Risk-managed time-series momentum: an emerging economy experience.
| Autor | Singh, Simarjeet |
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Introduction
Return predictability has remained a core theme in investment literature over the last four decades (Huang et al, 2020). Financial researchers have proposed several factors to forecast future returns, including size, value, momentum and quality (Basu, 1983; Fama and French, 1992; Jegadeesh and Titman, 1993; Sloan, 1996). Of these factors, momentum has proven to be the most pervasive and persistent (Blitz et al, 2020). It is simply defined as "the continuation of the trend" (Singh and Walia, 2022). According to Georgopoulou and Wang (2017), momentum anomaly has two dimensions: "cross-sectional momentum" (relative momentum) and "timeseries momentum" (absolute momentum). Most momentum anomaly researchers have concentrated on classical cross-sectional momentum. Jegadeesh and Titman's (1993) seminal work on cross-sectional momentum stated that "financial instruments that have outperformed (underperformed) their peers in the past will continue to outperform (underperform) in the immediate future". Time-series momentum is a relatively new version of the momentum anomaly that focusses on a financial asset's absolute (own) performance. Moskowitz etal (2012) proposed the concept of absolute momentum and concluded that a financial instrument's own performance in the previous year predicts its future performance. They argued that investors could earn substantial abnormal profits by going long (short) in financial instruments with positive (negative) cumulative returns in the preceding year.
Financial studies have demonstrated the importance of the absolute momentum effect in many geographic regions and time periods (Hurst etal, 2017; Georogopoulo and Wang, 2017; Lim et al, 2018; Eldomiaty et al, 2019; Guo and Ryan, 2021). Nevertheless, most of these studies were undertaken to this effect in mature markets. However, it will be fascinating to examine how absolute momentum techniques function in emerging and frontier markets. Moreover, most research publications on time-series momentum strategies have focussed on the return aspect; there has been minimal research on the risk aspect. Guo and Ryan (2021) have recently highlighted the potential risks associated with absolute momentum portfolios. Numerous financial academicians claim that absolute momentum techniques produce the best outcomes under severe market situations (Moskowitz et al, 2012). It will be intriguing to test this fact in emerging markets where information distribution is sluggish (Qin and Bai, 2013). Apart from that, the majority of research on risk-managed momentum techniques has been on relative momentum. Furthermore, executing these risk-managed momentum techniques necessitates the computation of intricate parameters and the provision of additional cash (Singh etal, 2021). All these considerations make existing risk-managed momentum techniques unattractive amongst practitioners. As a result, there is a need for a novel risk-managed time-series momentum approach that is simple to deploy and requires minimal funds (Salcedo, 2021).
The present study narrows down these gaps in time-series momentum literature by testing the absolute momentum strategies in an emerging economy research setting. The study focusses on time-series momentum payoffs amid severe market conditions. Recognising the significance of risk concerns, this study suggests a novel time-series riskmanaged momentum approach based on market conditions. In a nutshell, the current study addresses the following research objectives (RO):
RO1. Testing the profitability of time-series momentum strategies in the Indian Stock Market.
RO2. Investigating the performance of time-series momentum strategies during extreme market conditions.
RO3. Introducing a novel risk-managed time-series momentum approach for limiting massive absolute momentum losses.
The present study followed the methodological approach of Moskowitz et al (2012) proposed to formulate time-series momentum portfolios. The study reports a significant time-series return continuation effect in the Indian equity segment. It remains substantial even after incorporating standard risk factors. Nevertheless, time-series momentum portfolios, like relative momentum portfolios, are prone to significant losses from time to time. These catastrophic absolute momentum failures frequently occur during the crisis and recovery phases. These findings contrast with outcomes in mature markets (Lim et al., 2018), where time-series momentum strategies perform best amid severe market conditions. The gradual diffusion of information in emerging markets might explain these disparities (Zhang et al., 2020). Trading signals are delayed because of the sluggish diffusion of information, resulting in time-series momentum losses. Furthermore, the proposed time-series momentum approach proved to be a preferable alternative since it generates about 2.5 times higher returns than traditional time-series momentum and results in significant improvements in downside risks and higher-order moments. Though financial researchers propose a plethora of risk-managed momentum approaches, however, most of them are difficult to execute due to extensive computations. The proposed time-series momentum framework is a simple, easy-to-implement approach that promises togenerate almost the same returnsas existing risk-managed momentum approaches. The study makes three key contributions to the momentum literature considering the above. First, the current study is one of the first to look at the usefulness of time-series momentum strategies in the context of a developing economy. The research reveals similar effects to those reported in industrialised markets. Second, the study sheds insight into the negative aspects of time-series momentum. The author has carefully researched the return on time-series momentum portfolios in crisis and recovery stages. The findings of this investigation differ from those of the developed market. Finally, the study adds to the burgeoning literature on alternative momentum investment by providing a new risk-managed absolute momentum approach. The proposed time-momentum strategy predicts bullish (recovery) and bearish (crisis) periods and suggests which positions to take during these periods.
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Literature review 2.1 Time-series momentum
The time-series momentum phenomenon was initially studied by Moskowitz et al. (2012). The authors demonstrated, using 58 financial assets, that investors may generate statistically and economically significant returns by investing in assets that have provided positive returns over the previous 12 months and selling those that have produced negative returns. They also discovered that conventional asset pricing models could not explain time-series momentum returns. Later, Hurst et al. (2017) corroborated the findings of Moskowitz et al. (2012) by examining the time-series momentum impact on a larger class of financial assets. Financial researchers also explore the various explanations of the time-series momentum effect. He and Li (2015) suggest a behavioural model based on three kinds of traders, i.e. contrarian, fundamental and momentum traders. Absolute momentum strategies generate significant payoffs only when momentum traders are active in the market. Kim et al. (2016) showed that volatility scaling drives absolute momentum profits. Koijen et al. (2018) used absolute momentum returns as a rational factor to examine carry trades. Lim et al. (2019) inoculated machine learning-based neural networks into the traditional time-series momentum approach and demonstrated that their hybrid approach outperforms plain time-series momentum.Yang et al. (2022) established a link between information diffusion and time-series momentum. They prove that stocks with faster information diffusion speed exhibit higher time-series momentum returns. More recently, Huang et al. (2020) found no strong evidence of an absolute momentum effect.
Even thoughmost of the literatureon absolute momentum has focussedon futures and other liquid instruments, a handful of research papers also test the time-series momentum effect in equities. For instance, Bird et al. (2017) tested absolute momentum strategies in 24 developed countries and reported an impact of time-series momentum on these equity markets that were both statistically and economically significant. The authors also claimed that time-series return continuation strategies outperform conventional relative momentum strategies in terms of payoffs. Lim etal (2018) demonstrated the efficacy of time-series momentum strategies in the US market over a 100-year period (1927-2017). Cheema et al (2018) divulged that absolute momentum provides superior returns than relative momentum only when the market continues in the same state. Traditional relative momentum tactics outperform market changes. Goyal and Jegadeesh (2018) went one step ahead and identified the causes of the time-series momentum effect's superiority over the cross-sectional momentum effect. The authors illustrate that leverage is the primary source of variation in the performance of these two momentum approaches. They also found that traditional cross-sectional momentum performs better when leverage is fully integrated than time-series momentum. Schmid and Wirth (2021) recently revealed that trend strengths and correlations between various investment instruments determine which momentum strategy, i.e. time-series or cross-sectional, yields superior results. If most of the investment instruments have similar trends and low correlations, the absolute momentum approach takes precedence; otherwise, the cross-sectional momentum approach takes precedence.
2.2 Risk-adjusted momentum
In recent years, most of the literature on momentum anomaly has concentrated on the risk aspects of the traditional cross-sectional momentum strategies (Grobys and Kolari, 2020). Several financial academicians have reported the fatter left tails of...
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