Stock Price Informativeness Research Articles

Exchange Inquiry Letters and Stock Price Informativeness: Evidence from China

ABSTRACT Using the data of China A-share listed firms from 2015 to 2019, we examine the effect of exchange inquiry letter regulation on stock price informativeness. It is found that the stock prices of inquired firms contain more firm-level information after the receipt of inquiry letter. Further research demonstrates that attracting investors’ attention to more firm-specific information is the potential channel through which inquiry letters improve stock price informativeness. This paper provides empirical evidence for the effectiveness of exchange inquiry letter regulation, which has significant implications for listed firms improving information disclosure and for regulators playing a better role in emerging markets.

Is corporate social responsibility constrained by bank credit resource allocation?

AbstractCredit rationing theory suggests that the impact of bank credit allocation on corporate social responsibility (CSR) has the dual effect of adverse selection and moral hazard. In this study, the impact of credit allocation on CSR and its mechanisms are explored in the context of credit rationing theory based on Chinese listed companies from 2010 to 2019. The results show that the impact of bank credit allocation on CSR exhibits an inverted U‐shaped correlation, and there is a critical point (17.9134) at which the increase in credit allocation will promote the level of CSR until the critical point is exceeded, after which the level of CSR will gradually decrease. Credit allocation can influence CSR through information asymmetry (stock price informativeness, analyst tracking) and managerial overconfidence, respectively. Companies with overconfident managers and low transparency in non‐socially responsible weighting systems are more likely to have an inverted U‐shaped relationship between bank credit allocation and CSR. This study provides new empirical evidence to understand the impact of credit allocation on CSR and expands the theory of credit rationing to a certain extent, thereby providing a reference for CSR maximization.

영업권 손상인식과 주가의 미래이익정보성

In this study, we investigate whether discretionary recognition of goodwill impairment affects the stock price informativeness of future earnings. Using firm-year observations with a positive beginning goodwill balance from 2011 to 2016, we test whether the decision to recognize impairment loss inconsistent with impairment indication (accelerated impairment) improves the future earnings response coefficient (FERC). We find that the accelerated impairers exhibit greater FERCs, lower value relevance of pre-impairment goodwill, and higher persistence of reported EPS. We suggest that early recognition of impairment from potentially overstated goodwill assists investors in better predicting future earnings by mitigating the measurement error inherent in goodwill book value and smoothing the earnings time series. Our findings fill the literature gap by shedding light on the positive role of accounting discretion exercised under the context of low level fair value applied to goodwill impairment test.

A self-regulated generative adversarial network for stock price movement prediction based on the historical price and tweets

Stock price movement prediction is an important task of the financial prediction field. The current mainstream approaches usually apply financial texts and some corresponding stock price information to predict the stock price movement. However, the current methods usually suffer from two shortcomings: (1) To reduce the stochasticity in the stock price and financial text information, some researchers adopt generative models to better treat the stochasticity while enduring the overfitting problem during training. (2) Although the current state-of-the-art methods based on the generative adversarial network have been proposed to reduce the overfitting, they only concentrate on the overfitting problem of the stock price information and neglect the above problem of financial text information with higher stochasticity. In this paper, we propose a self-regulated generative adversarial network by combining the generative adversarial network and cooperative network for the stock price movement prediction. Furthermore, the proposed model can effectively reduce the stochasticity and overfitting problems simultaneously for the stock price and the financial text information. The experimental results on the currently commonly used stock dataset based on tweets confirm that the proposed method can achieve the novelly state-of-the-art performance compared with some current advances.

Managerial Ownership, Board Characteristics and Stock Price Informativeness in Indonesia Stock Market: Examination of the Pre-IFRS and Post-IFRS Adoption Periods

Managerial Ownership, Board Characteristics and Stock Price Informativeness in Indonesia Stock Market: Examination of the Pre-IFRS and Post-IFRS Adoption Periods

Insider stock pledging and stock price informativeness: Evidence from India

AbstractInsiders’ shares can act as collateral while raising funds from lenders. This study examines the impact of insiders’ stock pledging activities on stock price informativeness using a sample of 1835 Indian firms. Our findings report that insider stock pledging increases the informational efficiency of stock prices. This informational efficiency increases for larger firms with: (1) financial constraints (high leverage and low cash holdings); (2) greater reliance on trade credit; and (3) higher indulgence in related party transactions. We also provide evidence on abnormal share turnover as a trading mechanism through which insider stock pledging is related to stock price informativeness. Our findings are robust across different specifications and after accounting for endogeneity issues.

Bond market information disclosure and industry spillover effect

PurposeThe aim of this paper is to examine the effect of information disclosure by unlisted bond issuers on the stock price informativeness of listed firms in the same industry.Design/methodology/approachThis paper takes advantage of information disclosure during the bond issuance and examines the spillover effect of unlisted bond issuers' information disclosure on listed firms in the stock market. The sample is composed of A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2018. All the data are obtained from the China Stock Market and Accounting Research and WIND databases. The impact of bond market information disclosure on price informativeness of listed firms in the same industry is identified through multivariate regression analyses.FindingsEmpirical results show that price informativeness of listed firms has a significantly positive association with the information disclosure of same-industry unlisted bond issuers. Further analyses show that the above finding is more significant when information disclosure of bond issuers is a more important channel for acquiring industry information (i.e. when industry is more concentrated, when economic uncertainty is high, and when industry information is less transparent) and understanding the industry competitive landscape (i.e. when bond issuers are relatively large, when bond issuers and listed firms have more direct product competition, when bond issuance firms are large-scale state-owned business groups), and when there are more cross-market information intermediaries (i.e. more cross-market institutional investors and more sell-side analysts). This paper indicates that information disclosure of bond issuers has a positive spillover effect on the stock market.Originality/valueThe novelty of the research is that the authors examine industry information spillover from unlisted firms to listed firms leveraging on unlisted firms' information disclosure in bond markets.

Executive stock options, informative stock prices and innovation

Prior studies have not reasonably explained why executive stock options (ESOs) encourage innovation through vega rather than delta. This study re-examines the relationship among vega, delta and innovation performance when stock prices are informative. The findings indicate that informative stock prices amplify the delta effect on encouraging executives to improve innovation performance but that informative prices alleviate the traditional positive effect of vega on innovation. Moreover, when stock prices are informative, deep-in-the-money options reduce the positive effect of delta on innovation, whereas the state control, independent directors and manager and director shareholding enhance (reduce) the positive effect of delta (vega) on innovation. The results hold after conducting robustness tests.

An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices

For several years the modeling as well as forecasting of the prices of stocks have been extremely challenging for the business community and researchers as a result of the existence of noise in samples and also the non-stationary behaviour of information samples. Notwithstanding these drawbacks with improved deep learning, it is now possible to design schemes that will efficiently perform the feature learning task. For this work, we proposed a brand-new end to end algorithm labeled EHTS toward solving the stock price forecasting problem. The AB-CNN and CB-LSTM modules extract features from the stock price dataset and soon after amalgamating the results. Thus, the output of the concatenation stage was feed into the concluding stage which is a stand-alone MLP module. The inclusion of the LSTM and Attention Mechanism in our architecture is to extract long-range and exceptionally long-term stock price information. We experiment the proposed algorithm on two popular stocks both from the NYSE stock market namely “Johnson & Johnson” code-named, “JNJ” and the Bank of America (BAC). In terms of the rMSE, MAE and MAPE error metrics, our proposed scheme gives the lowest error value in all for all datasets. Also, five percentage training window sizes are experimented and EHTS outperforms all the baseline schemes for the different window sizes in all the two datasets with the 70% window size having the highest performance. In terms of number of epochs, EHTS uses the lowest number of epochs for training than the other schemes in all the datasets. Finally, we as well study our stock’s information to point out short-range trading opportunities by performing simulations on our stock price data. The metrics considered in the simulation are as follows: Moving Average (MA), Moving Average Convergence Divergence (MACD) curve, MACD histogram, Signal line, Relative Strength Index (RSI), Returns (R), Annual Returns (AR), Sharpe Ratio (SR), Annual Volatility (V), Maximum DrawDown (MDD) and Daily WinningRate (DWR). For all the aforementioned metrics, EHTS performs better than the baselines. Experimental results revealed that our proposed scheme outperformed the stand-alone deep learning schemes, statistical algorithms, and machine learning models from the beginning to the end.

Foreign analysts and managerial investment learning from stock markets

This paper provides evidence on the role of foreign analysts in improving the informativeness of stock prices for corporate managers. Using a large sample of 15,623 firms in 42 economies, we find that foreign analysts improve managerial learning from stock markets as measured in terms of investment-to-price sensitivity. Consistent with the flow of information from global firms to local firms, we directly find that foreign analysts reduces firms’ price delay to global market information. We also dismiss the concern that Tobin's Q captures no additional information that is reflected only in the stock price. Cross-sectional analyses indicate that the positive effect of foreign analysts on managerial learning is more pronounced for firms in developed economies, firms with better financial positions, firms in less competitive industries, and firms with less foreign ownership. Our findings provide cross-country evidence on the informative role of foreign analysts for managers, which in turn strengthens the real effect of financial markets.

Peran Tata Kelola Perusahaan Dalam Respon Investor Atas Penerapan Nilai Wajar Properti Investasi Dan Aset Tetap di Indonesia

Investors consider the good firm value of the company in the hope of providing additional wealth in the future. Company information announced to the public provides a signal for investors regarding company policies. Fair value accounting provides more timely and transparent information to investors to assess the financial position and the entity performance if it refers to the prevailing values. This study examines the association of applying fair value to investment properties and fixed assets with firm value. This research method employs a quantitative approach. In addition, this study examines the role of corporate governance in the relationship between the application of the fair value of investment properties and fixed assets with firm value. The research data sources from the financial statements of property and real estate sub-sector companies listed on the Indonesia Stock Exchange (IDX) are from www.idx.co.id. Moreover, stock price information is sourced from www.finance.yahoo.com from 2016 to 2019. Based on purposive sampling, the total sample of this study amounted to 52 observations. This study indicates that the implementation of fair value to investment properties is positively associated with firm value, while the application of fair value to fixed assets is negatively associated with firm value. Meanwhile, corporate governance cannot moderate the positive effect of implementing the fair value of investment properties and fixed assets with firm value. This research indicates that the Financial Services Authority needs to improve its corporate governance policies and monitor the implementation of corporate governance applied to companies listed on the IDX.

Managerial learning and the informational role of fair values

AbstractThis paper examines whether fair values provide useful information for management decision making. This study uses a sample of 9733 bank‐quarters, covering 2009–2016 to examine the relation. Empirical evidence suggests that fair values, particularly of liability items, affect for stock price informativeness for managers. The effects, however, depend on the account type (asset or liability) and the investment area. Information contained within level 3 items, in general, benefits management learning. Evidence also suggests that accounting discretion and macroeconomic uncertainty largely disrupt management learning from reported fair values and that the fair value effects on management learning persists over the long‐run.

Cost Of Capital, Corporate Tax Plannings, And Corporate Social Responsibility Disclosure

This study aims to determine the effect of tax management activities on the cost of capital and the role of social responsibility disclosure in moderating its impact. This study employs secondary data from financial statement data, stock price information, and 10-year government bond yields. The data was obtained from the websites www.idx.co.id, www.idnfinancials.com, www.finance.yahoo.com, and www.bloomberg.com. Using purposive sampling, the research sample is from manufacturing companies listed on the Indonesia Stock Exchange from 2016 to 2020, obtaining 325 observations. Data were analyzed employing multiple linear regression for panel data. This study indicates that tax avoidance and tax aggressiveness are not associated with the cost of capital, while tax risk is negatively related to the cost of capital. Corporate social responsibility disclosure does not succeed in moderating the effect of tax avoidance and tax aggressiveness. Still, it succeeds in moderating the association between tax risk and cost of capital.

Does stock market liberalization improve stock price efficiency? Evidence from China

AbstractIn this study, we examine whether liberalization of the stock market improves stock price efficiency using China's market liberalization pilot program as a shock. We find that investible firms exhibit a significant increase in price efficiency, as proxied by stock price non‐synchronicity, after stock market liberalization. The results are robust to a series of tests and remain unchanged after we address the issue of endogeneity. We identify two channels through which price efficiency can be improved: better disclosure by firms and the incorporation of more information into stock prices through the trading activities of foreign investors. We also find that investment becomes more sensitive to prices, further indicating that stock prices have become more efficient. Finally, we find that stock price informativeness also increases.

Managerial Discretion, Earnings Opacity, and Stock Price Informativeness

Managerial Discretion, Earnings Opacity, and Stock Price Informativeness

CEO Social Capital and Stock Price Informativeness: International Perspective

CEO Social Capital and Stock Price Informativeness: International Perspective

Role of stock price informativeness in shaping non-GAAP earnings disclosures

ABSTRACT Using 107,411 firm-quarter observations from 2004 to 2017, we find that managers are less likely to report non-GAAP earnings with the increase of stock price informativeness, suggesting that information from stock prices discourages non-GAAP reporting from firm managers. We further show that stock price informativeness influences managerial incentive of non-GAAP reporting through two channels: (a) direct monitoring and (b) the improvement in the information environment. Accordingly, we find that the constraining effect is more pronounced when a firm’s monitoring mechanisms are weak and when the pre-disclosure information environments are poor. In addition, we find that the quality of non-GAAP earnings improves with the increase of stock price informativeness. Collectively, our results are consistent with the notion that information from the secondary stock price plays an important role in guiding managerial non-GAAP earnings disclosures.

CEO Social Capital and Stock Price Informativeness: US Perspective

CEO Social Capital and Stock Price Informativeness: US Perspective

Business Data Extraction Using a Programming Language

In the era of great informational quantity, the presence of technologies that assist in the extraction, transformation, and loading of data has become increasingly necessary. The term Big Data, usually used to describe this volume of information, requires the user to have knowledge of multiple tools such as Excel, VBA, SQL, Tableau, Python, Spark, AWS, and so on. In this context, the present work aims to study data extraction techniques using different methodologies. At the end of the work, a library of functions in the Python language will be made available that will deliver a compilation of stock price information available on the Yahoo Finance website as well as balance sheets from financial institutions released by Bacen. The main resource used will be Web Scraping, which is a method that aims to automate data collection via the web. Once the collection of functions has been structured, it will be made available for public enjoyment through the GitHub platform.

Research on Financial Development Capability of Listed Companies Based on Multiple Regression Model——Take BYD as an Example

The stock price is an important criterion for the outside world to measure the development status of listed companies, and the financial index information regularly disclosed by listed companies reflects the company's operating results during the period to a certain extent, which will affect the outside world's assessment of the company's development capabilities. This paper selects BYD Co., Ltd. as the research object, fully excavates and measures the stock price information disclosed by Shanghai and Shenzhen stock exchanges and the financial indicators of various dimensions reflected in BYD's financial reports, conducts correlation analysis and selects effective indicators for building multiple linear regression Model, in-depth analysis of the relationship between the financial indicators of each listed company and its development ability is significant. The study found that cost control, cash flow manipulation and debt solvency improvement have a greater impact on the stock price of listed companies. This conclusion can be used as a reference standard for BYD and other similar listed companies to improve the performance of their development companies to a certain extent. Outside investors provide certain advice.