I am a young Statistics graduate. A few days ago as an interview question, I have been asked to evaluate the predictive power of a Signal time series (supposedly output by an Artificial Intelligence system) in predicting a stock price time series and I was provided with a sample data set as follows:
Date | Open | High | Low | Close | Adj Close | Signal |
---|---|---|---|---|---|---|
202106-01 | 627.80 | 633.80 | 620.55 | 620.55 | 623.90 | 85.11 |
2021-06-02 | 620.13 | 623.36 | 599.14 | 620.13 | 605.12 | 76.59 |
2021-06-03 | 601.80 | 604.55 | 571.22 | 571.22 | 572.84 | 68.73 |
2021-06-04 | 579.71 | 600.61 | 577.20 | 600.61 | 599.05 | 78.47 |
2021-06-07 | 591.83 | 610.00 | 582.88 | 610.00 | 605.13 | 78.63 |
and so on for some few weeks. No further information have been provided.
I then pointed out that without knowing the analytical formula of the function employed to predict the stock price from the Signal value
Pred(Signal) = Stock Price Prediction
it was not possible (at least for what I know) to calculate any prediction error and thus to evaluate the prediction power of Signal.
The reply from the interviewer was that the problem is solvable as it is.
FIRST Question: how could one evaluate the predictive power of Signal given only the data above?
As a further question from the interviewer I was asked to "review the quality of the data, list any potential errors, and propose corrected values. Please list each quality check error and correction applied."
Really I had no idea how to proceed in answering this Second Question. SECOND Question Anyone has any pointers on any possible data quality procedure that I could have applied?
Thank you for your kind suggestions.