1.
Direction, Trend 그리고 Momentum (2)에 이어지는 글입니다. 이제 모멘텀전략과 관련한 논문입니다. 먼저 Forecasting High-Frequency Futures Returns Using Online Langevin Dynamics입니다.
Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency.
다음은 Trend Filtering Methods for Momentum Strategies입니다.
This paper studies trend filtering methods. These methods are widely used in momentum strategies, which correspond to an investment style based only on the history of past prices. For example, the CTA strategy used by hedge funds is one of the best-known momentum strategies. In this paper, we review the different econometric estimators to extract a trend of a time series. We distinguish between linear and nonlinear models as well as univariate and multivariate filtering. For each approach, we provide a comprehensive presentation, an overview of its advantages and disadvantages and an application to the S\&P 500 index. We also consider the calibration problem of these filters. We illustrate the two main solutions, the first based on prediction error, and the second using a benchmark estimator. We conclude the paper by listing some issues to consider when implementing a momentum strategy.
세번째는 Absolute Momentum:a Simple Rule-Based Strategy and Universal Trend-Following Overlay입니다. relative strength price momentum와 absolute, time series momentum라는 개념을 사용하고 있습니다.
There is a considerable body of research on relative strength price momentum but relatively little on absolute, time series momentum. In this paper, we explore the practical side of absolute momentum. We first explore its sole parameter – the formation, or look back, period. We then examine the reward, risk, and correlation characteristics of absolute momentum applied to stocks, bonds, and real assets. We finally apply absolute momentum to a 60-40 stock/bond portfolio and a simple risk parity portfolio. We show that absolute momentum can effectively identify regime change and add significant value as an easy to implement, rule-based approach with many potential uses as both a stand- alone program and trend following overlay.
네번째는 Momentum Strategies in Futures Markets and Trend-following Funds입니다.
In this paper, we rigorously establish a relationship between time-series momentum strategies in futures markets and commodity trading advisors (CTAs) and examine the question of capacity constraints in trend-following investing. First, we construct a very comprehensive set of time-series momentum benchmark portfolios. Second, we provide evidence that CTAs follow time-series momentum strategies, by showing that such benchmark strategies have high explanatory power in the time-series of CTA index returns. Third, we do not find evidence of statistically significant capacity constraints based on two different methodologies and several robustness tests. Our results have important implications for hedge fund studies and investors.
위의 논문들이 그런 것은 아니지만 Momentum만으로 운용하는 독립된 전략은 너무 위험하다는 시각도 있습니다.
So what are investors to do with momentum? Our conclusion is that momentum is inadvisable as a stand-alone strategy due to the risk of precipitous losses. Rather, we suggest that long-term investors seeking to tap more than one source of equity premium choose another, more stable factor for their core investment strategy (value is certainly a strong candidate), and consider adding momentum as a short-term trading strategy when market conditions are favorable.
Hot Potato: Momentum As An Investment Strategy중에서
2.
Direction, Trend 그리고 Momentum 이라는 주제를 마칩니다.
이상에서 소개한 논문들중 재미있는 논문들이 많이 있습니다. 소개한 논문을 가지고 알고리즘아카데미 강사를 하실 분이 있으면 연락을 주세요.(^^)
모멘텀 팩터와 여타 팩터들이 상관계수가 음이라 분산효과가 있는 장점이 있죠. 독립적으로 쓰면 안된다는 주장은 모멘텀 팩터의 수익이 풋옵션을 매도한 페이오프와 비슷하기 때문이죠. 조금씩 9번 먹다 1번에 깨지는.