As algorithmic trading (AT) has become a dominant component in financial markets, it is important to understand its benefits and costs. We argue that AT enhances managers’ bad news hoarding by reducing monitoring from fundamental investors and exacerbating price reactions to bad news, thereby increasing stock price crash risk. Consistent with our prediction, we find that AT is positively related to future stock price crash risk. We also find that AT is associated with managers’ opportunistic financial reporting and disclosure decisions and that the effect of AT on crash risk is more pronounced when managers have greater ability or incentives to withhold bad news. We further validate our findings using an instrumental variable analysis and the SEC’s 2016 Tick Size pilot program as a quasi-natural experiment. Overall, our results suggest AT is positively related to firm-specific stock price crash risk, which could potentially have devastating effects on shareholder welfare.
AbdiF.RanaldoA. (2017). A simple estimation of bid-ask spreads from daily close, high, and low prices. The Review of Financial Studies, 30(12), 4437–4480. https://doi.org/10.1093/rfs/hhx084
2.
AhmedA. S.LiY.XuN. (2020). Tick size and financial reporting quality in small‐cap firms: Evidence from a natural experiment. Journal of Accounting Research, 58(4), 869–914. https://doi.org/10.1111/1475-679x.12331
BaginskiS. P.CampbellJ. L.HinsonL. A.KooD. S. (2018). Do career concerns affect the delay of bad news disclosure?The Accounting Review, 93(2), 61–95. https://doi.org/10.2308/accr-51848
5.
BaldaufM.MollnerJ. (2020). High‐frequency trading and market performance. The Journal of Finance, 75(3), 1495–1526. https://doi.org/10.1111/jofi.12882
6.
BarbopoulosL. G.DaiR.PutniņšT. J.SaundersA. (2021). Market efficiency in the age of machine learning. Available at SSRN 3783221.
7.
BauerA. M.FangX.PittmanJ. A. (2021). The importance of IRS enforcement to stock price crash risk: The role of CEO power and incentives. The Accounting Review, 96(4), 81–109. https://doi.org/10.2308/tar-2018-0375
8.
BhargavaR.FairclothS.ZengH. (2017). Takeover protection and stock price crash risk: Evidence from state antitakeover laws. Journal of Business Research, 70, 177–184. https://doi.org/10.1016/j.jbusres.2016.08.021
9.
BhattacharyaN.ChakrabartyB.WangX. F. (2020). High-frequency traders and price informativeness during earnings announcements. Review of Accounting Studies, 25(3), 1–44. https://doi.org/10.1007/s11142-020-09550-z
10.
BlankespoorE.deHaanE.MarinovicI. (2020). Disclosure processing costs, investors’ information choice, and equity market outcomes: A review. Journal of Accounting and Economics, 70(2-3), Article 101344. https://doi.org/10.1016/j.jacceco.2020.101344
11.
BoehmerE.FongK.WuJ. J. (2020). Algorithmic trading and market quality: International evidence. Journal of Financial and Quantitative Analysis, 56(8), 1–30. https://doi.org/10.1017/s0022109020000782
12.
BreckenfelderJ. (2020). How does competition among high-frequency traders affect market liquidity?Research Bulletin, 78.
13.
BrogaardJ. (2011). High frequency trading and volatility. SSRN eLibrary.
14.
BrogaardJ.HendershottT.RiordanR. (2014). High-frequency trading and price discovery. Review of Financial Studies, 27(8), 2267–2306. https://doi.org/10.1093/rfs/hhu032
15.
CadmanB. D.CampbellJ. L.KlasaS. (2016). Are ex ante CEO severance pay contracts consistent with efficient contracting?Journal of Financial and Quantitative Analysis, 51(3), 737–769. https://doi.org/10.1017/s0022109016000375
16.
CallenJ. L.FangX. (2015a). Religion and stock price crash risk. Journal of Financial and Quantitative Analysis, 50(1-2), 169–195. https://doi.org/10.1017/s0022109015000046
ChaboudA. P.ChiquoineB.HjalmarssonE.VegaC. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045–2084. https://doi.org/10.1111/jofi.12186
20.
ChangX.ChenY.ZolotoyL. (2017). Stock liquidity and stock price crash risk. Journal of Financial and Quantitative Analysis, 52(4), 1605–1637. https://doi.org/10.1017/s0022109017000473
21.
ChenC.KimJ.-B.YaoL. (2017). Earnings smoothing: Does it exacerbate or constrain stock price crash risk?Journal of Corporate Finance, 42(1), 36–54. https://doi.org/10.1016/j.jcorpfin.2016.11.004
22.
ChenJ.HongH.SteinJ. C. (2001). Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics, 61(3), 345–381. https://doi.org/10.1016/s0304-405x(01)00066-6
23.
ChordiaT.GoyalA.LehmannB. N.SaarG. (2013). High-frequency trading. Johnson School Research Paper Series(20–2013).
24.
ChordiaT.MiaoB. (2020). Market efficiency in real time: Evidence from low latency activity around earnings announcements. Journal of Accounting and Economics, 70(2-3), Article 101335. https://doi.org/10.1016/j.jacceco.2020.101335
25.
ChungK. H.LeeA. J.RöschD. (2019). Tick size, liquidity for small and large orders, and price informativeness: Evidence from the Tick Size Pilot Program. Liquidity for Small and Large Orders, and Price Informativeness: Evidence from the Tick Size Pilot Program (March 7, 2019).
26.
ConradJ.WahalS.XiangJ. (2015). High-frequency quoting, trading, and the efficiency of prices. Journal of Financial Economics, 116(2), 271–291. https://doi.org/10.1016/j.jfineco.2015.02.008
27.
deHaanE.ShevlinT.ThornockJ. (2015). Market (in)attention and the strategic scheduling and timing of earnings announcements. Journal of Accounting and Economics, 60(1), 36–55. https://doi.org/10.1016/j.jacceco.2015.03.003
28.
DengX.GaoL.KimJ.-B. (2020). Short-sale constraints and stock price crash risk: Causal evidence from a natural experiment. Journal of Corporate Finance, 60(1), Article 101498. https://doi.org/10.1016/j.jcorpfin.2019.101498
DonelsonD. C.McInnisJ.MergenthalerR. D. (2016). The effect of governance reforms on financial reporting fraud. Journal of Law, Finance, and Accounting, 1(2), 235–274. https://doi.org/10.1561/108.00000005
HendershottT.RiordanR. (2013). Algorithmic trading and the market for liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001–1024. https://doi.org/10.1017/s0022109013000471
37.
HirscheyN. (2020). Do high-frequency traders anticipate buying and selling pressure?Management Science.
38.
HirshleiferD.LimS. S.TeohS. H. (2009). Driven to distraction: Extraneous events and underreaction to earnings news. The Journal of Finance, 64(5), 2289–2325. https://doi.org/10.1111/j.1540-6261.2009.01501.x
39.
HobergG.PhillipsG. (2016). Text-based network industries and endogenous product differentiation. Journal of Political Economy, 124(5), 1423–1465. https://doi.org/10.1086/688176
40.
HobergG.PhillipsG.PrabhalaN. (2014). Product market threats, payouts, and financial flexibility. The Journal of Finance, 69(1), 293–324. https://doi.org/10.1111/jofi.12050
41.
HsuC.WangR.WhippleB. C. (2022). Non-GAAP earnings and stock price crash risk. Journal of Accounting and Economics, 73(2-3), Article 101473. https://doi.org/10.1016/j.jacceco.2021.101473
LuJ. (2022). Limited attention: Implications for financial reporting. Journal of Accounting Research, 60(5), 1991–2027. https://doi.org/10.1111/1475-679x.12432
56.
Martins PereiraC. (2020). Unregulated algorithmic trading: Testing the boundaries of the European Union algorithmic trading regime. Journal of Financial Regulation, 6(2), 270–305. https://doi.org/10.1093/jfr/fjaa008
57.
NagarV. (1999). The role of the manager’s human capital in discretionary disclosure. Journal of Accounting Research, 37(1), 167–181. https://doi.org/10.2307/2491351
58.
O'haraM.YaoC.YeM. (2014). What's not there: Odd lots and market data. The Journal of Finance, 69(5), 2199–2236. https://doi.org/10.1111/jofi.12185
ShkilkoA.SokolovK. (2020). Every cloud has a silver lining: Fast trading, microwave connectivity, and trading costs. The Journal of Finance, 75(6), 2899–2927. https://doi.org/10.1111/jofi.12969
66.
ShorterG. W.MillerR. S. (2014). High-frequency trading: Background, concerns, and regulatory developments.
67.
StephanA. (2024). The effect of algorithmic trading on management guidance. In The Accounting Review (pp. 1–29). AAA.
WellerB. M. (2018). Does algorithmic trading reduce information acquisition?The Review of Financial Studies, 31(6), 2184–2226. https://doi.org/10.1093/rfs/hhx137
70.
XuY.XuanY.ZhengG. (2021). Internet searching and stock price crash risk: Evidence from a quasi-natural experiment. Journal of Financial Economics, 141(1), 255–275. https://doi.org/10.1016/j.jfineco.2021.03.003
71.
YanH. (2019). The real effects of algorithmic tradingPhD thesis. Duke University.
72.
YangL.ZhuH. (2020). Back-running: Seeking and hiding fundamental information in order flows. The Review of Financial Studies, 33(4), 1484–1533. https://doi.org/10.1093/rfs/hhz070
73.
ZhangF. (2010). High-frequency trading, stock volatility, and price discovery. Available at SSRN 1691679.
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