Value Stocks Research Articles

“Superstitious” Investors

Abstract We reconsider the excess volatility puzzle through the lens of a model in which agents believe they can predict dividend growth when in fact they cannot. Besides excess volatility in the time series, the model explains the value premium, and the explanatory power of the value factor. In support of the model, we show that analysts’ earnings forecasts align with market valuation and that analysts are far more optimistic about growth stocks than they are about value stocks. Using both survey and price data, we show that the same mechanism can explain the excess returns earned by investing in high-interest rate currencies. JEL G12, G15, G41

Decoding Stock Price Movements: How Net Profit Margin and Debt-to-Equity Ratio Drive Value, with Earnings per Share as the Game-Changer

This research investigates the relationships between Net Profit Margin (NPM), Debt-to-Equity Ratio (DER), Earnings per Share (EPS), and stock prices within the context of the Indonesian stock market. Utilizing a quantitative analysis approach, the study employs panel data regression to assess the statistical significance of these financial metrics on stock valuations. The findings reveal that NPM has a meaningful and statistically significant positive impact on stock prices, indicating that higher profit margins are associated with increased investor confidence and market valuation. Conversely, the results show that DER does not significantly influence stock prices, suggesting that investors may perceive high debt levels as a risk that diminishes stock value. Furthermore, the study finds that EPS does not moderate the relationship between NPM and stock prices, nor does it strengthen the effect of DER on stock valuations. These conclusions contribute to understanding financial determinants in stock price movements, emphasizing the importance of profit margins while highlighting the limited role of leverage and earnings per share in this context. The research provides recommendations for investors and corporate managers and suggests avenues for future studies, including broader geographic comparisons and integrating qualitative factors into stock valuation analysis.

The Impact of the Russian-Ukrainian War on the Global Economy

The most unexpected thing happened just as the globe was beginning to recover from the COVID - 19 pandemic and there was hope for the economy to be rebuilt. The Russian invasion of Ukraine on February 24, 2022, was seen by the entire globe. The day of the invasion saw a decline in stock values. Not just the economy of Russia and Ukraine suffered during the initial months of the invasion, many other nations also suffered greatly because of the conflict. Food prices increased dramatically worldwide because of the conflict between these two nations. The cost of food increased as well as there was an increase in the price of crude oil. Thus, resulting in Global inflation. Numerous European nations placed harsh sanctions on Russia to lessen the impact of the conflict. However, this did not create much of an impact since These sanctions had a serious impact on both the European nations and the nations that were not involved in the conflict. This resulted in a serious inflation issue that affected both combatant and noncombatant nations worldwide. This paper focuses on the global economic effect of the Russia and Ukraine war and how the actions of one country can create a serious impact on the globe.

Cracking the Book Value Mystery: Understanding Earning Per Share, Price Earning Ratio, and Operational Efficiency in IDX80 Enterprises Indonesia

Research Aims: This study aims to elucidate the determinants of book value by investigating the effects of earnings per share (EPS), price-to-earnings (PER) ratios, and operational efficiency among companies listed in the IDX80 index from 2021 to 2023. Design/methodology/approach: The research utilizes a quantitative approach with an explanatory design, analyzing secondary data from the financial reports of 80 companies. Variables like EPS, PER, BV, and NPM are assessed using the Partial Least Squares (PLS) method via SmartPLS to uncover their relationships in the Indonesian capital market. Research Findings: Key findings highlight the significant impact of EPS on share valuation reflected in book value (BV) and the mediating role of NPM in this relationship. The study underscores the complexity of using PER alone to evaluate company worth within the IDX80 index, emphasizing the need for further exploration to enhance investment strategies and financial decision-making. Theoretical Contribution/Originality: This study enhances understanding of equity valuation in Indonesia's capital market by analyzing how EPS, PER ratios, and operational efficiency influence companies in the IDX80 index from 2021 to 2023. It integrates theoretical frameworks like signalling theory and mediation via NPM, showing that EPS significantly impacts share valuation through NPM. The research underscores limitations of using PER exclusively to evaluate company worth, emphasizing the intricate nature of stock valuation in the IDX80 index. Future studies should delve deeper into these factors to improve investment strategies and financial decision-making.

Predicting Market Share Patterns using LSTM

Forecasting stock prices is a significant challenge in the financial sector, marked by its complexity and the impact of numerous variables. In this paper, we undertake a comprehensive investigation into how machine learning algorithms can be effectively applied to address this challenge. Our main goal is to clarify the methodologies and strategies that can enhance the accuracy of stock market predictions. We explore the complex process of predicting stock values and scrutinize the various machine learning algorithms that have been suggested and employed for this purpose. By critically evaluating these algorithms, we aim to provide insights into their individual strengths and weaknesses, ultimately helping the reader make an informed decision about the most appropriate algorithm for their specific forecasting requirements. Beyond algorithm selection and attribute analysis, our review also considers external factors that can significantly influence stock prices. These factors include a broad range of variables, such as economic conditions, geopolitical events, corporate news, and market sentiment. Understanding the interaction between these external elements and stock market dynamics is essential for developing more robust and reliable prediction models.

Mathematical analysis of an anaerobic digestion model for biogas production from solid waste.

Municipal solid waste is one of the most significant sources of gas emissions. In a context of renewable energy production and greenhouse gas emission reduction, we focus on the amount of biogas produced by the anaerobic digestion of organic matter in a controlled landfill environment. We present a mathematical model describing this process from a microbiological and biochemical point of view, including the dynamics of the methane and carbon dioxide produced. A qualitative analysis of the dynamic system shows the existence of an infinite number of non-hyperbolic equilibria inducing an attractor that has been identified. Through numerical tests, we explore how the non-connectivity of the attractor resulting from the choice of growth functions can entirely transform the performance of the process, and highlight critical initial stock values that influence the amount of biogas produced.

Assessment of Carbon Stock and Sequestration Dynamics in Response to Land Use and Land Cover Changes in a Tropical Landscape

Quantitative analysis of LULC changes and their effects on carbon stock and sequestration is important for mitigating climate change. Therefore, this study examines carbon stock and sequestration in relation to LULC changes using the Land Change Modeler (LCM) and Ecosystem Services Modeler (ESM) in tropical dry deciduous forests of West Bengal, India. The LULC for 2006, 2014, and 2021 were classified using Google Earth Engine (GEE), while LULC changes and predictions were analyzed using LCM. Carbon stock and sequestration for present and future scenarios were estimated using ESM. The highest carbon was stored in forest land (124.167 Mg/ha), and storage outside the forest declined to 13.541 Mg/ha for agricultural land and 0–8.123 Mg/ha for other lands. Carbon stock and economic value decreased from 2006 to 2021, and are likely to decrease further in the future. Forest land is likely to contribute to 94% of future carbon loss in the study region, primarily due to its conversion into agricultural land. The implementation of multiple-species plantations, securing tenure rights, proper management practices, and the strengthening of forest-related policies can enhance carbon stock and sequestration. These spatial-temporal insights will aid in management strategies, and the methodology can be applied to broader contexts.

The Impact of Macroeconomic Factors on Stock Valuation

The widely accepted notion that capital markets are significantly influenced by macroeconomic factors underscores the importance of examining how these factors directly and indirectly affect stock valuations. Stock prices are inherently volatile, subject to the whims of the market, yet they are expected to converge with the intrinsic value of the shares they represent. Uncovering this fair value is essential for strategic investment decisions. This review article aims to provide an in-depth analysis of the impact of macroeconomic variables on the financial performance of specific companies, using macroeconomic data to elucidate the shifts in the fair value of their shares. By focusing on Baiyunshan, a leading pharmaceutical company in China, the article illustrates the tangible effects of macroeconomic factors on stock valuation. It offers a comprehensive exploration of various evaluation models, providing investors with a robust framework to assess the intrinsic value of publicly traded pharmaceutical companies. This study not only bridges the gap in current understanding but also equips investors with the necessary tools to navigate the complexities of the capital market with greater confidence and precision.

Value relevance of accounting information in Morocco: impact of profitability and earnings management strategies

PurposeThis study examines the value relevance of accounting information for publicly listed Moroccan companies and analyzes how profitability affects stock market valuation. Additionally, the study explores earnings management strategies used to avoid losses and earnings decreases.Design/methodology/approachEmpirical tests use the Ohlson (1995) Price model to assess the extent to which accounting information explains variation in stock prices for the period 2012–2021. Moreover, the study applies the Burgstahler and Dichev (1997) approach to analyze earnings management strategies used by listed Moroccan companies.FindingsThe findings suggest a positive and significant relationship between the book value, net earnings and the market value of stocks. Additionally, profitability is identified as a crucial factor significantly influencing the relevance of accounting figures. Moreover, the analysis of the distribution of earnings reveals that listed Moroccan companies engage in earnings management to avoid losses and decreases in earnings.Practical implicationsThe findings hold practical significance for investors, corporate leaders, accountants and stakeholders relying on accounting information for decisions. They also offer insights for Moroccan stock market regulators to improve accounting quality and market efficiency.Originality/valueThe study shows the extent to which stock prices reflect the accounting information from an emerging country perspective, contributing to a predominantly unexplored area of literature regarding economies in transition. Moreover, the study offers crucial insights into the influence of profitability on the market value of listed Moroccan companies and sheds light on the utilized earnings management strategies.

Analysis of Financial Fraud Based on the GONE TheoryA Case Study of Hangzhou Century Co., Ltd.

Convertible bonds allow creditors to convert their claims into company shares under certain conditions. Because of this debt-equity conversion feature, the issuance conditions, pricing and conversion value of convertible bonds are highly susceptible to financial information. Increasing the market value of a company's stock by exaggerating earnings or concealing debt may lead to convertible bond investment decisions based on misinformation, which in turn affects investors and the healthy operation of the market. Based on the GONE theory, this paper analyses the motivation of financial fraud in convertible bond documents and proposes corresponding preventive measures for its characteristics. Through the research and analysis of this type of financial forgery, the preventive governance of financial forgery can be further strengthened, which not only helps to protect the company's reputation and ensure the accuracy and reliability of the financial report, but also helps to attract and maintain the trust of investors, avoid possible legal risks and economic losses, and create a transparent and honest financial environment for the market to promote the efficiency of the market and the fair competition of enterprises.

Media pressure, corporate social responsibility, and the risk of share price crash

The investigation employs a compilation of data from 5085 listed entities over the period from 2013 to 2022 to explore the dynamics between media coverage, corporate social responsibility initiatives (CSR), and the risk of stock market crashes. The results suggest that greater media focus can elevate the chances of stock value plummeting, whereas robust CSR dedication tends to mitigate such risks. The extent of overall debt funding plays a significant role as an intermediary in the connection between CSR efforts and the threat of equity market meltdowns. Moreover, the influence of the audit services provided by the Big Four firms greatly alters the correlation between CSR endeavors and the probability of market downturns. Additionally, the influence of CSR on the risk of crashes varies based on the profitability of the company. The study proposes that enhancing CSR execution, maintaining moderate levels of financial leverage, and optimizing approaches to media relations can all contribute to reducing the likelihood of stock market crashes.

A Study on Impact of Persistent Inflation on Stock Valuations in Current Context

A Study on Impact of Persistent Inflation on Stock Valuations in Current Context

The Impact Of Stock Price On Investment Decision: Theory & Evidence

Purpose – The purpose of this paper is to present a behavioral explanation of excess stock price volatility relative to present value theory. Design/methodology/approach – The conceptual basis is the impact of effect on investor decisions. The empirical tests involve survey data collected from a sample of semi-professional Investors and investment advisors. Findings – It is suggested that affect causes investors to perceive an inverse ex ante relationship Between risk perceptions and expected returns. Thus, new good or bad information has an amplified effect on stock valuations. In addition, investors tend to extrapolate recent short-term market movements into the future. Practical implications – The primary implications are that ex ante perceptions of risk and return Vary inversely and that affect has a strong influence on valuation. This means that simple statistical Measures of risk are unlikely to fully capture risk perceptions and that market volatility can be Expected to be greater than a simple present value model would imply. Originality/value – This paper is unique as to the conclusion that risk and return perceptions vary Inversely ex ante and that affect can amplify stock price volatility

The Optimal Quantity of CBDC in a Bank-Based Economy

We show that the estimated effect of digital euro news on bank stock valuations and lending depends on the bank’s deposit reliance and the central bank digital currency (CBDC) design features. Using a quantitative DSGE model calibrated to the euro area economy that replicates such evidence, we find that CBDC issuance yields nontrivial welfare trade-offs between, on one side, the positive expansion of liquidity services and the improved stabilization of deposit funding and lending and, on the other side, a negative bank disintermediation effect. The optimal amount of CBDC lies between 15 and 45 percent of quarterly GDP. (JEL E23, E42, E58, F33, G14, G21)

Advancements in Stock Market Prediction Techniques: A Comprehensive Survey

The stock market, known for its unpredictable nature and complex workings, has been the focus of significant study. Conventional models frequently fail to fully capture the complex and diverse nature of stock market behavior. With the increasing complexity of financial markets, there is a rising demand for advanced strategies that can offer investors precise predictions and essential insight. There are several studies in this regard, from which we chose 24 modern studies from the year 2018 to the year 2023 in various ways to come out with results that are the focus of a new start in the prediction. This review collected different machine learning methods and deep learning techniques, including deep neural networks, which were then used to build powerful prediction models. Integrating behavioral finance theory with quantitative indicators and financial news provides a unique perspective on stock market dynamics. Also, The Efficient Market Hypothesis (EMH) highlights the significant impact of future information, such as news, on the valuation of stocks. Combining deep learning techniques in forecasting and examining the stock market represents an essential stage in financial research, it offers a more intricate comprehension of the intricacies of stock market dynamics. However, obstacles still need to be overcome, and it is essential to use prudence when applying reported accuracies to actual trading situations. The convergence of behavioral finance, efficient market hypothesis, and deep learning present opportunities for more investigation, offering a more comprehensive method of forecasting stock market trends

Model Building for Prediction of Valuation of Growth Stocks

It has been a great challenge for investors to evaluate growth stocks because these types of stocks are especially hard to forecast because of their inconsistencies and uncertainty. This paper establishes not only regression models, which include a regular Multivariable Linear Regression (MVLR) and a Fixed Effects (FE) Regression Model but also the application of a Panel Vector Autoregression (PVAR) Model on a dataset aiming to predict the Price-to-Sales Ratio (P/S) with several variables. Excel, Python, and Rstudio environments are used in the process of coding and model building. More specifically, Excel is used to conduct the MVLR model, Python is used for the FE model, and Rstudio is used for the PVAR model. By comparing different models of each type, the most optimized model was determined using a training set. After applying predictions with all types of models using another testing set, the best model is identified to be the best approach to the problem. Ultimately, all three models are compared. PVAR model and FE model are proved to be the more reliable ones out of the three models, because the R-squared for MVLR is 0.1859, lower than that of the FE model, 0.524, and the Mean Absolute Error (MAE) of for PVAR Model and FE model are respectfully 53.2139 and 55.1529. This project serves as a good starting point for future analysis and prediction of financial metrics like P/S ratio and is able to have great significance as it discovers some key points for the prediction of P/S value like its connection with time and entity despite the fact that the models themselves aren’t the best fit mainly caused by the limitations of data and variable access.

Tree carbon stock and seasonal influence on soil properties in shivpuri-nagarjun national park, nepal

The carbon stock stored within the biomass of tree species is vital in the forest ecosystem as it contributes significantly to the carbon balance. In addition, the physicochemical properties of soil play a critical role, in influencing overall ecosystem health. This study has analyzed the carbon stock and influence of seasonal change on soil physicochemical properties along soil depths in the Shivpuri Nagarjun National Park (SNNP), Nepal. The above-ground biomass carbon stock was found to be 227.09 t/ha and below-ground stock was 45.42 t/ha. Tree species Castanopsis tribuloides exhibited the highest values of above and below-ground tree carbon stock. The soil of the study site was sandy loam and slightly acidic. High temperature and moisture in the monsoon season were followed by an increased bulk density during the pre-monsoon with deeper soil layers. The sand, silt, and clay contents did not differ significantly across the seasons and depths. The key soil nutrients, like carbon, total nitrogen, phosphorus, and potassium were high during the monsoon season at the topsoil layer, which gradually declined with increasing depth in all seasons. The study highlights that the total tree carbon stock in the study site is 272.51 t/ha, with significant seasonal and depth-related variations in soil attributes. The monsoon season, characterized by maximum soil moisture and higher concentrations of essential soil components, is crucial in influencing soil physicochemical properties and offers important insights for forest conservation and management. Bangladesh J. Bot. 53(3): 567-576, 2024 (September)

Stock Valuation In Retail Trade Companies Listed On The Idx: Residual Income And Dividend Discount Model

Stock Valuation In Retail Trade Companies Listed On The Idx: Residual Income And Dividend Discount Model

A two-step method for assessing enhanced value in turnaround, spin-off, and value stocks

To assess outright and relative value opportunities in stocks and benchmark their performance against an index with global relevance, it is important to achieve and measure risk-adjusted excess returns. Academic and corporate research has focused quite extensively on analyzing stock returns and comparing the outperformance of specific investment strategies, with value investing being one of the most prominent and longest-known factor strategies. In this event study, to test for the existence of risk-adjusted excess returns, or alpha, a novel two-step approach is proposed to assess Enhanced Value in single stocks for three different investment approaches: plain value investing, investing in spin-offs, and investing in turnaround companies. While the first step of the two-step approach screens companies for a combination of financial company characteristics, the second step ranks and sorts them by either their price-earnings ratio or by their price-book ratio, thus “enhancing” the value assessment. Their short- and mid-term stock performance is investigated for an investment horizon of one year, three years, and five years. Stocks of value companies, spin-offs, and turnaround companies outperform the S&P 500 benchmark on average and on a risk-adjusted basis for all three investment horizons when tested for Enhanced Value with the novel two-step approach. The analysis results provide deeper insights into how the value factor in its different characteristics needs to be understood in the context of investment strategies and how it potentially can be applied to stock selection and portfolio construction, resulting in investment strategies showing a risk-adjusted outperformance.

Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices.

This paper objectives to improve stock market prediction accuracy by training data on sentiment analysis of tweets, overcoming volatility and complexity. Utilizing the Use of natural language processing (NLP) algorithms, the tweet's sentiments were classified into (negative - neutral - and positive). The stock value price was predicted by implementing Machine learning algorithms (KNN, SVM, GBM, LR, DT, RF, EL). Among the techniques of ML, (GBM) achieved the greatest results in terms of accuracy (96%). Its results were compared with the results of a deep learning algorithm that uses the same data where GBM got better results, and other algorithms showed results (KNN = 55%, SVM=90%, LR=82%, DT=90%, RF=90%, EL = 88%). The results obtained were superior to previous studies.