Predicting High-Frequency Industry Returns: Machine Learners Meet News Watchers

Jiang, Hao; Sz, Li; Yuan, Peixuan

doi:10.2139/ssrn.3700466

Cited by 1 publication

(1 citation statement)

References 42 publications

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“…For example, Chinco et al (2017) use 1-min iShares market ETF for market return, and iShares Russell 1000 and growth ETFs for size and value returns. Jiang et al (2020) use 30-min returns for 226 sectors and find predictive power in the first 30 min. Lachance (2021) finds order imbalances increase overnight returns in ETFs and are exploitable.…”

Section: Introductionmentioning

confidence: 99%

High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19

Gurrib

Kamalov

Alshareif

2022

IJEEP

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This study investigates intraday patterns in the eleven sectors of the United States (U.S.). Key contributions are (i) risk and return patterns at specific trading periods on the New York Stock Exchange (NYSE), (ii) whether a specific day return model can predict the next 15-minute positive return, and (iii) the impact of the first vaccination rollout in the U.S. on intraday Exchange-Traded-Funds (ETF) returns. Time-dependent regressions capture risk and return relationships, decision trees in machine learning compare return models, and impulse responses capture the effect of the 2019 coronavirus (COVID-19) vaccine rollout in U.S. 15-minute Standard & Poor’s Depository Receipts (SPDR) Select Sector ETF data is used over 12th March 2020-23rd February 2021. Findings support sector ETF returns fluctuate the most in the first and last 15 minutes. Average returns in the first 15 minutes are the highest, converging to near zero as the trading session continues. Overnight returns contribute the most to volatility. U-shaped patterns into both return and risk exist, especially on Mondays. Mondays and Fridays have the most significant positive returns 15 minutes after the open. Prediction scores using an all-return model were superior to any specific day return model. The first vaccination rollout has a positive effect only in energy, technology, and financial sector ETFs, however with a short-lasting effect on ETFs returns.

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Section: Introductionmentioning

confidence: 99%