Form Of Investment Research Articles

Is there a tradeoff between management earnings forecasts and sustainability reporting?

This study investigates the relationship between managers' incentives to disclose management earnings forecasts (MEFs) and the Global Reporting Initiative (GRI) sustainability reports; two types of voluntary disclosures that are produced by managers to provide financial and/or nonfinancial information to stakeholders. As the antecedent determinants of these two disclosures overlap, but the related risks and strategic foci differ, managers' decisions to disclose MEFs and GRI sustainability reports could be independent, cooperative, competing, or even conflicting. Using a sample of public firms from the United States between 2005 and 2018, we demonstrate that managers make a tradeoff decision between disclosing MEFs and disclosing GRI sustainability reports, which is aggravated by proprietary costs and risk exposure. We also find that disclosing both the MEF and GRI sustainability report provides more useful information for investors than no disclosures or producing only one type of disclosure. This indicates that while stakeholders desire more high-quality information, managers compromise on disclosure level in practice. Our study is consistent with the recent trend of appeals by regulators, practitioners, and academia for enhanced standards to improve the quantity and quality of voluntary disclosures for information users.

Ice-and-snow tourism in China: trends and influencing factors

Ice-and-snow tourism (IST) is central to the Chinese ice-and-snow economy and can help achieve the green transformation and expansion of the Chinese economy. However, IST has not been defined conclusively in China. In this study, the IST concept was analyzed, and the growth of both tourism types was described using an Event–Time–Space comprehensive analysis method. Two temporal phases with large increases were highlighted in both tourisms. The factors promoting their increases were analyzed from market demand, the Beijing 2022 Olympics Winter Games, and scientific and technological developments for ice and snow. The 2020–2021 decrease in IST was primarily attributed to the COVID-19 pandemic. In addition, IST has dual challenges related to natural and socioeconomic factors. Climate warming has a long-term adverse effect on IST, particularly on glacier tourism. Traditional outdoor skiing tourism in Northeast China (NEC) may also be affected by more competition owing to increased ski tourism in the pan-Beijing-Tianjin-Hebei region, the Xinjiang Uygur Autonomous Region (XJUAR) and Southern China, as well as by the development of indoor skiing and ice/snow entertainment. These natural and socioeconomic factors currently limit the ability of IST to promote socioeconomic development in alpine areas, particularly in western China. However, there are many opportunities for future IST growth, including warmed comfort climate at high altitudes in the NEC, XJUAR and the Qinghai-Tibet Plateau, major glacier resource potentials, and IST development driven by policies. The findings of this study provide important information for decision-makers and investors in IST and its related industries.

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022.

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION

Initial Public Offerings (IPOs) are a popular way for companies to raise capital and enter the public markets. However, many IPOs underperform and fail to meet the expectations of investors. In this research paper, we explore the use of different machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANN and SVM, for predicting IPO underperformance. We collect and pre-process a dataset of IPOs from the past few years, and use it to train and evaluate the performance of each model. Our results show that Artificial Neural Network model is better suited for predicting IPO underperformance. Additionally, our analysis provides insights into the factors that contribute to underperformance and highlights the importance of certain features in predicting IPO performance. Our research provides valuable information for investors and financial analysts interested in predicting the performance of IPOs and mitigating the risks associated with IPO investments. We have tested machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANN and SVM. After Comparing the accuracy of all the models, we arrived at the conclusion that ANN model performed the best with an accuracy of 68.11%.

ANALISIS FAKTOR PENENTU PERFORMA PERUSAHAAN MANUFAKTUR YANG TERCATAT BEI TAHUN 2017-2020

The purpose of this research is to obtain empirical evidence on the influence of leverage, asset structure, liquidity, company size, non-debt tax shields, and managerial ownership of the performance of manufacturing companies listed on the Indonesia Stock Exchange for the period 2017-2020. Sampling in this study used purposive sampling methods and obtained 48 samples of manufacturing companies listed on the Indonesia Stock Exchange for the period 2017-2020. The study used the Eviews 12 and Microsoft Excel 2016 software programs to manage data. The results of this study showed that leverage and asset structure had a negative influence on the company's performance, liquidity and non-debt tax shields had no negative influence on the company's performance while the size of the company and managerial ownership did not have a positive influence on the company's performance. The implication of this research is the need to reduce the use of debt to minimize the interest expense of the debt which will increase the company's profit so as to improve the company's performance which will provide good information for investors.

Accrual quality, investor reaction to earnings, and the confirmatory role of sales news

Purpose: Agency theory predicts that information asymmetry provides agents with an incentive to manipulate performance signals to maximize their utility, which gives principals reasons to distrust such signals. The accounting and finance literature finds empirical support for this prediction by studying how earnings reliability attributes affect investors' reactions to earnings an-nouncements. However, research pays less empirical attention to whether in-vestors skeptical of earnings reliability look for confirmatory signals in other parts of the income statement. This study aims at filling such this research gap. Design/methodology/approach: This study examines investors' combined use of earnings and sales news. It adopts an event-study methodology to ana-lyze whether sales news moderates the stock market response to annual earn-ings announcements. Findings: The results show that investors do not fully trust earnings news if earnings beat analyst expectations and the firm has a reputation for low accru-al quality. In this case, positive sales data alleviate investors' skepticism of earnings news and, thus, make them react more favorably. In contrast, sales data do not affect the market response if the earnings news is negative, or the firm accrual quality is high. These results are robust to different model speci-fications and explanations. Originality/value: The findings shed new light on how investors use sales data to complement earnings news and our understanding of the consequences of accruals quality on investor information processing.

Determinants of Company Value: Study on Pharmaceutical Companies on the Indonesia Stock Exchange

Company value is very important in investment transactions, and company management must understand the determinants to achieve company goals. The main operational goal of the company is to achieve the maximization of expected profits. Profit maximization is the company's only rational policy. This study aims to determine and examine the determinants of firm value: growth, firm size and financial performance. The research sample consisted of 9 pharmaceutical companies listed on the Indonesia Stock Exchange. Data is collected through financial statement documents for 4 years, 2018-2021. The number of data observed is 36 observational data. Data analysis used multiple regression analysis. The results showed that company growth and firm size positively and insignificantly affected firm value. Meanwhile, financial performance has a significant positive effect on firm value. The implications of the research results are theoretically as developing insight related to investment and portfolio management, then practically as information for investors in making investments.

잉여현금흐름정보와 ESG평가정보의 상호작용효과가 외국인지분율에 미치는 영향

The purpose of this study is to verify whether the interaction effect of free cash flow information and ESG evaluation information is related to foreign ownership and how it affects it.
 The research method is based on the free cash flow and foreign ownership ratio data from 2016 to 2020 provided by Fn-guide and ESG evaluation information data disclosed by the Corporate Governance Service, basic statistical analysis, correlation analysis, Regression analysis was performed.
 Summarizing the research results, the correlation analysis results show that the dependent variable foreign ownership ratio and variables of interest 1) free cash flow information and 2) ESG evaluation information (rating (R), environment (E), society (S), governance (G) category) showed a significant positive (+) correlation.
 And the results of the regression analysis show that the interaction effect information between free cash flow information and ESG evaluation information (rating (R), environment (E), society (S), and governance (G) sectors) excludes the social (S) sector. was found to have a significant positive (+) effect on the foreign ownership ratio. In other words, the higher the interaction effect between free cash flow information and ESG evaluation information, the higher the foreign ownership ratio.
 The implication of these research results suggests that foreign investors invest funds with interest in companies with high levels of free cash flow information and ESG evaluation information. Therefore, the interaction effect between free cash flow information and ESG evaluation information is expected to serve as useful accounting information for foreign investors in the capital market.

Reaksi Pasar Modal Akibat Pengumuman Kasus Positif Pertama Virus Covid-19 Varian Omicron Di Indonesia

The capital market reaction to an announcement or event containing information is an important thing for investors to pay attention to. Important information for investors to know for future decision making. This research aims to determine the capital market reaction caused by the announcement of the first positive case of the Omicron variant of the Covid-19 virus in Indonesia on December 16 2021. This research uses the event study method with a 7 day research period. The research sample was taken using the Slovin technique from 80 companies that are members of the Kompas100 Index. The hypothesis test used in this research is the One Sample T-test. The results of this research show that there is no market reaction as indicated by the insignificant abnormal returns obtained around the announcement event. The results of this research indicate that the capital market in Indonesia can take the form of an informationally efficient, semi-strong market.
 Keywords: Market Capital Reaction; Abnormal Return; Omicron Virus; Efficient Market Hypothesis; Signaling Theory

Dynamic Information Regimes in Financial Markets

We develop a model of investor information choices and asset prices in which the availability of information about fundamentals is time-varying and responds to investor demand for information. A competitive research sector produces more information when more investors are willing to pay for that research. This feedback, from investor willingness to pay for information to more information production, generates two regimes in equilibrium, one having high prices and low volatility, the other the opposite. The low-price, high-volatility regime is associated with greater information asymmetry between informed and uninformed investors. Information dynamics move the market between regimes, creating large price drops even with no change in fundamentals. In our calibration, the model suggests an important role for information dynamics in financial crises. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.01213 .

Human Capital Efficiency, Free Cash Flow, Corporate Sustainability Performance and Internal Auditors

Corporate Sustainability Performance is linked to the company's business strategy, which aims to benefit stakeholders while improving people's lives and protecting the environment. This study aims to examine the effect of Intelectual Capital and Free Cash Flow on Corporate Sustainability Performance with Internal Auditors as the moderating variable in this research. Intellectual Capital is proxied by Human Capital Efficiency, Structural Capital Efficiency and Capital Employed Efficiency. Meanwhile, Corporate Sustainability Performance is proxied by the CSR index which is based on the 2016 Global Reporting Initiative Standard. The population of this study are companies included in the LQ45 Index during the 2017- 2021 period. The sample was selected using purposive sampling method and 20 companies were selected as the research sample. The results of this study indicate that Human Capital, have no significant effect on the Company's Sustainability Performance. Meanwhile, Free Cash Flow has a significant positive effect on the Company's Sustainability Performance. Internal auditors can weaken the relationship between Human Capital and Corporate Sustainability Performance, the relationship between Free Cash Flow and Corporate Sustainability Performance. Meanwhile, the Internal Auditor can strengthen the relationship between Capital Employed and Corporate Sustainability Performance. The results of this study have an impact on company management to consider the company performance and as information for investors in determining investment decisions.

Leading and lagging role between financial stress and crude oil

PurposeThe crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.Design/methodology/approachThis paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.FindingsThe results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.Research limitations/implicationsThe Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.Practical implicationsThe linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).Originality/valueRecently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method

Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the stock market prediction problem has attracted significant attention from both researchers and practitioners. Conventional machine learning models often fail to interpret the content of financial news due to the complexity and ambiguity of natural language used in the news. Inspired by the success of recurrent neural networks (RNNs) in sequential data processing, we propose an ensemble RNN approach (long short-term memory, gated recurrent unit, and SimpleRNN) to predict stock market movements. To avoid extracting tens of thousands of features using traditional natural language processing methods, we apply sentiment analysis and the sliding window method to extract only the most representative features. Our experimental results confirm the effectiveness of these two methods for feature extraction and show that the proposed ensemble approach is able to outperform other models under comparison.

Effect of Corporate Social Responsibility on Portfolio Performance: Evidence From Turkey

This study investigates the performance of corporate social responsibility (CSR) portfolios based on ESG scores of stocks traded in Borsa Istanbul (BIST). It also evaluates the performance of a trading strategy that represents buying portfolios that include companies with high CSR performance and selling portfolios comprising companies with low CSR performance. Therefore, two portfolios with high and low CSR performance were formed from stocks traded in Borsa Istanbul. These portfolios’ performances were compared from July 2010 to June 2020. The findings show that the alpha coefficient of the portfolio containing stocks of companies with high CSR performance is generally positive and significant. Thus, companies’ past CSR performances provide valuable information for investors. Additionally, based on this, investors can achieve a superior performance against the market. However, the alpha of the difference portfolio, which shows the difference between the two portfolios’ performance, is, in most cases, insignificant. This suggests that there is no proof that the portfolio comprising companies with high CSR performance is superior to the portfolio including companies with low CSR performance. These findings are supported by additional testing. Consequently, our result provides supporting evidence for the “no effect” hypothesis.

Dividend policy effect on common stock price volatility: An empirical evidence from Indian companies

Despite empirical research, the relationship between dividend policy and stock price volatility is continuously debatable. This study investigated the relationship between dividend policy and stock price volatility of Indian listed companies. The study examined 260 listed companies based on the reliable dividend-paying manners of nonfinancial companies listed on the Bombay Stock Exchange (BSE) for the financial period from 2014–2015 to 2020–2015. To analyze the data, this study used the panel data models: fixed effects, random effects, and the Hausman test. Finally, this study applied the fixed effect model after careful examination of multicollinearity, endogeneity, and causality issues related to the dataset. The analyses revealed a significant negative relationship between dividend payout and stock price volatility meanwhile, dividend yield and stock price volatility have a positive association. The study outcomes provide information for investors and managers about dividend decision. This study provides an extensive understanding of the emerging stock market fluctuation on the relationship with the dividend policy.

The Effect of Non Audit-Services on Auditor’s Professional Scepticism: Stakeholders Perceptive

This study investigates the effect of the provision of non-audit services on Auditor’s professional scepticism evidence from accounting practitioners in Abia State. The study used the Institute of Chartered Accountants of Nigeria (ICAN) and Association of National Accountants of Nigeria (ANAN) domiciled in Abia State. The data for the study were generated from primary source: questionnaires were the major instrument for data collection. The instrument was subject to both content and faces validity by experts. Three questions were structured in Likert scale form and the formulated hypotheses were tested with ordinary Logistic regression using SPSS package. The study found that non-audit services have no effect on auditor’s scepticism, the auditor’s objectivity during his course of audit procedure. The study therefore recommends that the audit committee should have full oversight of the auditor’s scepticism including the nature and extent of the work they do and their fees. The auditors been a professional should strive to maintain his integrity during the course of his duties bearing in mind that good name is better than riches. The audit committee should make his charter public and this should give investor information about how the audit committee chooses its auditors and how it decides the type of services they can provide.

PENGARUH PELAPORAN TERINTEGRASI DAN PENGUNGKAPAN RISIKO TEKSTUAL TERHADAP NILAI PASAR

This study aims to determine whether integrated reporting has a positive impact and whether textual risk disclosure (TRD) negatively impacts the company's market value. Signaling theory is used in this study, which focuses on how integrated reporting and TRD provide positive signals to investors so that they can increase firm value. Purposive sampling was used when the samples were mining companies listed on the Indonesia Stock Exchange (IDX). There are 168 observations and 56 firms in the sample. Stata 14.2 is used to analyze and multiple regression to test the hypothesis. As a result, integrated reports and Textual Risk Disclosures (TRD) do not affect the company's market value. This research has implications for stakeholder policies where other factors such as financial performance, industry stability, and other macroeconomics are more dominant in influencing company value. Companies can consider increasing the quality and relevance of their risk disclosures to provide more helpful information for investors.

Intelligent Prediction Model of Shanghai Composite Index Based on Technical Indicators and Big Data Analysis

Technical indicators provide useful information for investors to study the stock market. Based on the Shanghai Composite index from November 1994 to March 2022, we construct 20 technical indicators based on the moving-average (MA) rules, momentum (MOM) rules and volume on balance (VOL) rules, and model and analyze through ordinary least squares and extracted principal components. We found that the technical indicators provide effective information for the forecasting of the excess return rate of the Shanghai Composite index both in- and out-of sample, and the model has more superior forecasting performance in the decline period of the business cycle. In addition, this paper finds that the information provided by the technical indicators can better predict the rise of the peak front and the trough front of the business cycle. Finally, this paper studies the economic significance of the yield forecasting using technical indicators, and finds that the information in technical indicators can help investors to obtain better investment returns without transaction fees.

Timberland transaction costs: survey results and insights

Timberland is a real asset that has been gaining significant attention and capital from investors over the last four decades. This paper describes and summarizes the results of a timberland investment survey conducted in early 2020. The survey polled timberland investment professionals active in the sector and provided insights into how transaction costs affect timberland investments according to the size and location of the timberland estate. Component costs were also obtained and summarized for each of the steps involved in typical timberland transactions. The survey also provided insight into investor sentiment toward timberland transaction costs as well as the process of accounting of these costs within the forest industry. Key results of the study showed differentiation in transaction costs by location and size of the timberland estates. The study also showed that transactions costs do not deter most investors from placing capital in this asset class. This manuscript provides a baseline of transaction cost information for investors and professionals interested in the timberland real asset space.

Institutional Investor Information Competition and Accounting Information Transparency: Implications for Financial Markets and Corporate Governance in China

Institutional Investor Information Competition and Accounting Information Transparency: Implications for Financial Markets and Corporate Governance in China