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Predicting risk from financial reports with regression
 In Proc. NAACL Human Language Technologies Conf
, 2009
"... We address a text regression problem: given a piece of text, predict a realworld continuous quantity associated with the text’s meaning. In this work, the text is an SECmandated financial report published annually by a publiclytraded company, and the quantity to be predicted is volatility of stock ..."
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Cited by 53 (11 self)
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of stock returns, an empirical measure of financial risk. We apply wellknown regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting
Recommended Citation Predicting Risk from Financial Reports with Regression
, 2009
"... We address a text regression problem: given a piece of text, predict a realworld continuous quantity associated with the text’s meaning. In this work, the text is an SECmandated financial report published annually by a publiclytraded company, and the quantity to be predicted is volatility of st ..."
Abstract
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of stock returns, an empirical measure of financial risk. We apply wellknown regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting
Predictive regressions
 Journal of Financial Economics
, 1999
"... When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's "nitesample properties, derived here, can depart substantially from the standard regression set ..."
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Cited by 452 (19 self)
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When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's "nitesample properties, derived here, can depart substantially from the standard regression
Financial Dependence and Growth
 American Economic Review
, 1998
"... This paper examines whether nancial development facilitates economic growth by scrutinizing one rationale for such a relationship; that nancial development reduces the costs of external nance to rms. Speci cally, we ask whether industrial sectors that are relatively more in need of external nance de ..."
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Cited by 1043 (29 self)
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to the highest value use without substantial risk of loss through moral hazard, adverse selection, or transactions costs { are an essential catalyst of economic growth. Empirical work seems consistent with this argument. For example, on the
Least angle regression
 Ann. Statist
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
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Cited by 1308 (43 self)
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to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
Noise Trader Risk in Financial Markets
 Jolurnial of Political Economy
, 1990
"... We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders ’ beliefs creates a risk in the price of the asset that deters rational ..."
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Cited by 858 (23 self)
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arbitrageurs from aggressively betting against them. As a result, prices can diverge significantly from fundamental values even in the absence of fundamental risk. Moreover, bearing a disproportionate amount of risk that they themselves create enables noise traders to earn a higher expected return than do
Quantile Regression
 JOURNAL OF ECONOMIC PERSPECTIVES—VOLUME 15, NUMBER 4—FALL 2001—PAGES 143–156
, 2001
"... We say that a student scores at the fifth quantile of a standardized exam if he performs better than the proportion � of the reference group of students and worse than the proportion (1–�). Thus, half of students perform better than the median student and half perform worse. Similarly, the quartiles ..."
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Cited by 937 (10 self)
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, the quartiles divide the population into four segments with equal proportions of the reference population in each segment. The quintiles divide the population into five parts; the deciles into ten parts. The quantiles, or percentiles, or occasionally fractiles, refer to the general case. Quantile regression
Financial Intermediation and Growth: Causality and Causes
 JOURNAL OF MONETARY ECONOMICS
, 2000
"... This paper evaluates (1) whether the exogenous component of financial intermediary development influences economic growth and (2) whether crosscountry differences in legal and accounting systems (e.g., creditor rights, contract enforcement, and accounting standards) explain differences in the level ..."
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Cited by 788 (71 self)
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This paper evaluates (1) whether the exogenous component of financial intermediary development influences economic growth and (2) whether crosscountry differences in legal and accounting systems (e.g., creditor rights, contract enforcement, and accounting standards) explain differences
Regression Shrinkage and Selection Via the Lasso
 Journal of the Royal Statistical Society, Series B
, 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
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Cited by 4055 (51 self)
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that are exactly zero and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also
Results 1  10
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