Sunday, October 22, 2017

Pockets of Predictability

The possibility of localized "pockets of predictability", particularly in financial markets, is obviously intriguing.  Recently I'm noticing a similarly-intriguing pocket of research on pockets of predictability.  

The following paper, for example, was presented at 2017 the NBER-NSF Time Series conference at  Northwestern University, even if it is evidently not yet circulating:
"Pockets of Predictability", by Leland Farmer (UCSD), Lawrence Schmidt (Chicago), and Allan Timmermann (UCSD).  Abstract:  We show that return predictability in the U.S. stock market is a localized phenomenon, in which short periods, “pockets,” with significant predictability are interspersed with long periods with little or no evidence of return predictability. We explore possible explanations of this finding, including time-varying risk premia, and find that they are inconsistent with a general class of affine asset pricing models which allow for stochastic volatility and compound Poisson jumps. We find that pockets of return predictability can, however, be explained by a model of incomplete learning in which the underlying cash flow process is subject to change and investors update their priors about the current state. Simulations from the model demonstrate that investors’ learning about the underlying cash flow process can induce patterns that look, ex-post, like local return predictability, even in a model in which ex-ante expected returns are constant.

And this one just appeared as an NBER w.p.: "Sparse Signals in the Cross-Section of Returns", by Alexander M. Chinco, Adam D. Clark-Joseph, Mao Ye, NBER w.p. 23933, October 2017.
http://papers.nber.org/papers/w23933?utm_campaign=ntw&utm_medium=email&utm_source=ntw
Abstract: This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling 1-minute-ahead return forecasts using the entire cross section of lagged returns as candidate predictors. The LASSO increases both out-of-sample fit and forecast-implied Sharpe ratios. And, this out-of-sample success comes from identifying predictors that are  unexpected, short-lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.

Here's some associated work in dynamical systems theory:  "A Mechanism for Pockets of Predictability in Complex Adaptive Systems", by Jorgen Vitting Andersen, Didier Sornette, Europhysics Letters, 2005.  https://arxiv.org/abs/cond-mat/0410762
 Abstract:  We document a mechanism operating in complex adaptive systems leading to dynamical pockets of predictability ("prediction days''), in which agents collectively take predetermined courses of action, transiently decoupled from past history. We demonstrate and test it out-of-sample on synthetic minority and majority games as well as on real financial time series. The surprising large frequency of these prediction days implies a collective organization of agents and of their strategies which condense into transitional herding regimes.

There's even an ETH Zürich master's thesis:  "In Search Of Pockets Of Predictability", by AT Morera, ‎2008
https://www.ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/Master_Thesis_Alan_Taxonera_Sept08.pdf

Finally, related ideas have appeared recently in the forecast evaluation literature, such as this paper and many of the references therein:  "Testing for State-Dependent Predictive Ability", by Sebastian Fossati, University of Alberta, September 2017.
 https://sites.ualberta.ca/~econwps/2017/wp2017-09.pdf
Abstract: This paper proposes a new test for comparing the out-of-sample forecasting performance of two competing models for situations in which the predictive content may be state-dependent (for example, expansion and recession states or low and high volatility states). To apply this test the econometrician is not required to observe when the underlying states shift. The test is simple to implement and accommodates several different cases of interest. An out-of-sample forecasting exercise for US output growth using real-time data illustrates the improvement of this test over previous approaches to perform forecast comparison.