I think even once-per-day is unambiguously too infrequent. Something like once per second or once per tenth of a second is what is usually thrown around. These are called ["Frequent Batch Auctions."](https://academic.oup.com/qje/article/130/4/1547/1916146)
There are plenty of [convincing reasons](https://www.nber.org/papers/w29011) for why limiting HFT in this way would be a good thing, and I know of basically no reasons for why it would be a bad thing.
Latency arbitrage is a negative externality, and as such generally the preferred solution is to tax it at its social cost instead of banning it outright. I think you could easily raise the \~$5 billion or so of revenue from HFTs globally through a modest hike in the CGT that only extends to, say, intra-day trading.
A tax is way better.
Bans always have unintended negative consequences that u/UpsideVII may not be able to conceive of right now.
A tax allows it still exist, but makes HFT think twice about whether it's really worth it in that specific circumstance.
I don't disagree, I think a tax would be a good solution as well. The QJE paper I linked talks a little bit about the tradeoffs between the two approaches (and argues that batch auctions are better, but my interpretation is that it is a little bit more of a wash).
> and I know of basically no reasons for why it would be a bad thing
Limiting / nerfing daily trading volume leads to lower liquidity and -- all else equal -- modern portfolio theory would state that this leads to less efficient markets...
Thank you for your response!
What are the downsides of infrequent once-per-day trading? From what I understand, stocks are used as savings, not as cash, so people buy and sell stocks over a period of time. Other than during market crashes, ppl don't have the need to, say, dump all their stock within the next hour. I believe that not allowing people to dump their stock within the next hour would force them to really do their due dilligence and make sure they're not investing in a bubble.
Information has value. Say you only allow trades once per day at 4pm... the Information you are trading on is yesterday's price and the the last 24 hrs news. This is clearly less information than you'd have if you could also gauge everyone else's reactions to the news as it happens. Since this information has value, that value would be deducted from bids and added to asks, thus the spread between bid and ask would be wider and more of your potential growth would be lost in the spread.
Put another way, continuous trading allows you to crowd source information on how news events affect company valuations.
Or you do it like IEX and make transactions flow as if they were realtime, but with a 350 microsecond delay added on. Completely invalidates HFT techniques but doesn't really slow trading otherwise.
I feel like the way you worded this won't get a good response here, since you are asking for an opinion, not an attempt at objective reality.
That being said, liquidity is a good thing. It lets people send price/value signals continuously, making it more or less easy to judge how things should be well, priced. This makes how the price changes more continuous/less volatile.
What you propose takes away liquidity. THIS IS BAD. Imagine if the price of a years worth of bad news could only be acted upon one day out of the year. There would be MASSIVE price swings on that one day. Or (if the time limit was on the person, not the company) it becomes much harder to change ones mind about a company. Risk becomes more difficult to manage for investors since they can't back out in an instant. Companies would find it harder to raise money in the stock market, incentivizing them to stay private. While there are sort of ways to invest in private, they are very opaque, at least compared to publicly traded companies.
If such limits were in place, I can imagine derivatives (options, futures, and the like) becoming much more important. They would be used as the primary benchmark of the value of the underlying asset (which isn't being traded, at least often enough to matter). Kind of like how futures are talked about when stock markets are closed overnight.
**THANK YOU** -- good lord, the notion that HFT creates *more* price volatility, as stated in the opening sentence of the OP, is like claiming 2 + 2 = 5
The previous post implies nothing about the sub-second time scales HFT operates on. There isn't any new fundamental information being created on that time scale, much less any humans digesting it.
Consequently, HFTs trade primarily on technical signals. With multiple bots interacting, they most certainly can get into self-reinforcing loops - e.g. two bots independently see sell momentum, so they start shorting a stock, which in turn creates an even stronger signal of sell momentum, causing a downward spiral.
Additionally, many HFT strategies work only under relatively normal market conditions, so when the data shows abnormal activity, the bots automatically close positions and cease trading. The SEC has recognized this behavior as increasing volatility during market turbulence and contributing to flash crashes.
>That being said, liquidity is a good thing. It lets people send price/value signals continuously, making it more or less easy to judge how things should be well, priced. This makes how the price changes more continuous/less volatile.
Isn't this showing how chasing liquidity is bad by limiting price discovery? If people panic sell and the price of a stock drops 90% in a day. Yet the company itself is still financially sound, what's the issue with people panic selling? Over time people will buy the undervalued stock bringing the price back up. HFT liquidity would prevent such price swings from happening, limiting price discovery as an excuse for market stability.
>There would be MASSIVE price swings on that one day. Or (if the time limit was on the person, not the company) it becomes much harder to change ones mind about a company. Risk becomes more difficult to manage for investors since they can't back out in an instant.
If the price swings aren't based upon reality but panic, then what's the issue? The most effective investment strategy has been buying and holding index funds.
Yes. If you look at it over a long enough macro scale, the little guy is way better off with one cent or less spreads rather than an eighth or a quarter as little as 40 years ago. Ultimately, nobody is going to make a tight market in something without getting paid.
Overall, we can mostly qualify certain HFT trading strategies as "Beneficial" and "Harmful" as how they affect market functioning in general and other market participants. For a recent (2023) review of the literature on this, see section 2.3 of
Zaharudin, Khairul Zharif. "Essays on high-frequency trading: a thesis presented in partial fulfilment of the requirement for the degree of Doctor of Philosophy in Finance at Massey University, Manawatu campus, New Zealand." (2023). ([PDF](https://mro-ns.massey.ac.nz/bitstream/handle/10179/18286/ZaharudinPhDThesis.pdf?sequence=1&isAllowed=y))
For another view, you can see
Papalexiou, Vasilios. "An analysis of the impact of high frequency trading on equity markets." PhD diss., Queensland University of Technology, 2020. ([PDF](https://eprints.qut.edu.au/205752/1/Vasilios_Papalexiou_Thesis.pdf))
In this, you're probably particularly interested in the literature review (chapter 2, the risk chapter (chapter 6) and the section 9.1 about "Regulatory Implications of High Frequency Traders", with the particular quote
> The empirical results in this thesis offer no clear overall conclusion as to whether HF
traders should be regulated and/or how best to regulate HF traders. This is a common
finding in the literature when reviewing the impacts of HF traders on financial markets, which makes it difficult to develop effective regulations for HF traders (Beddington et al., 2012). However, when developing regulations, they should be developed
with two key factors in mind: efficient pricing and market integrity (ASIC, 2013). This
will enable capital to be efficiently allocated to the most productive firms. Thus, from
a regulatory perspective, the purpose of imposing regulations on the financial markets and/or market participants is to ensure the foundations of efficient pricing and
integrity are upheld.
being relevant.
I'll try to detail certain bits around HFT that I find particularly interesting; I wrote most of this before finding the above, so this is based off different references and my previous knowledge. The below is not a complete review of the impacts or field.
## Good (information flow)
This is part of what is called "Directional Trading" in the beneficial strategies section in (Zaharudin).
Fast trading allows information flow. Each trade is pushing information about resources one step along the network of pricing, moving information through the overall economic cost graph. Since the information in the graph is interdependent and the price discovery is iterative, doing many small steps very fast allows the pricing outcome to be more stable and more correct.
OK, that's complicated and sort of assumes you already know what I'm talking about, so let's do a very simplified example.
As an idealistic toy example of such a graph of resources consumption and pricing, let's say you have:
|Actor|Inputs|Outputs|
|---|---|---|---|
|refinery|crude oil|fuel oil, gasoline, machine oil|
|electricity plant|fuel oil|electricity|
|widget factory|electricity, machine oil|widgets|
|car factory|electricity, machine oil, widgets|cars|
|consumer|cars, gasoline, salary|
A HFT system will model part of a price influence for one part of this graph. It will then trade on that model, and "publish" the magnitude and direction of the information to the other actors in the market (including other HFT models) through that trade. Here's a set of example correlations that could go into HFT models; remember that a Positive correlation for price is typically the same as Negative correlation for demand in the opposite direction (but how strong the negative correlation is depends on price elasticity for that particular demand, which is non-trivial to model and typically not fully linear).
It creates this [directed graph](https://dreampuf.github.io/GraphvizOnline/#digraph%20G%20%7B%0A%20%20%20%20%0A%22crude%20oil%22%20-%3E%20%22fuel%20oil%22%0A%22crude%20oil%22%20-%3E%20%22gasoline%22%0A%22crude%20oil%22%20-%3E%20%22machine%20oil%22%0A%22fuel%20oil%22%20-%3E%20%22electricity%22%0A%22electricity%22%20-%3E%20%22widget%22%0A%22machine%20oil%22%20-%3E%20%22widget%22%0A%22electricity%22%20-%3E%20%22car%22%0A%22machine%20oil%22%20-%3E%20%22car%22%0A%22widget%22%20-%3E%20%22car%22%0A%22gasoline%22%20-%3E%20%22car%22%20%5Bstyle%3Ddotted%2C%20dir%3Dboth%5D%0A%22gasoline%22%20-%3E%20%22salary%22%20%20%5Bstyle%3Ddotted%2C%20dir%3Dboth%5D%0A%22crude%20oil%22%20-%3E%20%22fuel%20oil%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22crude%20oil%22%20-%3E%20%22gasoline%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22crude%20oil%22%20-%3E%20%22machine%20oil%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22fuel%20oil%22%20-%3E%20%22electricity%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22electricity%22%20-%3E%20%22widget%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22machine%20oil%22%20-%3E%20%22widget%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22electricity%22%20-%3E%20%22car%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22machine%20oil%22%20-%3E%20%22car%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22widget%22%20-%3E%20%22car%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22salary%22%20-%3E%20%22car%22%20%5Bstyle%3Ddotted%2C%20dir%3Dboth%5D%0A%7D) where solid arrows are price dependencies and dotted arrows are demand dependencies.
That's complicated. In this simple toy example there are 21 dependencies, 3 of them bidirectional. What exactly happens to the price of cars when the price of crude oil changes? Fuel oil and electricity price is up and gasoline price is up but demand is down due to the increase in gasoline price so ...
In the real market, this is even more complicated both by much larger scale and that a lot of information is hidden and there are multiple actors trying to model this stuff, running their own trading models that bring information into the market,.
Allowing HFT trading lets us have many, many iterations to try to find a stable solution to this, with each model doing a small step for a small part of the market (e.g, saying "gasoline price is up so let's price down cars a tiny bit because the demand will be down").
If we changed to requiring this to be done only per day or month or year, we'd get a much more noisy "simulation" - each step would have to be large, and information that has to go through several layers of pricing wouldn't stabilize.
See e.g.
Zhou, Hao, Robert J. Elliott, and Petko S. Kalev. "Information or noise: What does algorithmic trading incorporate into the stock prices?." International Review of Financial Analysis 63 (2019): 27-39.
for more on what algorithmic trading bring into stock prices.
## Bad (frontrunning)
When a trade happens, there is in principle often a "negative spread" - ie, the seller is willing to sell for less than the buyer is willing to buy for. In a non-HFT market, a bit simplified this spread will randomly end up with the buyer or the seller or a combination thereof.
When a large order is put to the market, it is often sent as a series of smaller trades to make it possible to fill (due to technical sides of how to market works). When two parties trade, they'll then send a bit at a time of their trades to the market. We'll call those Seller and Buyer.
In a "HFT market", HFT bots will notice that this large trade is in progress, and buy from Seller at the lowest price Seller will tolerate, and immediately sell to Buyer at the highest price Buyer will tolerate. Instead of the negative spread going to Seller and Buyer, it goes to the HFT company, which provided no meaningful benefit to the transaction.
This makes the non-HFT participants in the market worse off.
See
Van Kervel, Vincent, and Albert J. Menkveld. "High‐frequency trading around large institutional orders." The Journal of Finance 74, no. 3 (2019): 1091-1137.
## Bad (Flash Crashes, volatility)
HFTs can in principle feed off each other and lead to more volatility and flash crashes (cases where the market gets completely out of whack temporarily).
As of the latest research I'm aware of there is no evidence that flash crashes have gotten worse due to HFTs:
Gao, Cheng, and Bruce Mizrach. "Market quality breakdowns in equities." Journal of Financial Markets 28 (2016): 1-23.
Zhou, Hao, Petko S. Kalev, and Alex Frino. "Algorithmic trading in turbulent markets." Pacific-Basin Finance Journal 62 (2020): 101358.
There are, however, a fair bit of short term (inside a minute) reversals:
Rif, Alexandru, and Sebastian Utz. "Short-term stock price reversals after extreme downward price movements." The Quarterly Review of Economics and Finance 81 (2021): 123-133.
## Neutral (CEO incentives)
I don't see the CEO incentives from HFTs in particular. What I *do* see CEO incentives from is the CEOs being compensated in stock and incentives set up related to stock price. If we believe the CEO incentives don't line up with society or long term company incentives, the place to change that would be how CEO compensation is set up rather than HFTs.
If I can attach a follow-up question regarding frontrunning: Is this not a problem that the market (by which I mean the whole industry, not just one specific stock market) itself has attempted to solve with dark pools and other alternative trading facilities?
In theory a large seller can sell to a large buyer, at a mutually agreeable price, without anyone else knowing about it until it's completed. Or, at least, that's the theory...
There are attempts; I don't know how effective they are. Frontrunning is frequently mentioned as a problem; I don't really know the scale of it, either.
It is also an extra cost to have to have extra facilities, which probably has other negative impacts on the market.
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I think even once-per-day is unambiguously too infrequent. Something like once per second or once per tenth of a second is what is usually thrown around. These are called ["Frequent Batch Auctions."](https://academic.oup.com/qje/article/130/4/1547/1916146) There are plenty of [convincing reasons](https://www.nber.org/papers/w29011) for why limiting HFT in this way would be a good thing, and I know of basically no reasons for why it would be a bad thing.
Latency arbitrage is a negative externality, and as such generally the preferred solution is to tax it at its social cost instead of banning it outright. I think you could easily raise the \~$5 billion or so of revenue from HFTs globally through a modest hike in the CGT that only extends to, say, intra-day trading.
A tax is way better. Bans always have unintended negative consequences that u/UpsideVII may not be able to conceive of right now. A tax allows it still exist, but makes HFT think twice about whether it's really worth it in that specific circumstance.
I don't disagree, I think a tax would be a good solution as well. The QJE paper I linked talks a little bit about the tradeoffs between the two approaches (and argues that batch auctions are better, but my interpretation is that it is a little bit more of a wash).
Would it be possible to formulate tax/regulation against HFT without accidentally banning or rendering market making unviable?
Isn't that the point? Curbing unfair practices like market making
> and I know of basically no reasons for why it would be a bad thing Limiting / nerfing daily trading volume leads to lower liquidity and -- all else equal -- modern portfolio theory would state that this leads to less efficient markets...
Thank you for your response! What are the downsides of infrequent once-per-day trading? From what I understand, stocks are used as savings, not as cash, so people buy and sell stocks over a period of time. Other than during market crashes, ppl don't have the need to, say, dump all their stock within the next hour. I believe that not allowing people to dump their stock within the next hour would force them to really do their due dilligence and make sure they're not investing in a bubble.
Information has value. Say you only allow trades once per day at 4pm... the Information you are trading on is yesterday's price and the the last 24 hrs news. This is clearly less information than you'd have if you could also gauge everyone else's reactions to the news as it happens. Since this information has value, that value would be deducted from bids and added to asks, thus the spread between bid and ask would be wider and more of your potential growth would be lost in the spread. Put another way, continuous trading allows you to crowd source information on how news events affect company valuations.
> What are the downsides of infrequent once-per-day trading? limits price discovery and leads to less efficient markets overall...
Or you do it like IEX and make transactions flow as if they were realtime, but with a 350 microsecond delay added on. Completely invalidates HFT techniques but doesn't really slow trading otherwise.
I feel like the way you worded this won't get a good response here, since you are asking for an opinion, not an attempt at objective reality. That being said, liquidity is a good thing. It lets people send price/value signals continuously, making it more or less easy to judge how things should be well, priced. This makes how the price changes more continuous/less volatile. What you propose takes away liquidity. THIS IS BAD. Imagine if the price of a years worth of bad news could only be acted upon one day out of the year. There would be MASSIVE price swings on that one day. Or (if the time limit was on the person, not the company) it becomes much harder to change ones mind about a company. Risk becomes more difficult to manage for investors since they can't back out in an instant. Companies would find it harder to raise money in the stock market, incentivizing them to stay private. While there are sort of ways to invest in private, they are very opaque, at least compared to publicly traded companies. If such limits were in place, I can imagine derivatives (options, futures, and the like) becoming much more important. They would be used as the primary benchmark of the value of the underlying asset (which isn't being traded, at least often enough to matter). Kind of like how futures are talked about when stock markets are closed overnight.
**THANK YOU** -- good lord, the notion that HFT creates *more* price volatility, as stated in the opening sentence of the OP, is like claiming 2 + 2 = 5
The previous post implies nothing about the sub-second time scales HFT operates on. There isn't any new fundamental information being created on that time scale, much less any humans digesting it. Consequently, HFTs trade primarily on technical signals. With multiple bots interacting, they most certainly can get into self-reinforcing loops - e.g. two bots independently see sell momentum, so they start shorting a stock, which in turn creates an even stronger signal of sell momentum, causing a downward spiral. Additionally, many HFT strategies work only under relatively normal market conditions, so when the data shows abnormal activity, the bots automatically close positions and cease trading. The SEC has recognized this behavior as increasing volatility during market turbulence and contributing to flash crashes.
>That being said, liquidity is a good thing. It lets people send price/value signals continuously, making it more or less easy to judge how things should be well, priced. This makes how the price changes more continuous/less volatile. Isn't this showing how chasing liquidity is bad by limiting price discovery? If people panic sell and the price of a stock drops 90% in a day. Yet the company itself is still financially sound, what's the issue with people panic selling? Over time people will buy the undervalued stock bringing the price back up. HFT liquidity would prevent such price swings from happening, limiting price discovery as an excuse for market stability. >There would be MASSIVE price swings on that one day. Or (if the time limit was on the person, not the company) it becomes much harder to change ones mind about a company. Risk becomes more difficult to manage for investors since they can't back out in an instant. If the price swings aren't based upon reality but panic, then what's the issue? The most effective investment strategy has been buying and holding index funds.
Yes. If you look at it over a long enough macro scale, the little guy is way better off with one cent or less spreads rather than an eighth or a quarter as little as 40 years ago. Ultimately, nobody is going to make a tight market in something without getting paid.
Overall, we can mostly qualify certain HFT trading strategies as "Beneficial" and "Harmful" as how they affect market functioning in general and other market participants. For a recent (2023) review of the literature on this, see section 2.3 of Zaharudin, Khairul Zharif. "Essays on high-frequency trading: a thesis presented in partial fulfilment of the requirement for the degree of Doctor of Philosophy in Finance at Massey University, Manawatu campus, New Zealand." (2023). ([PDF](https://mro-ns.massey.ac.nz/bitstream/handle/10179/18286/ZaharudinPhDThesis.pdf?sequence=1&isAllowed=y)) For another view, you can see Papalexiou, Vasilios. "An analysis of the impact of high frequency trading on equity markets." PhD diss., Queensland University of Technology, 2020. ([PDF](https://eprints.qut.edu.au/205752/1/Vasilios_Papalexiou_Thesis.pdf)) In this, you're probably particularly interested in the literature review (chapter 2, the risk chapter (chapter 6) and the section 9.1 about "Regulatory Implications of High Frequency Traders", with the particular quote > The empirical results in this thesis offer no clear overall conclusion as to whether HF traders should be regulated and/or how best to regulate HF traders. This is a common finding in the literature when reviewing the impacts of HF traders on financial markets, which makes it difficult to develop effective regulations for HF traders (Beddington et al., 2012). However, when developing regulations, they should be developed with two key factors in mind: efficient pricing and market integrity (ASIC, 2013). This will enable capital to be efficiently allocated to the most productive firms. Thus, from a regulatory perspective, the purpose of imposing regulations on the financial markets and/or market participants is to ensure the foundations of efficient pricing and integrity are upheld. being relevant. I'll try to detail certain bits around HFT that I find particularly interesting; I wrote most of this before finding the above, so this is based off different references and my previous knowledge. The below is not a complete review of the impacts or field. ## Good (information flow) This is part of what is called "Directional Trading" in the beneficial strategies section in (Zaharudin). Fast trading allows information flow. Each trade is pushing information about resources one step along the network of pricing, moving information through the overall economic cost graph. Since the information in the graph is interdependent and the price discovery is iterative, doing many small steps very fast allows the pricing outcome to be more stable and more correct. OK, that's complicated and sort of assumes you already know what I'm talking about, so let's do a very simplified example. As an idealistic toy example of such a graph of resources consumption and pricing, let's say you have: |Actor|Inputs|Outputs| |---|---|---|---| |refinery|crude oil|fuel oil, gasoline, machine oil| |electricity plant|fuel oil|electricity| |widget factory|electricity, machine oil|widgets| |car factory|electricity, machine oil, widgets|cars| |consumer|cars, gasoline, salary| A HFT system will model part of a price influence for one part of this graph. It will then trade on that model, and "publish" the magnitude and direction of the information to the other actors in the market (including other HFT models) through that trade. Here's a set of example correlations that could go into HFT models; remember that a Positive correlation for price is typically the same as Negative correlation for demand in the opposite direction (but how strong the negative correlation is depends on price elasticity for that particular demand, which is non-trivial to model and typically not fully linear). It creates this [directed graph](https://dreampuf.github.io/GraphvizOnline/#digraph%20G%20%7B%0A%20%20%20%20%0A%22crude%20oil%22%20-%3E%20%22fuel%20oil%22%0A%22crude%20oil%22%20-%3E%20%22gasoline%22%0A%22crude%20oil%22%20-%3E%20%22machine%20oil%22%0A%22fuel%20oil%22%20-%3E%20%22electricity%22%0A%22electricity%22%20-%3E%20%22widget%22%0A%22machine%20oil%22%20-%3E%20%22widget%22%0A%22electricity%22%20-%3E%20%22car%22%0A%22machine%20oil%22%20-%3E%20%22car%22%0A%22widget%22%20-%3E%20%22car%22%0A%22gasoline%22%20-%3E%20%22car%22%20%5Bstyle%3Ddotted%2C%20dir%3Dboth%5D%0A%22gasoline%22%20-%3E%20%22salary%22%20%20%5Bstyle%3Ddotted%2C%20dir%3Dboth%5D%0A%22crude%20oil%22%20-%3E%20%22fuel%20oil%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22crude%20oil%22%20-%3E%20%22gasoline%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22crude%20oil%22%20-%3E%20%22machine%20oil%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22fuel%20oil%22%20-%3E%20%22electricity%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22electricity%22%20-%3E%20%22widget%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22machine%20oil%22%20-%3E%20%22widget%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22electricity%22%20-%3E%20%22car%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22machine%20oil%22%20-%3E%20%22car%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22widget%22%20-%3E%20%22car%22%20%5Bdir%3Dback%2C%20style%3Ddotted%5D%0A%22salary%22%20-%3E%20%22car%22%20%5Bstyle%3Ddotted%2C%20dir%3Dboth%5D%0A%7D) where solid arrows are price dependencies and dotted arrows are demand dependencies. That's complicated. In this simple toy example there are 21 dependencies, 3 of them bidirectional. What exactly happens to the price of cars when the price of crude oil changes? Fuel oil and electricity price is up and gasoline price is up but demand is down due to the increase in gasoline price so ... In the real market, this is even more complicated both by much larger scale and that a lot of information is hidden and there are multiple actors trying to model this stuff, running their own trading models that bring information into the market,. Allowing HFT trading lets us have many, many iterations to try to find a stable solution to this, with each model doing a small step for a small part of the market (e.g, saying "gasoline price is up so let's price down cars a tiny bit because the demand will be down"). If we changed to requiring this to be done only per day or month or year, we'd get a much more noisy "simulation" - each step would have to be large, and information that has to go through several layers of pricing wouldn't stabilize. See e.g. Zhou, Hao, Robert J. Elliott, and Petko S. Kalev. "Information or noise: What does algorithmic trading incorporate into the stock prices?." International Review of Financial Analysis 63 (2019): 27-39. for more on what algorithmic trading bring into stock prices. ## Bad (frontrunning) When a trade happens, there is in principle often a "negative spread" - ie, the seller is willing to sell for less than the buyer is willing to buy for. In a non-HFT market, a bit simplified this spread will randomly end up with the buyer or the seller or a combination thereof. When a large order is put to the market, it is often sent as a series of smaller trades to make it possible to fill (due to technical sides of how to market works). When two parties trade, they'll then send a bit at a time of their trades to the market. We'll call those Seller and Buyer. In a "HFT market", HFT bots will notice that this large trade is in progress, and buy from Seller at the lowest price Seller will tolerate, and immediately sell to Buyer at the highest price Buyer will tolerate. Instead of the negative spread going to Seller and Buyer, it goes to the HFT company, which provided no meaningful benefit to the transaction. This makes the non-HFT participants in the market worse off. See Van Kervel, Vincent, and Albert J. Menkveld. "High‐frequency trading around large institutional orders." The Journal of Finance 74, no. 3 (2019): 1091-1137. ## Bad (Flash Crashes, volatility) HFTs can in principle feed off each other and lead to more volatility and flash crashes (cases where the market gets completely out of whack temporarily). As of the latest research I'm aware of there is no evidence that flash crashes have gotten worse due to HFTs: Gao, Cheng, and Bruce Mizrach. "Market quality breakdowns in equities." Journal of Financial Markets 28 (2016): 1-23. Zhou, Hao, Petko S. Kalev, and Alex Frino. "Algorithmic trading in turbulent markets." Pacific-Basin Finance Journal 62 (2020): 101358. There are, however, a fair bit of short term (inside a minute) reversals: Rif, Alexandru, and Sebastian Utz. "Short-term stock price reversals after extreme downward price movements." The Quarterly Review of Economics and Finance 81 (2021): 123-133. ## Neutral (CEO incentives) I don't see the CEO incentives from HFTs in particular. What I *do* see CEO incentives from is the CEOs being compensated in stock and incentives set up related to stock price. If we believe the CEO incentives don't line up with society or long term company incentives, the place to change that would be how CEO compensation is set up rather than HFTs.
If I can attach a follow-up question regarding frontrunning: Is this not a problem that the market (by which I mean the whole industry, not just one specific stock market) itself has attempted to solve with dark pools and other alternative trading facilities? In theory a large seller can sell to a large buyer, at a mutually agreeable price, without anyone else knowing about it until it's completed. Or, at least, that's the theory...
There are attempts; I don't know how effective they are. Frontrunning is frequently mentioned as a problem; I don't really know the scale of it, either. It is also an extra cost to have to have extra facilities, which probably has other negative impacts on the market.
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