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Dimension Reduction Methods for Financial Market Prediction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dimension Reduction Methods for Financial Market Prediction./
Author:	Deng, Yizhe.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	161 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: A.
Contained By:	Dissertations Abstracts International83-09A.
Subject:	Forecasting. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003131
ISBN:	9798209785934

Dimension Reduction Methods for Financial Market Prediction.
Deng, Yizhe.

Dimension Reduction Methods for Financial Market Prediction. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 161 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: A.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.

This item must not be sold to any third party vendors.

Return predictability is an important issue in finance and financial engineering.Investor's expectations play a central role in forming asset returns, but the challenge is that expectations are unobservable. This study attempts to obtaininvestor's expectations via two channels. To fully condense the informationembedded in the expectations, we consider dimension reduction approaches toextract the condensed latent factor that helps forecast stock returns.In the first channel, we construct an expected macroeconomic condition factorfrom survey-based forecasts of future macroeconomic activities, with the purposeof tracking the equity premium. This macro factor exhibits salient counter-cyclicaldynamics, produces an out-of-sample R2 of 3.4% for predicting quarterlystock market excess returns from 1984 to 2018, and dominates a wide array ofcommonly used macro and financial predictors. The long-term macro forecastsprovide incremental information about the time variations of long-horizon equitypremiums. A dynamic trading strategy that employs market timing in returnand volatility jointly based on the factor can yield a significant and sizable utilitygain to a mean-variance investor.In the second channel, we proxy the U.S. volatility risk by a single forward-looking factor extracted from the term structure of option-implied U.S. forwardvariances. We study the cross-country impact of the U.S. stock market volatilityrisk. A large increase in the U.S. volatility risk significantly predicts future stockmarket returns on 11 industrialized countries. We also find strong out-of-sample predictive ability of the U.S. volatility risk. Empirically, our U.S. volatility riskfactor can predict future U.S. macroeconomic conditions as well as local stockmarket volatility, suggesting that the source of the predictability we find stemsfrom the impact of U.S. volatility on the international investment opportunityset. This result is consistent with the international version of the inter-temporal capital asset pricing model and supports the leading unique role of the U.S. inthe international stock market risk spillover network.

ISBN: 9798209785934Subjects--Topical Terms:

547120
Forecasting.

Dimension Reduction Methods for Financial Market Prediction.
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300 $a 161 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: A.
500 $a Advisor: Wu, Lixin;Yang, Zhaojun.
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Return predictability is an important issue in finance and financial engineering.Investor's expectations play a central role in forming asset returns, but the challenge is that expectations are unobservable. This study attempts to obtaininvestor's expectations via two channels. To fully condense the informationembedded in the expectations, we consider dimension reduction approaches toextract the condensed latent factor that helps forecast stock returns.In the first channel, we construct an expected macroeconomic condition factorfrom survey-based forecasts of future macroeconomic activities, with the purposeof tracking the equity premium. This macro factor exhibits salient counter-cyclicaldynamics, produces an out-of-sample R2 of 3.4% for predicting quarterlystock market excess returns from 1984 to 2018, and dominates a wide array ofcommonly used macro and financial predictors. The long-term macro forecastsprovide incremental information about the time variations of long-horizon equitypremiums. A dynamic trading strategy that employs market timing in returnand volatility jointly based on the factor can yield a significant and sizable utilitygain to a mean-variance investor.In the second channel, we proxy the U.S. volatility risk by a single forward-looking factor extracted from the term structure of option-implied U.S. forwardvariances. We study the cross-country impact of the U.S. stock market volatilityrisk. A large increase in the U.S. volatility risk significantly predicts future stockmarket returns on 11 industrialized countries. We also find strong out-of-sample predictive ability of the U.S. volatility risk. Empirically, our U.S. volatility riskfactor can predict future U.S. macroeconomic conditions as well as local stockmarket volatility, suggesting that the source of the predictability we find stemsfrom the impact of U.S. volatility on the international investment opportunityset. This result is consistent with the international version of the inter-temporal capital asset pricing model and supports the leading unique role of the U.S. inthe international stock market risk spillover network.
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