Trading Stock Research Articles

Pengaruh Struktur Modal Dan Kebijakan Dividen Terhadap Nilai Perusahaan Dan Dampaknya Terhadap Harga Saham Pada Sektor Pertambangan Periode 2016-2020

The capital market is a facility that is prepared to trade stocks, bonds and various other types of securities. Before buying a stock, investors must conduct an analysis that is expected to affect the stock price. In addition, it must have a benchmark by investing in a company that will get the quality as expected. One of the factors that affect stock prices is the fundamental condition seen from the company's financial condition. 
 This study uses a comparative descriptive method with a quantitative approach. The data used are in the form of company financial statements through document studies. The sampling technique used is purposive sampling with predetermined criteria in order to obtain 10 samples of companies. Data analysis used path analysis with SPSS as the analysis tool. The purpose of the study was to determine the effect of capital structure and dividend policy on firm value and stock prices.
 The results of this study indicate that the capital structure has no effect on firm value dividend policy has a significant positive effect on firm value. Meanwhile, capital structure has a significant negative effect on stock prices, dividend policy has no effect on stock prices and firm value has a significant positive effect on stock prices. Firm value is not able to mediate capital structure on stock prices while firm value is able to mediate policy on stock price dividends.
 
 Keywords : Capital Structure, Dividend Policy, Firm Value, Stock Price.

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

Risk Analysis of A-H Share Connect Market Based on Deep Learning and BP Neural Network

China's Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs make it possible for investors to trade stocks within specified limits through the two stock exchanges. The A-H share exchange stock market is crucial to the opening of the Mainland market, but few studies have paid attention to the market risks of such stocks. Using deep learning and BP neural network algorithm, this study constructs a three-dimensional A-H share interconnection market risk prediction index system including stock price fundamental indicators, technical indicators, and macro indicators based on the CES300 Index. Taking the CES300 Index return as the output layer indicator, a BP neural network with a 21-10-1 structure is constructed, and the tan-sigmoid transfer function and the LM optimization algorithm training function are used for network training to predict the return of the A-H share interconnected stock market. The mean square error (MSE) converges to 10−6, and the goodness of fit R reaches 0.9928 and validates the prediction accuracy of the BP neural network model. It provides an efficient and accurate risk prediction model for the A-H share interconnected market, which facilitates the interactive development of the Mainland and Hong Kong markets.

Insiders’ characteristics and market timing capabilities: buying and selling evidence

PurposeThis paper aims to study individual managers’ market timing capabilities while trading (either buying or selling) stock from their portfolios, as well as the impact of gender, seniority and trading frequency on their market timing performance.Design/methodology/approachThis study uses a relative transaction price approach introduced by Dittmar and Field (2015) on 837 aggregated trades made by managers from their portfolios between 2010 and 2015. These were taken from publicly disclosed information through the Portuguese regulator. Furthermore, this study uses a median regression-based method to infer the authors’ conclusions.FindingsThe authors find that insiders buy (sell) at a relatively lower (higher) price when compared to other traders. This evidence shows that insiders have market timing capabilities. Moreover, this paper shows that contrarily to gender, both seniority and frequency help explain market timing performance and that insiders’ trades made over-the-counter (OTC) generally overperform the ones made on the open market (OM). Finally, this study finds a significant crisis-related influence on insiders’ market timing performance.Originality/valueThis study contributes to the literature by studying insider trading at the portfolio level, by analyzing the impact of personal characteristics of insiders (including gender, tenure and eagerness), focusing both on studying the buying and selling behavior across both OMs and OTC and analyzing firsthand the impact of a macroeconomic shift on insider trading performance.

Order Choices: An Intraday Analysis of the Taiwan Stock Exchange

This paper employs order-, trade-, and quote-level data to examine the determinants of order choices and the impacts of order choices on execution quality by various investor types in the Taiwan Stock Exchange. We find marketable-quote orders have a higher degree of price aggressiveness, larger order size, higher trade value, shorter duration, and higher fill rate than behind-the-quote orders. There exists a transient order serial correlation. Different types of investors have their own preferences in order choices, while market microstructure factors, such as transitory volatility, spread, market depth, and trading interval, significantly influence stock traders’ order choices. Findings show that marketable-quote orders tend to perform better in terms of order duration. Moreover, institutional investors spend less time on completing their trades than do individuals, particularly for foreign investors after controlling all other factors.

FEATURES OF THE APPLICATION OF FUNDAMENTAL ANALYSIS FOR A PORTFOLIO OF SHARES OF PUBLIC COMPANIES INVESTING IN CRYPTO ASSETS

The article discusses the strategy of indirect investment in cryptocurrency assets by compiling an investment portfolio of shares of public technology companies operating in the developing cryptocurrency market or investing in cryptocurrencies. When forming such a specialized portfolio, you can get the maximum level of profitability with a lower risk than when investing in cryptocurrencies directly, but only if you improve the methods of fundamental analysis. This is the goal set for achieving this research. The authors use the metrics of the world's largest analytical portals: GuruFocus, YahooFinance, Zacks, unfortunately, are not very popular among both Russian stock traders and portfolio investors. Each share, based on the values of the coefficients, is assigned a rating score, on the basis of which the coefficient of investment attractiveness is calculated. The purpose of portfolio diversification by sector and industry is to reduce non-systematic risk measured using the beta coefficient and the standard deviation of profitability. The results of calculations of the profitability and risk indicators of the formed portfolio are compared with the “benchmark” - the leading stock and cryptocurrency indices and conclusions are drawn about the feasibility of investing in this sector of the stock market.

Market Reactions to the Announcement of China’s Resource Tax Law

ABSTRACT This study employs event study methodology to examine the impacts of China’s Resource Tax Law on corporate firms, from the perspective of stock traders. Our results demonstrate that most sectors experience negative market reactions from 41 to 121 trading days around the announcement rather than immediately. Regarding the determinants of market reactions, we find that research and development (R&D) investments are positively related to cumulative abnormal returns. Moreover, our additional tests show that R&D investments have different impacts on various subsamples. Further, the empirical results remain robust when controlling for potential endogeneity issues.

Can investors profit by utilizing technical trading strategies? Evidence from the Korean and Chinese stock markets

The idea of this study is derived from observing the profitability of stock investments following the phenomena of continuously rising (or falling) prices of stocks and continuously overbought (or oversold) signals emitted by technical indicators. We employ the standard event study approach and technical trading strategies to explore whether investors would exploit profits in trading the constituent stocks of the Korea Composite Stock Price Index 50 and Shanghai Stock Exchange 50 when the aforementioned continuous phenomena occur. We find that both the Korean and Chinese stock markets are not fully efficient; this finding may enhance the robustness of the existing literature. In addition, we reveal that contrarian strategies are appropriate for the trading stocks listed on the Korean stock market for all the cases investigated in this study. However, momentum strategies are appropriate for the Chinese stock market when continuously rising stock prices and overbought signals are simultaneously observed. These findings imply that the difference in investor behaviors between the Korean and Chinese stock markets might result in dissimilar trading strategies being employed for these two markets.

Household investment diversification amid Covid-19 pandemic: Evidence from Chinese investors

This paper finds the degree of investor's investment diversification is found to be positively correlated with confirmed cases in the city where they live. This relationship is a result of stricter quarantine policy adopted by cities hit with more severe Covid-19 outbreak, which has led to increased time for investors to trade stocks and seek advisory. In addition, the employment of investors in these cities is often negatively affected. Therefore, they tend to hold a relatively pessimistic view on expectations of future income, and thus be more cautious and risk-averse in terms of investment decisions.

Reaksi Pasar Saham Jakarta Islamic Index Sebelum dan Sesudah Pengumuman Kabinet Indonesia Maju

The purpose of this study was to determine the significant differences in the TVA(trading volume activity) and AR(abnormal return) on the JII stock for the observation period before and after the announcement of Indonesia Maju Cabinet 2019. The data in this study were secondary data obtained from Indonesia Stock Exchange(IDX) and Yahoo Finance with event windows of 2 days before and 2 days after the announcement. Data processing in this study used paired sample t test and Wilcoxon signed rank test with the Shapiro Wilk test as a requirement to determine the normality point of the data distribution. The study results indicated that there were a disvergence in TVA and there was no significant disvergence in AR before and after the announcement of Indonesia Maju Cabinet 2019 which represents the market reaction to the event when viewed from trading activity and stock prices. Keywords: Trading Volume Activity, Abnormal Return, Jakarta Islamic Index

The relevance of using ABC inventory analysis in profitability management of the enterprise with the purpose of optimizing working capital

Introduction. The competition of goods and services increasingly goes beyond the market nowadays and is transformed into competition of enterprises for consumers. The consumer forms the volume of sales of the company's products, and as a result, this leads to an increase in its income and profitability. In turn, the company must maintain the appropriate amount of working capital - trade stocks, stocks, resources, because their excess makes the company inert, and the disadvantage - leads to a violation of the normal production process and opportunities for increasing sales volumes. Therefore, maintaining the optimal amount of working capital is one of the important problems of managing the company's current assets. Purpose. The purpose of the study is to study the possibilities of forming the optimal structure of commodity stocks, as one of the components of the Goal. The purpose of the study is to study the possibilities of forming the optimal structure of commodity stocks, as one of the components of the working capital of the enterprise. enterprises. Method. Scientific works on the analysis and management of the company's working capital are the methodological basis of the research. An example of the practical application of the ABC analysis method is given. This method is indispensable when forming the nomenclature of goods (product range) and their stocks, which make up the most specific weight in the structure of the working capital of the enterprise. The formation of the stages of the ABC analysis allows you to clearly demonstrate the order of choosing the most important factors when making a management decision regarding the optimal structure of the working capital of the enterprise. The versatility of this ABC analysis method is emphasized. It seems appropriate to note that such an economic category as the consumer value of the product, which directly affects the size of the company's reserves and the working capital of the company, can be analyzed using this method. The results. The adaptability of the ABC analysis to the industry specifics of the business entity and to the specified task of managing the production and economic process of the enterprise is considered. On the basis of empirical studies given in the example, the ratio of groups A, B and C according to the criterion of sales value is demonstrated. The expediency of using the results of ABC analysis in the formation of the optimal structure of working capital in the part of goods is substantiated on the example of a trading company. The practical implementation of ABC analysis will allow to form an appropriate information and analytical base for the formation of effective management actions aimed at increasing the production capacity of the enterprise and increasing its profit.

The Formulation of Stock Trading Strategy Summary

Stock traders can maximize their total income by making scientific and reasonable transactions. According to the daily price flow, this paper determines whether traders should trade the assets in their portfolios. By establishing prediction models of future output value of different investment financial projects and quantitative analysis and sensitivity analysis of losses, we can combine the investments of financial products and evaluate their future value .Finally, the sensitivity analysis, advantages and disadvantages evaluation, error analysis and improvement of the model are carried out in this paper, so that the whole model can better provide guidance and practice when dealing with stock market transactions.

The Impact of Online Trading from a Personal and Technical Perspective on Trade Stocks in Emerging Markets

The Impact of Online Trading from a Personal and Technical Perspective on Trade Stocks in Emerging Markets

Manipulating Citadel: Strategies to Profit at the Expense of Retail Stock Traders’ Market Makers

Manipulating Citadel: Strategies to Profit at the Expense of Retail Stock Traders’ Market Makers

Heterogeneous stock traders, endogenous bubbles, and economic fluctuations

The impact of stock market bubbles on the real economy has always been an important topic. By introducing heterogeneous transactions of behavioral economics into a standard dynamic stochastic general equilibrium (DSGE) model, this paper finds, first, that heterogeneous trading behaviors cause an endogenous stock bubble. Second, the endogenous inverted U-shaped bubble affects the real economy through the borrowing constraint channel, which causes an inverted U-shaped output and promotes the output to rise against the decline of the total factor productivity (TFP) shock. Third, the positive bubble and increasing bubble dominate in promoting the output and investment of the real economy.

Https://www.ijeast.com/papers/134-138,Tesma608,%20IJEAST.pdf

Time series data is an ordered set of data points, which are values of a variable at different points in time, spaced at equal intervals. In other words, time series represents values of a specific variable measured at a constant time frequency. An example is of temperature of a city measured at 11 AM every day for 30 consecutive days. Analysis of time series data to forecast and predict the future finds use across multiple industries. A very important application of time series analysis is to predict prices of stocks in the future. Stock markets are complicated financial structures where stocks of public companies are traded. Prices of stocks witness great deal of fluctuations and changes over time. Professional stock traders and investors analyze time series data to forecast prices of stocks in the future with reasonable accuracy, which helps them generate higher returns on their investments with minimal risk involvement. Stock prices are highly volatile and react very quickly to changes in the market. Time series forecasting is one technique that helps in developing a model that can accurately predict such drastic and quick changes. In our research, we have made an attempt to analyze the time series data of certain stocks from Indian stock market (NIFTY), using which we have developed a forecasting model to accurately and efficiently predict the prices of these stocks in the future.

NILAI TUKAR DAN INDEKS HARGA SAHAM SEKTORAL SUATU STUDI DI INDONESIA

This research aims to find the symmetric and asymmetric effects of exchange rate on sectoral stock price index in Indonesia. Using monthly data and non-linear autoregressive distributed lag (NARDL) approach, we find a symmetric effects of exchange rate (at the first lag) on infrastructure, mining and trading stocks price index in the short run. Otherwise, there is a symmetric effects of exchange rate (at the second lag) on finance, trade, and manufacturing stocks price index but asymmetric effects existed on infrastructure, mining and property

The external sector shocks and macroeconomics in Nigeria

The study examines the relationship between some key macroeconomic indicators in Nigeria and the external sector. During the period under review, it was discovered that crude oil had a lion’s share of Nigeria’s export earnings and the international demand for the country’s non-oil exports was unimpressive due to the development of synthetic alternatives, discriminative tariffs and new entrants in the global market (Central Bank of Nigeria, 2008). Consequently, most of the research on this topic hinged their framework on shocks from the oil sector (see Lukman and Olomola, 2016). In contemporary times, however, the contribution of crude oil to Nigeria’s gross domestic product has been dwindling. As at 2019, the entire oil and gas industry contributed less than 10% of Nigeria’s gross domestic product (Central Bank of Nigeria (CBN), 2019). There was the need to examine the external sector from a more comprehensive approach and framework. Therefore, this study evaluated the impact of shocks from Nigeria’s terms of trade and major foreign stock market index on macroeconomics in Nigeria. The methodology adopted for this study is the vector autoregressive technique, impulse response function and the error variance decomposition method. The findings show that the gross domestic product, price level and interest rate respond strongly in the short run (1-2 years), gradually fluctuates in the medium term (3-5 years) and become stable in the long run (6-10 years) due to shocks from the Dow Jones index and Nigeria’s terms of trade. Thus, intervention policies should focus on mitigating the impact of external sector shocks on macroeconomics in the short and medium terms when the impact is enormous.

How do investors trade R&D-intensive Stocks? Evidence from hedge funds and other institutional investors

We examine how institutional investors trade stocks with high research and development (R&D) expenses and investigate whether they can detect value-relevant R&D. We document significant differences between hedge funds and other institutional investors in their trading in high R&D stocks. We find that hedge funds (other institutional investors) invest more (less) in high R&D stocks compared to all other stocks. Moreover, hedge funds exhibit strong stock-picking ability in high R&D stocks, and hedge funds with larger allocations to high R&D stocks generate higher future fund returns. Our findings suggest that hedge funds have superior skill in identifying value-relevant R&D.

THE INFLUENCE OF INFORMATION ON TRADING BEHAVIOR WITH PERSONALITY TRAITS AS MODERATION VARIABLE FOR STOCK TRADERS

The development of investment in Indonesia has increased rapidly over the past few years. One of the key factors affecting stock traders' trading behavior is information. Information that was previously difficult to obtain by investors became easily obtained due to technological developments. In addition to information, the characteristics of traders also influence their trading behavior. The population used in this study is the entire citizen of Surabaya, and the sample of this study is stock traders who have a minimum of 1-year trading experience. Data analysis is done by using Partial Least Square with Smart PLS 3.0. The result of this study is that information has a significant effect on trading behavior, while neuroticism, extraversion, openness, agreeableness, and conscientiousness do not moderate the effect of information on trading behavior significantly.