- We had attempted to create a framework for Quant based momentum trading framework with a focus of enhanced risk-reward profile by applying an ‘active overlay’ to prototype momentum framework which focuses on improved the risk-reward profile by identifying price trending and reversion environments through multiple trend filters.
- We had back tested a broad range of Momentum strategies that span a range of trend filters , rebalance properties and risk management techniques and made an attempt to design a framework based on our selection of asset universe, type of Momentum signal, signal look back window, trend quality and trend maturity and various risk management methods.
- Broadly, the frameworks uses four quantitative parameters of the trend metrics to identify price trends which exhibits strong persistent return continuation
- Based on the well accepted academic research findings that the Intermediate-Term Momentum exhibits a continuation in returns, we consider Weekly RSI (14 weeks) as a intermediate momentum indicator that compares the magnitude of recent gains and losses over a specified time period (default time frame - 14 trading days / weeks ) to measure speed and change of price movements of a security. Thus, we use the following threshold to identify the positive and negative trend:
A positive broader trend is confirmed only when the weekly RSI is above the reading of 52
A negative broader trend is confirmed only when the weekly RSI is below the reading of 50[/size]
2nd Parameter - Short-Term Momentum
- Once, we shortlist the stocks which are in the Intermediate-Term Momentum trend zone , we attempt to identify a short term momentum in a time series as the attempt here is to create an active framework for initiating a trade by using a more reactive momentum variable than weekly RSI, the framework scales the momentum variables to more shorter timeframe i.e. it uses daily RSI levels to capture short term early stage of trend continuation.
- Thus, the trade initiation point is confirmed when the price move results in a breakout above the higher band of the momentum channel ; i.e.
- A positive trend is confirmed only when the price move results in a daily RSI above the higher band (Daily RSI crossing above 55) and
- A negative trend is confirmed only when the price move results in a daily RSI below the higher band (Daily RSI crossing below 55)
3rd Parameter – Moving averages ratio
- Thus, in order to optimize the trade initiation, framework uses ratio of short term and intermediate moving averages to determine trend exhaustion i.e. if the ratio of 10 - period Moving Avg. / 30 – period Moving Avg. is above a defined threshold for a stock , it is said to be in the matured phase of Trend Continuation and such trades are avoided even though it passes the previous two signal filters.
- So, this acts as a third level of filter to further optimize trade initiation in order to reduce risk and potential losses due to trend mis-identification i.e. avoiding instances of following a trend when it is about to revert.
4th Parameter – Trend Quality - Frog in the Pan metric
- Now we seek to identify the quality of momentum associated with the stocks identified from previous trend filters / Signal metrics. Thus, attempt here is to integrate the Frog In the Pan metric discussed below in the framework.
- Frog-in-the-pan (FIP) hypothesis predicts that investors are inattentive to information arriving continuously in small amounts. Intuitively, it hypothesize that a series of frequent gradual changes attracts less attention than infrequent dramatic changes. Consistent with the FIP hypothesis, it is found that continuous information induces strong persistent return continuation that does not reverse in the long run. Momentum decreases monotonically from 5.94% for stocks with continuous information during their formation period to –2.07% for stocks with discrete information but similar cumulative formation period returns.