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1 – 2 of 2Ricardo Vinícius Dias Jordão, Ewerton Alex Avelar and Marco Antônio Lúcio
This paper aims to analyze the impact of intellectual capital (IC) on Brazilian companies’ sustainable value creation (VC).
Abstract
Purpose
This paper aims to analyze the impact of intellectual capital (IC) on Brazilian companies’ sustainable value creation (VC).
Design/methodology/approach
An empirical study was performed using descriptive and multivariate statistics based on the finance, strategy and IC theories. This research is quantitative, explanatory, descriptive, applied and ex post facto and uses traditional economic-financial variables (derived from financial statements – FSs) linked to two established frameworks for IC analysis: the market-to-book ratio (IC-INDEX) and the MVAIC, a variation of the value-added intellectual coefficient (VAIC™).
Findings
The findings showed that the IC estimated through the IC-INDEX and the MVAIC frameworks is directly related to the VC of Brazilian companies throughout the entire period and revealed a consistent effect in all time frames analyzed. Both models were robust and complementary in assessing the company’s VC and sustainability. The conclusion shows that IC is the most relevant factor in explaining VC and its continuity over time, regardless of other traditional variables used to study the phenomenon.
Research limitations/implications
From a theoretical perspective, this study contributes to mastering the understanding of the subject by applying two important IC measurement frameworks to explain sustainable VC over time and examining the problem in the Brazilian market – paving the way for future investigations.
Practical implications
This study provides users of accounting and financial information and other market agents with a better understanding of the VC process and the central role of IC in this process. These findings suggest that these asset investments tend to be more qualified to create corporate wealth for shareholders and other stakeholders. Such a result can help improve decision-making processes, besides generating competitive benchmarking and assisting them in financial analysis and resource allocation in the economy.
Originality/value
The uniqueness of the research arises from applying two important IC measurement frameworks (IC-INDEX and MVAIC) simultaneously to explain sustainable VC over time and the analysis in a relevant and complex emerging market – both issues are unexplored in the literature.
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Keywords
Ewerton Alex Avelar and Ricardo Vinícius Dias Jordão
This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest…
Abstract
Purpose
This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.
Design/methodology/approach
Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.
Findings
All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.
Research limitations/implications
The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.
Practical implications
The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.
Originality/value
The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.
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