Stock Price Variance (Market Close, May 15, 2023)
One way to measure the riskiness of US-listed stocks is to measure the variance of stock price changes. Risk is a function of stock price variance, which is the degree which stock prices vary around their mean change. We believe that risky stocks with high variance require investors to demand a higher expected return to hold them. The high uncertainty presented by higher variance requires a higher expected return.
When stock price variance falls across all stocks, and for the major indices, then the expected return required by investors falls. At least, that is what the theory says.
We calculate variance based on daily, adjusted price changes over the past 253 trading days. Stocks that maintain a stable price or stocks that rise (or fall) steadily have lower variance than stocks that fluctuate aggressively. We used to calculate price variance off of a natural log, and recently transitioned to a power log (upon request). This makes our variance values smaller, but still comparable across stocks.
This is counter-intuitive in practice. As stock price variance rises, we expect to see stock prices fall, which increases expected returns for new investments.
There are many experimental questions and hypotheses around this topic:
1. What causes increased stock price variance?
2. Is there a linkage (and possibly a lag) between rising stock price variance and falling stock prices?
3. Do we see differences in stock price variance across stock populations?
4. Do indices provide significant protection against stock price variance?
We have additional data to what is reported here. For collaboration opportunities please direct message us.
When stock price variance falls across all stocks, and for the major indices, then the expected return required by investors falls. At least, that is what the theory says.
We calculate variance based on daily, adjusted price changes over the past 253 trading days. Stocks that maintain a stable price or stocks that rise (or fall) steadily have lower variance than stocks that fluctuate aggressively. We used to calculate price variance off of a natural log, and recently transitioned to a power log (upon request). This makes our variance values smaller, but still comparable across stocks.
This is counter-intuitive in practice. As stock price variance rises, we expect to see stock prices fall, which increases expected returns for new investments.
There are many experimental questions and hypotheses around this topic:
1. What causes increased stock price variance?
2. Is there a linkage (and possibly a lag) between rising stock price variance and falling stock prices?
3. Do we see differences in stock price variance across stock populations?
4. Do indices provide significant protection against stock price variance?
We have additional data to what is reported here. For collaboration opportunities please direct message us.
Our Observations:
We started measuring stock price variance in May 2022. Since then, the variance of daily price changes are elevated, and have recently plateaued. They are now declining. Stock price volatility is declining. This means that on any given day, stocks will move less than before, regardless of whether up or down, however the rate of growth in variance is negative. In other words, stock price variance is decreasing.
We have heard a great deal about market liquidity, and have a hypothesis that changes in liquidity impacted stock price variance. However, we do not know whether liquidity improved in December 2022, and that seems questionable.
Stock price variance is consistently and significantly higher for stocks of companies with negative net income (and may include companies with non-US HQ locations) than for companies with positive net income (and solely US HQ locations). We also see larger overall declines in unprofitable company stocks over the past year.
Higher price variance and larger price declines are significant in our datasets between populations of US-listed unprofitable companies than US-Listed and US HQ companies that make a profit.
The riskiness of the SPY (the S&P 500 Index ETF) is elevated, but is slightly better (lower) than an equally weighted set of all profitable stocks. We understand the hypothesis and anecdotal evidence that market capitalization weighted indices provide better risk-adjusted returns. We believe that the $SPY is providing better variance readings than an equally weighted set of profitable stocks because of that capitalization weighting.
Key Takeaways:
Key Research Question:
Why did stock price variance increase from May 2022 through December 2022, and why did it flatten into May 2023?
While price variance flattened, so did the absolute level of diversified indices. We entered a consolidation phase, or what we affectionately call 'beach chop' where prices rise and fall quickly, but in a small range.
We hypothesize that this is caused by multiple factors, including reduced liquidity, increased costs of investing (e.g., rising interest rates), and a lower cost of making speculative bets on US equities. We also expect that greater use of shorter-term options (0DTE) has an impact on reducing large price movements.
We started measuring stock price variance in May 2022. Since then, the variance of daily price changes are elevated, and have recently plateaued. They are now declining. Stock price volatility is declining. This means that on any given day, stocks will move less than before, regardless of whether up or down, however the rate of growth in variance is negative. In other words, stock price variance is decreasing.
We have heard a great deal about market liquidity, and have a hypothesis that changes in liquidity impacted stock price variance. However, we do not know whether liquidity improved in December 2022, and that seems questionable.
Stock price variance is consistently and significantly higher for stocks of companies with negative net income (and may include companies with non-US HQ locations) than for companies with positive net income (and solely US HQ locations). We also see larger overall declines in unprofitable company stocks over the past year.
Higher price variance and larger price declines are significant in our datasets between populations of US-listed unprofitable companies than US-Listed and US HQ companies that make a profit.
The riskiness of the SPY (the S&P 500 Index ETF) is elevated, but is slightly better (lower) than an equally weighted set of all profitable stocks. We understand the hypothesis and anecdotal evidence that market capitalization weighted indices provide better risk-adjusted returns. We believe that the $SPY is providing better variance readings than an equally weighted set of profitable stocks because of that capitalization weighting.
Key Takeaways:
- We can improve the variance of our portfolios by market capitalization weighting our data analysis, although this has a high computational cost and would reduce our data accuracy. You can gain this benefit by investing with the S&P 500 ETF.
- Invest only in profitable companies with US HQ locations if you want less stock price volatility, all else being equal.
- We have seen a casual relationship between rising stock price variance and falling stock prices. There is no proof of causality.
Key Research Question:
Why did stock price variance increase from May 2022 through December 2022, and why did it flatten into May 2023?
While price variance flattened, so did the absolute level of diversified indices. We entered a consolidation phase, or what we affectionately call 'beach chop' where prices rise and fall quickly, but in a small range.
We hypothesize that this is caused by multiple factors, including reduced liquidity, increased costs of investing (e.g., rising interest rates), and a lower cost of making speculative bets on US equities. We also expect that greater use of shorter-term options (0DTE) has an impact on reducing large price movements.
1. $SPY, or the S&P 500 Index ETF:
May 5: 3.8 x 10-5
May 15: 3.5 x 10-5
2. All stocks:
May 5: 6.3 x 10-5, for 3,397 stocks
May 15: 5.8 x 10-5 for 3,384 stocks
May 5: 3.8 x 10-5
May 15: 3.5 x 10-5
2. All stocks:
May 5: 6.3 x 10-5, for 3,397 stocks
May 15: 5.8 x 10-5 for 3,384 stocks
Assumptions: portfolios are evenly weighted, for 253 trading days, using adjusted closing stock prices.
Note (*): We continue to adjust our data validation parameters to help us select stocks that can be readily purchased and sold for our Chicago Quantum Net Score model portfolio / managed accounts / hedged fund.
Daily fluctuations in the number of stocks is likely due to changes in data validation parameters, followed by changes in earnings.
Thank you Finviz, Yahoo Finance and Intrinio.