Essays in life-cycle portfolio choice
File(s)
Author(s)
Zhang, Yuxin
Type
Thesis
Abstract
This dissertation presents three essays in life-cycle portfolio choice. Chapter 1 solves for optimal consumption and portfolio choice in a life-cycle model with short sales and borrowing constraints, undiversifiable labor income risk and a predictable, time-varying, equity premium and show that the investor pursues aggressive market timing strategies.
Importantly, it shows that, in the presence of stock market predictability, the conventional financial advice of reducing stock market exposure as retirement approaches is correct on average, but ignoring changing market information can lead to substantial welfare losses. Therefore, enhanced target-date funds (ETDFs) that condition on expected equity premia increase welfare relative to target-date funds (TDFs). Out-of-sample analysis supports these conclusions. \newline
Chapter 2 studies the effect of observable predictors that imperfectly predict conditional expected stock returns on optimal life-cycle consumption and portfolio choice in the presence of undiversifiable labor income risk. Investors filter the unobservable expected stock returns from realized predictive variables and stock returns. Young stockholders hold more conservative portfolios, better matching empirical observations, than models assuming a predictor perfectly delivering the conditional expected stock return or models assuming i.i.d. stock returns. Welfare losses from ignoring imperfect predictability can be substantial. \newline
Chapter 3 uses different stock return predictors at quarterly frequency to solve for optimal consumption and portfolio choice in a life-cycle model with short-sales and borrowing constraints and undiversifiable labor income risk. Both wealth accumulation and asset allocation look similar qualitatively to their i.i.d. unconditional averages, but are quantitatively different and depend on predictors in different ways. Therefore, enhanced target-date funds (ETDFs) that condition saving and portfolio choice on predictor variables can lead to substantial welfare gains.
Importantly, it shows that, in the presence of stock market predictability, the conventional financial advice of reducing stock market exposure as retirement approaches is correct on average, but ignoring changing market information can lead to substantial welfare losses. Therefore, enhanced target-date funds (ETDFs) that condition on expected equity premia increase welfare relative to target-date funds (TDFs). Out-of-sample analysis supports these conclusions. \newline
Chapter 2 studies the effect of observable predictors that imperfectly predict conditional expected stock returns on optimal life-cycle consumption and portfolio choice in the presence of undiversifiable labor income risk. Investors filter the unobservable expected stock returns from realized predictive variables and stock returns. Young stockholders hold more conservative portfolios, better matching empirical observations, than models assuming a predictor perfectly delivering the conditional expected stock return or models assuming i.i.d. stock returns. Welfare losses from ignoring imperfect predictability can be substantial. \newline
Chapter 3 uses different stock return predictors at quarterly frequency to solve for optimal consumption and portfolio choice in a life-cycle model with short-sales and borrowing constraints and undiversifiable labor income risk. Both wealth accumulation and asset allocation look similar qualitatively to their i.i.d. unconditional averages, but are quantitatively different and depend on predictors in different ways. Therefore, enhanced target-date funds (ETDFs) that condition saving and portfolio choice on predictor variables can lead to substantial welfare gains.
Version
Open Access
Date Issued
2017-05
Date Awarded
2017-09
Advisor
Michaelides, Alex
Kosowski, Robert
Publisher Department
Imperial College Business School
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)