1,969,228 research outputs found
Information Uncertainty and the Momentum Effect
No full textI identify simple proxies for uncertainty and attempt to determine if the returns to a momentum strategy vary with these proxies. The proxies identified include the stock’s daily 6-month historical return volatility, the magnitude of alpha in a 6-month historical regression of the stock’s daily returns on the Fama-French factors and the (1-R2) value of the regression. The exposures to each of the risk factors were also tested as possible proxies for uncertainty related to the factors. Using daily stock return data from CRSP from 1926 to 2006, stocks are first sorted into quintiles based on these proxies. A momentum strategy is pursued in each uncertainty quintile by taking long and short positions in the deciles with the highest and lowest past returns respectively over a 6 month ranking period, and holding these positions for a further 6 months. It was found that with greater volatility, momentum returns are higher. Similarly, as the magnitude of alphas rises, momentum returns increase. These results support the hypothesis that greater uncertainty contributes to momentum. Finally, momentum returns are higher with larger exposures to the market factor, but show no statistically significant trends with the size and book-to-market factors. When (1-R2) values increase however, momentum returns decline, in contradiction with the hypothesis that greater uncertainty contributes to momentum. Stocks were also sorted into industry groups according to Kenneth French’s twelve industry portfolio classification. The industries were ranked according to the volatility of their daily returns and the returns to a momentum strategy within the industry. There was no clear relationship between the volatility of daily returns and momentum returns of the twelve industry portfolios
Do Momentum Strategies Work?: - Australian Evidence
Get PDFThis paper investigates the profitability of momentum investment strategy and the predictive power of trading volume for equities listed in the Australian Stock Exchange. Recent research finds that momentum and trading volume appear to predict subsequent returns in U.S. market and past volume helps to reconcile intermediate-horizon “under reaction” and long-horizon “overreaction” effects. However, bulk of the evidence on this important relationship between past returns and future returns is limited to U.S. portfolios. This study provides an out of sample evidence by examining the relationship between “trading volume” (measured by the turnover ratio) and “momentum” strategies in an Australian setting. We document a strong momentum effect for the Australian market during the period 1988 through 2002 and find that momentum plays an important role in providing information about stocks. We also find that past trading volume predicts both the magnitude and persistence of price momentum. In summary, our findings are consistent with the U.S. evidence.
A Model of Momentum
Get PDFOptimal investment of firms implies that expected stock returns are tied with the expected marginal benefit of investment divided by the marginal cost of investment. Winners have higher expected growth and expected marginal productivity (two major components of the marginal benefit of investment), and earn higher expected stock returns than losers. The investment model succeeds in capturing average momentum profits, reversal of momentum in long horizons, as well as the interaction of momentum with market capitalization, firm age, trading volume, and stock return volatility. However, the model fails to reproduce procyclical momentum profits.
Portfolio structure and optimisation of momentum returns
No full textThis study analyses momentum returns in 54 countries covering 34 years. It is the first study where optimising programmes are applied to momentum returns and portfolio selection. Momentum returns have remained a contentious topic and a substantial amount of research purports to support and negate this anomaly. Momentum studies have previously concentrated on finding the cause of this anomaly or determining whether abnormal returns are present only in a particular dataset. However, there is no clear consensus regarding how to construct/implement a momentum strategy. To date it has been unclear as to how many portfolios should be created, whether or not the portfolios should be equally-weighted or value-weighted and how the momentum returns will change when a chosen strategy is constructed/implemented compared to an alternative strategy. This lack of precision concerning how to construct/implement momentum strategy potentially leads to confounding results. This current thesis contributes to an understanding of the structure impact effect by computing momentum returns for changing portfolio structures and observing the magnitude of the impact on returns on a specially crafted database of 52,593 stocks from 54 countries over the time period 1973-2007.
The two important empirical extensions are the industrial momentum and the 52-week high momentum models. Both the industrial momentum and 52-week high momentum strategies claim that their returns are superior to the traditional momentum return and possess superior explanatory power. Empirical evidence, to date, has not been available to attest to whether the results hold true when applied in different markets. This gap in knowledge motivated this research investigation of multiple countries addressing how different methods of calculating returns, different approaches to momentum strategy, different portfolio weighting process impact upon the robustness of results.
This research also addresses the question of whether momentum returns can be increased through the use of optimisation algorithms. Traditionally, little attention has been paid to the portfolio weighting with either an equal-weighted or value-weighted approach to allocating funds to the Winner and Loser portfolios. This study proposes an alternative way of allocating money to the Winner and Loser portfolios with the goal of generating increased returns. Eight different algorithms are applied to the share returns to determine whether one method is clearly superior to others in maximising the momentum returns for the synthesised portfolios over a period of time. This is the first study of its type where optimising programmes are applied to momentum returns and portfolio selection and covers several countries.
The results indicate that momentum returns are robust on a global scale and the returns are by and large statistically significant under different portfolio construction approaches. Both the industrial and 52-week momentum strategies remain positive and statistically significant but do not generate the same magnitude of returns as the conventional momentum return model. The optimisation of momentum return shows promising results as a number of optimisation techniques do enhance momentum returns. As the potential to increase returns becomes known, traders will quickly react and there is likely to be a range of new investment products arising
Momentum Cycles and Limits to Arbitrage Evidence from Victorian England and Post-Depression US Stock Markets
Get PDFWe evaluate the importance of “Limits to Arbitrage” to explain profitability of momentum strategies. Specifically, when the availability of arbitrage capital is in short supply, momentum cycles last longer, and breaks in momentum cycles are shorter. We demonstrate the robustness of our findings with a unique database of stock returns from1866-1907 London and the CRSP database. Momentum cycle durations are similar in both databases and all other momentum facts documented in the literature using the CRSP database hold for the Victorian period as well, except for the January reversal due to the absence of capital gains taxation.
Is Momentum Really Momentum? : International Evidence
Get PDFNovy-Marx (2010) finds that momentum is primarily driven by stock performance twelve to seven months prior to portfolio formation in the US market. We examine whether this finding holds in international stock markets. In particular, we investigate whether intermediate horizonpast performance is more dominant than 52-week high momentum strategy and recent pastperformance with individual stock data in international markets. Our results indicate that the intermediate past, recent past, and the 52-week high momentum effects are prevalent in international markets. The intermediate horizon past performance during the last twelve to seven months dominates in most of the markets studied. The 52-week high momentum and the recentpast performance during the last six to two months are highly correlated. However, they are not as important as stock performance during the last twelve to seven months.departmental bulletin pape
On the Impact of Spatial Momentum
Get PDFMomentum, the population growth that occurs after a fall in fertility to replacement level, has long been appreciated as a factor in the future population growth of many countries. This paper argues that another aspect of growing populations - their high proportion rural - is also a source of significant growth, and refers to the additional growth attributable to geographical redistribution as spatial momentum. Using simplifying assumptions, a model for analyzing spatial momentum is developed based on population composition, rates of growth, and levels of interregional migration. Calculations are then done using (i) hypothetical populations exhibiting a range of plausible demographic behavior, and (ii) the population of Mexico, 1970. The results show that spatial momentum can have a substantial impact on ultimate population size under commonly encountered circumstances.population growth, population momentum, spatial momentum, urbanization
Momentum Profits and Time-Varying Unsystematic Risk
Get PDFThis study assesses whether the widely documented momentum profits can be ascribed to time-varying risk as described by a GJR-GARCH(1,1)-M model. Consistent with rational pricing in efficient markets, we reveal that momentum profits are a compensation for time-varying unsystematic risks, common to the winner and loser stocks. We also find that, because losers have a higher propensity than winners of disclose bad news, negative return shocks increase their volatility more than it increases that of the winners. The volatility of the losers is also found to respond to news more slowly, but eventually to a greater extent, than that of the winners. Following Hong et al. (2000), we interpret this as a sign that managers of loser firms are reluctant to disclosing bad news, while managers of winner firms are eager to releasing good news.Momentrum profits, Common Unsystematic risk GJR-GARCH(1,1)-M
Orbital angular momentum: origins, behavior and applications
Get PDFAs they travel through space, some light beams rotate. Such light beams have angular momentum. There are two particularly important ways in which a light beam can rotate: if every polarization vector rotates, the light has spin; if the phase structure rotates, the light has orbital angular momentum (OAM), which can be many times greater than the spin. Only in the past 20 years has it been realized that beams carrying OAM, which have an optical vortex along the axis, can be easily made in the laboratory. These light beams are able to spin microscopic objects, give rise to rotational frequency shifts, create new forms of imaging systems, and behave within nonlinear material to give new insights into quantum optics
Time-series and cross-sectional momentum investment strategies: International evidence
No full textNumerous studies have found that profits that can be realised from following a momentum-based investment strategy of buying recent outperforming stocks (winners) and selling recent underperforming stocks (losers) (Jegadeesh & Titman, 1993, 2001). Momentum strategies have proved to be robust across time, countries and asset classes, leading Fama (1998) to observe that momentum remains the “premier unexplained anomaly”. The existence of the momentum abnormal returns continues to challenge the market efficiency theory.
The majority of momentum studies have investigated cross-sectional momentum strategies in which stocks are selected on the basis of their relative performances over some prior period. In a recent study, Moskowitz, Ooi, and Pedersen (2012) introduce a time-series momentum strategy which provides an alternative approach to security selection where stocks are chosen on the basis of their absolute performance over some prior period.
Although previous literature has evaluated momentum strategies in numerous markets settings, by far the bulk of these studies have concentrated on equity markets. Therefore, it is somewhat surprising that we are yet to see a comprehensive study that compares the two types of momentum strategies in this arena. The main objective of this study is to evaluate and compare the performances of the two momentum strategies in the security markets in order to examine validation of the market efficiency theory across international stock markets.
The study addresses the objective in three stages: before-transaction costs performance (raw returns), after-transaction costs performance (net returns) and after-transaction costsperformance adjusted for risks (risk-adjusted net returns). A further by-product of our research is that we utilise a large number of implementation alternatives for both momentum strategies and so provides an insight into the optimal way to implement both time-series and cross-sectional momentum strategies.
Before transaction cost (raw) return:
The first thing that has been found is that the time-series and cross-sectional momentum returns reduce as we increase the cut-offs used when choosing both winning and losing stocks and so increase the number of stocks in the momentum portfolios (i.e. including 32%, 60% and all stocks in either the winner or loser portfolios). The extension of the cut-offs from 32% to 100% results in a reduction in the returns on the momentum portfolios by approximately 50% on average based on the pooled data for the 24 markets. Having established this, we then use the 32% cut-offs over the remainder of our analysis.
The study finds that both time-series and cross-sectional momentum strategies produce significant positive outcomes under numerous implementations in the majority of developed stock markets with the major exceptions being Greece, Israel, Japan, Hong Kong, Portugal, Spain and the US. The time-series momentum strategy outperforms the cross-sectional momentum strategy under optimal implementations conditions in all markets and is statistically significant in half of these markets.
After transaction costs (net) returns, and risk-adjustment net returns:
We find that the transaction costs and standard risk explain most profitability of the time-series and cross-sectional momentum strategies. In terms of the Fama-French alpha determined using after-transaction costs return, about 6% on average of the implementations evaluated produce significant positive risk-adjusted net returns. There are absolutely no implementations that yield significant positive returns in Austria, France, Germany, Greece, Hong Kong, Israel, Japan, Norway, Portugal, Singapore, Spain and the US., while less than 5%of the implementations in Australia, Canada, and Ireland. The findings support that the market efficiency hypothesis still holds across the most markets in our sample and the existence of exploitable investment opportunities is rare.
The study particularly concentrates on the optimal implementations of both the time-series and cross-sectional momentum strategies across the 24 markets. Common characteristics of these optimal implementations for the risk-adjusted net returns are that they combine a formation and a holding period of between 15 and 18 months, a buy-and-hold portfolio construction policy and the use of either a market or inversed-volatility portfolio weighting scheme.
Based on the optimal implementation approach for each market, the overall performance of the two momentum strategies is eroded from 2.09% (raw return) to 1.34% (net return) and to 0.9% (Fama-French alpha) for the time-series momentum strategy, and from 1.43% (raw return) to 0.87% (net return) and finally to 0.51% (Fama-French alpha) for the cross-sectional momentum strategy. At each of the three steps along the way, this study finds that the time-series momentum strategy continues to outperform the cross-sectional momentum strategy; however the magnitude of the superior performance is diluted with an average difference from 0.66% (raw return) to 0.47% (net return) and to 0.39% (Fama-French alpha). In addition, the superior performance of the time-series momentum strategy relative to the cross-sectional momentum strategy comes during periods when the markets have been performing poorly but that this advantage also erodes as we proceed from raw returns to net returns to risk-adjusted net returns.
One possible explanation for the superiority of the time-series momentum strategy is that it forms portfolios of slightly smaller capitalization stocks with a greater spread in past performance between the winner and loser stocks. Both of these features suggest that thetime-series momentum strategy will outperform the cross-sectional momentum strategy. On the other hand, the transaction costs and risk from the time-series momentum strategy are higher than the costs from the cross-sectional momentum strategy which is largely a consequence of time-series momentum strategy selecting smaller and growth stocks, and generating a higher turnover over a market cycle, so it is not surprising that the outperformance of the time-series momentum strategy becomes smaller after adjusting for risk on an after-transaction costs basis
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