000198382 001__ 198382
000198382 005__ 20181203023500.0
000198382 0247_ $$2doi$$a10.1080/15427560.2014.877016
000198382 022__ $$a1542-7560
000198382 02470 $$2ISI$$a000332270500002
000198382 037__ $$aARTICLE
000198382 245__ $$aExcessive Volatility is Also a Feature of Individual Level Forecasts
000198382 260__ $$bRoutledge Journals, Taylor & Francis Ltd$$c2014$$aAbingdon
000198382 269__ $$a2014
000198382 300__ $$a14
000198382 336__ $$aJournal Articles
000198382 520__ $$aThe excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes. Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level. Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants' revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.
000198382 6531_ $$aLearning biases
000198382 6531_ $$aLeast-squares learning
000198382 6531_ $$aFinancial prediction
000198382 6531_ $$aReinforcement learning
000198382 6531_ $$aExcess volatility
000198382 700__ $$0244316$$g180075$$uEcole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland$$aNursimulu, Anjali
000198382 700__ $$uCALTECH, Pasadena, CA 91125 USA$$aBossaerts, Peter$$g181386$$0241931
000198382 773__ $$j15$$tJournal Of Behavioral Finance$$k1$$q16-29
000198382 909C0 $$xU11813$$0252272$$pSFI-PB
000198382 909CO $$pCDM$$particle$$ooai:infoscience.tind.io:198382
000198382 937__ $$aEPFL-ARTICLE-198382
000198382 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000198382 980__ $$aARTICLE