Random walk and breaking trend in financial series: An econometric critique of unit root tests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The present note sheds light on several pitfalls associated with unit root tests that are overlooked by a growing volume of literature in financial economics. Specifically, several studies have confused unit root tests with the Random Walk hypothesis. Unit root tests are not designed for such a task since they aim at investigating whether a time series is difference‐stationary or trend‐stationary and are not, therefore, predictability tests. Secondly, we emphasize some serious shortcomings associated with the widely used unit root test developed by Zivot and Andrews [Zivot, E. & Andrews, D.W.K. (1992). Further evidence on the great crash, the oil‐price shock, and the unit‐root hypothesis. Journal of Business and Economic Statistics , 10, 251–270.]. In particular, we stress that results from the Zivot–Andrews test are sensitive to the methods employed to calculate the critical values and to select the maxim lag k . Furthermore, Zivot–Andrews test imposes a one time structural break in a time series; however recent studies showed that not counting for other true structural breaks may bias the results and may cause a spurious rejection of the unit root null hypothesis. Finally, we support our arguments by an empirical example based on the findings of Narayan and Smyth [Narayan, K.P. & Smyth, R. (2004). Is South Korea's stock market efficient? Applied Economics Letters , 11, 707–710.] with regards to the efficiency of South Korean stock market. We show that contrary to what the authors claim, the KSE (KOSPI) price index is predictable, and hence the South Korean stock market is not informationally efficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it