Even though we classify momentum with a longer time frame than a day, it is necessary to point out that momentum can also exist within the day. Traders can find momentum during the day, as well as for longer time frames. The strategies that we analyze below have long-time horizon momentum.
A momentum strategy is based on identifying and following a price trend in the market. It is based on the premise that an asset price that is moving strongly in a direction will continue to move in that direction until the price trend loses strength. The momentum is determined based on the trading volume and rate of price change. Momentum can be classified into time-series momentum and cross-sectional momentum.
Time series momentum means that past returns are correlated with future returns.
In order to design a time-series momentum strategy, researchers calculate the correlation coefficient of the returns using a statistical approach, where the null hypothesis represents no correlation between returns.
The correlation coefficient of returns can vary across lags and sometimes the most positive correlation is between returns of different lags. For example, we can find that the correlation coefficient of returns in one day is negative, and the correlation coefficient of returns in a twenty day window is positive. So, while using this approach, it is necessary to take into account the different timeframes of the returns correlations.
Cross-sectional momentum is based on the relative performance of two prices series, where one price series outperforms the other. The hypothesis in this type of strategy support is that if one price series outperforms the other in the present, it is likely to keep doing so in the future.
Working with Momentum Strategies
Momentum strategies can be based on technical indicators and breakouts. For example, it is possible to define an entry signal to buy when the price reaches a new N-day high, or when the price exceeds the upper Bollinger band, or when the price exceeds the N-day Moving Average or Exponential Moving Average.
Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) are typical indicators used to find signals of oversold and overbought stocks that could lead to entry orders to go long or short.
Other classical momentum strategies generate signals to buy when the number of up days exceeds the number of down days in a moving period. A sophisticated approach would buy or sell the stock prior to the big movement by detecting chart patterns as well as resistance and support levels and make entries prior to the big move.
These types of strategies many times are supported by pipelines or screeners in order to find stocks that meet some criteria. For example, one pipeline could select stocks that are within 10% of their 52-week high and rank them from highest to lowest to this high. After getting this group of stocks, traders may wait for a pull back to enter into the market.
Momentum strategies involve a high level of risk because there is no guarantee that the price will continue to go higher in case of an uptrend or continue to go down in case of a downtrend. Statistics show that the number of winning trades in a momentum strategy are few compared to the number of losing trades. However, these types of strategies have a high reward/risk rate. So, a few winning trades can make the strategy profitable despite the big number of losing trades.
For this reason, these types of strategies require an integral trading plan with a strict definition of entry and exit points and knowledge about the current market regime. Usually traders set stop loss orders to exit position to prevent a big loss when the trend has a reversal and start to move in the opposite direction.
The strategy metrics of a momentum strategy will differ from those of other types of strategies such as a mean reversion strategy. Usually in a momentum strategy, the ratio of winning trades over losing trades is lower than 1 as there are more losing trades than winning trades. Momentum strategies, therefore, rely on a small number of winning trades with a greater profit. In contrast, the number of winning trades for a mean reversion strategy is higher than the number of losing trades. Mean reversal strategies are profitable based on the amount of winning trades and not the reward/risk relation of each trade.
- Introduction to Quantitative Trading
- Quantitative Trading - Advantages and Disadvantages
- Types of Quantitative Trading Strategies
- Momentum Strategies
- Mean Reversion Strategies
- Market Making Strategies and Day Trading Strategies
- How to Generate Trading Ideas
- Designing A Trading Strategy For Profit
- Backtesting a Trading Strategy - Considerations
- Risk Management of a Trading Strategy
- Risk Indicators - VIX Index and TED Spread
- Plotting the VIX Index and TED Spread in R
- Introduction to Quantmod in R
- Downloading Data Using Quantmod Package in R
- Creating Charts with Quantmod
- Data Analysis with Quantmod in R
- Measuring Overall ETFs Performance
- Quantstrat Example in R - EMA Crossover Strategy
- Quantstrat - EMA Crossover Strategy - Performance and Risk Metrics
- Quantstrat Example in R - RSI Strategy
- Quantstrat Case Study - Multiple Symbol Portfolio