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The purpose of this paper is to examine the comparative abilities of current period cash flows and earnings (and its components) to predict one‐year‐ahead cash flow from operations in Egypt.

The study uses the cash flow prediction models developed by Barth, Cram, and Nelson to examine the predictive abilities of earnings and cash flows for future cash flows. The first set of prediction models uses cross‐sectional regression to compare the predictive abilities of cash flows and aggregate earnings for one‐year‐ahead cash flow from operations. The second set of prediction models tests whether disaggregating earnings into cash flows and the major components of accruals enhances the predictive ability of earnings for one‐year‐ahead cash flow from operations.

The findings of the study reveal that aggregate earnings have superior predictive ability than cash flows for future cash flows. Also, the results reveal that disaggregating accruals into major components – changes in accounts receivable and payable, and in inventory, depreciation, amortization, and other accruals – significantly enhances predictive ability of earnings.

The study provides empirical evidence on the superiority of earnings in predicting future cash flows. The findings of the study should be considered in explaining the results of value relevance research Egypt. However, owing to relatively small sample size, given the thinness of the Egyptian capital market, these findings should be interpreted with caution.

The paper contributes to the limited body of research on the superiority of earnings and cash flows in predicting future cash flows by examining the predictive abilities of earnings and cash flows for future cash flows in Egypt as one of many emerging markets.

The purpose of this paper is to examine the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows in case of Shariah-compliant companies in India.

The study uses the list of CRISIL NSE Index (CNX) Nifty Shariah Index companies as its sample for a period of 10 years for conducting the analysis. The study utilizes the cash flow prediction models to examine the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows.

The study report that contrary to Financial Accounting Standard Board assertion, current cash flows have superior predictive ability of next period cash flows than current aggregate earnings in case of Shariah-compliant companies in India. The results further show that there are no gains from decomposing earnings into accruals and cash flows in predicting future cash flows. There is no increase in explanatory power (measured by adjusted R2) when aggregate earnings are disaggregated into accruals and cash flows to predict next period cash flows.

The empirical findings of the study will enable the Shariah compliant investors to understand the role of current earnings (and its components) and cash flows in predicting next period cash flows in case of Shariah-compliant companies in India.

To the best of author’s knowledge, this is the first study which examines the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows in case of Shariah-compliant companies in India.

The purpose of this paper is to explore whether intellectual capital affects the probability that a particular firm will default. The authors also test whether including intellectual capital performance in bankruptcy prediction models improves their predictive ability.

Using a sample of US public companies from the period stretching from 1985 to 2015, the authors test whether intellectual capital performance reduces the probability of bankruptcy. The authors use the VAIC as an aggregate measure of corporate intellectual capital performance.

The findings show that the intellectual capital performance is negatively associated with the probability of default. The findings also indicate that the bankruptcy prediction models that include intellectual capital have a superior predictive ability over the standard models.

This paper contributes to prior research on intellectual capital and firm performance. To the best of the knowledge, this is the first study to show that the benefits of intellectual capital extend from superior performance to long-term financial stability. The research can also contribute to bankruptcy studies. By using a time frame covering decades, the findings suggest that intellectual capital performance measures can be included in bankruptcy prediction models and can effectively complement traditional performance measures.

This paper highlights that intellectual capital is associated with long-term financial stability and a lower bankruptcy risk. Firms realising the potential of their intellectual capital can produce a virtuous circle between higher performance and greater financial stability.

The purpose of this paper is to examine the relative predictive ability of earnings, cash flow from operations as reported in the cash flow statement, and two traditional measures of cash flows (i.e. earnings plus depreciation and amortisation expense, and working capital from operations) in forecasting future cash flows for Australian companies. Further, an empirical investigation of the extent to which firm size, as a contextual factor, influences the predictability of earnings and cash flow from operations is presented.

The authors' sample includes 323 companies listed on the Australian Stock Exchange between 1992 and 2004 (3,512 firm‐years). They employ the ordinary least squares and fixed‐effects approaches to estimate their regression models. To evaluate the forecasting performance of the regression models, both within‐sample and out‐of‐sample forecasting tests are employed.

The authors provide evidence that reported cash flow from operations has more power in predicting future cash flows than earnings and traditional cash flow measures. Further, the predictability of both earnings and cash flow from operations significantly increases with firm size. However, the superiority of cash flow from operations to earnings in predicting future cash flows is robust across small, medium and large firms.

The authors' results, in terms of firm size, imply that the users of accounting information should be cautious in assessing the utility of earnings and cash flow measures in forecasting future cash flows as firm size decreases.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

The purpose of this paper is to examine the determinants of analysts' operating cash flow forecasts of Australian listed firms and whether or not such forecasts improve the usefulness of earnings and predictive ability of current cash flows.

The authors used a large sample of firms for which both cash flows and earnings forecasts were available over a period between 1993 and 2003, and employed both univariate and logistic regression analyses.

It was found that analysts forecast both operating cash flows and earnings when the firms are more complex in operations and when the size of the firm is relatively small. Further, it was found that cash flow forecasts improve the usefulness of earnings and predictive ability of current cash flows.

This study contributes to current understanding of analysts' forecast behaviour regarding dissemination of operating cash flow information and usefulness of cash flow forecasts.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.

Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.

The in-sample results reveal that the prediction model including the model bias can obtain bigger R², and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.

The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

This study investigates whether the skilled minority of active equity managers in emerging markets can be identified using a machine learning (ML) framework that incorporates a large set of performance characteristics.

The study uses a cross-section of South African active equity managers from January 2002 to December 2021. The performance characteristics are analysed using ML models, with a particular focus on gradient boosters, and naïve selection techniques such as momentum and style alpha. The out-of-sample nominal, excess and risk-adjusted returns are evaluated, and precision tests are conducted to assess the accuracy of the performance predictions.

A minority of active managers exhibit skill that results in generating alpha, even after accounting for fees, and show that ML models, particularly gradient boosters, are superior at identifying non-linearities. LightGBM (LG) achieves the highest out-of-sample nominal, excess and risk-adjusted return and proves to be the most accurate predictor of performance in precision tests. Naïve selection techniques, such as momentum and style alpha, outperform most ML models in forecasting emerging market active manager performance.

The authors contribute to the literature by demonstrating that a ML approach that incorporates a large set of performance characteristics can be used to identify skilled active equity managers in emerging markets. The findings suggest that both ML models and naïve selection techniques can be used to predict performance, but the former is more accurate in predicting ex ante performance. This study has practical implications for investment practitioners and academics interested in active asset manager performance in emerging markets.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.