Welcome to the DIY Momentum QnA and Discussion thread!
Momentum investing can be a highly effective strategy, but it requires a solid understanding of the market trends, entry-exit mechanisms, and risk management. This thread is dedicated to discussing various aspects of momentum investing, sharing insights, and answering questions.
Investment Thesis:
Momentum investing involves capitalizing on the continuation of existing market trends. Stocks that have performed well in the past are expected to continue their performance in the short to medium term. This strategy relies on the psychological factors driving market participants and aims to ride the wave of market sentiment.
Positives:
Trend Following: Momentum investing allows you to ride the trend and benefit from the collective market sentiment.
Flexibility: It can be applied across various asset classes and market conditions.
Performance: Historically, momentum strategies have outperformed in certain market environments, providing superior returns compared to traditional buy-and-hold strategies.
Negatives:
Volatility: Momentum stocks can be highly volatile, leading to significant drawdowns.
Short-term Focus: Requires frequent monitoring and rebalancing, which can be time-consuming and incur higher transaction costs.
Market Reversals: Momentum strategies can suffer during sudden market reversals, leading to potential losses.
Risks:
Market Timing: Incorrect timing can erode potential gains and amplify losses.
Overfitting: Reliance on historical data can lead to overfitting, where the strategy performs well on past data but poorly in real-time.
Liquidity: Investing in less liquid stocks can result in higher slippage and difficulty in executing trades at desired prices.
Interesting Aspects:
Screening Criteria: Share and discuss various screening methods to identify potential momentum stocks.
Entry-Exit Mechanisms: Debate the merits of different entry and exit strategies, such as weekly vs. monthly rebalancing.
Performance Metrics: Explore the use of metrics like the z-score to enhance momentum strategies.
I have been actively learning about momentum investing and have implemented a weekly rebalancing strategy in my portfolio. I am considering shifting to a monthly rebalancing approach and would love to hear your thoughts and experiences on this matter.
Let’s engage in a constructive discussion and help each other refine our momentum investing strategies. Please feel free to share your insights, ask questions, and contribute to the collective knowledge of the forum.
Looking forward to your valuable inputs!
Disclosure: I am currently invested in several momentum stocks and regularly rebalance my portfolio.
Feel free to make any adjustments or add more details specific to your experiences and knowledge!
Very good initiative. What is the number of stocks in your momentum portfolio? Usually, people suggest 10 stocks with 10% allocation for each.Have you tried with lower number of stocks or higher?
Yes, you are right, momentum generally persists for about 4-6 months, which aligns with the half-yearly rebalancing schedule of many momentum indices. This frequency helps capture the ongoing trends while avoiding excessive trading costs. By rebalancing every six months, these indices aim to stay invested in stocks that are currently performing well, based on the momentum factor, while periodically adjusting to reflect changes in market dynamics.
Personal pf rebalance I use to do monthly basis, but checking stock prices weekly.
Objective Measurement: The Z-score provides a standardized way to measure how far a stock’s price is from its mean, allowing for a more objective comparison across different stocks.
Statistical Rigor: Utilizing the Z-score incorporates statistical methods, making the investment process more data-driven and potentially reducing bias.
Enhanced Momentum Capture: By focusing on deviations from the mean, Z-score can help identify strong momentum plays, as stocks significantly above or below their historical average may indicate strong trends.
Risk Management: Z-scores can help identify outliers, potentially flagging extremely overbought or oversold conditions which can be useful for risk management.
Pros:
Quantitative Approach: Reduces emotional and subjective biases in stock selection.
Scalability: Can be easily applied to large datasets, making it suitable for institutional investors.
Versatility: Can be used in conjunction with other factors like value, quality, or size to enhance a multi-factor investing strategy.
Early Signal: Helps in identifying stocks that are starting to diverge from their historical performance, potentially catching trends early.
Cons:
Over-Reliance on Historical Data: Z-score is based on historical price data, which may not always predict future performance accurately.
Market Anomalies: Extreme market conditions can distort Z-scores, leading to false signals.
Complexity: Requires a good understanding of statistical concepts, which might not be suitable for all investors.
Volatility Sensitivity: Stocks with high volatility might frequently show significant Z-score deviations, potentially leading to more frequent trading and higher transaction costs.
While investing consider the following points:
Data Sources: Reliable and consistent data sources are crucial for calculating accurate Z-scores.
Backtesting: Share experiences and results from backtesting Z-score strategies to provide practical insights.
Integration with Other Factors: Discuss how Z-score can be integrated with other factors like earnings growth, price-to-earnings ratio, or other momentum indicators.
Real-World Application: Share examples of stocks that performed well using a Z-score approach and those that didn’t, to provide a balanced view.
Continuous Monitoring: Highlight the importance of regularly updating the Z-score calculations to reflect the most recent data and maintain the strategy’s relevance.
Engaging in discussions about these points can provide a comprehensive understanding of Z-score based factor investing and its practical applications.
Universe I track is Nifty smallcap 250, the weightage given to Z score is in proportional basis ie.
12 months to 6 months is 30: 70
Given, on basis of z score only rank is given,
There r many factors for entry and exit in momo(momentum ) pf but I prefer z score only.
Here is the reference information or video for ranking of stocks based on z score.
To rank stocks using the Z-score in Google Sheets, you can follow these steps:
Prepare Your Dataset: Enter your stock data into Google Sheets.
Calculate the Mean: Use the formula =AVERAGE(range) to find the average of your dataset. For example, if your data is in column A from row 2 to 100, use =AVERAGE(A2:A100).
Calculate the Standard Deviation: Use the formula =STDEVP(range) for the population standard deviation or =STDEV(range) for the sample standard deviation. For the same data range as above, use =STDEVP(A2:A100).
Calculate the Z-Score: Apply the Z-score formula for each data point using =(DataValue - Mean) / Standard Deviation. If the mean is in cell B1 and the standard deviation is in cell C1, for a data point in cell A2, the formula will be =(A2 - $B$1) / $C$1.
Interpret the Z-Scores: Positive Z-scores indicate values above the mean, while negative Z-scores indicate values below the mean. The absolute value indicates the number of standard deviations the data point is from the mean.
For video tutorials on how to calculate Z-scores in Google Sheets, you can check out the following resources:
Assume to calculate Z-scores in Google Sheets, start by entering your stock returns data into the sheet. Suppose you have five stocks with the following returns: 10%, 15%, 5%, 20%, and 12%. Enter the stock names in column A and their returns in column B.
Next, calculate the mean return. In a cell below your data, use the formula =AVERAGE(B1:B5) to find the average return. For this example, the mean return is 12%.
Then, calculate the standard deviation of the returns. In another cell, use the formula =STDEVP(B1:B5) to get the standard deviation. Here, it’s approximately 5.16.
Now, compute the Z-score for each stock’s return. In a new column, use the formula (DataValue - Mean) / Standard Deviation. For example, for the return in cell B1, the formula will be =(B1 - $B$6) / $B$7, where B6 contains the mean and B7 contains the standard deviation. Drag this formula down to apply it to all the returns.
After calculating, you will see Z-scores for each stock. A Z-score of -0.387 for stock A means its return is 0.387 standard deviations below the mean. A Z-score of 0.581 for stock B indicates its return is 0.581 standard deviations above the mean. This helps in identifying which stocks have significantly higher or lower returns compared to the average.
Hi…Can relative strength be used to identify the stocks in momentum? Relative outperformance compared to Nifty 500? Also is rebalnxing required or can we stay invested till it is in stage 2 ascending stage ?
Yes, you are right, infact relative strength can be used to identify momentum stocks. By comparing a stock’s performance against a benchmark like the Nifty smallcap 250 in my case, you can identify which stocks are outperforming. This helps in selecting stocks that are likely to continue their upward trajectory.
Rebalancing is often required in momentum investing to maintain exposure to the strongest performers. Regular rebalancing, such as weekly or monthly, monthly in my case (but monitoring weekly) ensures that you are always invested in stocks showing the highest momentum. However, some investors prefer to hold onto stocks as long as they remain in an ascending phase, or stage 2, which is characterized by a strong uptrend, and infact if you got a fundamentally good stock returns may be more than expected. So this approach can work but may require more active monitoring to ensure timely exits when the trend reverses.
So, both methods have their merits, and the choice depends on your investment strategy, risk tolerance, and how actively you want to manage your portfolio.
Not that we cannot mix two styles and create a hybrid model, but this is just momentum, once a set of rules are created, the focus is on the whole idea with perhaps making the process better, and not on one single stock, as the allocation is not particularly different between stocks.
So, while one can differentiate between stocks existing in the list, and allocate more, I don’t think we can say it will add any benefit to the overall process, also, there could be a time frame filter, if a stock consolidates and if it does not fit the criteria of the system, it should be exited so as to give place for another momentum stock. Of course, one can exclude such stocks and keep them separate and not look at them as part of the initial system, despite having found it in the system.
I think, at the end of the day, if a particular method is delivering returns as per or beyond our expectations, and if we know it is working for us, at least in the current environment, we can do it and continue doing it, and we can create many such methods using any and every source available
We’re discussing various momentum strategies on this thread, including @visuarchie’s specific momentum strategy. By evaluating different strategies, backtesting them, and analyzing their results, we’re aiming to find the optimal approach to momentum investing.
