Financial Stock Research Articles

Development and validation of a performance measurement system based on Islamic principles

The infamous malfeasance of corporate giants and the collapse of a financial system with global reverberations have further pressured the need for greater ethical discipline and consideration in business and finance. This study explored the motivations within firms as driven by their performance measurement systems (P.M.). Then, the study developed a newly proposed P.M.S. with greater ethical emphasis based on Islamic principles to be a foundation for augmented Sharia-compliant screening criteria for Islamic equities. Discourse analysis of the Islamic religious texts was used, followed by validation through interviews with scholars and practitioners. Results indicate that improving the current Sharia screening criteria is possible by expanding the qualitative and quantitative assessments to include indicators that consider shareholders, the board of directors and top management, business dealings and products, employee relations, community issues and environmental protection. This study has implications for regulators such as Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI), Islamic Financial Services Board (IFSB) and Sharia-compliant screening criteria users such as the Dow Jones Islamic Market (DJIM), Financial Times Stock Exchange (FTSE) and Standard and Poor's (S&P) that may consider expanding their current equity screening criteria which mainly depends on the issuer's business activity and narrow quantitative measures. This version: June 28, 2022.

A Comparison of Linear Regression, LSTM model and ARIMA model in Predicting Stock Price A Case Study: HSBC’s Stock Price

It is widely assumed that the stock market is a key aspect of the financial market and stock price forecasting has become a popular topic of an in-depth study by financial technologists. With the advent of financial markets, stock prices face several analytical and forecasting challenges. In this paper, the stock price trend forecast analysis is carried out and the survey object was the transaction data of HSBC from 2010 to 2019. Linear regression, LSTM model and ARIMA model are used to forecast stock price trends. The models’ prediction accuracy is demonstrated after the cross-validation process by combining error indicators and trading performance. As a consequence of the analysis, we discover that LSTM has the lowest error value, which implies that it is ideally suited to stock market forecasting. On the contrary, linear regression holds the largest mean square error, implying that this method has the weakest fitting for the stock market.

The Impact of Applying International Financial Reporting Standards on Improving Financial Performance in Commercial Banks

Over the past decades, the world has witnessed radical changes on many levels, including the economic, financial, capital markets, and the growth of multinational corporations, which led to an increase in the demand for financial and accounting information that is understandable, reliable, and transparent, and comparable across international borders. The International Reporting Standard (IFRS) Fifi helps in carrying out this task by comparing the financial performance of institutions across countries.The main objective of this study is to demonstrate the impact of the application of international financial reporting standards on the improvement of financial performance and to compare the effects of financial ratios results in the field of liquidity, profitability and indebtedness, as the study was applied in The commercial banks were used as a sample for the study, and the main performance indicators were used through the financial ratios of the selected banks for the data related to the annual report published according to the International Financial Reporting Standards (IFRS) for the past four years before and after the habituation (IFRS). Ratios are important for different stakeholders, especially investors, so this study focused on the extent of the difference between the results of the financial ratios applied to the financial data prepared under both the unified accounting system and the international financial reporting standards for a sample of commercial banks listed in the Iraq market for financial securities of the Iraqi Commercial Bank , and the Iraqi divine knowledge, and the study reached a set of conclusions, the most important of which is the difference in the accounting rules and practices in light of both the international accounting standards and the unified accounting system, as the results of the study showed that due to the lack of familiarity with the international financial reporting standards in full, it had a slight impact as the recommendations of the study On financial ratios, and there are still many restrictions in front of the application of these standards in their correct form, whether these restrictions are related to local accounting policies or related to an efficient market due to the availability of fair value for many items of the general budget when preparing financial reports. The study recommended the need for banks to fully apply the international financial reporting standards for the purpose of demonstrating their impact on financial performance, as well as the study recommending the existence of an independent supervisory body to ensure the extent of the commitment of banks listed in the financial stock market to apply the international financial reporting standard.

Fundamental Analysis of Financial Ratios in Stock Price: Do Loss and Firm Size Matter?

Purpose: This study aims to examine the relationship between financial ratios and stock price, and we further test the variables in the subsamples of loss or profit and the firm size.Method: This study used non-financial companies listed on Indonesia Stock Exchange (IDX) from 2010-2020.Findings: The result shows that all financial ratios used in this study are positively associated with the stock price, except the solvability ratio is negatively associated with the stock price. Furthermore, in the subsample of companies that experience losses, only a few financial ratios have a relationship with stock prices. Then, the companies that have a small size show an insignificant liquidity ratio. This result is robust using coarsened exact matching (CEM).Novelty: The results add to the literature regarding the ability of financial ratios to stock prices and especially provide new evidence from loss or profit and the firm size in Indonesia.

Volatility transmission dynamics between energy and financial indices of emerging markets: a comparison between the subprime crisis and the COVID-19 pandemic

PurposeThis paper aims to analyze the volatility transmission between an energy stock index and a financial stock index in emerging markets during recent high instability periods. The study considers the impact of both the period under analysis and the data frequency on the direction and intensity of the contagion, as well as the effect of the potential spillovers on the risk measures. These questions still lack definitive answers and have become more relevant in a context of financially unsettling events such as COVID-19 crises.Design/methodology/approachThis study employs an extension of the dynamic conditional correlation (DCC) model that allows for the time-varying dependence relationship between the variables. This dependence is analyzed at daily, weekly and monthly basis using data from the Bloomberg platform on energy and stock market indices for emerging markets between 2001 and 2021.FindingsThe results for a sample spanning from 2001 to mid-2021 show bidirectional volatility transmission on a daily basis, whereas only evidence of volatility transmission from the financial to the energy exists for weekly and monthly frequencies. However, considering different subsamples of daily data, the authors only find volatility transmission from financial (energy) index to the energy (financial) during the Great Recession (COVID-19) as a consequence of the different source of the shock and transmission channels.Originality/valueThis study reveals that volatility transmission between energy and stocks in emerging markets has changed and presents a unidirectional pattern from energy to financial markets during the COVID-19 period in contrast to calm and the sub-prime crisis intervals. These results differ from previous studies, focused on global markets, that show bidirectional spillovers during this period.

Identifying influential financial stocks using simulation with a two-layer network

Risk spillover from one stock to another tends to create a contagion effect in the stock market. Fire sales due to the overlapping portfolios of mutual funds can amplify the contagion risks, leading to a downward spiral of stock prices. In this paper, we simulate this downward spiral phenomenon for the Chinese financial stocks based on a two-layer network structure and aim to identify the influential financial stocks based on their individual induced systemic risks. Our findings show that stock liquidity and the concentration of funds’ holding on stocks play important roles in determining systemically important financial institutions. Our results also confirm the statements of “too-big-to-fail” and “too-interconnected-to-fail” of financial institutions in the Chinese market. Our results show that a more sensitive flow-performance relationship of mutual funds can amplify the contagion risk by 41%. However, the magnitude can be more drastic in a low market liquidity scenario, where the contagion risk is boosted by 160%.

The impact of displaced commercial risk on the performance of Islamic banks

Islamic banks are exposed to a particular risk that stems from one specific service that they offer: the collection and management of deposits in the form of unrestricted profit-sharing investment accounts. These accounts are the source of displaced commercial risk, which arises from assets managed on behalf of investment accountholders, effectively being transferred to the Islamic financial institution's own capital stock. This occurs because, when considered necessary, banks do not pay part or all of their due Mudharib shares, which would otherwise be paid to depositors, as a result of commercial pressure to increase returns. This study assesses whether this risk hinders the performance of Islamic banking institutions already struggling to compete for deposits in a system dominated by well-established conventional banks. Using the methodology developed by Beck and Katz (1995) to analyze data collected from Bahraini banks, the study demonstrates the existence of a negative and statistically significant relationship between DCR and the performance of Bahraini Islamic banks. This result suggests that increases in DCR are driven by dissatisfied profit-motivated depositors wishing to transfer their funds to more rewarding investments (either Islamic or conventional). Furthermore, an upsurge in this particular risk impels the bank to accumulate more investment reserves, which jeopardizes their profitability, especially when they are kept as idle cash or invested in illiquid assets. Therefore, our results provide regulators and decisionmakers in both Bahrain and other countries where Islamic and conventional financial institutions coexist with a better understanding of the unrestricted profit and loss sharing contracts of Islamic banks.

The resilience of Islamic banking efficiency in the face of the Covid-19 epidemic

Purpose - Since 2020, the COVID-19 pandemic has harmed the Efficiency Islamic banking industry. This study examines the effects of the COVID-19 pandemic on 33 Islamic stock-traded banks in Asia.Method - This study includes public financial statements and stock prices of Islamic banks in Asia. This study compares peer-to-peer of how the Covid-19 pandemic affect 33 Islamic financial institutions in 2019 and 2020. A dummy-covid separated pre-pandemic and post-pandemic periods and bank efficiency was a dependent variable. Least-squares panel data regression, fixed-effects, and random-effects models determine model parameters.Result - This panel regression analysis shows that Islamic banking's usefulness changed after the Covid-19 pandemic. Islamic financial institutions performed better than usual during the pandemic. The analysis results show this clearly.Implication - Islamic banking appears relatively unaffected by the current economic downturn. The Islamic banking sector, which differs from its conventional counterpart in that it is based on the principle of profit sharing, will fare better during economic contractions, according to these findings.Originality - This is the first study to use a cross-country sample of Islamic banks in Asia to analyze the efficiency of Islamic banking during COVID-19.

특수관계자 거래가 주가지연현상에 미치는 영향

[Purpose]Korean firms have a characteristic that ownership is concentrated on specific people and relatives, and it has been reported that controlling shareholders pursue private interests through related party transactions. Firms report related party transactions through footnotes in financial statements, but the disclosure details are different for each firm. Therefore, firms with larger related party transactions have greater information asymmetry, so stock prices are not expected to immediately reflect transaction information.
 [Methodology]The sample period used in this study was from 2011 to 2019, and an analysis was conducted on non－financial firms listed in the Korean stock market. Financial data and stock price data used in the analysis were extracted from the KIS－VALUE database, and data on related party transactions were collected from the TS2000 database. Referring to the study of Hou and Moskowitz (2005), stock price delay, a dependent variable of the study, was measured.
 [Findings]The level of related party transactions had a positive relationship with the stock price delay phenomenon. The result can be interpreted as a slower rate at which stock prices reflect corporate information because firms with larger related party transactions have lower information transparency. In addition, the positive relationship between the size of related party transactions and the stock price delay phenomenon was significant only in chaebol firms.
 [Implications]The results of this study call attention to investors so that they can make careful investment decisions in consideration of the fact that the larger related party transactions, the stronger the stock price delay may appear. In addition, it suggests that regulatory agencies need to reorganize related laws so that details of related party transactions are disclosed in order to prevent market abnormalities such as stock price delays.

THE EFFECT OF DIVIDEND POLICY ON SHARE PRICES OF BURSA MALAYSIA LISTED COMPANIES

The objective of this study is to determine the effects of dividend policies on share prices with an emphasis on companies listed on the FTSE Bursa Malaysia 100 (FBM100) index. These research aims to investigate and examine the effect of dividend policy on the share prices performance of listed companies under FTSE FBM100. This study measures a relationship between dividend policies (independent variables) which include dividend yield, volume traded, dividend payout, and company size, return on invested capital, free cash flow yield, volume traded (control variables) toward share price as dependent variable. The effects of dividend policy on share price movement are one of the most disputed topics in corporate finance. The share price is the most common factor for investors when making decision to buy shares. Various empirical studies can be found examining and analyzing the effect of dividend policy on share prices, however, the results are inconsistent. This study focused on the effect of dividend policy on share prices of companies listed on the FTSE (Financial Time Stock Exchange) FBM100 index from 2011 to 2020.. A total of 56 companies are sampled in this study after filtering, and data were analyzed using the regression model. The results of panel data regression model indicated that dividend payout has an insignificant effect on share prices, however, dividend yield has negative and significant effect on share price. Return on invested capital, volume traded, and company sizes by market capitalization have significant effect on share price while free cash flow yield had no significant effect. As a result, dividend policy has considerable impact on share prices. Thus, the result is important. The findings of this study are expected to serve as a reference for companies in establishing dividend policies that will improve company performance and for investors in considering whether it should retain, increase, or decrease their stake in the company.

Artificial Intelligence Optimization Algorithms in the Finance Sector

Increasing efficiency and security expectations with the technological developments in the financial sector have necessitated more effective solutions in issues such as financial failure prediction, portfolio selection, market analysis, risk assessment, and stock price estimation. At this point, artificial intelligence optimization algorithms come to the fore in terms of lower cost, higher data storage capacity, and transaction volume, performing the same operations in similar problems and creating an effective order to prevent problems that may occur in the future. For this purpose, the use of artificial intelligence optimization algorithms in the finance sector is discussed in this study. To guide their use in decision-making and problem-solving processes, Artificial Bee Colony Algorithm, Tabu Search Algorithm, Flower Pollination Algorithm, Shark Smell Optimization Algorithm, and Differential Evolution Algorithm were explained and examples from their application areas were shared. As a result of the study, the advantages of artificial intelligence optimization algorithms were revealed and their importance in the finance sector was emphasized. This study is especially widely used in the solution of engineering problems; however, it contributes to the literature in terms of explaining algorithms whose applications in the finance sector are still limited.

Novel COVID-19 Outbreak and Global Uncertainty in the Top-10 Affected Countries: Evidence from Wavelet Coherence Approach

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability.

Time Series and Data Science Preprocessing Approaches for Earthquake Analysis

Time series are frequently used today to analyze data that changes over time and to predict future trends. Usage areas of time series data include many applications such as financial market forecasts, weather forecasts, sales forecasts, medical diagnostics and stock management. Among the methods, there are techniques such as autoregressive integration, moving average, long-short-term memory neural network, time series condensation, wavelet transform and Frequency Domain. These techniques are chosen depending on the characteristics of the time series data and their intended use. For example, the ARIMA model is used for variable variance and non-stationary time series, while the LSTM model may be more suitable for capturing long-term dependencies. In this article, it has been tried to prove that time series based artificial intelligence systems can be built on fault movements, which are very difficult to predict on earthquake time series data, and it is quite possible to get useful results. In particular, deep learning methods are among the prominent methods in the article. Deep learning methods are used to detect complex structures and analyze large datasets to produce accurate results. These methods include multilayer perceptrons, long-short-term memory neural network, and radial-based function network. It is also emphasized that factors such as the selection of features used in earthquake prediction, data preprocessing, feature engineering and correct model selection are also important. As a result, the use of artificial intelligence techniques on earthquake time series data has great potential in estimating earthquake risk. Deep learning methods perform better, especially for large datasets, and more accurate results can be obtained with the right model selection. However, factors such as data preprocessing and feature selection also need to be considered.

Financial Literacy, Hassle Factor, and Stock Market Participation: Empirical Evidence from Indonesia Stock Exchange

The stock market plays an important role in economic development. Although stock investment can provide a high rate of return, in reality, stock market participation is still a big challenge in many countries, including Indonesia. This study aims to explore the influence of financial literacy and the hassle factors on the intention to invest and stock market participation. From the survey, valid feedback was obtained from 401 respondents, with domiciles spread across all major islands in Indonesia, including Java, Kalimantan, Riau, Maluku & Papua, Nusa Tenggara & Bali, Sulawesi, and Sumatra. The research method used in this study is a quantitative method with a 5-point Likert scale measurement. The data is processed using SmartPLS 3.0. The study's results found a positive and significant influence between financial literacy on intention to invest and stock market participation. Meanwhile, the hassle factor was found to have a negative and significant effect on the intention to invest, but there was no effect on stock market participation. Finally, the relationship between the intention to invest and stock market participation was found to have a positive and significant effect.

Disclosure of auditor risk assessments in expanded audit reports

PurposeThis rich descriptive study examines auditors' client risk assessment (i.e. “key audit matters”/critical audit matters) disclosures in expanded audit reports of 328 Financial Times Stock Exchange (FTSE) 350 companies. The study compares auditor-identified client risks with corporate risk disclosures identified in audit committee reports, in terms of number and type of risks. The research also compares variation in auditor-identified client risks between individual Big 4 audit firms. In addition, the study examines auditor ranking of their client risks disclosed.Design/methodology/approachThe study manually content analyses disclosures in audit reports and audit committee reports of a sample of 328 FTSE-350 companies with 2015 year-ends.FindingsAudit committees identify more risks than auditors (23% more risks). However, auditor-identified client risks and audit-committee-identified risks are similar (80% similar), as are auditor-identified client risks between the individual Big 4 audit firms. Only ten (3%) audit reports rank the importance of auditor-identified client risks.Research limitations/implicationsSample is restricted to one year, one jurisdiction, large-listed companies and companies audited by Big 4 auditors.Practical implicationsThe study provides important insights for regulators, auditors and users of financial statements by identifying influences on disclosure of auditor-identified client risks.Originality/valueThe paper mobilises institutional theory to interpret the findings. The findings suggest that auditor-identified client risks in expanded audit reports may demonstrate mimetic behaviour in terms of similarity with audit-committee-identified risks and similarity between individual Big 4 audit firms. The study provides important insights for regulators, auditors and users of financial statements by identifying influences on disclosure of auditor-identified client risks.

Comparing neural network with linear Regression for stock market prediction

There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results.

Magic Formula: Sebuah Tinjauan dalam Prediksi Perusahaan-Perusahaan Unggulan di Indonesia

Purpose: This study aimed at investigating the performance of a value investing strategy called the “Magic Formula”, introduced by Joel Greenblatt (2006), applied to the Indonesian Stock Exchange. Magic Formula is a simple stock selection strategy by ranking stocks based on calculating Return on Capital and Earnings Yield. Methodology: First, this study examined stocks listed on the Indonesia Stock Exchange represented by the Kompas 100 index measuring the price performance of 100 stocks that have good liquidity and large market capitalization. This step was followed by eliminating finance stocks because they have different debt postures which will affect the calculation in the next stage. The next step was to choose the top thirty stocks from the combined rank to become a portfolio. Portfolio rebalancing is conducted annually. Sharpe, Treynor, and Jensen’s Alpha analysis were used to measure returns adjusted for risk. Results: This study showed that using return on capital and earnings yields can produce higher average returns than the market index from April 2015 to April 2019. The Magic Formula portfolio yields an average return of 12.76% and the market produces average returns of 6.89% during the period under test. Limitations: The obtained data were derived from 5 years-data of the Indonesia Stock Market. in order to neglect the Efficient Market Hypothesis, the collection of wider data was required. besides, the result of this study is unusable to test the Efficient Market Hypothesis at weak or strong level. Contribution: This study contributes empirical evidence that the Magic Formula is a solid investment model especially considering its simplicity. Magic Formula can be used as an early stage in the process of selecting a company to invest in. Keywords: 1. Value Investing 2. Magic Formula 3. Earning Yield 4. Return on Capital 5. Investment Strategy

How Inflation Could Moderate The Effect of Financial Ratio on Stock Return

Stock return is something that is expected to be obtained by all investors who invest in the capital market. Financial stock returns will occur if the company's profits increase which makes the company distribute dividends. This study aims to test and obtain empirical evidence of the effect of Return on Equity, Debt to equity ratio, and Dividend Yield on stock returns of consumer goods companies listed on the Indonesia Stock Exchange.The research population is manufacturing companies listed on the IDX in 2019-2021. The sample in this study were 46 consumer goods companies which were determined based on purposive sampling method. The analytical tool used to test the hypothesis is moderate regression analysis.The results showed that Return on Equity and Dividend Yield had a positive effect on stock returns. Meanwhile, Debt to equity ratio has a negative effect on stock returns. Likewise, in inflation as a moderating variable where on the variable Return on Equity and Debt to equity ratio inflation does not moderate the effect of Return on Equity and Debt to Equity Ratio on Stock Returns while on the Dividend Yield variable inflation weakens the effect of Dividend yield on Stock Returns. Future research can develop this research by using other variables that in theory have an influence on stock returns, such as interest rates.

Prediction of KLCI Index Through Economic LASSO Regression Model and Model Averaging

The Financial Times Stock Exchange (FTSE) Bursa Malaysia KLCI Index is a key component in the development of Malaysia's economic growth and the complexity in terms of identifying the factors that have a substantial impact on the Malaysian stock market has always been a contentious issue. In this study, the macroeconomic factors of exchange rate, interest rate, gold price, consumer price index, money supply M1, M2, and M3, industrial production, and oil price were discussed by using economic LASSO regression and Bayesian Model Averaging (BMA) with monthly average and monthly end time-series data spanning from January 2015 to June 2021, with a total of 78 observations by using the R Studio. The findings demonstrate that month-end data is better suited for stock market prediction than month-average data and that the BMA model is more suitable than the LASSO model, as seen by lower Mean Square Error of Prediction, MSE(P) and Residual Mean Square Error of Prediction, RMSE(P) values. The exchange rate, gold price, and money supply have a negative association with the dependent variables, while the consumer price index has a positive relationship associated with the dependent variables. The consumer price index is the most significant contributing factor, whereas gold price is the least significant. The result depicted that the KLCI index has no significant relationship with the variables interest rate, money supply M2, M1, industrial production index, and oil price. In conclusion, investors could specifically focus on the positive contributor and put lesser attention on improving their portfolio return.

Estimation of stock market index volatility using the GARCH model: Causality between stock indices

This paper aims to model the volatility of returns for selected stock indices and examine the causal relationships between the markets using the historical daily prices of the Financial Times Stock Exchange (FTSE), Bursa Malaysia Kuala Lumpur Composite Index (KLCI), the Indonesia Stock Exchange Index (LQ45), and the Stock Exchange of Thailand (SET) from January 2008 to November 2019. The study employs univariate GARCH models that are prominent in capturing the volatility clustering of financial instruments in association with the Box–Jenkins methodology for better estimation. Generally, the ARMA-GARCH model is used to capture the volatility series, while the Granger causality test examines the causal directions between the markets. The findings revealed leverage effects on the markets, with the outperformance of the EGARCH in analyzing the empirical properties of stock returns. An initial test that yielded positive correlations suggests the existence of co-movement between the derived volatility series. The study concluded bidirectional causal relationships between the selected markets, and based on the resulting relationships, it is proposed that supervision of markets among the ASEAN members could be advantageous in predicting the corresponding market performance.