Earnings Prediction Research Articles

Modeling and predicting earnings per share via regression tree approaches in banking sector: Middle East and North African countries case

The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.

Using Natural Language Processing to Assess Text Usefulness to Readers: The Case of Conference Calls and Earnings Prediction

We examine whether support vector regressions (SVR), supervised LDA (sLDA), random forest regression trees (RF), and ‘tone’ extract narrative content from conference calls that correlates with useful information that a human reader would identify. We find that each narrative-content measure (along with a composite measure) explains a portion of analyst-forecast revisions for quarter q 1 issued after the conference call in quarter q. Correlation with analyst-forecast revisions improves when the composite measure adapts to context (positive/negative returns; high variance/low variance) and ignores sparse words. The correlation is comparable and incremental to that of financial signals (cash-flow changes, earnings surprises, and management forecasts), which suggests that the narrative content of conference calls as extracted by readers is economically significant. Our results suggest that models of narrative content have reasonable construct validity and that this validity is likely to be improved by further thought on the unique characteristics of text.

A machine learning approach to univariate time series forecasting of quarterly earnings

We propose our quarterly earnings prediction (QEPSVR) model, which is based on epsilon support vector regression (ε-SVR), as a new univariate model for quarterly earnings forecasting. This follows the recommendations of Lorek (Adv Account 30:315–321, 2014. https://doi.org/10.1016/j.adiac.2014.09.008), who notes that although the model developed by Brown and Rozeff (J Account Res 17:179–189, 1979) (BR ARIMA) is advocated as still being the premier univariate model, it may no longer be suitable for describing recent quarterly earnings series. We conduct empirical studies on recent data to compare the predictive accuracy of the QEPSVR model to that of the BR ARIMA model under a multitude of conditions. Our results show that the predictive accuracy of the QEPSVR model significantly exceeds that of the BR ARIMA model under 24 out of the 28 tested experiment conditions. Furthermore, significance is achieved under all conditions considering short forecast horizons or limited availability of historic data. We therefore advocate the use of the QEPSVR model for firms performing short-term operational planning, for recently founded companies and for firms that have restructured their business model.

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.

EARNINGS MANAGEMENT AND FUTURE EARNINGS

Abstract This research is aimed to examine the moderating effect of the cost of earnings management on the relationship between earnings management and future earnings. Research samples are manufacture companies listed in Indonesia Stock Exchange 2013-2015. The cost of accruals earnings management is auditor quality, while the costs of real earnings management are the market share and financial health. Based on the fixed effect regression test, auditor quality strengthens the positive effect of accruals earnings management on future performance, while market share and financial health weaken the negative effect of real earnings management on future earnings. It indicates that in the context of efficient contracting, high quality auditor provide better signal for earnings prediction compared to the low quality auditor. In addition, higher market share and higher financial health limit opportunistic real earnings management to reduce future earnings.

ANALISIS RASIO KEUANGAN DALAM MEMPREDIKSI PERUBAHAN LABA (Studi Empiris Perusahaan di Jakarta Islamic Index)

Financial ratio analysis is one of the techniques of financial statement analysis. Financial ratio analysis can be used to predict changes in earnings in the future. The research that will be conducted aims to test the ability of financial ratios to predict changes in earnings in the future. Prediction of profit changes in this study uses 31 financial ratios as independent variables and relative earnings changes as the dependent variable. The samples to be tested are companieslisted in the Jakarta Islamic Index for the 2014-2017 period by purposive sampling. Data analysis using the Stepwise Regression method, namely regression analysis that selects the variables that will be members of the regression equation by making selections based on the tolerance criteria of the variables. The conclusion of this study is that there are several financial ratios to predict changes in earnings in the future. These ratios OINIBT (Operating Income toNet Income Before Taxes) have a positive effect on future earnings predictions, OITL (Operating Income to Total Liabilities) has a negative effect on future earnings predictions, CATA (Current Assets to Total Assets) have a positive effect on earnings predictions in the future, and WCTA (Working Capital to Total Assets) has a negative effect on future earnings predictions.

The Role of Accruals in the Prediction of Future Earnings

Although past accruals can explain current earnings, whether current accruals can predict future earnings has not been examined. I introduce three regression-based prediction models to predict future earnings: using only cash flows only, both cash flows and accruals, and earnings, to determine whether current accruals contribute to the prediction of future earnings. I find a cash flows and accruals model is more precise than a cash flows-only model, but the earnings model is the most precise. This result suggests that current earnings are a more accurate predictor of future earnings, and that accruals’ persistence is less important in prediction of future earnings. I also document that predictions based on these models are more precise than time series-based predictions, but not as precise as analysts’ forecasts. Additionally, I investigate how current earnings management negatively affects prediction of future earnings and hypothesize that current accrual management and real earnings management are positively associated with the prediction errors of the three models. The result shows that only the measure of real earnings management is positively associated with prediction error.

Advertising, Earnings Prediction and Market Value: An Analysis of Persistent UK Advertisers

AbstractThis paper examines whether major media advertising expenditures help in predicting future earnings. We consider the role of media advertising in firms’ marketing efforts and posit that persistent advertisers are more likely to benefit from advertising activities in creating long‐lived intangible assets. Employing a sample of persistent UK advertisers over the period 1997–2013, we find that advertising expenditures are significantly positively associated with firms’ future earnings and market value. We also report size and sector‐based differences in the association between advertising and firms’ future earnings. Our additional analysis provides support for the arguments that despite the recent rise in digital advertising budgets, traditional advertising media are still effective in positively influencing firms’ performance. Overall, the results of this study are consistent with the view that advertising expenditures produce intangible assets, at least for firms in certain sectors. These findings have implications for marketers in providing evidence of the value generated by firms’ advertising budgets, for investors in validating the relevance of advertising information in influencing future earnings, and for accounting regulators in relation to the provision of useful insights for any future deliberations on financial reporting policies for advertising expenditures.

Predict Foreign Exchange Revenue from CPO Commodity Exports Using Backpropagation Neural Network

Export of CPO and its derivative products is one of the biggest revenues for Indonesian foreign exchange. Through this research, predictions of foreign exchange earnings from CPO exports are made using backpropagation artificial neural networks. The data used in the form of export data for the period September 2013 to December 2016. The final results of this research using epoch 10000, learning rate 0.2, and hidden layer 10.

Effects of winsorization: The cases of forecasting non‐GAAP and GAAP earnings

AbstractThis study examines how the winsorization procedure affects the performance of regression‐based earnings forecasting models. I find that the impact is multifaceted and depends principally on three factors: the level of data errors in the tails, the characteristics of firms affected by the process, and the use of scaling. For a non‐GAAP earnings yield specification, where data input errors exist, winsorization changes the information set in a non‐systematic way and helps to improve the performance of regression‐based forecasts, especially when the least squares estimator is employed. However, for a non‐GAAP earnings per share specification, with fewer data input errors found in the tails of the distribution, winsorization has a particularly strong effect on very large companies, lowering the economic value of earnings predictions. I observe similar results for corresponding GAAP earnings specifications. Robust estimators, such as least absolute deviation, high breakdown‐point and Theil‐Sen, appear to be a more effective solution than winsorization. Their earnings forecasts consistently yield significant positive abnormal returns across non‐GAAP and GAAP earnings specifications.

Equity Values and Prediction of Earnings with Disaggregation of Earnings in India

This article examined the relative performance of aggregated and disaggregated earnings for valuation of equity and prediction of earnings in India. We measured three levels of earnings disaggregation: aggregate earnings, total accruals and cash flows, and four major constituents of accruals, then we estimated pooled as well as individual industry-wise regressions. We adopted Barth, Beaver, Hand and Landsman’s (1999, Review of Accounting Studies, 4(3, 4), 205–229; 2005, Journal of Accounting, Auditing & Finance, 20(4), 311–345) linear information structure grounded on generalized version of Ohlson (1999) model. We compared our results with the studies based on developed market. Our findings say that aggregated earnings and its disaggregated components are value relevant, and the adjusted R-squares of every next disaggregated systems are higher than aggregated systems, but in varying range across industries. We also find that the investors are not capable of judging total accruals and cash flows separately for investment decisions in this emerging market.

Financial Derivatives in Corporate Tax Aggressiveness

The investor’s attention on net income numbers without regard to the procedures used to generate them, has encourage management to carry out earnings management. Includes within earnings management is income smoothing. Income smoothing can be viewed in term of the reduction in earnings variability over a numbers of periods, or within a single period, as the movement toward unexpected level of reported earning. Objective of this study to examine the market reaction on earnings announcement due to the income smoothing. This study examine ninety nine companies which listed in Jakarta Stock Exchange at least since 1990. Market reaction is measured as cumulative abnormal return five days surrounding the companies’ earnings announcement date. Overall, the result of this study indicate that there is significant market reaction surrounding the companies’ earnings announcement date and these market reaction significantly difference between smoother companies and non-smoother companies. This study is hopes to give contribution to the literature, that income smoothing practice can regard as a signal to better prediction of future earnings by investors and a mean to decrease market reaction on companies’ earnings anouncement.

The Impact of the Bullwhip Effect on Sales and Earnings Prediction Using Order Backlog

AbstractRecent work in the supply chain literature suggests that the variance in orders placed with suppliers will be larger than that of sales to buyers. This distortion in demand information increases as it is passed along the supply chain from customers to upstream suppliers and has been referred to as “the bullwhip effect.” In this paper, we argue that the bullwhip effect reduces the ability of order backlog to predict future sales and earnings for upstream suppliers. Results obtained from our empirical analysis support this proposition. We find that the impact of bullwhip on the predictive ability of order backlog is further accentuated in firms with longer operating cycles. Market intermediaries such as financial analysts, on average, are unable to fully account for differences in the predictive ability of order backlog. However, analysts belonging to employers that follow all firms in a vertical supply chain do a better job of understanding the impact of bullwhip on the predictive ability of order backlog. In additional tests, we find that the bullwhip effect also impedes the ability of inventory components to predict future sales and earnings.

The informativeness of impression management − financial analysts and rhetorical style of CEO letters

Purpose This paper aims to investigate the informativeness of rhetorical impression management patterns of CEO letters and examines whether these rhetorical features affect financial analysts’ forecasting behaviour. Design/methodology/approach The authors use textual analysis on a sample of 526 CEO letters of US firms and apply factor analysis on individual linguistic style measures to identify co-occurrence patterns of style features. Findings The authors identify three holistic style patterns (assertive acclaiming, cautious plausibility-based framing and logic-based rationalizing) and find that assertive rhetorical feature in CEO letters is negatively related with the dispersion of financial analysts’ earnings forecasts and positively associated with earnings forecast accuracy. CEOs’ use of a rationalizing rhetorical pattern tends to decrease the dispersion of financial analysts’ earnings, whereas a cautious plausibility-based rhetorical position is only marginally instrumental in getting more accurate earnings predictions. Practical implications Whilst impression management communication is often theorized as manipulative and void of real information content, the findings suggest that impression management serves both self-presentation and information-sharing purposes. Originality/value This paper elaborates on the co-occurrence of style characteristics in management communication and is a first attempt to validate the external ramifications of holistic style profiles of corporate narratives by focusing on an economic target audience.

How Informative Are Stock Prices of Islamic Banks?

Using a sample of 2,210 observations for 170 banks operating in 12 countries with dual banking systems over 2006-2017 period, we find that Islamic banks have lower stock return non-synchronicity, lower illiquidity ratio, and their current returns have lower future earnings prediction ability than conventional banks. Hence Islamic banks have less information content in stock prices than conventional banks that can be due to their lower degree of transparency mandated by their financial paradigm. This suggests that for Islamic banks, market discipline may not be as effective as it is for conventional banks and hence they require more direct supervision. Our findings have important implications for policy-makers and investors.

Impact of Segment‐level Natural Resource Operational Risk Reporting on Earnings Predictions

Non‐financial reports alert investors to operational risks associated with issues such as insufficient access to natural resource inputs and related costly interruptions to production, while segment‐level reports alert investors to operational risk distribution across a firm. An important issue, to date unexplored, is how segment‐level non‐financial reporting has an impact on earnings predictions. We report the results of an experiment used to examine how mining company segment‐level water reports affect investors' earnings predictions, where water reports indicate whether the firm and its segments will have access to sufficient water to meet production needs. We find that investors do not change their earnings predictions when firm and segment‐level reports indicate low water risk but they do revise down their earnings predictions when firm and segment‐level water reports indicate high water risk. This is consistent with investors responding to the additional information provided in segment‐level reports confirming that water risk is high across the firm. Regardless of whether firm‐level water reports indicate high or low water risk, when segment‐level reports indicate that one segment is low water risk and another is high water risk, investors revise down their earnings predictions. This is consistent with investors recognizing that natural resource operational risk concentration in one segment can affect earnings more than evenly‐distributed risk. Overall, our findings suggest that belief‐adjustment theory explains how investors react to prospective operational risk information contained in segment‐level water reports according to the similarity of the segment‐level risks, and that this information is factored into earnings predictions.

Mandatory IFRS Adoption and the Usefulness of Accounting Information in Predicting Future Earnings and Cash Flows

We examine whether the mandatory adoption of International Financial Reporting Standards (IFRS) has changed the usefulness of accounting information in predicting future earnings and cash flows out-of-sample. Using a sample of firms from European Union countries that mandatorily adopted IFRS in 2005, we find the out-of-sample earnings and cash flows forecasts derived from alternative accounting models become significantly more accurate after IFRS adoption. The accuracy, however, varies with the strength of legal and regulatory enforcement. Firms in strong enforcement countries experience larger improvements in earnings forecast accuracy than firms in weak enforcement countries but the opposite happens for cash flow forecasts. Accruals are useful in the prediction of both earnings and cash flows, but again their usefulness varies with the strength of the legal and regulatory environment. Portfolios of stocks based on the out-of-sample forecasts earn economically significant 12-month ahead hedge returns after IFRS adoption, which corroborates the detected forecast accuracy improvements. Overall, the study contributes to the IFRS literature by providing new evidence that an important dimension of accounting quality, predictive ability, has improved after mandatory IFRS adoption.

RESPON INVESTOR TERHADAP PENGUMUMAN LABA INDUSTRI PERBANKAN YANG MENGHADAPI KOS STICKINESS

This study aimed to analyze the response of investors to the announcement of the financial statements of the banking sector. The population in this study was the banking industry. This study used purposive sampling method. Based on sampling techniques the number of samples in this study was 11 banks. The research period was 2002-2014. The analysis technique used was panel data regression. The result showed that investor response to the financial statements of banks which faced cost stickiness was weak. The increase of cost stickiness on the banking sector led the weak investor response to the announcement of the financial statements of banks. In the banking companies facing cost stickiness problem, earnings prediction accuracy reduced. This causes the income statement would provide information that was less important to the earnings prediction in the next periods.

The Prediction of Earnings per Share through the Models of Random Walk and Random Walk with Drift: A Case Study of Jordan

The Prediction of Earnings per Share through the Models of Random Walk and Random Walk with Drift: A Case Study of Jordan

Usefulness of Accounting Estimates

This is an exploratory study to examine the quality or usefulness of accounting estimates of companies in China and India over time. Specifically, we examine how well the accounting estimates are able to predict future earnings and cash flows during the period 2003-2013. The results for India indicate that the out-of-sample earnings and cash flow predictions derived are more accurate and more efficient in the more recent period (2010-2013) than the earlier period (2003-2006). In contrast, the out-of-sample earnings and cash flow predictions for China are generally more biased, less accurate, and less efficient. The results indicate abnormal returns earned on hedge portfolios formed on earnings (cash flow) predictions for India in the recent period. In contrast, none of the portfolios for China earn positive returns. The results suggest that the accounting estimates in India in recent years have become better predictors of future earnings and cash flow than accounting estimates in the earlier period. However, the accounting estimates in China are not relevant for predicting earnings and cash flows over the years in the sample period.