Market Capitalization Weights When Excluding Preferred Shares
Summary
The document raises a portfolio construction question about weighting common shares by market value when a company also has publicly traded preferred shares. The data source reports separate market values for common and preferred stock, and the investor intends to exclude preferred shares from the investable universe. Including preferred capitalization in the weight denominator would make a common share’s portfolio weight reflect the full company’s equity value; using common capitalization alone would base the weight on the security class actually held.
The question highlights that the choice can materially affect firms with substantial preferred capitalization, but supplies no answer, cited literature, or comparison of portfolio results. It therefore identifies a methodological decision rather than prescribing a standard. The appropriate convention depends on the portfolio’s intended exposure and on how the research defines market capitalization and eligible securities; consistency and explicit documentation are necessary for interpretation.
Key ideas
- A value weighted portfolio can use common share capitalization alone or total common and preferred capitalization.
- Including preferred share value changes the weight assigned to the company’s common stock.
- The document does not establish a literature standard or offer an empirical comparison.
- The capitalization definition should match the portfolio’s intended investment universe and be documented.
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Full text
# Can I dynamically change hyper-parameters of a model?
# Can I dynamically change hyper-parameters of a model?
### Question
- Can I apply different hyper-parameters for different training sets?
- I can see the point of using the shared parameters but I cannot see the point of using shared hyper-parameters. The advantage of using the shared parameters is introduced in the following question: Recurrent NNs: what's the point of parameter sharing? Doesn't padding do the trick anyway?
### More Details
1) Explanations on My Model As my model is a stock trading model, I will explain to you how I trade stocks. Please bear with me for explaining how I trade stocks. It is not a long explanation.
- I am using Bollinger bands to trade stocks. (All the stock data in this example is daily).
- In short, I calculate simple moving average (SMA) and standard deviation of N days long stock prices. (Yes, I assume that the stock prices follow the Gaussian distribution.) The upper band is `SMA + k*stdev`, while the lower band is `SMA - k*stdev`.
- I buy the stock when the stock price is above the upper band ('Too_High_Buy') or below the lower band ('Too_Low_Buy'). For the 'Too_High_Buy' case, I sell the stock when the stock price goes below the SMA. For the 'Too_Low_Buy' case, I sell the stock when the stock price goes above the SMA.
- The parameters of my model is SMA and Stdev, while the hyper-parameters are N and k. N: It decides how smooth SMA (the yellow line) will be. k: It decides how far the upper and lower bands will be located from the SMA.
- As different values of N and K show different characteristics, we should search which values of N and K are good for stock price data.
2) How to decide the hyper-parameters(N & k)
- I use 'sliding steps' to decide the appropriate number for two hyper-parameters, N and K.
- Sliding steps use the fixed amount of training data to decide the hyper parameter and check the performance of these hyper-parameters on the validation dataset, which immediately follows the training dataset.
- I thought sliding steps is a good cross-validation tool to apply on stock data, because statistical properties of stocks can change while time goes by. For example, 30 years ago, McDonald and Coca-cola shows the similar price movements because they were sold together. However, nowadays, Coca-cola focuses on healthy drinks while McDonald stays as unhealthy food brands, they can show different price movements.
- The hyper-parameter here can be several things, but for the sake of simplicity, let's say the hyper-parameters that we should decide is the duration of training dataset (N). The duration of dropped and forecasting is set to 1 day.
- Using grid-search of different values of N and K, I calculate which values of N and K shows the best performance during the validation period in the training dataset.
3) My question
- Can I use different k for different training sets? In other words, can I dynamically change which value of K to use based on the performance of sub-training dataset?
- While performing the grid-search in the sliding step window method, we use the same value of K in all the training datasets to trade a stock during the validation period.
- However, we can use different values of K based on the performance of sub-training data.
- For example, let's say N is fixed to be 30 days. Then from the 1st Jan to 30th Jan, k=0.6 shows the best performance and we use this k=0.6 for the 31st Jan. Then from the 2nd Jan to 31st Jan, k=1.5 shows the best performance, then we use this K value for the 1st Feb, and so on.
- Why should we use the shared hyper-parameter K all across the model? For parameters, it makes sense because it allows reduction of the parameters that the model has to learn. (source: Recurrent NNs: what's the point of parameter sharing? Doesn't padding do the trick anyway?)
- But for using shared hyper-parameters, the amount of hyper-parameters is the same whether I use the shared hyper-parameters or not. It is only 1 hyper-parameter, which is K. The amount of computations needed is the same as well.
## Answer by Sergei Rodionov (score 1)
https://quant.stackexchange.com/a/61115
Quoting George Box, "All models are wrong, but some are useful." The model with static parameters would be at least somewhat explanatory, but not necessarily predictive. By parameterizing too much one might stumble upon a perfect fit, which will fail miserably on future data.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.