CPRT used car auction service floats to top driving factor 2 days before US tariff on auto parts comes into effect
CPRT joins the top four driving factors for the three US stock indices since 2025-05-01 market close. The US tariff on new auto parts comes into effect 3 May.
Particularly, it was driving factors computed to be most influential for the prices of the three ETFs on the day in 6 months from the date of calculation.
Just search for “QQQ.US; SPY.US; DIA.US”, then add “&asof=2025-05-01” or “&asof=2025-05-02” at the end of web link to see result calculated for those days.
As of 2025-05-01 market close, CPRT becomes No 3 driving factor
https://tsterm.com/?q=QQQ.US%3BSPY.US%3BDIA.US&asof=2025-05-01
As of 2025-05-02 market close, CPRT becomes No2 driving factor for the three stock indices
https://tsterm.com/?q=QQQ.US%3BSPY.US%3BDIA.US&asof=2025-05-02
For training, price history of up to Nov 2024 has been used to predict the day in six months. The system performed a lot of experiments behind to check the prediction performance when one “candidate” driving factor is present or not. For more information, refer to the white paper. It is possible around the Election season, Trump’s policy pledge had begun to impact various stocks’ prices.
Causal predictor selection: we trained many models
To predict. —————————————-| beginning of May 2025
with history. ———————| beginning of Nov 2024
Notes
Stock index: “A financial index produces a numeric score based on inputs such as a variety of asset prices. It can be used to track the performance of a group of assets.” Investopedia article
Why in tsterm.com there is not directly stock indices such as Nasdaq 100, but just those ETF (Exchange-Traded Funds) trying to replicate the performance of those indices? Sometimes stock indices are expensive to source. But usually ETF prices are available from any data provider.
The engine behind tsterm.com is generic. It can equally be configured to predict for different horizons using data of different granularity: daily, hourly, etc. It’ll be a matter of affording the data and compute resources.
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