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利用情感分析改进投资决策：概率排序框架AI 解读

We develop a probabilistic framework to extract sentiment information from text by training a model to predict and rank sentiments in newly encountered documents. Our approach imposes a joint semi-parametric model on text and ordinal response variables, addressing the challenges of sparse sentiment signals and complex response distributions. Through a word screening procedure and the use of normalized ranks, our approach achieves consistent sentiment ranking without…

文本选择AI 解读

Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high-dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than…

宏观与金融不确定性及波动率的对冲AI 解读

We study the pricing of shocks to uncertainty and volatility using a wide-ranging set of options contracts covering a variety of different markets. If uncertainty shocks are viewed as bad by investors, they should carry negative risk premiums. Empirically, however, uncertainty risk premiums are positive in most markets. Instead, it is the realization of large shocks to fundamentals that has historically carried a negative premium. In other words, we find that the return…

系统性风险与宏观经济：一项实证评估AI 解读

This article studies how systemic risk and financial market distress affect the distribution of shocks to real economic activity. We analyze how changes in 19 different measures of systemic risk skew the distribution of subsequent shocks to industrial production and other macroeconomic variables in the US and Europe over several decades. We also propose dimension reduction estimators for constructing systemic risk indexes from the cross section of measures and demonstrate…

三遍回归滤波：利用大量预测变量进行预测的新方法AI 解读

We forecast a single time series using many predictor variables with a new estimator called the three-pass regression filter (3PRF). It is calculated in closed form and conveniently represented as a set of ordinary least squares regressions. 3PRF forecasts are consistent for the infeasible best forecast when both the time dimension and cross section dimension become large. This requires specifying only the number of relevant factors driving the forecast target, regardless of…

动态等相关模型AI 解读

A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmatically applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, involves first adjusting for individual volatilities and then estimating correlations. A quasi-maximum likelihood result shows that DECO provides consistent parameter…