layout: post title: “Research Paper Are Cash Flows Better Stock Return Predictors Than Profits?” date: 2025-06-17 category: trading —————–

Summary

Author: Stephen R. Foerster, John Tsagarelis, Grant Wang Publication Year: 2017

The paper investigates whether direct method cash flow measures offer better predictive power for stock returns than traditional profitability measures like net income or return on assets. By transforming standard financial statements to estimate direct cash flows, the authors find that these disaggregated cash flow metrics significantly outperform traditional income-statement-based ratios in explaining future stock returns. Their approach enables investors to isolate recurring, value-generating activities from non-recurring or financing-driven cash flows.

Key Concepts from the Abstract

  • Direct method cash flow measures are stronger predictors of stock returns than income statement-based metrics.
  • Disaggregated cash flows (especially from operations, taxes, and capital expenditures) contain incremental predictive power.
  • High cash flow stocks outperform low cash flow stocks by over 10% annually on a risk-adjusted basis.
  • The results are robust across investment horizons, risk models, and sectors.

Key Ideas

Page 18, Paragraph 1

Direct method cash flow (DMCF) measures deliver higher future stock returns compared to traditional profitability ratios. Portfolios formed on DMCF metrics generate statistically significant long-short spreads, highlighting the advantage of using cash-based metrics over income-based ones.

Page 25, Paragraph 1

When tested over extended holding periods (3, 6, 12 months), DMCF measures remain effective. Although returns slightly taper, the reduction in transaction costs from lower turnover makes these measures even more appealing, maintaining their predictive superiority.

Page 27, Paragraph 3

Sector-neutral analysis confirms that the predictive power of cash flow metrics is not sector-dependent. Even when portfolios are sorted within industry groups, DMCF-based strategies outperform those based on gross profit or net income, underlining their broad applicability.

Data and Variables Used

The study utilizes data from the S\&P 1500 index from 1994 to 2013, obtained from the Xpressfeed North American database. Key data fields include:

  • Cash Flow Metrics: Operating cash flow (OANCF), capital expenditures (CAPX), depreciation (DP, DPC)
  • Accounting Metrics: Sales (SALE), Cost of Goods Sold (COGS), SG\&A (XSGA), Net income (IBCOM)
  • Balance Sheet Metrics: Total assets (AT), market value of equity (MVE)
  • Cash Flow Components: Accounts receivable changes (RECCH), tax payments (TXT, TXACH, TXDC), financing and non-operating items (XINT, SPI, DO, XI)

Potential Issues

1. Complexity of Direct Method Reconstruction

  • Requires transforming indirect statements not natively reported by companies.
  • Relies on granular data fields often aggregated or inconsistently disclosed across firms.

2. Sample Selection Bias

  • Focus on S\&P 1500 excludes microcap or international stocks.
  • Limiting the sample may affect generalizability beyond large U.S. firms.

3. Data Availability and Reporting Lags

  • Uses lagged accounting data to prevent look-ahead bias.
  • Still assumes timely and accurate disclosure from firms, which may vary.

4. Interpretability and Implementation for Practitioners

  • Investors may struggle to replicate the disaggregated DMCF process.
  • Requires robust data infrastructure and advanced modeling skills.

Reflection

This paper offers a compelling argument for shifting focus from accounting profits to disaggregated cash flows in equity analysis. The authors demonstrate that direct cash flows better capture the economic reality of firms’ operations and future prospects. For quantitative investors and portfolio managers, incorporating these insights could lead to more robust alpha generation. The evidence is clear: follow the cash.

Reference