Market Cap Research Articles

Is ESG investment rewarded or just doing good? Evidence from China

The question of whether ESG investment is rewarding is a key determinant of its attractiveness to investors. Despite extensive research, a consensus remains elusive. This paper delves into the potential costs and benefits of ESG preferences in the Chinese stock market by constructing three-dimensional portfolios that integrate risk, return, and investor ESG preference. Instead of providing a binary answer of whether ESG investment is rewarding, we offer a glide path of costs to investors for achieving their ESG goals. Furthermore, given the potential market cap bias, we examine the differences in costs between portfolios composed of large-cap and small-cap stocks. Our empirical findings are twofold: first, if investors have a relatively low preference for companies' ESG performance, their investment is as rewarding as those with no ESG preference. However, if they have a high level of preference for ESG performance, their investment may be just doing good. This means that if the level of ESG preference is below a certain threshold, the costs of ESG preference are minimal or even zero. However, when the threshold is crossed, the costs rise sharply. Second, these costs vary significantly between portfolios with different market capitalizations. Specifically, incorporating ESG factors into small-cap portfolios often results in higher financial sacrifices compared to large-cap portfolios. These insights are crucial for investors seeking to balance financial and non-pecuniary objectives, providing guidance for optimizing ESG integration strategies in their investments.

Shattering the Fetter of Main Markets on the London Stock Exchange: Is It a Benign Start for the Dual-Class Share Structure in the UK?

The dual-class share structure, influenced by the governance perception of government and pressure from institutional investors, was still the forbidden region most time in the UK. However, the bonus of reforms that the London Stock Exchange (LSE) enjoyed was declining swiftly reflected in the number of listed companies and market cap. To promote competition and attraction, the LSE conditionally relaxed the restrictions of dual-class shares on premium listing in 2021, but the effects were faint in practice as founders cannot genuinely achieve the value of dual-class shares. In 2023, the Financial Conduct Authority (FCA) drafted the proposal removing the classification of standard and premium listing to relieve the restrictions of dual-class shares. Although this is a positive attempt, the action is also a double-edged sword that may pass higher risks to public shareholders. This article argues the overall intrinsic value of dual-class shares outweighs its potential risks on corporate governance. Yet, the great shareholder protection cannot be ignored as the reputation of the LSE. The design of dual-class rules should focus on shareholder protection but not influence the accomplishment of value, which can keep a certain resilience to achieve a relatively flexible balance between founders and investors.

PENGARUH E-BANKING TERHADAP KINERJA KEUANGAN (STUDI KASUS PADA PERBANKAN DENGAN MARKET CAP TERATAS DI INDONESIA)

The use of technology in the banking sector is currently growing rapidly with the presence of E-banking. E-banking may affect financial performance and can increase banking profits. The purpose of this study is to determine the effect of E-banking on the financial performance of banks with the top market cap. ROA is used as the dependent variable to represent financial performance. The independent variables used are ATM, M-banking, DPK and BOPO by adding bank size as a control variable. The samples used are BBCA, BBRI, BMRI, BBNI and MEGA. The type of data used is secondary data with panel data regression analysis of the annual financial report data of each bank from 2015-2022. The results of this study are ATM, M-banking and BOPO have a significant effect on ROA while DPK has no significant effect on ROA. Simultaneously, ATM, M-banking, DPK, BOPO and Bank Size variables have a significant effect on ROA. Therefore, the implication of this research is that banks focus on improving and developing E-banking in order to increase bank profits which will improve the bank's financial performance.

Missing Financial Data

Abstract We document the widespread nature and structure of missing observations of firm fundamentals and show how to systematically handle them. Missing financial data affects more than 70% of firms that represent about half of the total market cap. Firm fundamentals have complex systematic missing patterns, invalidating traditional approaches to imputation. We propose a novel imputation method to obtain a fully observed panel of firm fundamentals that exploits both time-series and cross-sectional dependency of data to impute missing values and allows for general systematic patterns of missingness. We document important implications for risk premiums estimates, cross-sectional anomalies, and portfolio construction. (JEL C14, C38, C55, G12)

Application of deep learning and statistical methods in predicting Taiwanese stock trends

Since its inception, the stock market has been a topic of considerable interest. Its variation and the complexity of integrating technology into the stock market have made it difficult for stock market trends to be fully understood. Various metrics and analytical approaches have been proposed in response to such changes, ranging from purely technical metrics to hardware upgrades. The widespread application of deep learning in the stock market, from basic metrics (opening price, closing price, highest price, lowest price, trading volume) to machine learning in sentiment analysis, further increases the possibility of increasing profits. Some front-end techniques, such as noise reduction through mathematical models, enhance the accuracy of deep learning models. However, few studies have centered on predicting long-term stock price changes. The traditional moving average (MA) cannot rapidly reflect drastic changes on its curve even though it can display trends; therefore, this study proposes an MA-based approach that improves the 200-day MA such that its delayed response to actual prices in real-time can be overcome. This deep learning model training was performed by combining 200-day MA data with two other types of MA data, thereby creating a new approach to metric analysis. The sample consisted of stocks of 13 Taiwanese companies with a high market cap: Taiwan Semiconductor Manufacturing Co., Ltd., MediaTek Inc., Chunghwa Telecom Co., Ltd., Fubon Financial Holding Co., Ltd., Cathay Financial Holding Co., Ltd., Nan Ya Plastics Corp., United Microelectronics Corp., Delta Electronics, Inc., CTBC Financial Holding Co., Ltd., Mega Financial Holding Co., Ltd., Formosa Chemicals & Fibre Corp., Hon Hai Precision Industry Co., Ltd., and Formosa Plastics Corp. Through multiple evaluation metrics, the experimental results revealed that the proposed model performed better in general than the traditional MA model for all stocks.

ESG Risks and Financial Performance. Analysis of Romanian-Listed Companies

Abstract The term “sustainability” is often used interchangeably with the acronym ESG (Environmental, Social, Governance). This happens as investors trust ESG scores to provide an accurate measurement of a company's sustainability performance. The traditional approach for evaluating sustainability performance involves analysing the scores for a given company, for each of the 3 ESG pillars: Environmental, Social and Corporate Governance. Moreover, most studies show that these scores are positively correlated with financial performance. While this approach is rather straightforward, usually it is not exhaustive and fails to capture the risks behind a company's exposure to material ESG issues and its management. Therefore, this study proposes an approach based on material ESG risk, exposure and management scores. Particularly, this paper focuses on the sector-wide analysis of ESG risk scores and their correlation with financial performance, for Romanian-listed companies, using the most recent data from one of the largest ESG rating provider. The methods employed are data analysis & visualization and correlation analysis. The results reveal, on average, a year-on-year decrease of the ESG Risk Score for the selected companies, along with increased exposure to material ESG risks, yet better management of these risks. Interestingly, the results do not show a significant correlation between the selected ESG variables and financial performance, measured by ROE, ROA and Tobin's Q, though a significant positive correlation between the Exposure and Management scores and firm size, measured by Market Cap, is found. Although there are limitations to this paper, for example, related to sample size or timeframe, it provides valuable insights on both the sustainability performance of Romanian-listed companies and, more broadly, its relationship with financial performance from a regional perspective.

Adoption of decentralized trial elements in cancer clinical trials supporting FDA approvals during COVID-19.

e13802 Background: The COVID-19 pandemic significantly disrupted the conduct of cancer clinical trials. Facilitated by emergency FDA guidance, many sponsors integrated decentralized clinical trial (DCT) elements to prioritize patient safety and maintain access. The FDA’s Oncology Center of Excellence, in collaboration with the Clinical Trials Transformation Initiative (CTTI), assessed the adoption of DCT elements in registrational cancer clinical trials conducted during the pandemic that resulted in an FDA approval. Methods: CTTI sent trial sponsors a recruitment email with a link to an online survey and the name of the trial that resulted in an FDA approval. Sponsors were asked to select the DCT elements used in that trial prior to and in response to the pandemic, challenges faced when implementing these approaches, and the likelihood of using the approaches in the future. Data were collected from December 6, 2022 to January 17, 2023. Descriptive statistics were used to summarize sponsor responses. Results: Out of 52 eligible studies, 19 surveys were completed from 13 unique sponsors (37%; 12 pharmaceutical companies and 1 chose not to disclose). Sixty-three percent of sponsors identified as a large pharmaceutical company with a market cap of over $10 billion. The majority of cancer clinical trials (89%) occurred both in the United States and abroad, with almost all sponsors (95%) incorporating at least one DCT element during the pandemic. The most widely adopted DCT elements were remote site monitoring (89%), telemedicine (68%), remote laboratory assessments (63%), remote distribution of investigational product (58%) and local imaging assessments (47%). The main challenges in implementing DCT elements included site institutional policies (83%), site challenges with technology adoption (61%) and site regulatory restraints (56%). Many sponsors intend to incorporate some degree of decentralization in future cancer clinical trials, with remote site monitoring (95%), remote collection of patient-reported outcomes (68%), telemedicine (68%), and electronic informed consent (58%) being deemed "highly likely" or "likely" for future adoption. Conclusions: The COVID-19 pandemic accelerated the use of DCT elements in cancer clinical trials. This study represents the first systematic evaluation of the use of DCT elements in cancer clinical trials intended for regulatory submission and provides proof of concept that cancer trials with DCT elements can generate data suitable for FDA review and marketing approval. The appropriate use of DCT elements holds promise to expand clinical trial access and reduce patient burden. Further characterization and mitigation of clinical site-level challenges is warranted to encourage continued adoption of DCT elements in cancer clinical trials.

Optimization of Stock Investments in Indonesia Stock Exchange: Comparing the Method of Dollar Cost Averaging (DCA) and Lump Sum (LS)

The aim of this research is to find out how to optimize stock investment using the dollar cost averaging method and the lump sum method and also to compare the performance between the dollar cost averaging method and the lump sum method based on return results. This research uses a descriptive method with a quantitative approach. This research uses 2 companies with the largest market cap listed on the Indonesian Stock Exchange for the 2021-2023 period. The results of this research found that the lump sum method return in 2022 on BBCA shares reached 22.85% and on BBRI shares 18.19% higher than the return on the dollar cost averaging method on BBCA shares which was only 13.70% and on shares BBRI is only 10.86%. The lump sum method return in 2022 is the highest return between 2021 and 2023. These results prove that the lump sum method is more optimal than the dollar cost averaging method.

Testing the boundaries of applicability of standard Stochastic Discount Factor models

We provide a joint non-parametric test to gather insights on the boundaries of applicability of Stochastic Discount Factor (SDF) models. We find that a non-trivial class of models cannot price the U.S. stock market equally weighted portfolio, implying non-monotonic SDFs, especially over the last 50/60 years in (recessionary) periods characterized by higher market volatility. Stocks responsible for this rejection mostly belong to the smallest NYSE market cap decile, are characterized by high idiosyncratic risk, and typically cannot be priced via SDF models where the aggregate level of risk aversion is bigger then 9 or 10. Excluding these stocks increases the ability to explain the cross-section of returns without impairing the ability to span the mean–variance frontier.

Deciphering crypto interconnections using a multi-analytical approach

Blockchain technology has primarily underpinned cryptocurrencies that are used either as speculative investment vehicles or for transaction facilitation. There has been a keen interest in understanding the dynamics of interconnectedness and conditional correlations among cryptocurrency prices. While most studies have primarily focused on Bitcoin or the top few cryptocurrencies, this study adopts a comprehensive, multi-analytical approach, incorporating other smaller cryptos that appeal to small and medium investors. Pearson correlational analysis explores the interconnectedness among cryptos and investigates co-movement in crypto prices through their returns, volatility, volume traded, and the CCi30 index returns. Principal Component Analysis (PCA) is used to identify highly correlated clusters, summarizing cross-sectional information based on covariance within the predictors. The predictive regression model of Granger Causality test is applied as a vector autoregression (VAR) forecasting method to examine Granger causality of price movements within the clusters identified. The findings from the correlational matrices of returns and volatilities show no difference in behaviours between larger and smaller cap ones, whereas correlations in trading volumes indicate high correlations in large market-caps. Smaller market-cap cryptos exhibit stronger correlations in volatilities than the larger market cap ones. Two highly correlated clusters emerged from the PCA analysis, with Binance Coin (BNB) and Ripple (XRP) exhibiting greater influence than Bitcoin (BTC) and Tether (USDT) in the second cluster. The findings will enable cryptocurrency users and investors to grasp price mechanisms better, offering valuable insights to improve their decision-making abilities.

DAO Dynamics: Treasury and Market Cap Interaction

This study examines the dynamics between treasury and market capitalization in two Decentralized Autonomous Organization (DAO) projects: OlympusDAO and KlimaDAO. This research examines the relationship between market capitalization and treasuries in these projects using vector autoregression (VAR), Granger causality, and Vector Error Correction models (VECM), incorporating an exogenous variable to account for the comovement of decentralized finance assets. Additionally, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is employed to assess the impact of carbon offset tokens on KlimaDAO’s market capitalization returns’ conditional variance. The findings suggest a connection between market capitalization and treasuries in the analyzed projects, underscoring the importance of the treasury and carbon offset tokens in impacting a DAO’s market capitalization and variance. Additionally, the results suggest significant implications for predictive modeling, highlighting the distinct behaviors observed in OlympusDAO and KlimaDAO. Investors and policymakers can leverage these results to refine investment strategies and adjust treasury allocation strategies to align with market trends. Furthermore, this study addresses the importance of responsible investing, advocating for including sustainable investment assets alongside a foundational framework for informed investment decisions and future studies in the field, offering novel insights into decentralized finance dynamics and tokenized assets’ role within the crypto-asset ecosystem.

Intuitionistic fuzzy time series forecasting method based on dendrite neuron model and exponential smoothing

Methods based on artificial neural networks for intuitionistic fuzzy time series forecasting can produce successful forecasting results. In the literature, exponential smoothing methods are hybridised with artificial neural networks due to their simple and efficient structures to improve the forecasting performance. The contribution of this paper is to propose a new forecasting approach combining exponential smoothing methods and intuitionistic fuzzy time series. In this study, a forecasting algorithm based on the dendrite neuron model and simple exponential smoothing methods is proposed for modelling intuitionistic fuzzy time series. In the fuzzification stage of the proposed method, the intuitionistic fuzzy c-means method is used. The proposed method is a modular method using two separate dendrite neuron model neural networks and the grey wolf optimisation algorithm is used to estimate all parameters of the method. The performance of the proposed method is applied on four different random time series obtained for Index of Coin Market Cap and the performance of the method is compared with some other fuzzy forecasting methods. As a result of the analyses, it is concluded that the proposed modular method has better forecasting results than other methods.

The Distinction of Evaluation on Companies in Different Fields

This paper explores the variations in Return on Equity (ROE) and Price-to-Sales Ratio among publicly traded companies across different time periods and market capitalizations. The analysis employs Linear Regression and Correlation Coefficients to examine the relationships between ROE, Price-to-Sales Ratio, Market Capitalization, and economic conditions during these periods. The findings reveal that Price-to-Sales Ratio serves as a reliable indicator of a company's market capitalization. Furthermore, companies within the same industry exhibit substantial disparities in Market Capitalization, ROE, Price-to-Sales Ratio, Price-to-Earnings (P/E) Ratio, and PEG Index due to differing corporate strategies and backgrounds. For instance, in the new energy sector, both BYD and NIO display a negative correlation between market capitalization, while Tesla contradicts this trend. This discrepancy is attributed to internal factors such as asset composition, liabilities, and shareholder structure, which significantly influence a company's financial performance. The Market Capitalization of these new energy companies is predicted to rise due to global interest in AI technology, with technology companies being at the forefront of AI development. In the technology sector, companies like Google have solidified their positions by advancing AI concepts. New entrants, like Apple, combining Artificial Intelligence and Robotics, are gaining consumer support, indicating potential Market Cap. growth. The fashion industry also demonstrates distinctive strategies, with companies like NIKE, Adidas, and Anta focusing on sustainability and carbon neutrality. Therefore, future company evaluations should consider both financial indices and a company's long-term vision and sustainability practices to predict their future value effectively.

The Impact of the COVID-19 Epidemic on the U.S. Senior Housing Market—Analysis of Changes in Brookdale’s Market Capitalization Based on Smoothing Splines Regression

The U.S. population is aging, with a large portion of the population already entering retirement age. As a result, demand in the senior living industry continues to increase. This article explores the impact of the COVID-19 pandemic on the senior living industry, focusing on changes in market capitalization for industry leader Brookdale. Based on Brookdale's market cap data from 2005 to 2023, smoothing spline regression was used to analyze the impact of the pandemic on the company's market worth. Three different cross-validation methods including ordinary cross-validation (OCV), approximate cross-validation (ACV) and generalized approximate cross-validation (GACV) were tested for regression parameter selection. The study found that ACV provided the best model fit with an R-squared of 0.7181. The results show that Brookdale's market capitalization has experienced a significant decline during the COVID-19 outbreak, and the predicted values are consistent with the actual reduction in Brookdale's market capitalization. Factors contributing to the decline include lower occupancy rates in senior care facilities and the impact of the pandemic on the overall economy. This underscores the likelihood of challenges within the senior living industry during public health emergencies, warning both investors and operators to prepare for potential risks. The employment of the smoothing spline model in this study enhances the precision of market trend representation and facilitates rational decision-making among industry stakeholders.

A comparative study on classification models for stock rating prediction

The digital transformation in the stockbroker industry has led to a significant increase in retail investors, who often lack the expertise to analyse stocks thoroughly. This research addresses the challenge by proposing a classification model to predict stock ratings such as "Reduce", "Hold", "Moderate Buy", and "Buy”, allowing retail investors to make informed decisions. The data analysed is collected from the S&P 500 index through web scraping using Beautiful Soup, resulting in a dataset used for training and testing the classification model. Popular stock indicators are used as attributes in predicting the rating of the stock, which includes the Exchange, Price, Volume, Market Cap, ROE, ROA, P/E Ratio, EPS, Annual Sales, Net Income, Net Margins, and PB Ratio of the stock. The models selected for classification include K-Nearest Neighbors (k-NN), Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest. GridSearch is employed to maximize each algorithm's parameters for optimal performance. Results indicate that the k-NN model outperforms others, achieving the highest accuracy (0.618644) and weighted F1-score (0.605011). However, all models exhibit relatively low accuracy, suggesting the complexity of predicting stock ratings due to external factors not considered in the study.

Market Manipulation of Cryptocurrencies: Evidence from Social Media and Transaction Data

The cryptocurrency market cap has experienced a great increase in recent years. However, large price fluctuations demonstrate the need for governance structures and identify whether there are market manipulations. In this article, we conduct three analyses—social media data analysis, blockchain data analysis, and price bubble analysis—to investigate whether market manipulation exists on Bitcoin, Ethereum, and Dogecoin platforms. Social media data analysis aims to find the reasons for price fluctuations. Blockchain data analysis is used to find detailed behavior of the manipulators. Price bubble analysis is used to investigate the relation between price fluctuation and manipulators’ behavior. By using the three analyses, we show that market manipulation exists on Bitcoin, Ethereum, and Dogecoin. However, market manipulation of Bitcoin is limited, and for most of Bitcoin’s price fluctuations, we found other explanations. The price for Ethereum is the most sensitive to technical updates. Technical companies/teams usually hype some new concepts (e.g., ICO, DeFi), which causes a price spike. The price of Dogecoin has a high correlation with Elon Musk’s X (formerly known as Twitter) activity, showing that influential individuals have the ability to manipulate its prices. In addition, the poor monetary liquidity of Dogecoin allows some users to manipulate its price.

Synthetic cap rate indices (1991-Covid era)

We introduce a method that combines Euclidean distancing and OLS techniques to project synthetic capitalization rate indices (‘SCXs’) for metropolitan statistical areas in the US. SCXs are projected independently of market prices, asset specific characteristics and geographic location (ex-ante). In contrast to market cap rates, driven by geographic proximity and market comparables, our new method is driven by economic proximity. We find SCXs provide better forward guidance than market cap rates for commercial real estate (‘CRE’) defaults and CRE values before and during the Covid pandemic. Our method establishes CRE benchmark cap rate indices across property types that explicitly connect CRE valuation at the MSA level to macroeconomic indicators through economic proximity.

Why does price deviate from net asset value? The case of Singaporean infrastructure REITs

This paper examines the determinants of the changes in premiums to Net Asset Value (NAV) in infrastructure REIT (infra-REIT) share prices in Singapore over the 2017–2021 period. Samples of 11 listed Singaporean REITs (S-REITs) are selected to create a balanced panel data for the analysis. Our finding shows that infrastructure S-REITs have generally been trading at premiums to their NAVs. The first part of the analysis shows that REIT premiums are positively related to REIT size (market cap) and percentage of institutional ownership. On the other hand, REIT premiums are negatively related to dividend yield. Volatility, volume, ROE and liquidity are found to be insignificant in explaining price and NAV divergence. The second part of the analysis finds that there is a higher proportion of uninformed (noise) traders when infrastructure S-REIT prices are diverging from their NAV. This finding supports the Noise theory which implies that there is some evidence that noise traders are present in the infrastructure S-REIT market and their actions might cause irrational departure of REIT prices from the underlying NAV.

Monetary momentum and risk management in stock market

Purpose- This study aims to investigate the relationship between monetary interest rate decisions, liquidity mechanisms and risk management issues. As a core interest the significance of a structural change in the data around the FOMC meetings is analyzed. By the help of continuous time models we analyze the kind of dynamics, which can be observed in the stock returns, i.e. conditional volatilities and jumps. A further central interest is given to investment decisions and risk management issues. This encompasses the elaboration of the hedging strategies to achieve higher performance. Methodology- The study employs GARCH-Ito and GARCH-Ito-Jump models to analyze the stock market returns and their related volatilities on the day of a FED interest decision announcement. The continuous time GARCH model setting allows to model stock market returns with a high flexibility, therefore these models are abled to capture jump dynamics in the stock returns. Findings- The analysis reveals that persistence in conditional volatilities change according to alternative stocks. These stocks can be classified according to alternative market capitalization sizes. Mega market capitalization stocks are better governed by no jump GARCH-Ito models regardless the monetary policy changes, that is, changes in interest rates or not. Conclusion- Based upon the analysis, it may be concluded that risk management applications effectively might perform under the consideration of stock types in terms of market sizes. The persistence in the conditional volatility massively decreases if a jump component is introduced into the model. Since most of the mega market cap stocks perform better without a jump part component, it might be conjectured that persistence in the conditional volatility for mega cap stocks play a more important role compared to large cap stocks. Regardless the case whether there is an interest rate change or not, the persistence in conditional volatility remains in mega cap stocks, and thus, these stocks are prone to the involvement of prices jumps. Keywords: Monetary policy, risk management, jump detection, investment decisions JEL Codes: C22, E49, G11

The Relationship Between Key Financial Ratios To The Revenue Growth And Financial Performance Benchmarking Analysis Of Oil & Gas Companies Listed In The Indonesian Stock Exchange

The global oil and gas sector, a crucial driver of economic vitality, remains a focal point for investors navigating the dynamic energy market. According to the Handbook of Energy and Economic Statistics of Indonesia, the oil and gas industry constitutes a significant portion, approximately 43.5%, of the country's energy mix in 2022, equivalent to about 797 million barrels of oil equivalent (BoE). Projections from Indonesia’s Long-Term Strategy for Low Carbon and Climate Resilience (LTS-LCCR) 2050 anticipate an increase to 1.4 billion BoE in 2050, even under the strictest emission scenarios. Given the pivotal role of oil and gas in Indonesia's energy landscape, it is imperative to examine investor interest in companies within this industry. Regarding the oil & gas companies in the Indonesian Stock Exchange (IDX), investors' attention has been drawn to only a few stocks in particular due to the attractiveness of profitable prospects in the oil and gas sector, which brings up the question: do these “popular” oil and stocks really perform admirably financially? Overall, this research uses the quantitative method. This research has the purpose to identify and analyze the financial ratios that have a significant impact on the revenue growth of Indonesian oil & gas companies, to determine which Indonesian oil & gas companies with significant ratios exhibit high potential and possess a high market capitalization, to identify Indonesian oil & gas companies with significant ratios that demonstrate high potential and have a low market capitalization, to evaluate Indonesian oil & gas companies with significant ratios that exhibit low potential but possess a high market capitalization, and to compare the financial performance of the high potential Indonesian oil & gas companies with highly reputable oil & gas companies that are not listed on the Indonesian Stock Exchange. To complete the first objective, this research uses regression analysis, which resulted in Asset Turnover and Assets Growth as the significant ratios to the Revenue Growth. Using these significant ratios to measure the potential, the High Potential – High Market Cap category included AKRA, RAJA, ELSA, and HITS while in the High Potential – Low Market Cap category, this research has found out KOPI is qualified into this category. In the Low Potential – High Market Cap category, TAMU and BULL are included. Lastly, when compared to worldwide industry leaders, the IDX-listed oil & gas companies which are marked as High Potential have found some interesting findings: AKRA have much better Asset Turnover ratio than industry leaders, ELSA’s Assets Growth are observably better than Halliburton and Schlumberger, and HITS’ profitability (Gross Profit Margin) is still better than the spectacularly growing Pertamina International Shipping.