Biggest Market Capitalization Research Articles

Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation

The Bitcoin, one of the most discussed event of recent times, is the first cryptocurrency to be released. Bitcoin has the biggest market capitalization in the cryptocurrencies, and it is a decentralized payment tool that cannot be controlled by any government institution. Cryptocurrencies operate on top of a decentralized network, recording and verifying transactions using a technology called Blockchain. Many bubble price formations occurred in financial markets throughout history. Cryptocurrency markets are prone to manipulation because they are not connected to any center. This study aims to to investigate the buble formations in Bitcoin (BTC) US Dollar price for the period of 11 December 2017 - 31 July 2023, and also to determine the main financial variables affecting Bitcoin prices. ARDL Bounds test proves cointegration between Bitcoin price and dependent variables. Error Correction Model determines positive connection between BTC and Nasdaq100 index. A daily change in Nasdaq100 index leads to a 0.58% increase in BTC price. Model captures also negative relationship between Volatility Index, USD Index and BTC price. No long-term relationship emerges in the results. In the long run, unique and different variables shape BTC price formation.

A multi-attribute decision-making fusion model for stock trading with customizable investor personality traits in a picture fuzzy environment

In this paper, a fuzzy logic-based machine learning (ML) algorithm is introduced. This proposed ML algorithm accepts picture fuzzy sets (PFS) as the fuzzified input and incorporates genetic algorithm (GA) during the training process. The proposed ML algorithm is then incorporated into two well-known decision-making methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). These two decision-making methods and the proposed ML algorithm are then applied to solve a multi-attribute decision-making (MADM) problem related to the evaluation and ranking of public listed companies based on their stock performance, in accordance with investors’ personalities. The actual daily closing stock price of five public listed companies from the big market capitalization (Big Cap) category traded in the Kuala Lumpur Stock Exchange (KLSE) for a period of 10 years is used as the datasets for this study. Monte Carlo simulation is used to verify the accuracy of the results. In addition, a comprehensive comparative study of some recent PFS-based decision-making methods in the existing literature and the proposed methods is conducted, and all the typical instances of the investors’ personalities are observed. The results obtained through this comparative study corroborates the results obtained via the proposed methods, and this proves the effectiveness of the proposed methods. The differences in the results obtained via the different methods are analyzed and discussed, and this again proves that the results obtained via the proposed methods are effective and consistent with the judgments of human experts.

A Study on CNN Feature Extraction for Stock Price Prediction

The return on investment for investors in the stock market is highly dependent on the investor's timing strategy, that is, the decision of what time to buy or sell a stock. A successful timing strategy requires investors to accurately identify the price movement of a company. As a result, some investment professionals have created technical analysis analytical methodologies to forecast the short-term trend of a stock. However, technical analysis approaches are prone to subjectivity, such as the selection of technical indicators and indicator periods. This essay attempts to utilize a convolution layer in deep learning to extract features as an alternative to technical indicators and to reduce subjective elements' effect on prediction bias. Several stock predictions are evaluated between a standard LSTM model and an LSTM model with convolution layers (CNN-LSTM model) in this research. The experimental results show that the CNN-LSTM model outperforms the standard LSTM model in predicting the price of certain stocks with a big market capitalization and high liquidity.

The Pricing of ESG: Evidence From Overnight Return and Intraday Return

By featuring the link of investor heterogeneity to the persistence of the overnight and intraday components of returns, we examine the ESG–overnight (intraday) alpha relation in the Chinese stock market. The empirical results show that ESG score has a significantly negative effect on the expected stock overnight returns in Fama–MacBeth regression. Consistently, given the biggest market capitalization and the least illiquidity subsamples, the trading strategies by going long (short) the top (bottom) ESG quintile would yield negative profits. In addition, we conduct the implication of the ESG pricing by dividing the full sample into green stock subsample and sin stock subsample, and the empirical results present that the ESG pricing is pervasive of the green-type stocks. These conclusions verify the pricing of ESG and support the conjecture that green stocks have lower expected returns because ESG investors value sustainability.

ANALYSIS OF THE EFFECT OF FIRM SIZE, GROWTH, TANGIBILITY, LIQUIDITY, AND PROFITABILITY ON STOCK RETURN WITH DEBT LEVEL AS INTERVENING VARIABLES ON LQ45 SHARES IN INDONESIA STOCK EXCHANGE

At the moment, investment is primarily defined as capital investment, and it has an essential meaning in the domestic and global economy. One of the investment types selected by society is the stock investment in the capital market. Investing in the capital market requires a barometer to provide investors with a general illustration of the stock price index. One of the stock indices in the Indonesia Stock Exchange is LQ45. The LQ45 index measures the price performances of 45 stocks with high liquidity and big market capitalization supported by the excellent company fundamental aspect. The study aimed to discover the effect of Firm Size, Growth Tangibility, Liquidity, and Profitability on Stock Return with Debt Level as the intervening variable in LQ45 stocks. This study is expected to provide suggestions and considerations for LQ45 firms to decide to improve the company stock return. The study employed a quantitative method with data processing using SPSS. The data collection was carried out using Company Public Financial Statements from the IDX website. Sample criteria were non-banking firms listed in LQ45 from 2014-2018. The study results reveal that seven variables mainly influenced Debt Level and Stock Return, i.e., Firm Size has a significant negative effect on Debt Level with statistic t-value (-2.362) and sig. value (0.019); Firm Size has a significant positive effect on Stock Return with statistic t-value (2.288) and sig. value (0.023); Growth has a significant positive effect on Debt Level with statistic t-value (3.779) and sig. value (0.000); Tangibility has a significant negative effect on Debt Level with statistic t-value (-5.520) and sig. value (0.000); Tangibility has a significant positive effect on Stock Return with statistic t-value (2.161) and sig. value (0.032); Liquidity has a significant negative effect on Debt Level with statistic t-value (-7.581) and sig. value (0.000); Profitability has a significant negative effect on Debt Level with statistic t-value (-3.158) and sig. value (0.002). Therefore, investors should consider these variables to increase Debt Level and Stock Return.

ESTIMATING THE EFFECT OF THE COVID-19 OUTBREAK EVENTS ON THE INDONESIA SECTORAL STOCK RETURN

COVID-19 has become a global issue that bought a simultaneous effect all over the world. This outbreak has caused a significant impact on the economic stability including the stock market performance. This research used event study methodology to evaluate the Indonesia’s sectoral stock market performance which is represented by companies with the biggest market capitalization in Indonesia. Estimating the COVID-19 outbreak events, the sample of the study consisted of daily sectoral indices stock data from 1 December 2017 to 14 April 2020. The market model is used to predict expected stock returns and simple regression to get the parameters of the equation. By using a t-test, it can be concluded whether the virus outbreak caused an abnormal return on the sectoral indices. The finding showed that the abnormal returns were found in the Basic Industry and Chemicals sector, Infrastructure, Utilities and Transportation sector, Agricultural sector and Mining sector. Furthermore, Basic Industry and Chemical sector, which is represented by Barito Pasific Ltd., give the greatest reaction to the stock market performance in Indonesia due to COVID-19. Keywords: abnormal return, event study, stock return, COVID-19, Indonesia

PERAN EKSISTENSI SAHAM DI LQ45 PADA KINERJA PROFITABILITAS PERUSAHAAN

Many companies in Indonesia compete to improve the financial performance to be listed in LQ45 index. LQ45 index is the house of 45 stocks with high liquidity, big market capitalization and good financial performance. This paper aims to investigate whether the existence status of stocks in LQ45 index in the past affects the current profitability performance of the companies. This study relies on semesterly data from 19 companies dated from 2012 to 2015. Using panel data regression model of Net Profit Margin (NPM), the findings suggests that the existence of stocks in LQ45 index in previous period significantly affect the NPM. The results are robust and consistent across specifications either using fixed effect or random effect approaches. The magnitude ranges from 22,77 to 26,87 percentage points.

Book-To-Market Decomposition, Net Share Issuance, and the Cross Section of Global Stock Returns

This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market—changes in book value, changes in price, and net share issues. This conclusion is supported using data representing North America, Europe, Japan, and Asia. Results are highly consistent across all global regions and hold for small and big market capitalization subsets as well as in different subperiods. Variables measured over the past twelve months are more relevant than variables measured over the past thirty-six months, demonstrating that recent news is more important than old news.

Finding Size Factor and Value Factor in Indonesia Stock Exchange

This study conducted by gathering data from Indonesia Stock Exchange (IDX) with 2 specifics model, Capital Market Pricing Model (CAPM) and Fama French 3 Factors Model (FF3FM). These model was estimated by classify 557 stocks in Jakarta Composite Index (JCI) to 6 classes: S/L class is class with small size and low Book to Equity (BE) to Market Equity (ME), S/M class is class with small size and medium in BE/ME, S/H class is class with small size and high in BE/ME, otherwise B/L class is class with big size and low in BE/ME, B/M class is class with big size and medium in BE/ME, B/H class is class with big size and high in BE/ME. With F test, t test and classic assumption test, best class and best model were B/L class and FF3FM. The result was confirmed size factor and value factor in Indonesia Stock Exchange (IDX). Size factor are confirmed in 3 classes (S/M, S/H and B/L), and value factor are confirmed in 4 classes (S/M, S/H, B/L and B/H). Therefore, classes with size and value factor are S/M, S/H and B/L. With BE/ME is 1/PBV and PBV indicating the stock price relative to its book value, so in Indonesia Stock Exchange the size factor and value factor confirmed in market with small market capitalization with low to medium in stock price relative to its book value and market with big market capitalization with high stock price relative to its book value.

REAKSI PASAR MODAL INDONESIA TERHADAP PERISTIWA BENCANA ALAM TSUNAMI DI ACEH TANGGAL 26 DESEMBER 2004

As one of an instrument of economy, capital market can not be separated from influence that amend in its environment, either occur in macro economic, micro economic or non-economic environment. This research is one of the event study which examined in how the reaction of Indonesian capital market toward event that occur in an non-economic environment which is tsunami natural disaster in Aceh, December 26th, 2004.The purpose of this research is to examine stock prices reaction and trading volume activities in Jakarta Stock Exchange (JSX) toward tsunami natural disaster event in Aceh, also to examine whether there are any differences in average abnormal return and average trading volume activity before and after tsunami natural disaster event in Aceh. The samples are generated from stocks that have the biggest market capitalization in JSX which are often listed in LQ45 in period August, 2003 until January, 2008.The result of test using SPSS with 95% confidence level shows that JSX was not responded toward tsunami natural disaster event in Aceh. This is shown by no abnormal return during event period, no difference of average abnormal return before and after event, and no difference of average trading volume activity before and after tsunami natural disaster event in Aceh.

Investment Style Preference and its Effect Upon Performance of Tracking Portfolios

Any task of portfolio creation requires that a suitable pre‑selection of assets is made, out of which the resultant portfolio is to be formed. Several approaches in passive investing implemented through portfolio tracking are applied in practice, and assets are pre‑selected frequently on the basis of their capitalization or value/growth potential. The paper studies to which extent the investment style practiced by a small investor affects the performance of the tracking portfolio. The design of the analysis is experimental and hinges on tracking the S & P 500 Index in three different periods with assets pre‑selected by diverse investment styles. Taking the approach of with linear and quadratic tracking, two factors are analyzed on that occasion: the investment style (big vs. small market capitalization, value vs. growth assets, Fama‑French stratas of assets) and the number of assets (10, 20, 30, 40, 50 assets). It is found that while small market capitalization portfolios were preferable in the first two parts of the investigated time frame, this pattern ceased to hold in the third last part with no guidance for a recommendable investment style.

Book-to-Market Decomposition, Net Share Issuance, and the Cross Section of Global Stock Returns

This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market—changes in book value, changes in price, and net share issues. This conclusion is supported using data representing North America, Europe, Japan, and Asia. Results are highly consistent across all global regions and hold for small and big market capitalization subsets as well as in different subperiods. Variables measured over the past twelve months are more relevant than variables measured over the past thirty-six months, demonstrating that recent news is more important than old news.

Pengaruh Jumlah Saham Beredar, Harga Saham dan Presentase Saham Publik Terhadap Likuiditas Saham Perusahaan Yang Listing di JII Periode 2013

The increase on market capitalization, number of traded shares, frequency of stocks trading and stocks price index influence the stock liquidity. Stocks which have a high market capitalization and that index prices always increase reflected in the Jakarta Islamic Index (JII). It is because the majority shares on JII are member of LQ45 which have a big market capitalization and good track record. Therefore interesting to do research to see the effect of the number of shares, the stock price and the percentage of public stock to the stock liquidity of listed companies on JII. The approach used in this research is quantitative with multiple linear regression analysis technique. The independent variables are the number of shares, the stock price and the percentage of public stock, and the dependent variable is stock liquidity. The samples are 30 listed companies on JII of period 3 June to 29 November 2013. Simultaneously, the number of shares, the price stock and the percentage of public stock have significant effect on stock liquidity. Partially, only the price stock and the percentage of public stock that affect on stock liquidity.

Which is the Right 'Market Beta'? 1,385 US Companies and 147 Betas/Company in a Single Date

We report betas of 1,385 US companies on March 31, 2014, calculated with the S&P500. We calculate 147 betas for each company using monthly, weekly and daily returns and using different intervals: from a year to five years. The average (median) of the difference [maximum beta - minimum beta] was 1.17 (1.03). The 30 companies with biggest market capitalization had an average (median) of the difference [maximum beta - minimum beta] of 0.81 (0.72).We show that comparing two companies, if asked “Which company had a higher beta on March 31, 2014?” you can answer anything you want choosing the appropriate returns (daily, weekly or monthly) and the appropriate period (from 1 to 5 years).Ranking the companies according to their betas, we find that the average of the [maximum ranking - minimum ranking] for the 1,385 companies is 786.

ANALISIS PENGARUH MARKET CAPITALIZATION TERHADAP HARGA SAHAM PADA PERUSAHAAN YANG TERDAFTAR DI BURSA EFEK INDONESIA

This study aimed at testing and analyzing how both the big market capitalization and the small one had an effect on the price stock. The population of this study was all companies listed in BEI in the period of 2007 – 2011. The purposive sampling with a quota sampling was applied in the technique as sampling, while the selection result of sampling was 36 companies with big market capitalization and 36 companies with small market capitalization. The method of data selection of this study was documentation because the data was taken from other sides. The dependent variable used in this study was price stock and the independent variable was market capitalization. The tools for data analysis applied Vector Error Correction Model (VECM). The result of this study indicated that the big market capitalization had a positive effect on the price stock and gave an effect of 69.91% on the price stock at the end of the period. While the small market capitalization had a positive effect on the price stock at 85.09%.

The Influence of Ownership Structure on the Corporate Performance of Malaysian Public Listed Companies

The main objective of this study is to examine the impact of ownership structure on the corporate performance of the Malaysian public listed companies (PLC). Empirical evidence suggests that insider and institutional shareholding do not influence the corporate performance of the Malaysian PLC. The results seem to suggest that institutional shareholders have failed in their monitoring role and principal agent problem will not be solved by increasing the director's shareholding in the company. Despite segmenting the company according to market capitalization, results do not differ significantly. The relationship between corporate performance and insider and institutional shareholding is only significant in big market capitalization companies.

Equilibrium “Anomalies”

AbstractMany empirical “anomalies” are actually consistent with the single beta capital asset pricing model if the empiricist utilizes an equity‐only proxy for the true market portfolio. Equity betas estimated against this particular inefficient proxy will be understated, with the error increasing with the firm's leverage. Thus, firm‐specific variables that correlate with leverage (such as book‐to‐market and size) will appear to explain returns after controlling for proxy beta simply because they capture the missing beta risk. Loadings on portfolios formed on relative leverage and relative distress completely subsume the powers of the Fama and French (1993) returns to small minus big market capitalization (SMB) portfolios and returns to high minus low book‐to‐market (HML) portfolios factors in explaining cross‐sectional returns.