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We examine firm value and performance implications of the growing trend of nontechnology companies engaging in activities relating to digital technologies. We measure digital activities in firms based on the disclosure of digital words in the business description section of 10-Ks. Digital activities are associated with a market-to-book ratio 8%–26% higher than industry peers, and only 25% of the differences in market-to-book is explained by accounting capitalization restrictions. To control for selection bias, we implement lagged dependent variable and IV regressions, and our market-to-book findings are robust to these specifications. Portfolios formed on digital activity disclosure earn a Daniel et al. The Journal of Finance 52 (3): 1035–1058 ( 1997 )-adjusted return of 30% over a three-year horizon and a monthly alpha of 44-basis-points. On the other hand, we find weak evidence of near-term, positive improvements in fundamental performance, as we find some evidence of interim productivity increases but declines in sales growth conditional on digital activities.

Electricity is currently one of the most popular sources of energy. Considering such widespread use of electric energy, we may ask, what is the economic cost of producing and supplying it? The climate crisis and the social pressure associated with it have triggered the necessity to make further investments in renewable and low-emission energy sources, while the COVID-19 pandemic has abruptly limited electricity consumption in industry. All these factors can have an impact on disruptions or loss in the liquidity of companies responsible for supplying electricity to end users. Guaranteeing cash flow for energy sector entities is a prerequisite for energy supply continuity. In this context, the selection and application of reliable sources of information are vital for the management of the financial liquidity of energy sector entities. The aim of this article is to prove the value of the financial information of individual (IFR) and consolidated financial statements (CFR) essential for the indicative liquidity assessment of Polish energy groups in 2018–2021. The hypothesis of this study is that individual and consolidated statements do not offer coincident analytical data due to the diversified role of their parent undertakings. We have applied indicative liquidity assessment analysis from a static and dynamic perspective to 2018–2021, on the basis of individual and consolidated financial statements. The results clearly show high dysfunction in the application of indicative liquidity assessment in the case of the individual financial statement of the parent company. This is mainly due to the role parent companies play in Polish energy sector groups, as they are mainly responsible for support processes.

This paper examines the effect of the mandatory adoption of International Financial Reporting Standards (IFRS) by the European Union on financial analysts' information environment. To control for the effect of confounding concurrent events, we use a control sample of firms that had already voluntarily adopted IFRS at least two years prior to the mandatory adoption date. We find that analysts' absolute forecast errors and forecast dispersion decrease relative to this control sample only for those mandatory IFRS adopters domiciled in countries with both strong enforcement regimes and domestic accounting standards that differ significantly from IFRS. Furthermore, for mandatory adopters domiciled in countries with both weak enforcement regimes and domestic accounting standards that differ significantly from IFRS, we find that forecast errors and dispersion decrease more for firms with stronger incentives for transparent financial reporting. These results highlight the important roles of enforcement regimes and firm-level reporting incentives in determining the impact of mandatory IFRS adoption.

This paper examines the information content of the forward-looking statements (FLS) in the Management Discussion and Analysis section (MD&A) of 10-K and 10-Q filings using a Naïve Bayesian machine learning algorithm.

DuPont analysis, a common form of financial statement analysis, decomposes return on net operating assets into two multiplicative components: profit margin and asset turnover. These two accounting ratios measure different constructs and, accordingly, have different properties. Prior research has found that a change in asset turnover is positively related to future changes in earnings. This paper comprehensively explores the DuPont components and contributes to the literature along three dimensions. First, the paper contributes to the financial statement analysis literature and finds that the information in this accounting signal is in fact incremental to accounting signals studied in prior research in predicting future earnings. Second, it contributes to the literature on the stock market's use of accounting information by examining immediate and future equity return responses to these components by investors. Finally, it adds to the literature on analysts' processing of accounting information by again testing immediate and delayed response of analysts through contemporaneous forecast revisions as well as future forecast errors. Consistent across both groups of market participants, the results show that the information is useful as evidenced by associations between the DuPont components and stock returns as well as analyst forecast revisions. However, I find predictable future forecast errors and future abnormal returns indicating that the information processing does not appear to be complete. Taken together, the analysis indicates that the DuPont components represent an incremental and viable form of information about the operating characteristics of a firm.

Fundamental analysis is an approach for evaluating a firm for its investment-worthiness whereby the firm's financial statements are subject to detailed investigation to predict future stock price performance. In this paper, we propose an approach to combine financial statement data using Data Envelopment Analysis to determine a relative financial strength (RFS) indicator. Such an indicator captures a firm's fundamental strength or competitiveness in comparison to all other firms in the industry/market segment. By analysing the correlation of the RFS indicator with the historical stock price returns within the industry, a well-informed assessment can be made about considering the firm in an equity portfolio. We test the proposed indicator with firms from the technology sector, using various US industries and report correlation analyses. Our preliminary computations using RFS indicator-based stock selection within mean-variance portfolio optimization demonstrate the validity of the proposed approach.

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

In order to accurately assess the financial status of a company and identify potential anomalies, this paper first implements unsupervised classification of financial transaction data based on Support Vector Machines, which automatically classifies the data into normal and abnormal categories. Histograms are introduced in combination with LightGBM to quickly fuse data from multiple sources. The most suitable first layer is selected by different algorithms, and the outputs of these algorithms are combined with industry-wide common abnormal features as inputs for LightGBM's second layer identification. With this two-layer structure, the model not only takes into account the industry characteristics, but also the common anomaly features. Empirical results show that in the accuracy of smart financial statement generation, the sensitivity of this paper's model iterates to 99.99% at 41.25% specificity, and the accuracy of this paper's model is as high as 0.98 when dealing with financial private information, macroeconomic, and market information.In the identification of financial transaction anomalies, the number of anomalous weeks is identified to be 24, 29, 34, and 36, and the fusion of multi-source data effectively identifies the large amount of financial transactions, fluctuating transactions and other suspicious abnormal transactions.

The objective of the conducted research includes examining importance of financial statements and financial statements analysis in business decision-making process. Conducted empirical research is focused on analysis of determining and evaluating the frequency of using accounting data and annual financial statements within the business decision-making process. According to obtained results, it can be concluded that more than 60% of examines frequently use accounting information and information available from annual financial statements within business decision-making process, and that they are familiar with methods of using technics of financial statements analysis for purposes of evaluating financial position and business efficiency.

This paper primarily focuses upon the adoptability of set of governance mechanisms evolved by the Securities and Exchange Commission of Pakistan (SECP) by the listed firms at Pakistan Stock Exchange (PSX). Purposefully, one of the heavily contributing industries of Pakistan, textile spinning industry has been targeted. Empirical estimation underwent the data of listed firms at PSX for the period of 2010-2018. Sources of the data were annually audited financial statements published by firms, Balance Sheet Analysis (BSA) and Financial Statement Analysis (FSA) published by the State Bank of Pakistan (SBP). Descriptive analytical reasoning along with panel data methodological adaptations including location and time fixed effects were conducted. Conclusions drawn upon the basis of estimation deduced that governance mechanisms carved by SECP, influenced the performances of listed firms positively and significantly.