Hydrogen is considered a key component of the renewable energy transition for the 21st century, with potential applications using fuel cells in the transportation sector, decentralized heating systems, and energy storage. However, the conversion from fossil fuels to hydrogen implies comprehensive research to address technological and socio economic challenges, enabling its widespread adoption. This paper discusses the economics of fuel cells. A cost analysis of the polymer electrolyte membrane fuel cells (PEMFC) is performed, and current market data and developments are presented.

The transition from a vertically integrated industry to a horizontally integrated open market system changes the operational planning activities of generation companies (GENCOs). This transition, along with the strategic bidding decision process that must be employed by a GENCO, changes the objective from cost minimization to profit maximization. This change requires considering not only the technical aspects of unit operation, such as capacity limits, but also information about other market participants and the volatility of market prices. These additional factors are significant, especially in an oligopolistic market, because they influence the amount of electricity bought and sold, thus affecting net profit. This paper proposes an approach that data mines historical and current market data. The context is a deterministic four-market-participant environment. This model uses an auction simulator for 120 time periods. Results suggest that the data mining approach be extended to the reduction of epistemic uncertainty in VaR/PaR inferences using information gap theory.

The European Union's decarbonisation strategy emphasizes the full integration of its electricity markets to enhance flexibility, competition, and overall efficiency. This study compares the existing Flow-Based Market Coupling (FBMC) mechanism with a theoretical model that incorporates all grid restrictions endogenously in the market clearing, avoiding heuristic assumptions. We address two primary research questions: how would market outcomes, prices, and the generation mix evolve under perfect market coupling, and what challenges persist in the current FBMC design? A three-month case study during the European energy crisis in 2023 provides the empirical basis. Our results reveal significant price convergence-with average prices decreasing by nearly 30 €/MWh-improved integration of renewable energy sources, and enhanced cross-border trade. However, the simplified grid representation to reduce the complexity of the problem may lead to congestion and welfare losses, underscoring the need for more efficient models and more active consideration of congestion management in the cross-border capacity allocation in the integrated European electricity market.

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock’s price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. Firstly, we propose a new pattern network construction method for multivariate stock time series. The price volatility combination patterns of the Standard & Poor’s 500 Index (S&P 500), the NASDAQ Composite Index (NASDAQ), and the Dow Jones Industrial Average (DJIA) are transformed into directed weighted networks. It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market. Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K‐nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next‐day volatility patterns of a single stock. The results show that the optimal models corresponding to the two algorithms can be found through cross‐validation and search methods, respectively. The prediction accuracy rates for the three indexes in relation to the testing data set are greater than 70%. In general, the prediction ability of SVM algorithms is better than that of KNN algorithms.

Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated decision tree-adaptive neuro-fuzzy hybrid automated stock market trend prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Selected features are then subjected to dimensionality reduction and the reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market trend prediction. The proposed system is tested on four major international stock markets. The results show that the proposed hybrid system produces much higher accuracy when compared to stand-alone decision tree based system and ANFIS based system without feature selection and dimensionality reduction.

Purpose: Managing stock market risk and making an optimal investment decision in a stock market requires study- ing the dynamics of this market and analyzing the fluctuations of its benchmark index in order to avoid heavy damage in the event of crises. This paper aims to study and analyze the fluctuations of the main index of the Casablanca Stock Exchange "MASI" to explore its efficiency and stability in the normal financial context (especially before the recent pandemic crisis). Design/methodology/approach: To carry out this study, two methods are proposed, the first one, how evolves this index over time depending on some random data generation processes widely used for stock prices: A Random Walk with a drift RW(α) and an autoregressive process of order 1 AR(1). Based on the actual MASI returns series used (2007-2018), we estimate each equation parameter according to the process chosen to generate the artificial MASI returns series to know the most relevant data generation process in the case of The Moroccan financial market. The second method focuses on the technique of "simple moving average" as a method of stock prices fluctuation analysis to make its investment decision, choosing the proper order on the same series. Findings: The results and findings of our econometric study show, in the first method, that either the RW(α) or the AR(1) process cannot adequately model MASI fluctuation. However, the results of the second method affirm the utility of the simple moving average to identify trends, their strength and buy / sell signals, using some techniques known in this field, in order to make decisions and draw interpretations in investment terms and risk management, which can prove that the market is less efficient and stable. Research limitations/implications: An important implication of this study is the need to explore the efficient models to describe the MASI return series. Originality/value: This study offers empirical evidence in relation to the estimation of the econometric model to describe MASI and how to make adequate investment decisions in the Moroccan stock market. Moreover, contributes to future research to find other more appropriate models.

This research paper explores the effectiveness of fundamental and technical analysis in predicting stock market returns. While fundamental analysis emphasizes the intrinsic value of stocks based on economic indicators, financial statements, and broader macroeconomic trends, technical analysis focuses on historical price trends, trading volume, and market psychology to anticipate future movements. Drawing on recent academic studies, practitioner insights, and comparative case analyses across different markets, this paper critically evaluates the predictive power, inherent limitations, and practical relevance of each approach under varying market conditions. The findings suggest that while each methodology offers unique strengths, combining both analytical frameworks may provide a more robust and holistic investment strategy—particularly in volatile, uncertain, or information-asymmetric markets. Additionally, the integration of artificial intelligence and machine learning with these traditional methods is highlighted as a promising advancement in enhancing forecasting accuracy and decision-making efficiency in modern financial markets. Keywords: Fundamental analysis, technical analysis, stock returns, market prediction, investment strategies.

Mean-Reversion Across MENA Stock Markets: Implications for Derivative Pricing

PurposeThe credibility of valuation is enhanced by the use of quality and reliable property market data. Nonetheless, the availability of quality and reliable market data remains a problem for valuers in Ghana, as current market data sources are tainted with issues of reliability. The purpose of this study is to examine the possibility of establishing property market data banks in Ghana, to help solve the market data problem faced by valuers.Design/methodology/approachThis study adopted a mixed-method approach consisting of the identification and review of literature, interviews with officials from the Lands Valuation Division of the Lands Commission and a questionnaire survey of property valuers.FindingsThe study finds that it is possible for property market data banks to be established, and in the absence of facilitating legislation, partnerships among various stakeholders are the best way of achieving this. The study identifies the lack of initiative and the lack of partnerships among the various stakeholders inter alia as the major factors limiting the establishment of property market data banks. The findings imply a need for stakeholders to take initiative to establish a property market data bank aimed at improving the quality and reliability of market data to enhance the valuation practice.Originality/valueThe study asserts the possibility of creating a property market data bank in Ghana, notwithstanding the limiting factors. The findings will provide a basis for relevant institutions and agencies to take cooperative action for the establishment of property market data banks, towards enhancing the valuation practice in Ghana as well as in other developing countries. The study will also prompt research into various tools and mechanisms to be adopted towards the establishment of property market data banks through participatory means.

Using reported large short position data from Korea stock market, we studied stock price movements around the short covering trades. We found a significant negative relationship between the short covering trades and the stock return on the previous five trading days. trading days. It means that short sellers engage in contrarian trading when they cover short positions. Short coverings are associated with positive returns on the trading day, confirming that there is a positive transient price impact at the coverings of large short positions. We also found that stock prices decline steadily in the 60 trading days following short coverings. This indicates that some short sellers are forced to prematurely close out their positions, thus suffering opportunity losses. Particularly, when a sudden surge in stock prices causes involuntary large-scale short coverings to occur in the short squeeze, the price impact gets larger and short sellers bear greater opportunity losses. This study sheds light on short selling strategies in practice and market stabilizing policies. Key words: Short Covering; Contrarian; Price Impact; Timing Ability; Short Squeeze JEL Classification: G10, G14