Can Money Flows Predict Stock Returns?

“Money flow” is defined as the difference between uptick and downtick dollar trading volume. Despite little published research regarding its usefulness, the measure has become an increasingly popular technical indicator. Our analysis demonstrates that money flows are highly correlated with same-period returns. We also found strong evidence of “money flow momentum,” in that lagged money flows can be used to predict future money flows. Most important is our finding that money flows appear to predict cross-sectional variation in future returns. Their predictive ability is sensitive, however, to the method of money flow measurement (e.g., the exclusion or inclusion of block trades) and the forecast horizon. “Money flow,” defined as uptick dollar volume minus downtick dollar volume, is a technical indicator first developed in the 1970s. Although the measure has become increasingly popular in recent years, its efficacy has not yet been rigorously tested. In this research, we provide an analysis of the relationship between money flow and returns in the U.S. stock market. Our results suggest that money flow, measured properly, can assist portfolio managers in the security selection process.We began the study by computing three types of daily money flow for NYSE-listed companies: money flow based on all trades, money flow based on block trades, and money flow based on nonblock trades. We normalized each by dividing by the corresponding dollar volume of trading (e.g., normalized block money flow was calculated as block money flow divided by block volume) to create a total of six money flow measures. Although we found each of the six money flow measures to be positively correlated with same-period returns, we found normalized nonblock money flows to be most strongly related to returns.Given the strength of the relationship between contemporaneous money flows and returns, we next examined the predictability of money flows. Using cross-sectional regressions, we found that money flow displays strong persistence: Companies with high money flow in the past tend to have high money flow in the future. Specifically, we found positive relationships between cumulative money flow measured over 1-, 5-, 10-, 20-, 30-, and 40-day periods and lagged money flow measured over the past 1, 5, 10, 20, 30, and 40 days. The strength of the relationship increased with the interval length; that is, longer-term money flow exhibited greater predictability than shorter-term money flow. We show that money flow persistence can be exploited through the formation of stock portfolios that subsequently experience high money flows.We next examined whether money flows can be used to predict returns. Using cross-sectional regressions, we examined the relationships between future returns and past money flows (with combinations of past and future periods of various intervals). Our results reveal that future returns are positively related to past money flows. As when predicting money flows, the strength of the relationship increases with the length of the period over which money flows and returns are measured: Money flows measured over 40 days predict subsequent 40-day returns better than money flows measured over 5 days predict subsequent 5-day returns. Moreover, controlling for past money flows, we found that past returns contain little useful information regarding future returns but that past money flows, even after we controlled for past returns, do contain useful information for predicting future returns.As a final test of the usefulness of money flow, we formed portfolios of money flow “winners” and “losers.” We used 10-, 20-, 30-, or 40-day measurement periods to form the winners and losers and held the portfolios for the subsequent 10, 20, 30, or 40 days. Using the 16 possible combinations of measurement period plus holding period for each of the six money flow measures, we examined the outcomes of the self-financing strategy of taking long positions in money flow winners and short positions in money flow losers. The winner portfolios outperformed the loser portfolios in 74 of the 96 cases we examined.In summary, money flow contains information beyond the information contained in returns and can be a useful tool in security analysis and portfolio management.