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"Momentum is consistent with value maximization of firms. The neoclassical theory of investment implies that expected stock returns are connected with expected marginal benefits of investment divided by marginal costs of investment. Winners have higher expected growth and expected marginal productivity (two major components of marginal benefits of investment), and consequently earn higher expected stock returns than losers. The investment-based model succeeds in capturing average momentum profits, reversal of momentum in long horizons, and the interaction of momentum with firm characteristics. However, the model fails to reproduce procyclical momentum profits. Overall, our evidence suggests that momentum can be understood within a framework in which markets are efficient and managers maximize the market value of equity"--National Bureau of Economic Research web site

We investigate the link between corporate investment and the momentum effect in stock returns. We argue that the momentum effect in a firm's stock returns tends to be generated as a result of a series of information exchanges between stock market investors and firm insiders regarding the firm's investment opportunities. Our theoretical setup predicts that past winners (losers) are likely to increase (decrease) their net investment, and the more they invest (disinvest) the more likely they are to realize positive (negative) returns in subsequent periods. Our empirical tests using a large sample of firms in 1984–2017 provide support for our hypotheses. We further design a momentum trading strategy, which buys past winners with positive predicted changes in net investment and sells past losers with negative predicted changes in net investment, and demonstrate that this trading strategy generates superior returns compared to a simple momentum trading strategy.

PurposeExisting empirical studies that document momentum trading strategies do not provide any insight on how investors choose the time horizon that is used to compute the past stock returns. Indeed, since past returns over overlapping time periods are positively correlated, it is hard to identify the exact historical time period on which investors base their trading strategies and to investigate whether such a period is unique. The purpose of this paper is to investigate this and reach some conclusions.Design/methodology/approachIn this paper the author uses experimental setting to analyze how investors choose which of the past returns to use as a basis for their trading strategies and whether this choice depends on their investment horizon. The advantage of this experimental setting over the existing empirical research is the ability to control for the investment horizon of the subjects and the ability to provide the subjects with a hand‐picked set of stocks with uncorrelated past returns over overlapping time periods. In the study subjects were asked to make short‐term investment decisions based on historical short‐term realized returns over two time intervals of different lengths. In each treatment the subjects were divided into two groups based on the lengths of their investment horizons, which were set to match the lengths of time intervals used to compute the historical returns.FindingsIt was found that subjects followed momentum trading strategies based on both historical returns provided to them and paid more attention to the historical returns over the shorter time period. In addition, some evidence was found that subjects with longer investment horizons rely less on momentum strategies.Originality/valueA wide sample was used to create an original set of observations and conclusions.

This study examines several aspects of the momentum
strategies, such as profitability, risk-based explanation, and
decomposition of the momentum profits. For this purpose, we use weekly
and monthly data of 581 firms listed at the Pakistan Stock Exchange
(PSX) for the period 2004-2014. We found the presence of momentum
profits over short and long-horizons, while majority of the contrarian
profits were observed only in the presence of penny stocks that have
share prices of PKR 10 or less. As a robustness check, we computed
returns through the weighted relative strength scheme (WRSS) procedure
and average cumulative abnormal returns (ACARs). Interestingly, the
results reported through WRSS have shown a similar pattern to that
obtained through average cumulative abnormal returns (ACARs). Further,
to know which factor contributes more to momentum and contrarian
profits, we used the model proposed by Lo and MacKinlay (1990). Our
findings show that the overreaction effect is the largest contributing
factor of contrarian profits in PSX, while cross-sectional risk is the
second largest factor and negatively affects the contrarian profits.
Moreover, the lead-lag effect contributes positively to the contrarian
profits. Similarly, the largest contributing factor for momentum profits
is the underreaction effect, whereas cross-sectional risk is the second
largest factor that positively affects momentum profits. Unlike
contrarian profits, lead-lag effect reduces the momentum profits in the
PSX.

Crafting investment models through contradictory value and momentum investment strategies by artificial intelligence
Professor Chien-Feng Huang discusses how Artificial Intelligence could provide innovative models and strategies to solve investment problems. Over the past decade, Professor Chien-Feng Huang has been working on several investment problems using Artificial Intelligence (AI). Big Data technology, data mining and machine learning play crucial roles in his research, where he hopes to discover niches and innovative solutions to bring about blue ocean strategies for investment. One major line of research Professor Huang has been pursuing is to combine seemingly contradictory strategies to generate emerging solutions for investment. For example, the momentum investing strategy might advise buying a stock, yet the value investment strategy advises against it. So, which one is right?