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This scholarly investigation examines the efficacy of Building Information Modelling (BIM) in enhancing cash flow forecasting (CFF) among construction Small and Medium-sized Enterprises (SMEs) in Saudi Arabia, with a specific focus on fostering innovation for sustainable economic advancement. In so doing, it seeks to strengthen the long-term viability of SMEs within the rapidly growing Saudi construction sector, thereby contributing meaningfully to broader economic goals. A quantitative research methodology was employed, with empirical data gathered through a questionnaire survey administered to one hundred construction stakeholders within Saudi Arabian SMEs. Quantitative data analysis techniques were applied to elucidate key themes and pressing issues in current CFF practices. The findings highlight critical challenges faced by Saudi Arabian SMEs in cash flow management, notably a scarcity of financial resources, a lack of advanced CFF expertise, and resistance to technological adoption. Integrating BIM into CFF processes emerges as an effective solution, addressing these challenges by providing accurate, timely financial data, improving project planning and execution, and enabling more informed decision-making, thereby fostering sustainable business operations. The proposed BIM integration strategy offers a practical roadmap for SMEs to adopt BIM for enhanced CFF, aligning with and advancing the sustainable economic objectives outlined in Saudi Arabia’s Vision 2030. By focusing on the unique context of Saudi Arabian construction SMEs and their specific cash flow management challenges, this study enriches the existing literature with substantive insights. It critically illustrates how BIM adoption can transform traditional financial management practices, presenting a robust framework for promoting sustainable economic development through innovation in CFF. Furthermore, these findings have significant implications for other developing economies seeking to leverage technological advancements as drivers of long-term growth.

Motivated by recent FASB, IASB, and CFA Institute comments, we explore the predictive value of direct method cash flow disclosures. A primary stated purpose of the direct method is to better forecast future performance. To examine this purpose, we first document that direct method line items, such as cash received from customers, are not reliably estimable using income statements and either balance sheets or indirect method statements of cash flows. When these estimation (articulation) errors are included in cash flows and earnings forecasting models, forecasting performance significantly improves. In addition, employing a future ERC (FERC) methodology, we find evidence suggesting that market participants utilize direct method disclosures for their stated purpose: to better forecast future operating performance. After conducting several tests for self-selection concerns, we conclude that the direct method is valuable to investors when forecasting future cash flows and earnings.

We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices have both in and out of sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well-recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears to stem from investors' biased beliefs about future cash flows.

Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross-section of book-tomarket ratios, we find an out-of-sample return forecasting R² of 13% at the annual frequency (0.9% monthly). We document similar out-of-sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios' exposures to economic shocks are key to identifying predictability and are consistent with duration-based theories of the value premium.

Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the historical average. Arguing that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models, we recommend combining individual forecasts. Combining delivers statistically and economically significant out-of-sample gains relative to the historical average consistently over time. We provide two empirical explanations for the benefits of forecast combination: (i) combining forecasts incorporates information from numerous economic variables while substantially reducing forecast volatility; (ii) combination forecasts are linked to the real economy.

This study develops a firm life cycle proxy using cash flow patterns. The patterns provide a parsimonious indicator of life cycle stage that is free from distributional assumptions (i.e., uniformity). The proxy identifies differential behavior in the persistence and convergence patterns of profitability. For example, return on net operating assets (RNOA) does not mean-revert (spread of 7 percent after five years between mature and decline firms) when examined by life cycle stage, which has implications for growth rates and forecast horizons. Further, determinants of future profitability such as asset turnover and profit margin are differentially successful in generating increases in profitability conditional on life cycle stage. Finally, investors do not fully incorporate the information contained in cash flow patterns and, as a result, undervalue mature firms. The cash flow proxy is a robust tool that has applications in analysis, forecasting, valuation, and as a control variable for future research.

The study utilises the International Labor Organization’s SMEs COVID-19 pandemic business risks scale to determine whether Artificial Intelligence (AI) applications are associated with reduced business risks for SMEs. A new 10-item scale was developed to capture the use of AI applications in core services such as marketing and sales, pricing and cash flow. Data were collected from 317 SMEs between April and June 2020, with follow-up data gathered between October and December 2020 in London, England. AI applications to target consumers online, offer cash flow forecasting and facilitate HR activities are associated with reduced business risks caused by the COVID-19 pandemic for both small and medium enterprises. The study indicates that AI enables SMEs to boost their dynamic capabilities by leveraging technology to meet new types of demand, move at speed to pivot business operations, boost efficiency and thus, reduce their business risks.

Organization capital is a production factor that is embodied in the firm's key talent and has an efficiency that is firm specific. Hence, both shareholders and key talent have a claim to its cash flows. We develop a model in which the outside option of the key talent determines the share of firm cash flows that accrue to shareholders. This outside option varies systematically and renders firms with high organization capital riskier from shareholders' perspective. We find that firms with more organization capital have average returns that are 4.6% higher than firms with less organization capital.

Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable by external stakeholders. In this study, we investigate whether the quality of managers' externally reported earnings forecasts can be used to infer the quality of their corporate investment decisions. Relying on the intuition that managers draw on similar skills when generating external earnings forecasts and internal payoff forecasts for their investment decisions, we predict that managers with higher quality external earnings forecasts make better investment decisions. Consistent with our prediction, we find that forecasting quality is positively associated with the quality of both acquisition and capital expenditure decisions. Our evidence suggests that externally observed forecasting quality can be used to infer the quality of capital budgeting decisions within firms.

A central issue in finance is whether stock prices move because of revisions in expected cash flows or discount rates, and by how much of each. Using direct cash flow forecasts, we show that stock returns have a significant cash flow news component whose importance increases with the investment horizon. For horizons over two years, cash flow news is more important. These conclusions hold at both the firm and aggregate levels, and diversification plays a secondary role in affecting the relative importance of cash flow and discount rate news. Our findings highlight the importance of cash flows in asset pricing.