By Michael Lewis

The tsunami of inexpensive credits that rolled around the planet among 2002 and 2008 was once greater than an easy monetary phenomenon: it used to be temptation, delivering complete societies the opportunity to bare elements in their characters they can now not in general have the funds for to indulge.

Icelanders desired to cease fishing and develop into funding bankers. The Greeks desired to flip their kingdom right into a pinata filled with money and make allowance as many voters as attainable to take a whack at it. The Germans desired to be much more German; the Irish desired to cease being Irish.

Michael Lewis's research of bubbles past our seashores is so brilliantly, unfortunately hilarious that it leads the yank reader to a snug complacency: oh, these silly foreigners. but if he turns a cruel eye on California and Washington, DC, we see that the narrative is a catch baited with humor, and we comprehend the reckoning that awaits the best and greediest of debtor countries.

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Extra resources for Boomerang: Travels in the New Third World

Example text

Diverges from the consensus forecast. 18 I also 16 തതതതതതതത௜௧ିଵ ൌ ଵ σ௃௝ୀଵ ܴ‫ܥܥܣ‬௜௝௧ିଵ , instead of I use ܴ‫ܥܥܣ‬ ௃ ଵ ௃ σ௃௝ୀଵ ܴ‫ܥܥܣܮ‬௜௝௧ , as ܴ‫ܥܥܣܮ‬௜௝௧ can only be calculated for those firms analyst i has also covered in the previous period. As my investigation is on the analyst level, I തതതതതതതത௜௧ିଵ attempt to measure the average forecast accuracy of all firms analyst ݅ has covered in year ‫ݐ‬. ܴ‫ܥܥܣ‬ comprises all firms covered in ‫ ݐ‬െ ͳ. 17 തതതതതതതത௜௧ିଵ ൌ The average boldness is defined as mean boldness over all firms analyst ݅ covers in year ‫ݐ‬: ‫ܦܮܱܤ‬ ଵ ௃ σ௝ୀଵ ‫ܦܮܱܤ‬௜௝௧ିଵ Ǥ 18 ௃ Two kinds of approaches are commonly used in the literature to examine forecast boldness.

For my examination I use the following regression model based on OLS: ‫ܳܧܴܨܴܩ‬௜௧ ൌ ߛ଴ ൅ ߛଵ ̴ܲܰ‫ܨܹܧ‬௜௧ +ߛଶ ‫̴ܷܧܸܱܯ̴ܣ‬௜௧ ൅ߛଷ ‫ܦ̴ܧܸܱܯ̴ܣ‬௜௧ ൅ ߝ௜௧ . (12) I calculate ̴ܲܰ‫ܨܹܧ‬௜௧ as the percentage of firms in analyst ݅’s forecast portfolio that she covers for the first time in year ‫ ݐ‬and thereby measure how unfamiliar she is with her forecast portfolio. ‫̴ܷܧܸܱܯ̴ܣ‬௜௧ [‫ܦ̴ܧܸܱܯ̴ܣ‬௜௧ ] is a dummy variable set to 1 if analyst ݅ has changed to a brokerage house of higher [lower] prestige within the last two years before year ‫ݐ‬, and set to 0 otherwise.

4,779 med. 3217 med. 4630 effort level (terciles) mean number forecast acc. (ܴ‫)ܥܥܣ‬ of obs. 5315 AGE group 9 mean forecast acc. 4444 effort level (terciles) low effort (ܶͳ) med. 3522 med. 4845 Notes: First, I divide the data sample into deciles with respect to forecast age, measured via ܴ‫ܧܩܣ‬. Separately for each age decile, I partition the data sample into terciles according to general forecast effort (‫[ )ܳܧܴܨܴܩ‬firm-specific forecast effort (ܴ‫])ܳܧܴܨ‬, in Panel A [Panel B]. Observations with the lowest (highest) values in forecast effort are assigned to tercile ܶͳ ሺܶ͵ሻǤ For each tercile, I report the mean value of range-adjusted forecast accuracy ሺܴ‫ܥܥܣ‬ሻ.

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