The Stock Market and the Business Cycle in Periods of Deflation, (Hyper-) Inflation, and Political Turmoil: Germany, 1913–1926
Bibliographic record
Abstract
INTRODUCTION With the onset of a deflation in stock prices beginning in early 2000, economists and policy makers have begun to worry that this development might eventually spill over into the goods market, possibly leading to a recession, if not outright depression, and that it is symptomatic of a “bad” deflation (see Bordo and Redish in this volume). The fact that political turmoil, and its attendant uncertainties, is also a feature of current events, stemming from the terrorist attacks of September 11, 2001, the subsequent wars in Afghanistan and Iraq, and a general economic malaise in Europe and the United States just to name a few events, only adds to the fears that deflation ought to be avoided at all costs. Implicit in such views is that financial markets in particular, and economies more generally, operate differently in a deflationary environment than in conditions of inflation. Although it is too early to tell how ongoing developments in asset prices will unfold, it may be useful to examine a period in economic history that has all of these elements to try and learn how widely accepted views about the determinants of asset prices, and their potential links to the real economy, fare under conditions of deflation, inflation, or even hyper-inflation. More precisely, the aim of this chapter is to provide empirical evidence on the long run validity of the present value model of asset price determination and the characteristics of the short run dynamics away from the long run equilibrium. We also investigate the long run and short run behavior of the link between stock prices and the business cycle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".