Stock Volume Research Articles

Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies.

The Extension and Improvement of the Forest Land Net Present Value Model and Its Application in the Asset Evaluation of Cunninghamia lanceolata Forest Land

In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this area, the technical system of the forest land asset evaluation was enriched. The forest land returns were divided into two parts, i.e., the segmented forest land return price from the growth of the actual forest stand to the end of the growth cycle (Bu1), and the segmented forest land return price for an infinite number of growth cycles after the growth of the actual forest stand to the end of the growth cycle (Bu2). Through structure, the forest land gain price expansion model was obtained, and the stand quality including the average diameter at breast height, average height, stock volume, and outturn of stand as dummy variables were used to construct the growth harvest model related to asset evaluation. Taking Cunninghamia lanceolata forest land as an example, the traditional asset evaluation methods were comparatively analyzed. The residual standard deviation (RSD) of the growth model was less than 10%, the total relative error (TRE) and mean system error (MSE) were within ±10%, the mean prediction errors (MPE) were less than 5%, and the mean percentage standard errors (MPSE) were less than 10%, respectively. Combining the forest land net present value expansion model with the traditional evaluation method, the evaluation value of the forest land assets was subsequently calculated, and accordingly, the forest land asset evaluation prime stand factors were predicated. It was found that the valuation results of the forest land net present value expansion model were consistent with the actual situation. The forest land net present value expansion model can therefore be used for asset evaluation of tree forest land (including natural uneven-aged forest land), bamboo forest land, shrub forest land, and economic forest land, and provide new technical support for forest land asset evaluation.

Liquidity, Volume, and Order Imbalance Volatility

ABSTRACTWe examine the dynamics of liquidity using a comprehensive sample of U.S. stocks in the post‐decimalization period. Motivated by a continuous‐time inventory model, we compute a high‐frequency measure of order imbalance volatility to proxy for the inventory risk faced by liquidity providers. We show that high‐frequency order imbalance volatility is an important driver of liquidity and explains the often positive time‐series relation between spread and volume for large stocks, which seems to run counter to most theoretical models. Furthermore, order imbalance volatility is priced in the cross‐section of stock returns.

Comparison of Three Active Microwave Models of Forest Growing Stock Volume Based on the Idea of the Water Cloud Model

Forest growing stock volume (GSV) is an essential aspect of ecological carbon stock monitoring. The successive launches of spaceborne microwave satellites have provided a broader way to use microwave remote sensing to monitor forest accumulation. Currently, the inversion parameterization models of active microwave remote sensing stock volume mainly include the interferometric water cloud (IWCM), BIOMASAR, and Siberia. Among them, the IWCM introduces backscattering and coherence, the BIOMASAR model only introduces backscattering, and the Siberia model only introduces coherence. Although these three models combine the backscatter coefficient and coherence of SAR to estimate volume accumulation, the performance of the models has not been evaluated at the same time in the same area. Therefore, this article starts from the perspective of the three combinations of coherence and backscattering, relies on three models that do not require measured data, and evaluates the accuracy of the models’ overall inversion of GSV. In addition, we combine precipitation meteorological information, vegetation types, and seasonal variation to separately explore model performance. The comparison results show that the IWCM model is relatively stable in the process of stock volume inversion and is more sensitive to the vegetation types of coniferous and deciduous forests. The influence of seasons and precipitation on the model is weak, and the accuracy of the multi-time-series model is slightly improved. The Siberia model has a good storage volume inversion effect in this study area, but the multiple time series did not improve the model accuracy. The BIOMASAR model is simple, and its performance was slightly inferior in this study area. Precipitation can negatively affect BIOMASAR. The model results for multiple time series outperform those for single time. In summary, the stability of IWCM is more suitable for research with unknown information. The BIOMASAR model is simple, does not require coherence calculations, and is ideal for the estimation of large-scale national or world-level storage distributions. The Siberian model performs better in small regions and smaller spatiotemporal baselines.

Texture Features Derived from Sentinel-2 Vegetation Indices for Estimating and Mapping Forest Growing Stock Volume

Forest growing stock volume (GSV, m3/ha) is an indispensable variable for forest resources supervision. Precise measurement of GSV is conducive to monitoring forest dynamics. Nevertheless, little research has explored the pattern pixel values and the function of texture features derived from a vegetation index (VI) for GSV prediction. In this study, we investigated combining linear regression or Random Forest with Sentinel-2 spectral predictors and image textures derived from spectral bands and vegetation indices, which were based on standard deviation values or mean values at the plot level, for predicting GSV of Masson pine, theropencedrymion and all the survey plots in Anji County, China. Specifically, ten groups of experiments encompassing combinations of spectral parameters and texture measures were established for detecting the potential of image textures based on two different image pixel statistics. The results showed that texture measures derived from VI were superior to spectral parameters or texture measures derived from spectral bands for estimating the GSV of single tree species. Moreover, texture features based on shortwave infrared bands or their related VI and the standard deviation at pixel level for spectral bands/indices were emphasized. Finally, the mean value at the plot level exhibited slightly stronger potential than the standard deviation in general.

Whole Stand Variable Density Yield Equations for Oak-Gum-Cypress Bottomland Hardwood Forests

Abstract Variable density yield equations were constructed using fuzzy linear regression for bottomland oak-gum-cypress forests along the US Gulf Coast and lower Mississippi River Delta region. The USDA Forest Service (Forest Service)’s Forest Inventory and Analysis program provided plot data (n = 526). Predictors included stand age, growing stock basal area per acre, sweetgum site index (base age 50 years), and US Environmental Protection Agency ecoregion dummy variables located in Alabama, Arkansas, Louisiana, Mississippi, Tennessee, and Texas. Dependent variables were per acre growing stock cubic foot yield (GSV) and Doyle board foot sawlog yield (SLV). Plots averaged 58 years, 90 ft2/ac basal area, 79 ft site index, 2,556 ft3/ac GSV, and 11,183 Doyle bf/ac SLV. Adjusted R2 were 0.98 (GSV) and 0.77 (SLV). Basal area possessed fuzziness in the GSV model, whereas the SLV model’s intercept was fuzzy. Six ecoregions possessed fuzziness in each model, but these were not identical across models.

Presentation of Financial Assets in the Statistics of Foreign Economic Relations

The purpose of the study. To characterize the level and dynamics of the country’s economic development, its sovereignty and, accordingly, the adoption of effective management decisions, quantitative information is needed on economic assets, in particular financial ones, their presentation in national accounts, monetary statistics and indexes of the external sector.The aim of the paper is to systematize theoretical and practical developments on the definition and presentation of data on financial assets in modern statistics of Russia’s foreign economic relations, to identify areas for improving methods for assessing and analysis of financial assets, a system of indexes characterizing their presence, structure and dynamics.Materials and methods. In the paper, the authors considered the definitions, specifics, classifications and categories of financial assets and liabilities, applied structural and dynamic data analysis, as well as methods of theoretical research in the form of generalization, comparison and special analytical procedures based on official statistics from Rosstat, the Bank of Russia, the Ministry of Finance and international statistical organizations.Results. The paper identifies the main directions for the statisticalstudy of financial assets, based on current international standards, taking into account national practice. Topical issues of theory and practice of observations and presentation of data on financial assets in foreign economic statistics, application of definitions and classifications of international standards to Russian official statistical activities are considered. The features of the data presentation on financial assets in macrostatistics, in the system of indexes of statistics of foreign economic relations, are formulated. The authors conducted a study of the structure and dynamics of indexes of Russia’s foreign economic statistics that characterize the state and movement of financial assets and liabilities representing them.Conclusion. Statistical analysis of foreign economic transactions with financial assets allow us to identify not only the main trends in the development of these processes, but also makes it possible to analyze the relationship between the subsectors of the Financial corporations’ sector, between this sector and other sectors, as well as a comprehensive change in the volume and composition of stocks and flows of financial assets as a result of the exchange between residents and non-residents.The authors comprehensively reviewed the methodological framework for constructing the financial account of the Balance of payments, International investment position, including in the context of financial instruments. The Balance of payments and International investment position, through their system of statistical indexes, reflect international economic relations and represent an important tool for the study of financial assets. The groupings of items of the financial account of the Balance of payments are based not only on the categories of financial instruments, but also on the functional categories of investments and classifications of institutional sectors, which serve the purposes of a comprehensive reflection of financial assets in foreign economic relations. Thus, it is obvious that the main task of such a presentation is to reflect financial assets in terms of the type of instruments, as well as to analyze the role of a particular sector in the implementation of relevant transactions.In the International investment position, financial assets are also fully reflected in that part of them that is involved in economic transactions between residents and non-residents. Groups of financial assets and liabilities are given in a classification similar to that used in the Balance of payments: by functional purpose, by financial instruments, by institutional sectors, by maturity. Together with the financial account, account figures for other changes in financial assets and liabilities explain the total amount of changes: increases or decreases in the value of financial assets / liabilities, their occurrence or disposal, resulting from transactions, revaluation and other changes in volume.The issues of theory and practice of presenting indexes of financial assets in the considered sections of macrostatistics and their analysis are a relevant and promising direction for improving national statistical accounting, incl. due to the spread of remote work, the collection of information via the Internet, the emergence of a large number of new financial instruments and operations.The authors of the article have repeatedly referred to the problems of macroeconomic financial statistics in their studies, which gave them the opportunity to comprehensively consider the topical issues of representing financial assets in the statistics of foreign economic relations.

The impact of investors attention on stock price

From the perspective of investor attention to market stocks, this paper analyzes investors' visitor volume of certain stocks and sentiments of stock indexes. I compare the search vol-ume and stock prices of Apple and Tesla to specifically observe the relationship between stock prices and investor attention in individual companies. Then I examine the search vol-ume and index of SP500 and SSEC to explain the significant disparity in the impacts of in-vestor attention in different regions. It shows that increased attention is positively related to stock price volatility, i.e., positive emotional signals (negative emotional signals) will lead to higher (lower) stock prices.

Land cover changes, biomass loss, and predictive causes of massive dieback of a mangrove plantation in Lampung, Sumatra

A new massive dieback case of Avicennia marina was observed in the area of the Sunda Strait, which started in 2020, and the cause is still unknown. This research was conducted to report the rate of mangrove degradation, measure biomass loss, and predict the causes of the dieback. A sequential imagery analysis (2019–2022) was conducted to interpret the area changes using a research area of 200 ha from the total 528.69-ha mangrove, using the plant senescence reflectance index (PSRI) and normalized difference vegetation index (NDVI). Field data were collected before and during the dieback event (2020 and 2021) at seven research stations including, live mangroves, partial dieback, and full dieback sites. Biomass loss was measured as standing stock volume (SSV) and total biomass carbon (TBC). A literature study was conducted to predict the cause of the dieback. Based on the results, the mangrove dieback caused a rapid and significant loss of healthy stands (results of the PSRI) and very-high-density stands (results of the NDVI). The rate of healthy stand loss was 13.43 ha month−1 (during July–October 2020) and that of very-high-density stand loss was 14.99 ha month−1 (October 2020–April 2021), which affected 126.62 ha or 24% of the total area (last measurement in January 2022). The SSV before dieback was 118.70 (±46.1) m3 ha−1, but within 14 months, it decreased to 20.8 (±6.0), 79.80 (±52.3), and only 1.0 (±1.0) m3 ha−1 for live, partial dieback, and full dieback stands, respectively. The TBC of live mangroves (before dieback) was 51.6 (±24.4) Mg C ha−1, but within the same period, it changed to 30.70 (±3.80), 69.50 (±14.6), and 51.60 (±9.4) Mg C ha−1 for live, partial dieback, and full dieback stands, respectively. Based on the literature study and field observation, the dieback was probably related to less flushing and long-term inundation by the decrease in rainfall intensity, in addition to nitrogen (NO3−N and NO2−N) enrichment, an over-supply of N from fishpond wastes. Sustainable silvofishery may be introduced to ameliorate water and soil quality.

A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China

Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension of the distribution patterns and variations of FSV is crucial in the assessment of the sequestration potential of forest carbon and optimization of the management programs of the forest carbon sink. In this study, a novel vegetation index based on Sentinel-2 data for modeling FSV with the random forest (RF) algorithm in Helan Mountains, China has been developed. Among all the other variables and with a correlation coefficient of r = 0.778, the novel vegetation index (NDVIRE) developed based on the red-edge bands of the Sentinel-2 data was the most significant. Meanwhile, the model that combined bands and vegetation indices (bands + VIs-based model, BVBM) performed best in the training phase (R2 = 0.93, RMSE = 10.82 m3ha−1) and testing phase (R2 = 0.60, RMSE = 27.05 m3ha−1). Using the best training model, the FSV of the Helan Mountains was first mapped and an accuracy of 80.46% was obtained. The novel vegetation index developed based on the red-edge bands of the Sentinel-2 data and RF algorithm is thus the most effective method to assess the FSV. In addition, this method can provide a new method to estimate the FSV in other areas, especially in the management of forest carbon sequestration.

Coronavirus Disease (COVID) -19 Vaccinations and Indonesian Capital Market

This study aims to compare the Indonesian capital market responses in term of abnormal return, cumulative abnormal return, and trading volume activity due to Coronavirus disease (COVID)-19 Vaccinations. The event study method is used in this research to study Indonesia's capital market response during the event period. The event period is divided into 2, i.e. 10 days before and after the event and also 30 days before and after the event. The sample was selected by using the purposive sampling method and obtained from 10 pharmaceutical sub-sector companies listed on IDX. The secondary data used in this study are daily data of closing price for each stock, closing price JCI (IHSG), stock volume trading, and outstanding share. The market model is used to predict expected returns. The results found that there were differences in abnormal returns before and after the implementation of the first COVID-19 vaccination in Indonesia. However, there was no difference between cumulative abnormal returns before and after the event. In 10 days before and after the event, there was no difference in trading volume activity. The opposite result was obtained from the 30 days, there were differences between trading volume activity before and after the event.

Forest Resources Projection Tools: Comparison of Available Tools and Their Adaptation to Polish Conditions

Over the years, various methods for estimating and projecting forest resources have been developed and are used by countries where the forest sector is important. Therefore, the obligation to report and account for forest resources, including changes in carbon stocks in a forest area, has gained attention. The latest regulations (Land Use, Land Use Change and Forestry—LULUCF) requires European Union (EU) members to annually report and publish national accounting plans estimating emissions and removals from managed forest areas (Regulation EU 2018/841). The major challenge is to choose and adapt a unique tool for this accounting. At the same time, they need to provide reliable estimates that are recognized by regulators and control authorities. This study focuses on comparing the adaptation of two accounting frameworks: the Operational-Scale Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) and the European Forest Dynamics Model (EFDM). Both tools are based on National Forest Inventory (NFI) data. It is assumed that the EFDM can provide similar results to the CBM-CFS3, which is already used in Poland. Implementing the EFDM and adapting it to Polish conditions could facilitate forest management decision-making and the preparation of forest policies. The main objective of this study was to compare and validate the accuracy of the results obtained with the EFDM framework. Metrics compared using both tools included growing stock volume, biomass of growing stock expressed in carbon units and age–class distribution over area. The comparison was based on the agreement of EFDM with CBM-CFS3 results. The volume of logging was taken from the EFDM and compared with the values obtained by Statistics Poland. This study also provides a guide for framework parameterization directly from the Polish National Forest Inventory data from the 2010–2015 cycle. Our main findings are that the results of the two models are reasonably comparable (the extent of deviation is acceptable). Moreover, the first implementation of the EFDM showed that it is an easy-to-use open-source program that allows forest managers to implement their own settings according to their needs. This document elucidates the concept of using both frameworks under Polish conditions and provides an impression of their performance for future modelers, students and researchers.

Pengaruh Pandemi Covid-19 terhadap Fluktuasi Harga Saham dan Volume Transaksi Saham Supermarket di BEI

This study aims to determine the decline or increase in stock prices and stock transaction volume after the announcement of the arrival of Covid-19 in Indonesia. The companies studied were companies belonging to the D113 supermarket sector, consisting of AMRT, HERO, MIDI, MPPA, AND RANC. The research time is 35 days of stock transactions before March 2 2020 and 35 days of stock transactions after March 2 2020. The research approach uses quantitative analysis. This study uses secondary data, namely stock price data and stock transaction volume data. The analytical method used is multiple linear regression using SPSS, namely the Normality Test followed by the T-Test Paired Samples Test. The results of the study found that stock prices and stock transaction volume had different results. Share prices have decreased after the entry of Covid-19, but the volume of stock transactions has actually increased. Supermarket management to take steps to adjust quickly and accurately to changes that occur as a result of the government's mobility restrictions. It is hoped that the stability of sales in supermarkets will be able to increase sales so that investors want to buy shares, so that share prices rise again. Keywords: Stock Price, Stock Volume, Supermarket

A comparative analysis of grid-based and object-based modeling approaches for poplar forest growing stock volume estimation in plain regions using airborne LiDAR data

ABSTRACT Poplar (PopulusL.) is one of the most widely distributed tree species planted in the plains of China and plays an important role in wood products and ecological services. Accurate estimation of poplar Growing Stock Volume (GSV) is crucial for better understanding the ecological functions and economic values in plain regions. However, the striped distribution feature of poplar forests in plain regions makes traditional grid-based GSV modeling methods highly uncertain. This research took Lixin County and Yongqiao District as case studies to examine the advantages of using object-based GSV modeling approach over the traditional grid-based approaches for poplar GSV estimation. The canopy height variables and density variables were extracted from airborne LIDAR-derived Canopy Height Model (CHM) data through different grid sizes and segmentation unit for constructing the poplar GSV estimation models using the linear regression. The results indicate that (1) Significantly linear relationships exist between GSV and height percentile variables; (2) The estimation accuracy in Lixin can be effectively improved by incorporating the CHM density variables into height variables, with the coefficient of determination (R 2 ) increasing from 0.46 to 0.71 and Root Mean Square Error (RMSE) decreasing from 20.23 to 14.94 m3/ha when a grid-based approach was implemented at grid size of 26 m by 26 m (plot size). However, CHM density variables have no effect on estimation modeling in Yongqiao district. The patch sizes and shapes considerably affect the selection of modeling variables and accuracy of modeling prediction; (3) The object-based mapping approach outperforms the grid-based approach in solving the mixed plot problem. This is especially valuable in the study areas with striped forest distribution. This study shows that differences in poplar stand structure affect the selection of modeling variables and GSV modeling performance, and an object-based modeling approach is recommended for GSV estimation in the plain areas.

The impact of climate policy on U.S. environmentally friendly firms: A firm-level examination of stock return, volatility, volume, and connectedness

This paper investigates the green stock market reaction to climate policy events associated with the Paris Agreement and the U.S. presidential elections. We document abnormal returns, volatility and volume reactions to climate policy events among green stocks. However, the magnitude of the reactions varies between the tightening and loosening of climate policy and across subgroups of the green stock markets. Our connectedness analysis further investigates the spillover patterns among individual green stocks and confirms their heterogeneous natures when responding to the occurrence of these climate policy events. By constructing a minimum connectedness portfolio based on the estimated connectedness among these green stocks, we find that investors can substantially reduce their risks. Our findings have strong implications for policy makers in designing policies to effectively promote green investments and mitigate climate change.

Формирование рубками ухода биологически устойчивых сосняков защитного назначения в Северном Казахстане

The article deals with the effect of thinning in a permanent trial plot (PTP). The objects of care were natural clean even-aged pine forests growing in arid conditions of the Kazakh Uplands (case study of the Burabay National Park). The first thinning led by A.A. Weisman was carried out in 1994 in 25–30-year-old stands with the care intensity from 12.5 to 49.7 %. Besides the operating sections, the trial plot laid out during the research had control sections, where thinning was not carried out. The inventory indicators of the stands were determined, and the effects of thinning were analyzed in all the control and operating sections 70 years after the experiment began. The experiment proves that in dry pine forests of Northern Kazakhstan, the stock of stands in the operating sections, even 70 years after the care does not reach the volume of stock in the control sections. This also applies to the sections covered by low-intensity thinning. At the same time, thinning reduces the stock of ground flammable materials and deadwood. It increases the average diameter of trees relative to the care intensity. This enhances the fire resistance and recreational attractiveness of the plantations. An expanded distribution series of trees by natural diameter classes is observed in all sections: the number of natural diameter classes is 1.8 times greater than the normative distribution, which is one of the proofs of the specificity of the structure of the studied pine forests. The use of thinning with high intensity by the stock is the most promising. Dead standing, diseased and stunted trees are removed from the stand in the course of thinning. Thinning is recommended for 20–25 and 40–50 years old trees.

Stock Return Response to Changes in MSCI Standard and Small Cap Index Composition for Turkey

The effect of index reconstitution on stock return and volume have been one of the controversial subjects in finance. Although, there are several researches, which are analyzing the domestic stock market index effect on stocks, a few studies focused on the effects of an international index. By the use of event study methodology, this paper analyzes the price effect of changes in constituents of two MSCI indexes (Standard Index and Small Cap Index) for Turkish companies as of announcement and effective dates. Overall, there are four analyses; namely, addition to Small Cap Index with respect to announcement (effective) day, deletion from Small Cap Index with respect to announcement (effective) day, addition to Standard Index with respect to announcement day (effective), and deletion from Standard Index with respect to announcement day (effective). The findings of the study present that index effect may appear or disappear, depending on the index, on which index reconstitution is being analyzed. Moreover, existence of the index effect is also subject to event day definition, which may be either announcement date or effective date of the index event.

Share repurchases on trial: Large‐sample evidence on share price performance, executive compensation, and corporate investment

AbstractUsing a large sample of US stocks covering more than three decades, we empirically examine common criticisms of and rationales for stock repurchases. Repurchases account for a tiny fraction of the trading volume in a typical stock, making their price impact too small to generate short‐term price manipulation. Price appreciation following repurchases is modest and does not reverse on average, suggesting the small price increases following repurchases signal firms’ good prospects. Also, we find no evidence that CEOs of repurchasing firms are paid excessively or that repurchases crowd out valuable investment opportunities. Because repurchases do not appear to be systematically abusive, enforcement action should be sufficient to deal with any bad actors, and significant regulation seems unwarranted.

Option‐implied information and quality of patents

AbstractThis research explores how option‐implied information predicts quality of patents. Using several measures of option‐implied information, we find that only the option to stock volume (O/S) ratio positively and significantly predicts quality of patents around patent grant announcements. The findings are not entirely driven by information from the stock market and the probability of informed trading. Further investigations show that the predictability of O/S on patent quality is stronger when market sentiment is high, firms have a higher short‐sale cost, and the quality of patents is relatively high.

Efeito da atenção do investidor na eficiência do mercado brasileiro de ações

ABSTRACT The objective of this article was to evaluate the relationship between investor attention, both professional and non-professional, and the efficiency of the Brazilian stock market, as measured by the predictability of daily returns. The role of investor attention in the capital market is a controversial issue, since some studies show that it is capable of inducing efficiency, while others point out that it essentially contributes to greater volatility due to behavioral biases. This study contributes to the behavioral finance literature by pioneering the analysis of the effect of investor attention on Brazilian stock prices. Moreover, we found no previous studies that compare the effect of investor attention on market efficiency across investor classes, as measured by access to different financial information providers. The analysis of investor attention, as measured by the volume of searches on the internet, is relevant given the growing abundance of data and digital inclusion. The evidence of an effect of attention on market efficiency contributes to the critique of this classical hypothesis. The methodology consisted of estimating autoregressive models using daily data from 2018 to 2021 on search volume, returns, and transaction volume for Brazilian stocks, including the interaction between returns and search volume, to assess the influence of attention on price dynamics. In this work, we find that in the Brazilian stock market, investor attention contributes to greater market efficiency, as measured by lower predictability of returns, but the most pronounced effect comes from the attention of non-professional investors. Understanding how attention affects the incorporation of information into prices contributes to a critique of the market efficiency hypothesis, as well as allowing for profits to be made by exploiting opportunities based on the level of attention of a particular class of investors.