Fundamental Analysis Research Articles

Microstructural and rheological influence of different strategies to mitigate oil migration in chocolate pralines during storage in limiting conditions

Chocolate pralines are metastable systems in which oil migration, particularly at high storage temperatures, represents the main contributor to quality loss. In this study, two strategies were compared to retard this migration respect to a control sample (C); the use of a barrier consisting of cocoa butter high-melting (CB) and the use of an oleogel realized with beeswax and sunflower oil (CA). Samples were stored in thermal abuse conditions (28 °C) and subjected to: microstructural, rheological and ability to retain oil in the filling analysis and colorimetric evaluation. Microstructure highlighted the highest percentages of oil in CA samples filling during storage, respect to C and CB, as confirmed by oil release. According to fundamental rheological analysis, C and CB showed the highest filling yield stress values respect to CA, which showed a linear and homogenous trend. Dynamic rheological analysis underlined the presence of an increase in the elastic modulus during storage, suggesting matrix aggregation and gummy filling cream, which was lowest for CA, supporting the positive effect exhibited by this barrier. For empirical-imitative analysis, CA showed a more spreadable and creamy filling. Results revealed the ability of beeswax oleogels to improve quality attributes of pralines during storage, also at higher temperatures.

Isolation and characterization of Thymus serpyllum L. essential oil and mass fragmentation analysis of major constituents

ABSTRACT The essential oils were isolated from Thymus serpyllum L. leaves, stem, flowers and whole plant using hydrodistillation technique and % yield was determined. The chemical constituents were studied using Gas Chromatography-Mass Spectrometry and Gas Chromatography-Flame Ionisation Detection. The analysis revealed the presence of 38 compounds in the essential oils obtained from different plant parts. Identification of compounds was achieved by detailed analysis of mass spectrum and retention indices. The major constituents of essential oil from the whole plant were thymol (35.25%), p-cymene (27.57%), thymol acetate (8.03%) and γ-terpinene (6.92%). In leaves they were thymol (45.59%), p-cymene (24.26%), thymol acetate (6.41%), and β-bisabolene (5.91%); whereas in flowers these were thymol (68.74%) and thymol acetate (7.40%) and in the stem, these were thymol (55.15%) and thymol acetate (8.01%). Specifically, it is the first report wherein essential oil constituents of T. serpyllum L. whole plant, leaves, flowers, and stems have been analyzed separately. Further, fundamental analysis of complex mass fragmentation patterns of terpenes and terpenoids using EI-MS has been achieved and the logical reason for the fragmentation pattern has been elaborated. Detailed elaboration of the mass fragmentation pattern may strengthen the EI-MS-based compound identification of terpenes and terpenoids from essential oils.

A Fundamental and Technical Analysis of the US and Chinese Stock Markets

This paper provides a comprehensive comparison between the stock markets of the United States and China, the two largest economies globally. The study examines key aspects such as market mechanisms, investor strategies, and market efficiencies. The U.S. stock market, characterized by its large market capitalization and robust growth, operates through dealer and auction market mechanisms with a strong regulatory framework and high openness to foreign investors. In contrast, the Chinese stock market, having developed more recently, primarily uses auction market mechanisms and imposes daily price limits to manage volatility. Historical developments show the U.S. market's long history of financial development and crisis management, while China's market has seen rapid development since economic reforms began in 1978. The analysis of market efficiency through fundamental and technical analysis reveals that the U.S. market more effectively reflects company performance in stock prices compared to the Chinese market, which still shows areas for potential improvement. This comparison highlights the distinct characteristics and developmental paths of the U.S. and Chinese stock markets, providing valuable insights for investors and policymakers in understanding the differences and guiding future market development and investment strategies.

Fundamental experimental analysis of the ignition risk of sparks generated during collisions between small UAV propellers and personal protective equipment

Small unmanned aerial vehicles (UAVs) are increasingly utilized in a variety of environments, including those containing flammable substances, where the risk of ignition is a significant concern. In this study, we investigate the risk of sparks generated by UAV propeller collisions with personal protective gear to ignite these gases. We utilized a high-speed infrared camera to measure the temperatures of sparks produced during such collisions. The experiments involved collisions between various types of protective gear and rotating propellers, during which spark generation was observed upon impact. The high-speed infrared camera recorded the spark temperatures at the moment of impact. The sparks produced by these collision reached temperatures ranging from 375 [°C] and 900 [°C], exceeding the auto-ignition temperatures of hydrogen, diesel oil, and gasoline vapors. Notably, even protective gear that did not produce visible sparks reached temperatures up to 375 [°C], which surpasses the auto-ignition temperatures of some flammable substances, such as gasoline and kerosene. Based on the experimental results, we discuss the potential risks of igniting hydrogen, diesel oil, gasoline, and kerosene due to propeller collisions with protective equipment.

Output Regularization With Cluster-Based Soft Targets.

While supervised learning of over-parameterized neural networks achieved state-of-the-art performance in image classification, it tends to over-fit the labeled training samples to give inferior generalization ability. Output regularization deals with over-fitting by using soft targets as additional training signals. Although clustering is one of the most fundamental data analysis tools for discovering general-purpose and data-driven structures, it has been ignored in existing output regularization approaches. In this article, we leverage this underlying structural information by proposing Cluster-based soft targets for Output Regularization (CluOReg). This approach provides a unified way for simultaneous clustering in embedding space and neural classifier training with cluster-based soft targets via output regularization. By explicitly calculating a class relationship matrix in the cluster space, we obtain classwise soft targets shared by all samples in each class. Results of image classification experiments under various settings on a number of benchmark datasets are provided. Without resorting to external models or designed data augmentation, we get consistent and significant reductions in classification error compared with other approaches, demonstrating that cluster-based soft targets effectively complement the ground-truth label.

Sentiment to Stocks: Rule-Based and Deep Learning Sentiment Analysis for LSTM-Driven Stock Prediction

How to accurately predict stock prices is a persistent problem in the financial realm. According to behavioral economics, human actions in the stock market tend to be irrational, emotional, and easily misled. In such context, this papers approach involves adopting various sentiment analysis models in subsequent sections of the paper, which aims to synthesize a precise methodology for forecasting stock prices aligned with the available data, namely structured technical analysis, while considering and quantifying investor sentiment, namely unstructured fundamental analysis. This work points out that by adopting sentiment analysis using the VADER and FinBERT models separately, there are accuracy improvements in both integration of VADER or FinBERT and transactional data compared with merely doing stock price prediction by forecasting using historical data alone. This outcome resemble an valuable proposition of modeling and prediction on irrational human behavior. Nevertheless, this paper provides insights on future possible enhancements in this area, which analyzing additional emotional dimensions in textual data and recognizing the multifaceted nature of human emotions is needed.

The efficacy of Relative Strength Index (RSI) in KLSE market trade

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.

Effect of Investment Decisions on Efficiency of Deposit Taking Savings and Credit Cooperative Societies in Nairobi County, Kenya

With growing competition globally, SACCOs are directing their energies on investments to enhance their efficiency and so as to survive extreme competition. However, the decision to invest is subjective and a wrong investment decision can lead companies even to bankruptcy. Investment choices can also be made in compliance with the conditions in the markets, the portfolio level divergence, the results of fundamental and technical analysis, along with what the investors and managers expect and prefer. This study’s intent was to determine how investment decisions impact the efficiency of deposit taking SACCOs in Nairobi. The study’s population was all the 43 DT-SACCOs in Nairobi County, Kenya. The independent variable for the study was investment decisions with four measures; investment in real estate, investment in government securities, investment in fixed deposit and investment in shares. The control variables were liquidity, firm size and liquidity. Firm efficiency was the response variable which was the primary focus of the study. The study utilized secondary data from 2014 to 2018 (5 years) on annual basis. A descriptive cross-sectional design together with the multiple linear regression model were used for the analysis of the variables. For this analysis the researcher used the SPSS version 21 software. The findings gave an R-square value of 0.317 meaning that 31.7 percent changes in the efficiency of DT-SACCOs in Nairobi, Kenya can be attributed to the seven selected predictor variables while 68.3 percent changes of efficiency of DT-SACCOs was attributable to other factors outside the scope of the study. It was further revealed that the predictor variables showed a strong correlation with efficiency (R=0.563). ANOVA results show that the F statistic was substantial at 5% level with a p=0.000. This shows the model was suitable for the study to provide an explanation of the variables. The results also showed that investment in real estate, investment in government securities and investment in shares produced positive and statistically substantial values for this study while investment in fixed deposits, liquidity, firm size and age were found to be statistically insignificant determiners of efficiency. This study recommends that measures should be put in place to enhance investment in government securities, real estate and shares as these three have a substantial influence on efficiency of DT-SACCOs in Nairobi. Keywords: Investment decisions, firm efficiency, liquidity, firm size, firm age

Adopting automated bug assignment in practice — a longitudinal case study at Ericsson

[Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR’s first bug assignment without human intervention happened in April 2019. [Objective] Our study evaluates the adoption of TRR within its industrial context at Ericsson, i.e., we provide lessons learned related to the productization of a research prototype within a company. Moreover, we investigate 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. [Method] We conduct a preregistered industrial case study combining interviews with TRR stakeholders, minutes from sprint planning meetings, and bug-tracking data. The data analysis includes thematic analysis, descriptive statistics, and Bayesian causal analysis. [Results] TRR is now an incorporated part of the bug assignment process. Considering the abstraction levels of the telecommunications stack, high-level modules are more positive while low-level modules experienced some drawbacks. Most importantly, some bug reports directly reach low-level modules without first having passed through fundamental root-cause analysis steps at higher levels. On average, TRR automatically assigns 30% of the incoming bug reports with an accuracy of 75%. Auto-routed TRs are resolved around 21% faster within Ericsson, and TRR has saved highly seasoned engineers many hours of work. Indirect effects of adopting TRR include process improvements, process awareness, increased communication, and higher job satisfaction. [Conclusions] TRR has saved time at Ericsson, but the adoption of automated bug assignment was more intricate compared to similar endeavors reported from other companies. We primarily attribute the difference to the very large size of the organization and the complex products. Key facilitators in the successful adoption include a gradual introduction, product champions, and careful stakeholder analysis.

The Impact of Fundamental Analysis on the IT Industry: A Comparative Study Using Wipro, TCS, and Infosys as Examples

The Impact of Fundamental Analysis on the IT Industry: A Comparative Study Using Wipro, TCS, and Infosys as Examples

The Influence of Earning Per Share (EPS) and Price Earnings Ratio (PER) on Stock Prices in the Food and Beverage Sector on the Indonesian Stock Exchange (BEI) for the 2015-2019 Period

The objective of this study is to examine the impact of earnings per Share (EPS) and Price Earnings Ratio (PER) on the stock prices of companies in the food and beverage industry listed on the Indonesia Stock Exchange (BEI) from 2015 to 2019. EPS is utilized as a metric to gauge the profitability of each share, whereas PER characterizes the market's assessment of the earnings generated per share. The research employed a descriptive verification analysis method, utilizing a sample of seven prominent organizations within the researched sector.The data utilized is derived from the yearly financial reports of each company, specifically secondary data. The investigation employed multiple regression approaches to examine the hypothesis regarding the impact of EPS and PER on stock prices. The test utilizes the coefficient of determination to ascertain the extent to which the regression model can account for the fluctuations in stock prices.The research findings indicate a substantial correlation between EPS (Earnings Per Share), PER (Price Earnings Ratio), and firm share prices within the food and beverage industry on the IDX (Indonesia Stock Exchange). The R^2 value of 0.753 suggests that 75.3% of the changes in stock prices can be accounted for by the independent variables, EPS and PER. In this industry, share prices are primarily influenced by the profitability per share and market valuation.In addition, this study emphasizes that additional variables not investigated in this research can significantly impact stock prices, including macroeconomic conditions, market sentiment, government regulations, and other external factors. Stable macroeconomic conditions can lead to more favorable circumstances for increased stock values, whereas government policy or market sentiment alterations can profoundly influence a company's stock performance.The recommendations derived from this study are intended for investors and corporate executives operating in the food and beverage industry on the IDX. Investors are advised to consider the earnings per share (EPS) and price-to-earnings ratio (PER) components of their fundamental analysis to make better-informed investment choices. It is recommended that companies prioritize enhancing their financial performance and ensuring openness in their financial reporting to boost market trust and raise the value of their shares.Subsequent investigations might broaden the range by including supplementary variables like Dividend Yield or Return on Equity (ROE) while doing a more extensive analysis of qualitative elements that impact market perceptions of EPS and PER. This research contributes to understanding the factors that impact share prices in the Indonesian capital market, particularly within the food and beverage industry.

Optimized Deep Learning Model Architecture for the Feature Extraction to Predict Trend in Stock Market

Predicting stock trends is a complex task influenced by various factors such as market sentiment, economic indicators, and company performance. Analysts often employ technical analysis, studying historical price patterns and trading volumes, as well as fundamental analysis, assessing financial statements and industry trends. Deep Learning models have also gained popularity for predicting stock trends, using algorithms to identify patterns and relationships in large datasets. Deep learning algorithms, particularly neural networks, excel at recognizing intricate patterns and relationships within complex datasets, making them well-suited for predicting stock prices, identifying trends, and managing risk. Hence, this paper proposed a Bird Swarm Optimization ARIMA LSTM (BSO-ARIMA-DL) model for stock trend prediction. The proposed BSO-ARIMA-DL model performance is applied in the company datasets Apple, Amazon, and Infosys for stock trend prediction. With the proposed BSO-ARIMA-DL model features are optimized for the identification of features in the dataset for the evaluation of optimal features. Upon the estimation of features, the ARIMA model with the LSTM architecture is implemented for the stock trend analysis. The proposed BSO-ARIMA-DL model deep learning model is implemented for the stock trend prediction in the companies. The results demonstrated that the proposed BSO-ARIMA-DL model exhibits a minimal error of ~10% minimal to the conventional ARIMA model.

Fundamental Diagram and Stability Analysis of Mixed Traffic Considering Heterogeneous Car-Following Behaviors and Platoon Factors

Abstract With the advancement of connected automated vehicles (CAVs), it is anticipated that mixed traffic environments, where human-driven vehicles (HVs) coexist with CAVs, will become prevalent in the future. The study aims to investigate the impact of heterogeneous car-following behaviors of HVs (e.g. aggressive, normal, and mild driving styles) and platoon factors of CAVs (i.e. platoon intensity and maximum platoon size) on the fundamental diagram and stability of mixed traffic. Firstly, a Markov chain approach is employed to describe the probability distributions of different leader-follower combinations, enabling us to construct a comprehensive mixed traffic model. Subsequently, a general modeling framework based on the mixed traffic model is established to examine the effects of heterogeneous car-following behaviors and platoon factors on the fundamental diagram and stability of mixed traffic. The results from numerical experiments reveal several findings: (i) an increase in the proportion of aggressive driving style enhances both the capacity and stability of mixed traffic; (ii) larger platoon intensity and maximum platoon size contribute to improved capacity, particularly in scenarios where a large fraction of HVs exhibit aggressive driving behavior; (iii) platoon intensity has a positive impact on traffic flow stability, while larger maximum platoon size leads to reduced stability; (iv) increasing CAV penetration without considering platoon intensity may lead to reduced stability compared to scenarios with a substantial proportion of aggressive drivers.

Profitability Analysis of Long Coriander Crop Share by Small-Scale Producers in Battambang, Cambodia

This paper is intended to introduce Long Coriander, the best-known leafy vegetable in Southeast Asia. It is a member of the Coriander family. It has a strong coriander scent and adds flavor to common ingredients in Central and Southeast Asian cuisine, particularly in soups, curries, and noodle dishes. Beyond its culinary uses, Long Coriander also has many benefits in medicine. To meet the criteria of this study, researchers seek to investigate the profitability of the Long Coriander crop for small-scale producers in Cambodia's Battambang Province. It weighs the revenue from producing Long Coriander against the costs of planting and harvesting its crop. The study also illustrates the estimated total revenue and costs for each planting. Producing Long Coriander may beneficially result in large sums of earnings for farmers, with a profit margin of approximately 57%. Furthermore, according to an economic efficiency (EE) analysis, the cost-to-revenue ratio is higher than 1. This proves that the total amount of revenue made from the sale of Long Coriander outweighs the costs of planting and harvesting, so small-scale producers are convincingly supposed to earn more money from this crop. This research study, through dedication and commitment, aims to address the profitability potential of Long Coriander production by integrating the fundamental economic analysis methods to gather rich data that may enhance and influence further agricultural research.

Neural Network forAutomatic Encryption Using Key Component Analysis to Detect Network Intrusion inCloud Computing

Cloud computing is one of the networks that has attracted more people's attention than other networks in today's world, and the reason for this is that it stores data in itself. This network consists of different computers that are scattered in different places and each user will be able to be a member in this space by registering in it. Among the many important issues related to the environment is the issue of security, which has become a very complex challenge. In order to secure networks, tools have beencreated, and one of these tools is the use of artificial intelligence methods to establish security in this environment. In most of the researches carried out in this field, either the duration of the algorithm execution was very long or the researches did not have enough accuracy, so article, we tried to detect the penetration in cloud computing. This was done in two different scenarios for feature selection and reduction. In one scenario, we reduced the dimensions using the fundamental analysis method, and in the other scenario, we did this using the bat meta-heuristic algorithm. In both scenarios, we used self-encrypting neural network for classification, and the structure of this network was the same in both scenarios. The reason for using these two scenarios was that we want to compare the accuracy obtained and the execution time of the algorithm for the dimensionality reduction method with principal component analysis and meta-heuristic algorithm. , the obtained results showed that the accuracy of the dimension reduction method with the meta-heuristic algorithm was more accurate than the principal component analysis method, but its execution time was approximately 13 minutes more than the principal component analysis method.

Technical and fundamental analysis of insurance stock prices on the Indonesian Stock Exchange (2018–2022)

Purpose: This study was conducted to determine the influence of fundamental (ROA, TATO, DER, NPM, and EPS) and technical (past share price, share volume, and past share volume) variables on share prices. in insurance companies listed on the IDX. Method: This study uses secondary data in the form of panel data, namely a combination of time series data for quarterly periods for five years, namely 2018-2022 and cross-sectional data from 11 insurance companies on the Indonesia Stock Exchange. The relationship between the research variables was tested using a panel data regression analysis. Results: The t-test results from the fixed effects model show that TATO and DER have a positive and significant influence on stock prices. ROA, NPM, and EPS do not significantly influence share prices. Past share prices and share volume have a significant positive influence on stock prices, whereas past share volume has a significant negative influence on share prices. The F-test results from the fixed effects model show that there is a significant influence of the independent variables, namely fundamental and technical variables, on stock prices. Limitations: This study only analyzes insurance companies (other than reinsurance) listed on the IDX from to 2018-2022, issue quarterly financial reports continuously from to 2018-2022, and have never been delisted during that period. Contribution: The contribution of this research is that it considers investors in making investment decisions, especially in selecting insurance company shares.

Shape Morphing of Re‐Entrant Honeycomb Metamaterials for Linear Auxetic Behaviors

A re‐entrant honeycomb structure stands out as one of the most prevalent auxetic metamaterials, characterized by its negative Poisson's ratio. While re‐entrant auxetic structures are capable of achieving tunable Poisson's ratios, they tend to vary with the magnitude of applied strain, thereby exhibiting nonlinear auxetic behaviors. This study proposes a novel re‐entrant structure aimed at achieving linear auxetic behavior by mathematically modifying the shape of a re‐entrant cell. To achieve this objective, a sigmoid‐based shape morphing function is introduced to modify the morphology of the hinge connections within the re‐entrant honeycomb cell. The deformation behavior of the shape‐morphed re‐entrant cell is investigated using finite element analysis (FEA), with variations in the morphing parameter. Two FEA models, namely the unconstrained and constrained models, are developed for fundamental analysis of cell deformation and experimental validation, respectively. Compared to the pure re‐entrant honeycomb structure, the proposed shape morphing reduces the relative variation of Poisson's ratio by 70%, while maintaining its magnitude higher than 1.0. This achievement of linear auxetics with a high Poisson's ratio has the potential to broaden the applications of the proposed auxetic structures to various functional components, including sensors with high linear sensitivity and soft actuators with tunable deformation characteristics.

Improved rapid gas decompression of novel NBR-CTS polymer blends

Nitrile rubber (NBR) and polysulfides are two classes of polymers known for their resistance against swelling when in direct contact with hydrocarbon-based oils and non-polar solvents. In this study nitrile rubber (NBR) and a cyclic tetrasulfide (CTS) are combined with a goal of achieving a rubber blend that has superior properties than of each component. The role of CTS in the blend is discussed, as it presents several functions. This study represents a fundamental analysis of NBR-CTS blends, starting with polymeric phase analysis by Scanning Electron Microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX). Thermodynamic compatibility studies are deployed in this study to illustrate the materials’ compatibility. Mechanical properties with an emphasis on Payne effect were also studied for blends of NBR-CTS. The synergistic effect found between NBR and CTS can be further expanded in a practical application of rapid gas decompression test as a side-by-side comparison for NBR and NBR-CTS blend compounds. This type of polymer blend represents a potential replacement of fluoroelastomers.

Understanding Investment Decision-making: A Qualitative Inquiry into High-Frequency Trading, Investment Strategies, and Portfolio Performance in the Financial Market

This qualitative study explores investment decision-making in the context of high-frequency trading (HFT), investment strategies, and portfolio performance within the financial market. The research aims to provide insights into the complex dynamics influencing investment decisions and their implications for market participants. Adopting a qualitative research design, the study conducts a comprehensive review and analysis of existing literature from academic journals, books, and conference proceedings. The data collection process involves synthesizing insights from diverse sources to uncover underlying themes, patterns, and contradictions surrounding investment decision-making. Through thematic analysis and constant comparison, the study identifies key findings related to the role of HFT, diversity of investment strategies, and evaluation of portfolio performance. The results highlight the transformative impact of HFT on market liquidity, efficiency, and stability, while also raising concerns about market fragmentation and systemic risks. Additionally, the study explores the evolution of investment strategies, ranging from traditional approaches like fundamental analysis to emerging techniques such as algorithmic trading and quantitative strategies. It underscores the importance of tailoring investment strategies to individual preferences and market conditions for optimizing portfolio performance. Furthermore, the study evaluates alternative frameworks such as post-modern portfolio theory (PMPT) and factor investing, offering promising avenues for enhancing portfolio resilience and risk-adjusted returns. Overall, the research contributes to a deeper understanding of investment decision-making processes and informs stakeholders in the financial industry about effective strategies for navigating the dynamic landscape of the financial market.

Utilizing Genetic Algorithms in Conjunction with ANN-Based Stock Valuation Models to Enhance the Optimization of Stock Investment Decisions

Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits.