Data Insights

Stock Price Trading Dynamics and Splits

The recent announcements that several large corporations with high stock prices will split their stocks, following last year’s splits in Apple and Tesla, has sparked renewed interest in stock splits from issuers. In this article, we examine structural and behavioral market features which are impacted by stock price. These features can influence a stock’s market quality and therefore may influence a decision to split or not to split.

Why have several firms started to focus on splits?

A nearly unabated 13-year bull market has resulted in stock prices attaining record price levels. The average stock price1 in 2003 was $22.48, and now stands at $55.77. Large-cap stocks, as measured by the S&P 500, have seen an even bigger gains, touching $117.25 in March, versus $26.70 in 20032 . At the start of 2003, there were 32 stocks that closed at or above $100. By the end of the first quarter of 2022, there were 843 securities in that category and 54 that closed above $500.

Forward stock splits3 were not particularly newsworthy in the past, although announcements were often lauded as signs that a company’s outlook was positive. Issuers used to regularly execute stock splits. During the early 2000s, prior to Regulation NMS, there were more than 100 splits per year; recent years rarely approached even 50 splits. There are many theories as to why companies split their stocks more often in the past: Retail investors tend to prefer lower-priced stocks, and splits may have helped make stocks more enticing to these investors. A lower stock price means a smaller up-front investment for a 100-share purchase. With the advent of commission free trading, and fractional shares, a security’s price point has become less of a barrier for retail investors.

We do not consider stock price performance for this article. While stock prices tend to rise on split announcements, this is likely attributable to investors assuming that companies split their stock when there is good news in the offing. In the past, firms that split their stocks did maintain relatively better performance for at least a year, but a recent study suggests the longer-term price out performance is now fleeting4.

Methodology and Results

Our analysis categorizes stocks5 into several categories based on average trading price and daily volume. We compare several measures:

One academic study7 suggests that the optimal price of a stock is where it would trade when its average consolidated quoted spread is two times the minimum tick size. For securities trading at or above $1.00, that means the optimal price is where a stock would trade with a $0.02 spread8.

We highlight spread measures in currency terms and basis point terms. We also specifically calculate a weighted average spread term for several VWAP categories and volumes to allow us to estimate an overall optimal price based on the Li and Ye formula: [(spread in cents -1)/split factor^2] + 1, to find the level where we get closest to $0.02.

Chart 2 above shows the average consolidated quoted spread in currency terms (vertical axis) for stocks based on their average price9 (line color) and the consolidated average volume (horizontal axis). We limit consideration to stocks that had a VWAP over the study period of at least $25 and up to $500, and volume starting at 100,000 shares per day up to ten million. Not surprisingly, stocks in the lowest price category exhibit the tightest spreads in currency terms. Interestingly, at the highest volumes, securities in the $250 VWAP range had narrower spreads than those in the $200 category. Note however, that in the lowest price category, many of these stocks are arguably tick-constrained.

When we think of the cost to trade relative to spread, it is more reasonable to think in terms of spreads in basis points (percentage terms). If you are trading a stock which has a $0.02 spread that trades at $10.00, and you, for example, pay the offer price for all of your executions10, that costs 0.2% or 20 basis points each trade. A stock trading at $100 percentage cost is 1/10th the $10 stock. We see that as volumes rise, the spread differential in basis points terms is very small. A $50 stock that trades five million shares per day averages 4.28 basis points, while a $500 stock is slightly wider at 5.63bps. At lower volumes, higher priced securities appear to have an advantage. Stocks trading one million shares per day in the $250 range average 8.7bps spread, but at $50, they are nearly double at 14.1bps.

The apparent conflict between between spreads in currency and percentage terms highlights why additional market quality measures are important. While quoted spread is an important component in determining the cost to trade, available liquidity at that price is also important.

One way to better capture the impact on different prices on the cost to trade, is to consider the ratio of dollar value available at the best price to trading volume. We calculate the average dollar value quoted at the National Best Bid and Offer (NBBO) and divide that by the average intraday volume executed per second11. A higher ratio means that the liquidity available at the inside is better able to withstand temporary volume surges. Lower volume stocks exhibit much higher ratios.

As Chart 4 shows, liquidity coverage is strong regardless of price level for stock trading below 500,000 shares per day. For example, the liquidity coverage range for stocks trading in our lowest volume category of 100,000 - 250,000 ranges between 31X for stocks in the $500-$1000 group versus 55X for the $25-$50 category. At each volume level, lower prices offer higher coverage. For stocks priced between $500 and $1,000, the inside liquidity is not even sufficient to handle one second average volume for securites that have a CADV between five and ten million shares.

The final statistic we consider is quote volatility. Quote volatilities are impacted by lower prices in the same way percentage spreads are; lower prices generally result in higher quote volatilities. This is not surprising, especially for less liquid stocks, which have even wider spreads, as a small price move results in a large percentage change. Although illiquid stocks on average have higher quote volatility, even at volumes below 100,000 shares, there is not a perfect relationship between volatility falling as prices rise. For example, the $50-$75 price category shows lower quote volatility than the $500-$1000 and the $200-$250 group. Overall, volatility seems to show a generally U-shaped curve at all but the two lowest price levels. Based on this observation, we do not think there is any obvious advantage to split or not to split based purely on quote volatility, although the lowest-priced stocks do generally exhibit higher volatilities.

Finally, we applied the formula from the Li and Ye academic paper, which we referenced in footnote seven, to the median spreads we calculated over multiple price and volume categories. According to their model, a ratio above 1.5 would mean a forward split would improve liquidity and trading costs, while a ratio below 1.0 would indicate a need for a reverse split. For example, a ratio of 0.5 would indicate a need to split 1 for 2. These stocks, based on their median spreads, are tick constrained12.

Our first observation is that low volume stocks do not fit well into the rubric of the model. Splitting a $25-$50 stock 2:1, as suggested for the 1,000,000 CADV category, is almost certainly not advisable as the market quality improvement is not likely worth the time and expense of splitting the stock. However, the data do consistently recommend splitting higher priced stocks. The greatest advantage may be for more liquid securities. A $500-$1,000 stock split of 5:1 is recommended by the model, which would take the stock’s price down to the $100-$200 range. Most of the recommended splits would target post-split prices above $50 and up towards $100.

Conclusion

One size does not fit all when it comes to stock price. There is no single optimal price, but rather the best price for a given security depends on its volume and liquidity characteristics. We find that, in general, targeting stock prices in the $50-$100 range for liquid securities likely offers the best market quality results, though issuers that are concerned about price volatility may wish to consider targeting a post-split price closer to the upper end of that range. However, the evidence is less convincing to split less liquid stocks. Liquidity coverage differences are small, regardless of price, while our data shows spreads in percentage terms and quote volatility are less favorable to lower priced stocks.

1 We use volume-weighted average prices (VWAP).

2 Although the average stock price is up less than three-fold, stocks have, on average, risen much more than that. IPOs, stock splits and return of capital mute average stock price gains.

3 A forward stock split is when a company increases its shares outstanding, lowering its stock price by an equivalent ratio. For example, a 2:1 split would double the shares outstanding and halve the stock price. Reverse splits are the opposite - shares outstanding are decreased and price increases. A 1:10 split results in 1/10th of the shares outstanding and increases the stock price ten-fold. This article focuses on forward stock splits.

4 “Fractional Trading”, Zhi Da and Vivian Wang (2021). This paper ties changes in retail trading to the introduction of fractional trading and posits that this has resulted in a decrease in the longer-term positive price impact post-split. The result is based, however, on a limited number of observations.

5 We limit the population to common stocks, ADRs and ADSs.

6 Quoted spreads are consolidated and averaged across the chosen date range for core trading hours. Quote volatility takes the average midpoint quote each second and calculates the average second-to-second percentage change from one second to the next and annualizes that figure. Quoted size coverage divides the average number of shares available at the National Best Bid and Offer across all displayed exchanges by the average intraday shares traded per minute.

7 “The Optimal Nominal Price of a Stock: A Tale of Two Discretenesses”, Sida Li and Mao Ye.

8 The aforementioned paper is also dependent on round lot size and quoted spreads as reported to the Consolidated Tape. If the round lot size for a stock was decreased, the quoted spread would also fall without performing a split. However, our research shows that the changes in round lot size in the SEC MDI rule would result in only very small decreases in quoted spreads for stocks trading below $1,000.

9 We use volume weighted average price or VWAP, which is ∑price*shares traded/∑shares traded.

10 This is crossing the spread. In this situation, you are aggressively taking the best price available, as opposed to passively putting a bid in the market and waiting for an aggressive seller to come to you. Passive orders are less likely to receive an execution.

11 “Average intraday volume executed per second” excludes auctions such as the Opening Auction and Closing Auction.

12 See our paper “The Impact of Tick Constrained Securities on the U.S. Equity Market”.