Profitability of time series momentum

We propose a continuous-time heterogeneous agent model consisting of fundamental, momentum, and contrarian traders to explain the significant time series momentum. We show that the performance of momentum strategy is determined by both time horizon and the market dominance of momentum traders. Specifically, when momentum traders are more active in the market, momentum strategies with short (long) time horizons stabilize (destabilize) the market, and meanwhile the market under-reacts (over-reacts) in short-run (long-run). This provides profit opportunity for time series momentum strategies with short horizons and reversal with long horizons. When momentum traders are less active in the market, they always lose. The results provide an insight into the profitability of time series momentum documented in recent empirical studies.     

Intraday momentum in Chinese commodity futures markets

In this paper, we study whether the intraday momentum exists in Chinese commodity futures markets. We first construct an open-interest-weighted index with the high-frequency data of all commodity futures traded and then examine the predictability of the last half-hour return with both in-sample and out-of-sample tests. Consistent with findings in other markets, we show that the first half-hour return can readily predict the last half-hour return. We further demonstrate that the magnitude of this intraday momentum varies with volatility, trading volume, trade size, the sign of the first half-hour return, the type of commodity futures, and the launch of night trading. Additionally, this intraday momentum does not result from data snooping but has economic significance and remains robust under different index weighting and predictor calculation methods.     

Time series momentum and moving average trading rules

We compare and contrast time series momentum (TSMOM) and moving average (MA) trading rules so as to better understand the sources of their profitability. These rules are closely related; however, there are important differences. TSMOM signals occur at points that coincide with a MA direction change, whereas MA buy (sell) signals only require price to move above (below) a MA. Our empirical results show MA rules frequently give earlier signals leading to meaningful return gains. Both rules perform best outside of large stock series which may explain the puzzle of their popularity with investors, yet lack of supportive evidence in academic studies.     

Risk-managed 52-week high industry momentum,momentum crashes and hedging macroeconomic risk

This is the first study to investigate the profitability of Barroso and Santa-Clara’s [J. Financial Econ., 2015, 116, 111–120] risk-managing approach for George and Hwang’s [J. Finance, 2004, 59, 2145–2176] 52-week high momentum strategy in an industrial portfolio setting. The findings indicate that risk-managing adds value as the Sharpe ratio increases, and the downside risk decreases notably. Even after controlling for the spread of the traditional 52-week high industry momentum strategy in association with standard risk factors, the risk-managed version generates economically and statistically significant pay-offs. Notably, the risk-managed strategy is partially explained by changes in cross-sectional return dispersion, whereas the traditional strategy does not appear to be exposed to such economic risks.     

Exploiting commodity momentum along the futures curves

This study examines novel momentum strategies in commodities futures markets that incorporate term-structure information. We show that momentum strategies that invest in contracts on the futures curve with the largest expected roll-yield or the strongest momentum earn significantly higher risk-adjusted returns than a traditional momentum strategy, which only invests in the nearest contracts. Moreover, when incorporating conservative transaction costs we observe that our low-turnover momentum strategy more than doubles the net return compared to a traditional momentum strategy.     

Time series reversal in trend-following strategies

This paper empirically studies the reversal pattern following the formation of trend-following signals in the time series context. This reversal pattern is statistically significant and usually occurs between 12 and 24 months after the formation of trend-following signals. Employing a universe of 55 liquid futures, we find that instruments with sell signals in the trend-following portfolio (‘losers’) contribute to this type of reversal, even if their profits are not realised. The instruments with buy signals in the trend-following portfolio (‘winners’) contribute much less. A double-sorted investment strategy based on both return continuation and reversal yields to portfolio gains which are significantly higher than that of the corresponding trend-following strategy.     

Time series momentum trading strategy and autocorrelation amplification

This paper investigates why general Moving Average (MA) trading rules are widely used by technical analysts and others. We assume general stationary processes for prices and we derive the autocorrelation function for an MA trading rule. Based on our results, we conjecture that autocorrelation amplification is one of the reasons why such trading rules are popular. Using simulated results, we show that the MA rule may be popular because it can identify price momentum and is a simple way of assessing and exploiting the price autocorrelation structure without necessarily knowing its precise structure. This paper then, provides empirical evidence of autocorrelation amplification using 15-year daily price data for 11 major international stock indices.     

Factor pricing in commodity futures and the role of liquidity

This paper empirically investigates the pricing factors and their associated risk premiums of commodity futures. Existing pricing factors in equity and bond markets, including market premium and term structure, are tested in commodity futures markets. Hedging pressure in commodity futures markets and momentum effects is also considered. This study combines these factors to discuss their importance in explaining commodity future returns, while the literature has studied these factors separately. One of the important pricing factors in equity and bond markets is liquidity, but its role as a pricing factor in commodity futures markets has not yet been studied. To our knowledge, this research is the first to study liquidity as a pricing factor in commodity futures. The risk premiums of two momentum factors and speculators’ hedging pressure range from 2% to 3% per month and are greater than the risk premiums of roll yield (0.8%) and liquidity (0.5%). The result of a significant liquidity premium suggests that liquidity is priced in commodity futures.     

Price behavior of small-cap stocks and momentum: A study using principal component momentum

This study analyzes why the negative momentum effect appears in Asian (China, Japan, Korea) stock markets, contrary to the U.S. market. We use principal component momentum (PMOM), a newly devised momentum measure. The PMOM is constructed by extracting commonalities from traditional momentum measures using principal component analysis. The results show evidence of positive and negative momentum profits in the U.S. and Asian markets, respectively. Negative momentum profits in Asian markets are attributable to the strong performance reversal of small stocks in the loser portfolio. Conversely, the positive momentum profits of the U.S. market are driven by the performance continuity of small stocks in the winner portfolio. The PMOM strategy is significantly more advantageous than traditional momentum strategies, based on the economic and statistical perspectives of momentum profits. These results are robust to changes in empirical designs.     

Conditional risk and performance evaluation: Volatility timing, overconditioning, and new estimates of momentum alphas

Unconditional alphas are biased when conditional beta covaries with the market risk premium (market timing) or volatility (volatility timing). We demonstrate an additional bias (overconditioning) that can occur any time an empiricist estimates risk using information, such as a realized beta, that is not available to investors ex ante. Calibrating to U.S. equity returns, volatility timing and overconditioning can plausibly impact alphas more than market timing, which has been the focus of prior literature. To correct market- and volatility-timing biases without overconditioning, we show that incorporating realized betas into instrumental variables estimators is effective. Empirically, instrumentation reduces momentum alphas by 20-40%. Overconditioned alphas overstate performance by up to 2.5 times. We explain the sources of both the volatility-timing and overconditioning biases in momentum portfolios.     

Asymmetry and downside risk in foreign exchange markets

Abstract

This paper evaluates the double gamma distribution as a means of modelling asymmetry in the conditional distribution of financial data. To do this the model is applied to ten exchange rate series covering mature and emerging market countries. A second contribution of this paper is to highlight the link between the double gamma distribution and the measurement of the second lower partial moment (or semi-variance). The resulting empirical performance of the double gamma model is found to be mixed when compared to a symmetric GARCH-t model. Estimates of conditional downside risk based on the double gamma model are constructed for each series. The results for the Malaysian Riggit, Zimbabwe Dollar and the Korean Won demonstrate the extreme downside volatility experienced by these countries during the emerging markets currency crisis.     

Clustering financial time series with variance ratio statistics

This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. Simulation results show that this metric aggregates time series according to their serial dependence structure better than a metric based on the sample autocorrelations. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates stock markets reasonably well according to size and the level of development. Furthermore, despite the substantial evolution of individual variance ratio statistics, the clustering pattern remains fairly stable across different time periods.     

Economic policy uncertainty and momentum

We show that a news‐based measure of economic policy uncertainty (EPU) negatively forecasts momentum. A 1‐standard‐deviation increase in EPU is associated with a 1.11% decrease in risk‐adjusted momentum returns. The predictive power of EPU is robust after controlling for previously documented economic state variables and macroeconomic uncertainty. We provide an explanation for these results from the perspective of a fund flow‐induced trading mechanism and offer direct empirical support. The literature documents that momentum can be partially attributed to performance‐chasing mutual fund flows. We find that this flow‐induced mechanism functions more effectively in low EPU states, thereby generating stronger stock momentum.     

Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Commodity markets are a widely researched topic in the field of finance. In this paper, we investigate the co-movement of return and volatility measures in different commodity futures markets and how these measures are affected by liquidity risk. First, we find that commodity returns display co-movement and that liquidity risk plays a key role in shaping asset return patterns. Moreover, we show that the volatilities of commodity returns co-move, and we demonstrate the role of liquidity risk in this joint pattern. We also find that the commodity markets we investigated share a common volatility factor that determines their joint volatility co-movement. Because liquidity risk affects both commodity returns and volatility shocks, it might be interpreted as the common causal factor driving both measures simultaneously. Therefore, we affirm the view that liquidity shocks are firmly related to two residual risks originating from both market return and market volatility. Finally, we also show that liquidity spillovers can significantly drive cross-sectional correlation dynamics.     

An examination of the relation between asymmetric risk measures,prior returns and expected daily stock returns

We use a sample of individual firm stock returns over the 1988–2009 time period to determine whether: (1) expected daily returns are related to asymmetric risk measures, (2) expected daily returns are related to the directional change of the prior day's price, and (3) our results are robust to the addition of firm size, book-to-market equity and liquidity. We find that investors are compensated for asymmetric risk; however, the positive risk–return relation is present only for our smallest firm quintile. We find a short-term return reversal present in all subgroups, except for the largest firms in our sample. We also document that the low volatility anomaly may be related to firm size and liquidity.     

Currency momentum,carry trade,and market illiquidity

This study empirically examines the effect of equity market illiquidity on the excess returns of currency momentum and carry trade strategies. Results show that equity market illiquidity explains the evolution of currency momentum strategy payoffs, but not carry trade. Returns on currency momentum are low following months of high equity market illiquidity. However, in the recent decade, illiquidity positively predicts the associated payoffs. The findings withstand various robustness checks and are economically significant, approximating in value to one-third of average monthly profits.     

Quant GANs: deep generation of financial time series

Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.     

Herding,momentum and investor over-reaction

In this paper we study the impact of noise or quality of prices on returns. The noise arises from herding by market participants beyond what is justified by information. We construct a firm-quarter-specific measure of speculative intensity (SPEC) based on autocorrelation in daily trading volume adjusted for the amount of information available, and find that speculative intensity has a significant positive impact on returns. Both cross-sectional and time series variation in SPEC are consistent with conventional wisdom, and with implications of theories of herding as in DeLong et al. (1990, J Political Econ 98(4):703–738). We find that high-SPEC firms drive the returns to momentum trading strategies and that investor over-reaction is significant only in the case of high-SPEC firms.

Information percolation,momentum and reversal

We propose a joint theory of time-series momentum and reversal based on a rational-expectations model. We show that a necessary condition for momentum to arise in this framework is that information flows at an increasing rate. We focus on word-of-mouth communication as a mechanism that enforces this condition and generates short-term momentum and long-term reversal. Investors with heterogeneous trading strategies—contrarian and momentum traders—coexist in the marketplace. Although a significant proportion of investors are momentum traders, momentum is not completely eliminated. Word-of-mouth communication spreads rumors and generates price run-ups and reversals. Our theoretical predictions are in line with empirical findings.