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The literature on financial statement analysis attempts to improve fundamental analysis and to identify market inefficiencies with respect to financial statement information.

In this paper, the author reviews the extant research on financial statement analysis.

The author then provides some preliminary evidence using Chinese data and offer suggestions for future research, with a focus on utilising unique features of the Chinese business environment as motivation.

The author notes that there has been no work that the author could locate specifically on Chinese FSA research. The unique business environment in China, relative to the US where the vast majority of this work has been conducted, should motivate any studies, especially given the author documents the robust finding in terms of the mean reversion in profitability.

Fundamental analysis is aimed at assessing the intrinsic value of companies based on publicly available accounting information. Thus, the main purpose of fundamental analysis research should be identifying the main determinants of value, establishing empirical relations between them and fundamental accounting variables, and finally, predicting the future behavior of such variables to estimate the value of the firm. Traditional fundamental analysis ignores the existence of some intangible assets, such as intellectual capital, that are currently considered as the main determinants of value. Human capital is an essential component of intellectual capital. This paper provides evidence on the extent to which the quality of human resources is related to the value of accounting variables that are used in fundamental analysis due to their perceived usefulness as proxies for investors' expectations on the firm's future profitability and growth in both, earnings and shareholders' equity.

This paper examines the profitability and performance measurement of U.S. regional banks during the period 1994–2011, using the GMM estimator technique. Our study extends prior research by including several factors not previously considered using U.S. data.

We use bank-specific, industry-specific, and macroeconomic determinants of profitability contemporaneous with our performance indicators. We follow the accounting fundamental analysis path in explaining the bank performance.

Among the performance measures, the efficiency ratio and provisions for credit losses are negatively and equity scaled by assets is positively related to profitability. However, these relationships either reverse (efficiency ratio and provisions for credit losses) or become insignificant (equity scaled by assets) when the target becomes change in profitability. The level of nonperforming assets is negatively related to profitability across all measures of profitability used. Macroeconomic variables are largely unrelated to profitability during the year they are measured. However, they have a significant relationship with earnings change measures, suggesting they have a lagged effect on profitability. The slope of the yield curve is especially strong in this regard.

We use our determinants to model changes in bank profitability one year ahead, in addition to including several factors not previously considered, using the predictive focus of the fundamental analysis research.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

A one-year-ahead price change forecasting model is proposed based on the fundamental analysis to examine the relationship between equity market value and financial performance measures. By including book value and six financial statement items in the valuation model, current firm value can be determined and the estimation error can predict the direction and magnitude of future returns of a given portfolio. The six financial performance measures represent both cash flows – cash flows from operations (CFO), cash flows from investing (CFI), and cash flows from financing (CFF) – as well as net income – R&D expenditures (R&D), operating income (OI), and adjusted nonoperating income (ANOI). This study uses a 10-year sample of the Taiwan information electronic industry (1995–2004 with 2,465 firm-year observations). We find hedge portfolios (consisting of a long position in the most underpriced portfolio and an offsetting short position in the most overpriced portfolio) provide an average annual return of 43%, more than three times the average annual stock return of 12.6%. The result shows the estimation error can be a good stock return predictor; however, the return of hedge portfolios generally decreases as the market matures.

This study examines the ability of fundamental summary measure Pr to predict earnings change for the subsequent year, the association of Pr and stock returns, and the relationship between Pr and risk factors beta and size. Pr is a probability index generated by logistic model and financial statement data. Beta effect is minimized by grouping firms into beta portfolios while size is controlled through incorporating size as an independent variable in the regression models. Evidence from the study indicates that Pr has a strong ability to predict future earnings change and has a positive and significant association with adjusted market returns, after controlling for beta. Pr's association with adjusted market returns is mitigated when beta and size are controlled simultaneously.

Identifying the fundamental characteristics of meaning and deriving an automated meaning‐analysis procedure for machine intelligence.

Semantic category theory (SCT) is an original testable scientific theory, based on readily available data: not assumptions or axioms. SCT can therefore be refuted by irreconcilable data: not opinion.

Human language involves four totally independent semantic categories (SC), each of which has its own distinctive form of “Truth”. Any sentence that assigns the characteristics of one SC to another SC involves what is termed here “Semantic Intertwine”. Semantic intertwine often lies at the core of semantic ambiguity, sophistry and paradox: problems that have plagued human reason since antiquity.

SCT is applicable to any endeavour involving human language. Research applications are therefore somewhat extensive. For example, identifying metaphors posing as science, or natural language processing/translation, or solving disparate paradox types, as illustrated by worked examples from: The Liar Group, Sorites Inductive, Russell's Set Theoretic and Zeno's Paradoxes.

To interact successfully with human language, behaviour, and belief systems, as well as their own environment, intelligent machines will need to resolve the semantic component/intertwines of any sentence. Semantic category analysis (SCA), derived from SCT, and also described here, can be used to analyse any sentence or argument, however complex.

Both SCT and SCA are original. Whilst “category error” is an intuitive notion, the observably precise nature, number and modes of interaction of such categories have never previously been presented. With SCT/SCA the rigorous analysis of any argument, whether foisted, valid, or obfuscating, is now possible: by man or machine.

The purpose of this paper is to investigate the effect of share pledging by controlling shareholders on earnings informativeness.

Using a sample of 23,120 firm-year observations from 2003 to 2019 in China, this paper examines how share pledging by controlling shareholders affects earnings informativeness, measured by earnings persistence and earnings response coefficients.

This paper finds that share pledging by controlling shareholders makes earnings less informative. The adverse impacts are more pronounced when share pledging distorts incentives of controlling shareholders to a greater extent and when the signaling of share pledging about a firm’s weak future performance is stronger. Finally, this paper further shows that the 2018 new regulation on share pledging effectively alleviates the negative impacts of share pledging on earnings informativeness.

First, this paper adds to the growing literature on the economic consequence of share pledging by documenting the adverse impacts of share pledging on earnings informativeness. The literature on the economic consequence of share pledging is often mixed, which justifies further research on the impacts of share pledging on earnings informativeness. Second, this paper documents a new signaling channel through which share pledging affects earnings informativeness. Third, the finding of this paper on the 2018 new regulation on share pledging may be interesting to research agencies, such as the Chartered Financial analyst institute and Institutional Shareholder Services institute that recommend tightening regulations on share pledging.

The purpose of this paper is to examine securities analyst independence in China's capital market and the effect on analyst independence of institutional investors’ shareholding and separation between control rights and cash flow rights of ultimate controller.

Using data of China's listed companies from 2006 to 2012, the authors empirically tested the relationship between analyst following and volatility of stock return. And based on the test, the authors investigated the role played by institutional investors’ ownership and separation between control rights and cash flow rights of ultimate controller.

According to the empirical results, there is a significant negative correlation between analyst following and volatility of stock return. Also, shareholding of institutional investors and the separation between control rights and cash flow rights of ultimate controllers will have an impact on the relationship between analyst following and volatility of stock return. When institutional investors hold higher proportion or the separation between control rights and cash flow rights of ultimate controllers keeps at a high level, the negative correlation between analyst following and volatility of stock return will weaken.

First, based on the theory of market intermediation, the paper examined analyst independence by investigating and analyzing the relationship between analyst following and volatility of stock return. Second, it analyzed the factors affecting analyst independence by integrating enterprise characteristic variable and market characteristic variable on the basis of introducing two variables – shareholding of institutional investors and the separation between control rights and cash flow rights of ultimate controllers.

A research and development project disseminated ecological technologies to approximately 3,250 vegetable farmers through farmer field schools (FFS) in four districts of Bali and East Java provinces of Indonesia. This article aims to assess the economic sustainability of vegetable production after FFS participation.

A survey randomly sampled 500 farmers, comprised of FFS participants (50%) and non-FFS participants (50%). Based on 1,000 farm operations, this analysis employed input-saving technology as the fundamental model examined using the double-difference method. Simultaneous reduction of agrochemicals and improvement of productivity represent indicators of economic sustainability.

Results indicate that pesticide use decreased without jeopardising farm productivity; moreover, vegetable production increased. These findings indicate that the ecological technologies transferred through FFS significantly improved economic sustainability performance.

This study purposively selected farmers who grew tomato and chilli. Thus, the outcomes are not generalisable to other crops.

FFS continues to be an effective method for transferring agricultural technologies to farmer communities. Policymakers are recommended to use FFS for disseminating beneficial and sustainable technologies to broader agricultural communities.

The adoption of ecological technologies provides positive economic and ecological milieus.

This study employs a double-differences approach to verify input-saving technological progress. Therefore, the performance of economic sustainability attributable to the project intervention is theoretically justified.