Equity Market or Bond Market—Which Matters the Most for Investment? Revisiting Tobin’s q Theory of Investment
Bibliographic record
Abstract
Recent experience seems to have shown that credit markets are more important than equity markets for investment and macrodynamics. This paper examines the effect of Tobin’s equity q and bond q on investment. More specifically we study the role of Tobin’s equity (usual) q, average q and bond q for aggregate investment over the period 1953: Q4-2011: Q1. Employing bond q and equity q, or alternatively bond q and average q, shows that these variables are very relevant in explaining investment. Yet, the time scale matters too. Examining the relationship of these variables over a long time scale, at low frequencies, we can show that the combination of bond q and average q are the most significant determinants of aggregate investment. Moreover, for the longer time scale the two variables, bond q and average q, result in the highest goodness of fit demonstrating good in-sample forecasting properties. As to the individual determinants of aggregate investment over the period 1953: Q4-2011: Q1, bond q is by far the most influential variable at all frequencies since it always has the highest correlation with investment and this correlation is always statistically significant. Similarly, the greater significance of average q, as compared to equity q, is probably an outcome of the financing instruments for investment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".