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The aim of this study is to examine the sub-variants of price momentum strategies. The paper recommends which sub-variants post above average returns for Australian Stock Exchange. It also analyzes the return behavior of short-term momentum effect among sub-variants of price momentum strategies. It has been found that monthly price momentum strategies result in above average abnormal returns, whereas weekly price momentum strategies should be used in combination with monthly price momentum strategies. Trading volume-based momentum investment strategies should not be used at all.

We study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%-26%) lower return from publication-informed trading. Post-publication declines are greater for predictors with higher in-sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with other published-predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.

We find consistent value and momentum return premia across eight diverse markets and asset classes, and a strong common factor structure among their returns. Value and momentum returns correlate more strongly across asset classes than passive exposures to the asset classes, but value and momentum are negatively correlated with each other, both within and across asset classes. Our results indicate the presence of common global risks that we characterize with a three-factor model. Global funding liquidity risk is a partial source of these patterns, which are identifiable only when examining value and momentum jointly across markets. Our findings present a challenge to existing behavioral, institutional, and rational asset pricing theories that largely focus on U.S. equities.

Financial intermediaries trade frequently in many markets using sophisticated models. Their marginal value of wealth should therefore provide a more informative stochastic discount factor (SDF) than that of a representative consumer. Guided by theory, we use shocks to the leverage of securities broker-dealers to construct an intermediary SDF. Intuitively, deteriorating funding conditions are associated with deleveraging and high marginal value of wealth. Our single-factor model prices size, book-to-market, momentum, and bond portfolios with an R² of 77% and an average annual pricing error of 1%—performing as well as standard multifactor benchmarks designed to price these assets.

When assessing a fund manager's skill, sophisticated investors will consider all factors (priced and unpriced) that explain cross-sectional variation in fund performance. We investigate which factors investors attend to by analyzing mutual fund flows as a function of recent returns decomposed into alpha and factor-related returns. Surprisingly, investors attend most to market risk (beta) when evaluating funds and treat returns attributable to size, value, momentum, and industry factors as alpha. Using proxies for investor sophistication (wealth, distribution channels, and periods of high investor sentiment), we find that more sophisticated investors use more sophisticated benchmarks when evaluating fund performance.

A five-factor model that adds profitability (RMW) and investment (CMA) factors to the three-factor model of Fama and French (1993) suggests a shared story for several averagereturn anomalies. Specifically, positive exposures to RMW and CMA (stock returns that behave like those of profitable firms that invest conservatively) capture the high average returns associated with low market β, share repurchases, and low stock return volatility. Conversely, negative RMW and CMA slopes (like those of relatively unprofitable firms that invest aggressively) help explain the low average stock returns associated with high β, large share issues, and highly volatile returns.

I propose and test a capital-flow-based explanation for some well-known empirical regularities concerning return predictability—the persistence of mutual fund performance, the \"smart money\" effect, and stock price momentum. First, I construct a measure of demand shocks to individual stocks by aggregating flow-induced trading across all mutual funds, and document a significant, temporary price impact of such uninformed trading. Next, given that mutual fund flows are highly predictable, I show that the expected part of flow-induced trading positively forecasts stock and mutual fund returns in the following year, which are then reversed in subsequent years. The main findings of the paper are that the flow-driven return effect can fully account for mutual fund performance persistence and the smart money effect, and can partially explain stock price momentum.

The last several decades have witnessed a shift away from a fully rational paradigm of financial markets toward one in which investor behavior is influenced by psychological biases. Two principal factors have contributed to this evolution: a body of evidence showing how psychological bias affects the behavior of economic actors; and an accumulation of evidence that is hard to reconcile with fully rational models of security market trading volumes and returns. In particular, asset markets exhibit trading volumes that are high, with individuals and asset managers trading aggressively, even when such trading results in high risk and low net returns. Moreover, asset prices display patterns of predictability that are difficult to reconcile with rational-expectations–based theories of price formation. In this paper, we discuss the role of overconfidence as an explanation for these patterns.

We identify two types of risk premia in commodity futures returns: spot premia related to the risk in the underlying commodity, and term premia related to changes in the basis. Sorting on forecasting variables such as the futures basis, return momentum, volatility, inflation, hedging pressure, and liquidity results in sizable spot premia between 5% and 14% per annum and term premia between 1% and 3% per annum. We show that a single factor, the high-minus-low portfolio from basis sorts, explains the cross-section of spot premia. Two additional basis factors are needed to explain the term premia.

We test a frog-in-the-pan (FIP) hypothesis that predicts investors are inattentive to information arriving continuously in small amounts. Intuitively, we hypothesize that a series of frequent gradual changes attracts less attention than infrequent dramatic changes. Consistent with the FIP hypothesis, we find that continuous information induces strong persistent return continuation that does not reverse in the long run. Momentum decreases monotonically from 5.94% for stocks with continuous information during their formation period to −2.07% for stocks with discrete information but similar cumulative formation-period returns. Higher media coverage coincides with discrete information and mitigates the stronger momentum following continuous information.