Earnings Reports Research Articles

Future trends of AI stocks prediction using ARIMA model

Abstract. This study uses an autoregressive integrated moving average (ARIMA) model to forecast the stock movements of artificial intelligence-related companies using IBM's historical stock price data from 2019 to 2024. Due to the high volatility and unique externalities of AI stocks, traditional financial models may not provide accurate forecasts. In this study, the ARIMA (1,1,0) model is chosen based on analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF). The prediction results indicate that IBM's stock price will trend upward in the short term. The model predicts that IBM's stock price will remain on an upward trend through late 2024 and is likely to exceed $200 with an optimistic market status. However, this prediction is more uncertain because the ARIMA model does not take into account external factors such as economic policies or earnings reports, which may affect the stock price. In conclusion, this study uses ARIMA to forecast AI stock trends, providing an analytical tool for investors and financial analysts, as well as recognizing the importance to incorporate external factors to improve forecast accuracy in high volatility stock market.

Transparency and altruistic punishment in an experimental model of cooperation to corruption through economic games

This work integrates cooperation, punishment, damage to the treasury, and the transgression of norms in a single experimental model of corruption. Participants formed words with predetermined letters, receiving a reward for each word, and, if they reached a goal, an extra taken from the common fund. Manipulation in the letters made it impossible to reach the goal, so reporting exceeding it implied cheating for a benefit. Three studies model the effects of signaling, descriptive norms, and the possibility of punishing or investigating (transparency) corruption acts. 248 participants were randomly assigned to the conditions of each study. Significant differences were found in reports of words and earnings in Studies 1 and 3, but not in Study 2. The experimental model reveals the potential of transparency as an alternative of lower social cost than altruistic punishment to diminish corruption. The relevance of these results for implementing public policies was discussed.

Impact of investor trust on public firms’ stock price efficiency and cost of capital: Insights from a firm-level measure for investor trust

Although studies have suggested that investor trust positively influences stock price efficiency and subsequently reduces the cost of capital for public firms, few studies have employed a direct firm-level measure of investor trust to offer empirical support for this proposition. Because stock prices are more efficient surrounding earnings announcements, we construct this measure by examining how prices change when unexpected earnings reports are provided. Using US market data from 1990 to 2019 to determine how strongly investor trust affects public firms’ stock price efficiency and cost of capital, we find that stock prices of firms with higher investor trust reflect market news more efficiently and predict earnings more accurately. Furthermore, our results suggest that companies trusted more by investors can raise money more cheaply. Collectively, our findings underscore the importance of fostering investor trust as a means of enhancing stock price efficiency and reducing the cost of capital.

The Puzzle of UK (Under‐) Investment: Is Investment Short‐Termism Just a Supply‐Side Problem in Capital Markets?

AbstractInvestors and financial market intermediaries have been blamed for under‐investment, low growth and low rates of innovation in the UK, with their behaviour being attributed to short‐termism. Various reasons for short‐termism have been identified, including undervaluing long‐term earnings, increased financial obstacles associated with longer investment horizons, and the adoption of financial control systems to meet investors’ demands for quarterly earnings reports. As a result, firms may opt for suboptimal short‐term investment projects while neglecting potentially valuable long‐term initiatives. Most research has focused on large corporates, which constitute a small fraction of the economy and involve multiple stakeholders. There is a significant knowledge gap regarding small owner‐managed firms that rely primarily on internal financing and bank debt for investment. Our study fills this gap by analysing a comprehensive UK finance and investment decision‐making survey of 1501 firms across all classes. The survey reveals that investment appraisal relies on a ‘payback’ period. We find that 58.8% of firms choose a payback period of 3 years or less, with shorter payback periods more prominent among the smallest firms. This suggests that financial frictions impact the investment behaviour of the smallest firms, while shareholder‐driven short‐termism influences the largest firms but only in relation to research and development projects.

Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets.

Sentiment Analysis for Financial Markets

Abstract: Predicting movements in the stock market is a novel use of sentiment analysis's growing body of knowledge. The purpose of this study is to investigate the potential of NLP for predicting stock market movements by analyzing textual data sources such as news articles, social media posts, as well as earnings reports. The research examines current approaches, applications, and difficulties in sentiment analysis by drawing on extensive surveys and reviews [1], [2]. It also investigates the use of pre-trained models in NLP and the potential biases of such models [6]. Important research findings [3], [17] suggest that NLP-based sentiment analysis models, especially those employing deep learning architectures, show promising results in financial forecasting. There are, however, several difficulties associated with these models. These include the requirement of huge datasets and the elimination of biases. This study has far-reaching ramifications. One benefit is a more nuanced comprehension of the potential and pitfalls of natural language processing for sentiment analysis in the financial markets, which is useful for both analysts and investors. Second, it provides opportunities for more study to enhance the precision and dependability of such models, which ultimately aids in the development of more steady and well-informed monetary judgements.

The impact of co-opted executives on earnings management

PurposeThe authors seek to analyze the impact of weak corporate governance by top executives of a firm on the firm's earnings reports. This research is meant to further emphasize the impact of co-opted executives on a firm, primarily through their impact on earnings management.Design/methodology/approachUsing financial data from 11,473 firm-year observations, the authors utilize ordinary least squares (OLS), 2-stage IV regressions, propensity score matching (PSM) and entropy balancing to analyze the impact of a co-opted top management team on discretionary accruals and restatements.FindingsThe authors find empirical evidence that firms with weak corporate governance from top executives are more likely to manipulate reported earnings and have lower financial reporting quality. The authors also find that the effect of co-opted executives on earnings management is weaker when a chief executive officer's (CEO’s) incentives are not aligned with those of top executives, suggesting that executives prevent earnings management due to reputational concerns. Co-opted chief financial officers (CFOs) increase the magnitude of earnings management in a firm but are not solely responsible for the authors' results.Originality/valueThe authors' results suggest that the top executive team provides an important first defense in the prevention of earnings management and corporate wrongdoing. Co-option of the top executive team may be an important consideration when doing research into corporate governance.

Analysis of Business Negotiation Strategy Application -Taking Nike as an Example

Nike has released its 2020 Impact Report, which reviews the achievements in the fiscal year 2020 and the achievement of previously set five-year (2016-2020) goals. Although the set goals were well underway, the company's Annual Earnings report was not satisfactory. Nike failed to meet its projected revenue of $50 billion set for 2020, so this paper analyzes and suggests how the company can effectively negotiate with organizations and customers based on Nikes interests and positions of different footwear products. Based on the growth-share matrix, Nike's footwear products are divided into four categories and how Nike shall negotiate pricing is analyzed in turn. This paper can concludes from the analysis of the four categories of footwear products that both interest-based negotiations and position-based negotiations represent Nike's overall interests. In interest-based negotiation, market research should be conducted to understand the demand for each product, and prices should be set accordingly to maximize revenue without losing customers. In position-based negotiations, the most important thing to grasp should be the hard indicator of profitability. When negotiating, Nike should set a fixed price for each product based on its own needs and goals. This price must be determined based on factors such as production costs, profit margins and product positioning. Therefore, in negotiations with suppliers and customers, Nike can change Nike's footwear pricing strategy according to its own interests and position.

Research on Financial Reports of Visual Health Industry

Today's visual health industry is growing rapidly due to its vast market and contemporary technological advances. This paper will analyze the financial reports of several companies of different sizes and segments to understand the market patterns as well as the shortcomings and improvements of the industry. This paper will use some formulas to calculate the data from the companies' earnings reports to find out the different indices and then compare them to reflect the problems. Ultimately, it is concluded that different key data points should be focused on different segments of the industry in order to come up with different solutions.

Marketing Challenges and Countermeasures of Streaming Media in the Post-Epidemic Era: Taking Netflix as an Example

Since 2019, the outbreak of the epidemic has brought huge customer subscriptions to Netflix, and the stock price has also risen as a result. However, according to its Q1 2022 earnings report, Netflix experienced its first single-quarter loss of sub-scribers in a decade, with high numbers. Using the case analy-sis method, this paper takes the sharp decline in subscribers of Netflix in the latest quarter as the starting point, thinks about how to retain users, analyzes the three reasons for the decline in the number of Netflix users, gives solutions with Product and Promotion as the core, and finally plans new marketing ideas for the streaming media in the post-epidemic era. It pro-vides a marketing model that can be promoted for the industry to retain users, return to peak performance, and develop sus-tainably under the normal epidemic situation.

Automatic Generation of Sentences Expressing Stock Price Trends Using Stock Prices and Earnings Reports

Automatic Generation of Sentences Expressing Stock Price Trends Using Stock Prices and Earnings Reports

Firm Attributes and Earnings Predictability of Listed Manufacturing Companies in Nigeria

Earnings predictability is a measure of how well the past earnings of a firm can explain its current earnings. Earnings measurement is very important in accounting since the performance of the firm can be seen by a wide range of users. The primary role of the financial statement is to disclose the financial statement information to internal and external users in a timely and reliable manner. This study was to ascertain the relationship between firm attributes and earnings predictability of quoted manufacturing companies in Nigeria. The population of the study consisted of all the listed manufacturing companies in Nigeria for the year ending 2021. Purposive sampling was used to sample 12 manufacturing firms that were continuously listed and actively trading on the floor of the Nigerian Exchange Group (NXG) Ltd. during the period 2017 to 2021 and whose financial statements are available and have been consistently submitted to NXG for the period under study. The ex post facto research design was used to establish the effect of firm attributes on earnings predictability. The firm attributes reviewed were firm size, age, leverage and liquidity, while earnings predictability was measured by operational cash flows on total assets. Panel regression data using a pooled estimate of ordinary least squares method was used for data analysis. The result revealed that firm age, firm size, firm leverage and firm liquidity all have weak effects on the dependent variable. Based on the findings, it was concluded that firm attributes have weak but positive effects on earnings predictability. It was recommended that manufacturing companies should provide quality earnings reports stating the earnings per share, operational cash flow, and total assets. This will showcase the accrual quality which could assist investors in estimating future earnings thereby making decisions to avoid security mispricing.

Analysis of Facebook Related to COVID-19

Digital advertising, including display advertising and search advertising, is a very important part of Internet social media companies. The global outbreak of Covid-19 hit this space, causing digital advertising to grow exponentially. Advertisers are spending more on social media with the average price of Facebook ad spots up 30% from the previous year. As a result, in the long run, Facebook has more than 9 million advertising partners, and small and medium-sized advertisers are its main source of revenue. Meta's core business appears to be experiencing declining user adoption and struggling revenue, as well as inflation and supply bottlenecks disrupting advertisers' budgets. Apple became the first Internet technology company to change course on protecting user privacy, and Facebook's monthly active users, at 2.91 billion in the fourth quarter, did not grow, dropping to below 5% year-over-year growth, according to its fourth-quarter 2021 earnings report. The new privacy policy has become a negative influence for all Internet technology companies and advertisers.

Ownership Structure and Earnings Management Practices: An Empirical Evidence

This paper aims to analyse the mitigating effects of ownership structure on earnings management (EM) practices. Data were collected from firms listed on Bursa Malaysia’s main market, covering the years 2011 through 2021. Panel Regression was employed to analyses the data, with the aid of STATA software version 17. The finding of this study confirmed significant negative association between foreign ownership (FOW) and EM of listed firms in Malaysia. Additionally, managerial ownership (MOW) and ownership concentration (OC) were found to be insignificantly related to EM. Similarly, the two control variables included in the analysis, only firm size (FISZ) was found to be significantly related to EM practices. Practically, this study offers an effective framework for OC, MOW, FOW and EM to reduce executive manager's opportunistic behaviour. The findings from this study supports the need for broader understanding so that investors and other stakeholders can see through earnings reports and, as a result, make informed contractual decisions, particularly when those decisions pertain to non-owner-controlled firms. In addition, the study’s findings provide helpful information to stakeholders in Malaysian listed companies on the value of FOW and it influence on EM mitigation.

Quality shareholders versus transient investors: The alarming case of product recalls

In this installment of Organizational Performance, we draw attention to two types of shareholders that tend to push executive decision-making in different directions. Quality shareholders (QSs) invest in a small number of companies and hold their shares over time. QSs offer patient capital that allows executives to focus on building and sustaining competitive advantages. Transient institutional investors (TIIs) hold dispersed shareholdings across a wide array of companies and frequently trade in and out of any given stock. TIIs impose pressure on quarterly earnings reports that induce managerial myopia and inhibit strategic thinking. We consider the influence of these investors on how many consumers are harmed before a defective product is pulled from the market. The good news is that for every 1% increase in QS shareholding, prerecall consumer harm decreases by 2%. Unfortunately, for the same amount of increase in TII shareholding, prerecall consumer harm increases by 6%—a frightening prospect. The case of product recalls draws the difference between QSs and TIIs into stark contrast. In response, we offer practical recommendations to assist managers in navigating these two types of powerful institutional investors.

Stock Price Movement Prediction Using Machine Learning

Machine learning based stock price prediction is a way of foreseeing the upcoming value of stock traded on a stock exchange in order to profit from it. It is difficult to predict stock prices with great accuracy because there are many elements at play. This is where machine learning plays a crucial role. For the majority of researchers in this field, market forecasting provides excellent profit opportunities. The majority of researchers utilize either technical or fundamental analysis to forecast the market. Technical analysis concentrates on studying price trends to forecast future prices, whereas fundamental analysis relies on studying unstructured textual data from sources like financial news and earnings reports. With time, more and more useful market data has been made available online.

Europe’s woes hit earnings

Earnings reports continuing to trickle in for chemical makers show that high European energy prices were the industry’s biggest obstacle for the third quarter. DuPont says that its sales in the period climbed 4% from a year earlier and that earnings were up 39%. The firm declared the results “better than expected” considering the harsh market conditions. At Celanese, sales rose 2%, while profits plummeted 62%. Celanese CEO Lori Ryerkerk noted in an announcement that demand “rapidly deteriorated across the third quarter in Europe and, to a lesser extent, Asia.” Huntsman’s sales were down 4% versus a year ago, and earnings slipped 49%. The firm is looking to cut costs by $40 million.

Investor institusional dan keinformatifan laba di Indonesia dan Jepang

This study examines the existence of institutional investors who can play a role in increasing earnings informativeness through income increasing-earnings management and income smoothing. The hypothesis was developed on the basis of the view that the motivation behind earnings management decisions is to increase the informativeness of earnings reports. The study employs sample of manufacturing companies listed on the Indonesia Stock Exchange and the Japan Stock Exchange for the period of 2010-2018. Ordinary least square and weighted least square regression models are used in cross-sectional statistical testing, which is carried out annually throughout the 2014-2018 observation period. In Japan, institutional ownership has a positive and significant effect on income increasing- earnings management, and on reverse income smoothing, as well, while these are not the case in Indonesia. This study reveals that in Japan, the institutional investors play a role of monitoring that can encourage managers to increase earnings informativeness through earnings management-income increasing followed by reverse income smoothing. This condition is not found in Indonesia. It is possible that in Indonesia, the institutional investors play a role of controlling, that may promote managerial opportunistic behavior.

Research on Financial Strategy and Financial Capability of Grassroots Power Supply Enterprises Based on Stochastic Matrix Model

On the basis of the generation, evolution, and development of the basic power supply enterprise’s financial core competence, this article determines the connotation and essence of the basic power supply enterprise’s financial core competence, so as to play a guiding role in fully and correctly grasping the basic power supply enterprise financial core competence. This article focuses on the formation mechanism and factors of the financial core competence of grassroots power supply enterprises and reviews the latest research hotspots and their theoretical significance in academia. By applying the random matrix model to evaluate the core financial capabilities of grassroots power supply enterprises, it proposes a path for cultivating and improving the core financial capabilities of grassroots power supply enterprises. Through a detailed and powerful discussion, it is possible to effectively improve the financial management efficiency of grassroots power supply enterprises and quickly adapt to the needs of market changes, so as to meet the interests of relevant stakeholders of grassroots power supply enterprises as much as possible. Considering the market competition environment, it is found that the positive relationship between corporate responsibility information disclosure and the degree of earnings management is only significant in low-competitive markets, which also shows that with the intensification of market competition, the degree of information asymmetry decreases. The ability of power supply is also increasing, which will weaken the mechanism of grassroots power supply enterprises to conceal earnings management behavior through corporate responsibility information disclosure. The results show that corporate responsibility information may become a tool to cover up the management of grassroots power supply enterprises to manipulate earnings and reduce the quality of earnings reports, and this behavior will be different in grassroots power supply enterprises with different characteristics and external environments. This will help to form an objective understanding of corporate responsibility information disclosure behaviors, identify the motives of grassroots power supply companies’ responsible behaviors for regulators, standardize corporate responsibility disclosure norms, and take differentiated measures for different situations.

Investor Protection and Post-Disclosure Disagreement: International Evidence

Synopsis The research problem Adding to the literature on investor protection, this study investigates whether nationwide institutional features may explain cross-country variation in post-disclosure disagreement. Motivation Previous research has revealed that the release of financial statements aggravates investor disagreement rather than attenuating it. However, most studies only obtain empirical evidence in the context of the United States; no work has examined this research question in an international setting. Another motivation for this paper is the attempt to understand the contradiction in the literature, which emphasizes micro-level determinants. By contrast, minimal attention has been paid to the macro-level institutional factors of a country’s information environment, which presumably prompts investors to shape heterogeneous beliefs. The test hypotheses H1: Countries with greater corporate transparency are associated with a lower level of post-disclosure investor disagreement. H2: Countries with stronger legal protection are associated with a lower level of post-disclosure investor disagreement. Target population Various stakeholders include firm managers, financial analysts, regulatory watchdogs, and users of earnings reports. Adopted methodology Ordinary Least Squares (OLS) Regressions Analyses Gao et al. ( 2012 ) to measure investor disagreement while we quantify corporate transparency (legal protection) by extracting the first principal component of nine countrywide characteristics pertinent to disclosure requirements (legal systems). Using a global sample from 38 countries, we perform a cross-sectional regression of post-disclosure disagreement on two proxies for investor protection after accounting for firm-specific control variables. Findings We find clear evidence of post-disclosure disagreement in all countries. Next, we document a negative relationship between corporate transparency (legal protection) and post-disclosure disagreement. Additional tests confirm that both better disclosures and strong regulations enhance information precision, accelerate information dissemination, and reduce informed trading, thus leading to a lower level of post-disclosure disagreement.