Financial Stock Research Articles

Financial Structure and Stock of Infrastructure, Utilities and Transportation Companies

This study aims to analyze the factors that affect the stock prices of companies in the infrastructure sector. This study utilizes a panel data regression method conducted from 2015 to 2022, using quarterly reports from seven different companies. According to the findings, four elements have a major impact on the stock price. The return on assets (ROA), price to book value (PBV), and total asset turnover (TATO) have a positive impact on stock prices, while debt to equity ratio (DER) has a negative impact. These findings are consistent with previous research on factors that affect stock prices. Companies should pay close attention to consistency in financial ratio indicators that affect stock prices, as demonstrated by this study. Financial ratio volatility is associated with higher risk and lower investor trust.
 Keywords: Company; Performance; Stock; Infrastructure; Ratio
 
 Abstrak
 Tujuan utama penelitian ini adalah untuk mengidentifikasi faktor-faktor yang berkontribusi terhadap fluktuasi harga saham. Penelitian ini menggunakan metode regresi data panel yang dilakukan dari tahun 2015 sampai dengan 2022, dengan laporan triwulanan dari tujuh perusahaan yang berbeda. Price to book value (PBV), return on assets (ROA), dan total asset turnover (TATO) memiliki pengaruh positif terhadap harga saham, sedangkan debt to equity ratio (DER) memiliki pengaruh negatif. Temuan ini konsisten dengan penelitian sebelumnya mengenai faktor-faktor yang mempengaruhi harga saham. Perusahaan harus memperhatikan konsistensi indikator rasio keuangan yang mempengaruhi harga saham, seperti yang ditunjukkan oleh penelitian ini. Volatilitas rasio keuangan dikaitkan dengan risiko yang lebih tinggi dan kepercayaan investor yang lebih rendah.
 Kata Kunci: Perusahaan; Kinerja; Saham; Infrastruktur; Rasio

Retail Financial Innovation and Stock Market Dynamics: The Case of Target Date Funds

ABSTRACTTarget date funds (TDFs) are designed to provide unsophisticated or inattentive investors with age‐appropriate exposures to different asset classes like stocks and bonds. The rise of TDFs has moved a significant share of retirement investors into macrocontrarian strategies that sell stocks after relatively good stock market performance. This rebalancing drives contrarian flows across equity mutual funds held by TDFs, stabilizing their funding, and reduces stock returns for stocks disproportionately held by these funds when stock market returns are relatively high. Continued growth in TDFs and similar investment products may dampen stock market volatility and increase the transmission of shocks across asset classes.

Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model

Accurately predicting volatility has always been the focus of government decision-making departments, financial regulators and academia. Therefore, it is very crucial to precisely predict the realized volatility (RV) of the stock price index. In this paper, we take the RV sequences of Shanghai Stock Exchange Composite Index (SSEC), Standard & Poor 500 index (SPX) and Financial Times Stock Exchange Index (FTSE) as the research objects, and propose a predictive model based on optimized variational mode decomposition (VMD), deep learning models including deep belief network (DBN), long short-term memory network (LSTM) and gated recurrent unit (GRU), and reinforcement learning Q-learning algorithm. Firstly, the original RV sequence is decomposed by using the VMD ideal parameters optimized by grey wolf optimizer (GWO) to obtain the intrinsic mode functions (IMFs). Then, DBN, LSTM and GRU are used to predict same IMF simultaneously. Finally, the optimal weights of the above three models are determined by the Q-learning algorithm to construct an integrated model, and the final results are obtained after accumulating the predicted values of each IMF. The predictive performance of the model was evaluated by four loss functions: the mean average error (MAE), mean squared error (MSE), heterogeneous mean average error (HMAE), heterogeneous mean squared error (HMSE) and modified Diebold and Mariano test (MDM). The experimental results show that the constructed GVMD-Q-DBN-LSTM-GRU method has better performance that the comparison model in both emerging and developed markets.

THE INFLUENCE OF CURRENT RATIO ON STOCK PRICE IN MANUFACTURING COMPANIES IN MULTIPLE INDUSTRY SECTORS LISTED ON THE INDONESIA STOCK EXCHANGE FOR THE 2018-2020 PERIOD

This study aims to find out how the IDX's Current Ratio affects stock prices in manufacturing companies in various industrial sectors for three years, from 2018 to 2020. The company's financial statements and stock prices for the 2018-2020 period, use quantitative secondary data types. There are 11 business populations in this study. This study involves information on financial reports with a time series for the previous 3 years distributed from www.idx.co.id. sample for this study using purposive sampling. This study used a sample of 11 business actors from the previous three years, out of a total of 48 manufacturing business actors from various industries. Based on the findings of this study, stock prices are not significantly affected by the Current Ratio.

PENGARUH RASIO LIKUIDITAS, PROFITABILITAS, SOLVABILITAS DAN HARGA SAHAM TERHADAP PENGUNGKAPAN ISLAMIC SOCIAL REPORT DI PERUSAHAAN SEKTOR KESEHATAN

ISR as a report that is not mandatory makes this disclosure interesting to be examined and juxtaposed with financial ratios, namely liquidity, profitability, solvency and stock price ratios. This study examines these financial ratios against simultaneous and partial ISR disclosures in health sector companies listed in ISSI for the 2017-2021 period. This study applied quantitative methods and multiple linear regression, the samples in this study were selected by purposive sampling techniques. The companies gathered are 12 companies. The results of the regression analysis carried out were simultaneously and partially variable liquidity, profitability, solvency, and stock price ratios, which did not significantly affect ISR disclosure.

Does Capital Structure, Profitability, and Dividend Policy Improve Stock Prices?

Purpose: The aim of this study is to investigate how capital structure, profitability, and dividend policy impact the stock prices of companies in the consumer goods manufacturing industry sector listed on the Indonesia Stock Exchange from 2018 to 2020 Theoretical framework: The theoretical framework is based on agency theory and financial management. It focuses on the relationship between financial structure, profitability, dividend policy, and stock prices. Financial structure is measured by the debt-to-equity ratio, profitability by return on assets, and dividend policy by the dividend payout ratio. The framework highlights the fundamental factors that affect a company's financial performance, which in turn influence its stock price. Design/methodology/approach: The data utilized for this research is quantitative in nature, consisting of financial ratios and approached descriptively. Descriptive statistics and panel data regression analysis were employed for data analysis with the aid of the Eviews 12 Student version application. Findings: The Results of the research indicate that capital structure, profitability, and dividend policy have a simultaneous impact on the closing price of stocks. Partially, the debt-to-equity ratio, which proxies for capital structure, has a significant and negative effect on the stock price. However, return on assets, which proxies for profitability, does not seem to have any influence on the stock price. Moreover, the dividend payout ratio, which proxies for dividend policy, also does not seem to have an impact on the stock price. Research, Practical & Social implications: The findings can be used as a basis for further research on the impact of other financial factors on stock price. Investors can use the results to make investment decisions, such as adjusting their portfolio based on the capital structure and dividend policies of the company. The study contributes to the development of the Indonesian stock market by providing insights for investors, analysts, and companies on the factors that affect the stock price Originality/value: The emphasis on the importance of considering not only financial ratios but also external factors when making investment decisions. It highlights the need for companies to maintain good financial performance and keep their DER ratio under control to maintain investor confidence and ensure financial stability.

THE INFLUENCE OF LIQUIDITY, SOLVENCY, PROFITABILITY, AND COMPANY SIZE ON STOCK PRICES

Providing empirical evidence regarding liquidity, solvency, profitability, firm size on stock prices is the purpose of this research. Secondary data through the collection of financial statements of energy companies for 2017-2021 obtained through the official website of the Indonesia Stock Exchange (www.idx.co.id) and the Indonesia Finance Market (www.finance.yahoo.com) are data used in research. Using data in the form of company financial statements and company stock prices. The sampling technique used was purposive sampling. Path analysis with WarpPLS 7.0 software as the analysis tool is applied. With the results of the observation is the effect of liquidity, profitability, and company size with stock prices, however, solvency has no effect on stock prices.

Financial Ratios and Stock Return in the Food and Beverage Company

This study uses financial ratio analysis to investigate the company's stock returns. This study aims to determine the dominant influence of several financial ratios on stock returns. Financial ratios are helpful in evaluating relied returns. The quantitative research approach is used to analyze how financial ratios affect stock returns. The population of this study is companies in the food and beverage sector listed on the Indonesia Stock Exchange. The analysis was carried out from 2017 to 2020 using secondary data sources. Purposive sampling is used in this study, with the sample chosen based on predetermined standards and subjected to multiple linear regression analysis. The analysis' findings demonstrate that DER and TATO have no significant effect on stock returns, while CR, ROA, and ROE have a significant effect on stock returns. optimal corporate profitability is not always achieved by having too many current assets. Additionally, unproductive management of surplus and unused funds will impact declining stock returns. The results of this study indicate that idle current assets cannot generate company profitability because the current ratio is too high indicating an excess of current assets relative to fixed assets.

GOVERNMENT POLICY REGARDING COAL AND NICKEL EXPORT BAN THE IMPACT ON THE INDONESIAN MINING INDUSTRY

Financial performance is a prospect for the company's financial growth. The company's financial performance will always be different in each company and each period. This study aims to analyze the effect of government policy regarding the ban on the export of coal and nickel in the form of raw materials on the company's financial performance by looking at the stock prices and sales of related companies. In this study, researchers used secondary data by processing data through eviews-12. The company's annual financial report data were obtained from www.idxchanel.com and stock price data obtained from id.investing.com. company's financial performance affects stock prices. The research results prove that government policies affect the company's financial performance and company's stock price. In this study it can be seen that the return on assets and debt ratios have an effect on stock prices while the current ratio has no effect on stock prices.

Stock Market Prediction from Sentiment and Financial Stock Data Using Machine Learning

Forecasting stock values is challenging due to market volatility and numerous financial variables, such as news, social media, political changes, investor emotions, and the general economy. Predicting stock value using financial data alone may be insufficient. By combining sentiment analysis from social media with financial stock data, more accurate predictions can be achieved. We use an ensemble-based model employing multi-layer perceptron, long short-term memory, and convolutional neural network models to estimate sentiment in social media posts. Our models are trained on AAPL, CSCO, IBM, and MSFT stocks, using financial data and sentiment from Twitter between 2015-2019. Results show that combining financial and sentiment information improves stock market prediction performance, achieving a next-hour prediction performance of 74.3%.

Shock transmission between crude oil prices and stock markets

This paper investigates the connectedness between crude oil prices and several financial stock markets from January 2000 to February 2023, applying a Dynamic Conditional Correlation Skew Student Copula model and the connectedness index by Diebold and Yilmaz (2012). The Kendall’s Tau correlation findings show that prior to the Global Finance Crisis, equity markets were less linked to BRENT than after the crisis, likely because of economic growth reducing the importance of oil prices. However, from late 2008 to 2023, global uncertainty due to various crises and conflicts led to greater correlation among oil prices and major equity indices. Importing countries showed negative pairwise dependence with BRENT more frequently than exporting countries, which can be attributed to their economic structure and dependence on oil. Thus, BRENT can serve as a hedge asset for equity investments in importing countries and just as a diversifier for exporting countries due to its weak negative correlation. Additionally, stock markets within oil-exporting countries, such as Norway (OSEAX) or Russia (RTS), are net receivers. The study concludes with a detailed analysis of connectedness in terms of the returns and volatility among crude oil prices and stock markets not only during different political and financial events but also in the aftermath of natural disasters that have affected the interdependencies between them. The results prove that the occurrences that influence the correlation between stock markets and BRENT do not have such a significant impact, but the spillover effect of these events dissolves at a leisurely pace. The findings of this study clearly show implications for various market participants, policymakers, and portfolio managers.

Stock Market Forecasting

Abstract: Stock market forecasting seeks to determine the worth of a firm’s financial stocks in the future. Machine learning is being used in recent developments in stock market forecasting technology to produce forecasts based on the values of current stock market indices by training on their previous values. Future stock price projections can be difficult to make when trying to anticipate the stock market. It is incredibly challenging to forecast the stock market since shares fluctuate so frequently. Every day and frequently, stock. Foreseeing trends in the stock market is often correct using this method. This study forecasts the closing prices of numerous corporations using Long-Short Term Memory (LSTM) methodologies. These models are assessed using RMSE, which is one of the commonly used error measures. LSTM works better than SVR, as shown by the experiment's findings

Stock Price Prediction Using Time Series Forecasting

Abstract: Accurately predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and increasing computing power, programmed forecasting methods have proven to be more efficient in predicting stock prices. In this work, artificial neural networks and random forest techniques are used to, predicted the next day's closing prices for five companies in various industries. Financial Data: Use the opening, high, low and closing prices to create new variables that will be used as inputs to the model. The model is evaluated against the standard strategy metrics RMSE and MAPE. The low values of these two indicators indicate that the model is efficient in predicting the closing price of the stock. Accurately predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and increasing computing power, programmed forecasting methods have proven to be more efficient in predicting stock prices. In this work, artificial neural networks and random forest techniques are used to, predicted the next day's closing prices for five companies in various industries.

Whether to Invest in Luxury goods based on Risk, Background, and Market

The luxury goods industry, the aviation industry, and the hotel industry all fall into the category of discretionary goods in people's lives. However, in the last 3 years, the spread of Covid-19 has affected all three sectors to a greater or lesser extent. Through the method of comparison, we can intuitively show the differences in the investment of financial stocks in these three industries. LVMH, AAR.Corp and Marriott International are representatives of luxury goods, aerospace, and hotels respectively, among which AAR.Corp is a small-cap stock with more characteristics. Analyse from a risk perspective, risk: LVMH<Marriott International<AAR.Corp.Analyse from a profitability perspective, profitability: AAR.Corp<Marriott International<LVMH.Analyse from a market perspective: value investors will invest in AAR.Corp.PEG investors may invest in Marriott, and LVMH would be on their watch list until its PEG ratio is under 2. Income investors won't invest in these three stocks since their dividend yield is less than 3%. This paper can make investors more clear about the stock market situation of non-essential goods, to help them choose suitable stocks.

GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk. However, the lack of nonlinear structure in most approaches means that conditional variance is not adequately represented in the model. On the contrary, the recent rapid development of deep learning methods is able to describe any nonlinear relationship in a clear way. We propose GARCHNet, a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators in GARCH. The variance distributions considered in the paper are normal, t and skewed t, but the approach allows extension to other distributions. To evaluate our model, we conducted an empirical study on the logarithmic returns of the WIG 20 (Warsaw Stock Exchange Index), S&P 500 (Standard & Poor’s 500) and FTSE 100 (Financial Times Stock Exchange) indices over four different time periods from 2005 to 2021 with different levels of observed volatility. Our results confirm the validity of the solution, but we provide some directions for its further development.

The Impact of Financial Distress on Corporate Performance in the Chinese Securities Market

This research aims to provide an in-depth investigation into the effects of financial distress on the operational and financial performance of companies. It employs a longitudinal study on a broad sample of listed corporations in the Chinese security market, analyzing their financial reports, stock performance, and various distress indicators over the past decade. The study also factors in macroeconomic conditions, regulatory changes, and industry-specific issues. The paper seeks to understand how financial distress influences firm performance, profitability, and survival, while also highlighting the strategies that distressed companies have employed to navigate these challenging circumstances. Furthermore, the research explores the role of governmental policies and corporate governance in mitigating the adverse effects of financial distress. This study will be particularly beneficial for stakeholders in the Chinese securities market, providing crucial insights for decision-making in financially turbulent times.

Financial Indicators and Stock Market Performance in Nigeria

This study examined financial indicators and stock market performance in Nigeria. The low performance of the stock market has been attributed to so many factors, ranging from financial, macroeconomic to political and institutional. Hence, the study examined the relationship between financial indicators and stock market performance, using the econometric analytical technique. Annual time series data sourced from the Central Bank of Nigeria and Nigerian Stock Exchange were employed. Stock market performance was measured by all-share index while financial indicators were represented by stock market profitability, liquidity and efficiency. The bound testing and Autoregressive Distributed Lag model estimation techniques were employed for the analysis. It was revealed that a long run relationship exists among the variables in the estimated model. A positive relationship exists between market profitability, liquidity, efficiency and stock market performance in Nigeria with coefficients of 58.98, 879.58 and5152.51, respectively. The implication of the findings is that market profitability, market liquidity and market efficiency have positive impact on stock market performance in Nigeria. Hence, the study recommends that; there is need for implementing policy such as good corporate governance that will maximize profit for investors in the market, make it attractive and hence deepen the Nigerian stock exchange market; the provision of windows for firms to raise cash through issuance of new shares, debentures, loans and deposits, when needed and enhance the ability of the firms to convert assets to cash to pay its current liabilities and its debt capacity;while a high trading volume should be maintained to enhance efficiency of the Nigerian capital market.

Assessing the impacts of Covid-19 on stock exchange, gold prices, and financial markets: Fresh evidences from econometric analysis

This research analyses the relative efficacy of gold price, financial market, and stock exchange hedging against sectoral and industry-level global stock market returns. Incorporating Gold into equity-based asset allocation techniques and assessing the stock market and financial sector during the COVID-19 epidemic is one way to diversify your portfolio and reduce risk. After orthogonalizing raw returns concerning a robust collection of relevant universal variables, we conduct our analysis inside a bivariate GARCH(p, q) framework. To further assess ideal portfolio proportions and the efficacy of hedging methods, we expand the volatility spillovers study by calculating the optimal weights for a minimal risk portfolio and determining the hedge ratio. In high-volatility environments, our results show which financial market and stock exchange sectors and industries investors should prioritize to minimize the risk and maximize reward. Use of country-specific macroeconomic variables indices to supplement the worldwide index, (3) separate analysis for the COVID-19 first wave due to the existing argument that the pandemic raises unexpected market events and our early data showing co-movement among the three unpredictability metrics during the pandemic. These findings have important implications for portfolio entrepreneurs and business investors looking to buy international equities.

Pengaruh Profitabilitas, Solvabilitas dan Rasio Pasar terhadap Harga Saham pada Perusahaan Perbankan yang Terdaftar di Bursa Efek Indonesia Periode 2018-2021

When it comes to a country's economic development and stability, banking, the beating heart of the global economy, plays a crucial role. Therefore, many existing and potential investors are attracted to invest in this industry. But before jumping into the capital market, investors need to pay attention to several factors, including a thorough examination of the company's financial statements. This study will examine the relationship between profitability, solvency, and market ratios seen in banking businesses' financial statements and stock prices. Purposive sampling was utilized to acquire data from 47 of the financial institutions included in the IDX for the years 2018-2021. The hypotheses were evaluated by a battery of tests employing linear regression analysis. Hypothesis testing reveals that profitability, solvency, and market ratios all have an impact on the stock prices of Indonesia Stock Exchange-listed banking businesses from 2018 to 2021. (simultaneously). Then, throughout the 2018-2021 time, the market ratio to stock prices of IDX-listed banking firms has a positive and substantial influence on stock prices, while the profitability and solvency of IDX-listed banking companies have a negative and large effect on stock prices.

Financial System, Audit and Corporate Governance During and After the Financial Crisis

A financial system is a set of institutions, such as banks, insurance companies, and stock exchanges, that permit the exchange of funds. Financial systems exist on firm, regional, and global levels. Borrowers, lenders, and investors exchange current funds to finance projects, either for consumption or productive investments, and to pursue a return on their financial assets. The financial system also includes sets of rules and practices that borrowers and lenders use to decide which projects get financed, who finances projects, and terms of financial deals. The concept of "internal control" conveys a difficult and complex process. Different countries have different interpretations of the internal control system, and in accordance with their tradition use different methods, techniques and philosophies for its implementation.The financial system of our economy, although it must be acknowledged that it is not very modern and developed, is still quite complex. In support of a complete system with all components, there is a need for harmonized investment in many directions at once. Financial crises and stock market crashes have clearly demonstrated the impact of investors’ sentiment on asset pricing and stock markets’ efficiency. Herd behavior, which is behavioral similarity based on individuals’ interaction that leads to convergence of action and correlated trading (Hirshleifer and Teoh, 2003), is one of the most important behavioral biases that is more likely to occur during periods of market stress when individual investors prefer to follow the market consensus, being reluctant to follow their own knowledge or beliefs (Christie and Huang, 1995). Management on all levels must have full confidence in the integrity and independence of the audit process.Financial system, audit and corporate governance (CG) has come to the forefront of academic research due to the vital role it plays in the overall health of economic systems. The wave of U.S. corporate fraud in the 1990s was attributed to deficiencies in corporate governance. The recent 2008-2009 global financial crisis, triggered by the unprecedented failure of Lehman Brothers and the subprime mortgage problems, renewed interest on the role of corporate governance in the financial sector. The main purpose of this scientific paper is to study financial situation, audit and governance and strategies for optimizing investment, concretelly in the albanian financial environment. Keywords: Financial crisis, corporate governance, emerging economies etc. DOI: 10.7176/RJFA/14-10-01 Publication date: May 31 st 2023