Stock Price Informativeness Research Articles

Do stock prices affect corporate tax behavior?

Purpose This study aims to examine how firm-specific information in stock prices influences corporate tax behavior. Design/methodology/approach This study uses ordinary least squares (OLS) regressions to examine the association between stock price informativeness and effective tax rate (ETR) volatility. Findings Stock price informativeness is positively associated with short-term tax avoidance as measured by ETR volatility. Originality/value While prior research examines the association between market activity and the level of ETRs, this study examines other various aspects of tax behavior.

Does industry co-location improve corporate information environment?

This study examines how the co-location of firms within the same industry enhances their information environments. We hypothesize that benefits arise from two effects: the industry peer effect, involving intra-industry information spillovers, and the geographic peer effect, related to shared local economic conditions with nearby non-industry peers. Our analysis evaluates how these effects, both separately and jointly, impact analysts’ forecast properties and stock price informativeness regarding future earnings. We find that the industry peer effect is negatively related to forecast dispersion and positively related to forecast accuracy, while the geographic peer effect shows no significant correlation. In addition, stock price informativeness about future earnings is positively associated with the industry peer effect but not with the geographic peer effect. We also find that greater proximity to industry or local peers enables analysts to incorporate more private (relative to public) information into their earnings forecasts, thereby enhancing the firm’s information environment. JEL CLASSIFICATION: M41, D80, G10

Effect of ESG rating disagreement on stock price informativeness: Empirical evidence from China's capital market

We assess the level of environmental, social, and governance (ESG) rating disagreement among Chinese listed companies using ESG data from eight ESG rating providers and examine the impact of ESG rating disagreement on stock price informativeness. Our results indicate a significant and positive correlation between ESG rating disagreement and stock price synchronicity. This suggests that different ESG ratings impede the integration of company-specific details into stock prices, thereby reducing the stock price's informativeness. The opacity of corporate information and agency costs can both be used to interpret this result. We also explore the transmission channels through which ESG rating disagreement affects stock price informativeness. Our findings reveal that such disagreement lowers the quality of analysts' predictions and impedes investors' access to information, affecting the informativeness of stock prices. Furthermore, we observe that ESG rating disagreement has a greater impact on stock price synchronicity for companies with high ESG ratings and those operating in highly competitive industries. Increased media coverage would mitigate the effect of ESG rating disagreement on stock price synchronicity. Overall, our findings contribute to the literature on ESG rating disagreement and demonstrate the critical role of ESG rating disagreement in the informativeness of stock prices.

Textual Dimensions of Sustainability Information, Stock Price Informativeness, and Proprietary Costs: Evidence from Integrated Reports

We examine whether integrated report quality, IRQ, is negatively associated with stock price synchronicity, an inverse measure of firm-specific information, and the extent to which the relation between IRQ and synchronicity is attenuated by proprietary costs. We measure IRQ using machine-based textual analysis along four dimensions: textual attributes, topical content, integrated reporting capitals, and financial versus sustainability information. We find that measures of IRQ based on seven textual attributes are negatively related to synchronicity, which is consistent with higher quality text containing more firm-specific content. Using PhraseLDA to identify topics in integrated reports, we find that contents related to the three most common categories—governance, performance, and risks and opportunities—are negatively associated with synchronicity. We find similar results for all integrated report capitals, except manufactured capital. Further, we find that sustainability information has a larger negative association with synchronicity than financial information. We also find that proprietary costs stemming from product market competition attenuate the association between IRQ and synchronicity, which suggests the informativeness of integrated reports varies with a firm’s competitive environment. Our results may inform the International Sustainability Standards Board as it considers the role of the Integrated Reporting Framework in developing sustainability standards.

Dung beetle optimization with deep learning approach for solving inverse problems in predicting financial futures

Leveraging deep learning (DL) to inverse problems has proven transformative in predicting financial futures, mainly in stock price prediction. In terms of financial markets, where predicting stock price is a challenging inverse problem, DL methods like long short-term memory networks (LSTM) and recurrent neural networks (RNN) demonstrate notable prowess. These techniques successfully capture complex patterns and dependencies in historical stock price information, learning to discern subtle correlations and fundamental market trends. DL facilitates the extraction of nuanced features by leveraging large datasets and sophisticated techniques, enabling accurate prediction of future stock prices. This intersection of advanced ML approaches and financial predicting signifies a paradigm shift in leveraging technology to navigate the complexity of the financial market and improves decision-making for traders and investors. This manuscript introduces a Dung Beetle Optimization with a Deep Learning Approach for Solving Inverse Problems in Predicting Financial Futures (DBODL-SIPPFF) technique. The DBODL-SIPPFF technique resolves the inverse problem and predicts the stock prices adequately. In addition, the DBODL-SIPPFF technique exploits data consistency (DC) optimizer to solve inverse problems. Primarily, linear scaling normalization (LSN) is applied for data normalization. The honey badger algorithm (HBA) is utilized for the feature selection. Moreover, convolutional long short-term memory (ConvLSTM) is used for stock price prediction. Lastly, the DBO technique is utilized for the optimum hyperparameter selection of the ConvLSTM model. The empirical assessment of the DBODL-SIPPFF technique takes place under diverse stock datasets. The obtained values stated that the DBODL-SIPPFF technique performs optimally over compared methods.

Devil in FIPA: the motivations and consequences of frequent inter-industry portfolio adjustments in the Chinese mutual fund market

ABSTRACT Based on the equity asset allocation of Chinese open-end active funds, we examined the potential motivations and economic consequences of frequent inter-industry portfolio adjustments (FIPA) by funds. The research findings indicate that the fund tournament effect compels managers to engage in FIPA. Funds with higher managerial ownership exhibit lower levels of FIPA, suggesting that managers themselves do not favour FIPA. Instead, evidence suggests that fund managers cater to investors’ preferences for FIPA, but there exists an asymmetric effect between top-performing and underperforming funds. Specifically, top-performing funds attract more capital inflows after engaging in FIPA, while underperforming funds tend to engage in more aggressive FIPA, leading to net outflows of funds. Furthermore, the results exclude the motive of funds engaging in FIPA for risk hedging purposes. Finally, we explored the impact of FIPA on the stock market, revealing that fund FIPA delays the incorporation of negative company-specific information into stock prices, ultimately reducing stock price information efficiency.

Do corporate managers glean information from their stock prices? New evidence from China's strategic emerging industries

Strategic emerging industries (SEIs) have become the pillar industries in China. However, prior studies argue that the Chinese stock market fails to adequately support the development of SEIs by primarily considering its financing function while ignoring its market feedback role. Based on data from 2007 to 2020, our study fills this gap by examining whether managers of Chinese SEI firms glean information from stock prices to guide their investment decisions. We find a significantly positive relationship between stock price informativeness (proxied by the volume-synchronized probability of informed trading) and investment-to-price sensitivity in the SEI firms only, indicating that managers of the SEI firms exhibit prominent learning behaviors. We further reveal that high growth prospects and high sensitivity of corporate fundamentals to macroeconomic factors contribute to the learning behaviors in China's SEIs. Our study sheds light on the role of the Chinese stock market in supporting the SEIs' evolution.

Digital technology adoption and investment sensitivity to stock price

This paper investigates whether and how digital technology affects the sensitivity of corporate investment to stock price in China. Employing text analysis, we measure firm-level adoption of digital technology. Our findings show that digital technology has a strong positive effect on the investment-to-price sensitivity. After addressing endogeneity problems and conducting a set of robustness tests, the main effect remains consistent. Mechanism tests suggest that digital technology enhances stock price informativeness, thereby increasing the investment sensitivity to stock price. Furthermore, this positive effect is more pronounced among firms with lower information transparency, higher institutional ownership, and higher managerial ownership. Our paper contributes to the literature by demonstrating that digital technology helps to improve the investment-to-price sensitivity.

DCEnt‐PredictiveNet: A novel explainable hybrid model for time series forecasting

This work presents a novel hybrid framework called DCEnt-PredictiveNet (deep convolutional neural network (DCNN) + entropy + support vector regressor (SVR)) that concatenate both deep and handcrafted features for time series data analysis and forecasting. From the discrete wavelet transform coefficients of input time series data, computed four different handcrafted entropy features, which were then concatenated with deep features extracted using a modified DCNN. The concatenated deep and handcrafted feature vector was then fed to a SVR for prediction. The DCEnt-PredictiveNet framework was trained and tested on three time series datasets of real-world COVID-19, stock price and traffic information, and achieved mean absolute percentage errors of 0.03 %, 1.53 % and 11.41 % for daily cumulative COVID-19 positive cases, closing stock price, and hourly traffic (vehicle numbers) at one junction predictions, respectively. In addition, we incorporated local interpretable model-agnostic explanations and Shapley additive explanations methods into DCEnt-PredictiveNet to enable visualization of significant features that contributed to the model’s decision-making, thereby enhancing its explainability. Our DCEnt-PredictiveNet model yielded promising and interpretable forecasting results, which can facilitate advance resource planning in hospitals for incoming COVID-19 patients, stock market investment planning, and efficient traffic control management.

Stock liquidity and the signaling value of patents: Evidence from china's national equities exchange and quotations market

Young and technology-oriented firms suffer from asymmetric information in seeking access to finance. An increase in stock liquidity can improve a firm's stock price informativeness and mitigate such information problems. We expect that firms will patent less when they face an increase in liquidity if patenting is used as a costly signal mechanism to demonstrate firm quality. By examining an exogenous policy shock that dramatically improves the stock liquidity of treated firms in China's National Equities Exchange and Quotations (NEEQ) market, we document evidence that treated firms patent less after the shock. Our findings reveal the importance of liquidity in reducing the deadweight costs caused by asymmetric information and the signaling value of patenting, and urge caution regarding the sole use of patent applications to measure innovation performance in entrepreneurial firms.

Stock Liquidity Sidedness and Share Repurchase

We show that stock liquidity sidedness plays a vital role in share repurchase decisions. Specifically, we investigate whether share repurchases are affected differently by upside and downside liquidity, which respectively correspond to positive and negative return days. Using a sample of U.S. publicly-listed firms over four decades, we demonstrate that upside liquidity has a larger effect on repurchase initiation, amount, and ratio. Managers of repurchasing firms are more concerned with upside liquidity because they expect their repurchase activities to propel stock prices upward rather than down. The relationship between liquidity sidedness and repurchase decisions is more pronounced for financially constrained firms but less so for firms with higher stock price informativeness. We mitigate potential endogeneity concerns by using the market and industry liquidity sidedness as instruments and exploiting the 2003 Amendment to SEC Rule 10b-18 as an exogenous shock to the information advantages of repurchasing firms. Overall, our findings underscore the importance of market microstructure in shaping corporate policies.

Disclosure Regulation and Price Informativeness: Evidence from Industry-Information Disclosure Guidelines in China

We examine whether China’s industry-information disclosure guidelines (IIDGs) improve firms’ stock price informativeness and the quality of their textual disclosure. Employing a stacked difference-in-differences design, we find that applicable firms respond to IIDG implementation by including additional IIDG-related and firm-specific information in the Management Discussion and Analysis (MD&A). Our results also show that the stock market reacts to the enhanced disclosure and incorporates more firm-specific (rather than market-wide or industry-level) information into the stock price, as evidenced by a decrease in stock return synchronicity. Further analyses show that firms with fewer increases in textual disclosure following IIDG implementation are more likely to receive disclosure-related comment letters. Additionally, the extent to which firms change their textual disclosure varies with the industry concentration and firms’ profit margins due to the proprietary costs of disclosure.

Pengaruh Analisis Fundamental dan Risiko Sistematik terhadap Harga Saham pada Pasar Modal Syariah

The Islamic capital market has attractive investment potential for investors. Therefore, investors need to understand stock price information, including fundamental analysis (ROE, DER, EPS) and systematic risk that occurs from outside the company (beta), before deciding to buy or sell shares of a stock. The differences between theory and current phenomena are blurring further research. The formulation of the problem is to find out whether there is any influence of fundamental analysis (ROE, DER, and EPS as well as BETA systematic risk) which can influence the share price of a company. This type of research uses quantitative methods. The data used is secondary data such as company securities reports. This population includes all companies listed on the Jakarta Islamic index (JII) in 2020 - 2022. From these findings, we can conclude that in some cases only ROE and EPS affect share prices. Meanwhile, DER and systematic risk (BETA) have no effect on share prices

Comparable but is it informative?Accounting information comparability and price synchronicity

Increasing accounting information comparability (AIC) theoretically facilitates investors’ analysis of firm performance and improves stock price informativeness by incorporating more firm-specific information. However, achieving the purported purpose empirically is subject to firms’ institutional environment and corporate governance. We propose that under weak legal systems and less developed market environments, higher AIC may adversely affect price informativeness due to managers’ incentives and ability to obfuscate information and investors’ “hallo” effect. Using a large sample from China, we show that the AIC is positively related to price synchronicity, an inverse measure of price informativeness. Additionally, the positive impact is significantly greater for firms located in regions with weak legal systems and less developed market environments. The positive relation is also significantly greater when the business environment and economic policy uncertainties are high.

The short-termism trap: Catering to informed investors with limited horizons

Does the stock market exert short-term pressure on listed firms, do they respond, and is this response value reducing? We show that limited investor horizons indeed have those consequences, as follows. First, informative stock prices increase firm value; in our model, they reduce the agency cost of incentivizing managers. Second, short project maturity improves stock price informativeness by catering to informed investors with short horizons. Third, since informed trading capital is a scarce resource, attracting informed investors cannot increase an individual firm’s price informativeness in equilibrium: it simply destroys shareholder value. This “short-termism trap” can potentially destroy up to 100% of the benefits of stock market listing.

The effect of geopolitical uncertainty on stock liquidity: evidence from Taiwan misfired missile event

ABSTRACT We investigate the effect of geopolitical uncertainty on stock liquidity by using the Taiwan misfired missile event as a natural experiment. This exogenous event provides us with a unique opportunity to overcome the challenges of time recognition and confounding economic effects in the identification. We find that geopolitical uncertainty has a significantly negative effect on the stock liquidity of Taiwan-related firms. The mechanism test shows that the increased stock price risk and information asymmetry are the two main channels through which geopolitical uncertainty decreases stock liquidity. Moreover, we find that firms’ higher information transparency and regulators’ timely information disclosure on geopolitical events can effectively mitigate the negative impact of geopolitical uncertainty on stock liquidity.

ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF))

Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.

Firm-specific investor sentiment and stock price informativeness

This paper investigates the influence of firm-specific investor sentiment on stock price informativeness in China. Using a composite firm-specific investor sentiment index, we find that sentiment positively influences stock price synchronicity and future price crash risk for opaque stocks. We use firm-specific investor sentiment proxy to develop a market sentiment proxy, which could predict aggregate price synchronicity beyond extant market sentiment indicators. Our results suggest that the composite sentiment index is a reliable proxy and investor sentiment negatively affects price informativeness.

The Effect of Algorithmic Trading on Management Guidance

ABSTRACT I investigate whether algorithmic trading (AT) affects the provision of management guidance. Existing research finds that AT decreases fundamental information acquisition before earnings announcements and consequently reduces the informativeness of prices. To compensate for reduced information acquisition, I predict and find that managers at firms with more AT activity increase the quantity and quality of guidance issued at earnings announcements. Evidence is consistent with managers responding to reduced information acquisition, as opposed to changes in liquidity, and results suggest guidance in response to AT is effective at reducing information asymmetry. These findings identify a new channel through which AT affects stock price informativeness by documenting a link to managers’ disclosure decisions. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; G19; G10.

Military experience and subsequent effectiveness as a director

Wars inevitably result in an increase in the workforce of those with military experience. The military training associated with such experience promotes leadership skills and emphasizes a unique value system encompassing integrity, duty, selflessness and self-discipline. We hypothesize that directors with military experience encourage timely disclosure of firm-specific information, which leads to higher stock price informativeness. Based on a sample of listed firms in 46 countries over the period 1999–2016, we document that having a military director on board is positively related to stock price non-synchronicity, informed trading and shorter price response delay. Exploiting exogenous variations in the likelihood of having military directors that arise as a result of variations in the military talent pool, we confirm the causal inference of these results. Our findings suggest that military directors reduce corporate misconduct and improve the timely disclosure of information.