Does Pension Wealth Peak at the Age of Early Retirement?

Once an employee has qualified for early retirement, continuing to work involves an implicit tax in the form of forgone pension payments. We explore this implicit pension tax in a Canadian setting and find that, contrary to Lazear (1983), but consistent with most U.S. studies, pension wealth generally does not peak at the date of first eligibility for early retirement. We highlight the importance of the earnings path posited for older workers, anticipated enrichments to flat benefit formulas, and the distinction between reduced and unreduced early retirement as determinants of this result.     

A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network-pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with the no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of S&P 500 futures options from 1987 to 1991.     

Review Essay

What Do We Produce in the “Knowledge Factory”and for Whom? A Review Essay of The Knowledge Factory by Stanley Aronowitz     

The Effect of Government Regulations on Personal Loan Markets: A Tobit Estimation of a Microeconomic Model

The purpose of this paper is to analyze both theoretically and empirically the effect of selected government regulations on a high-risk personal loan market. Unlike previous studies, which have generally relied on a loosely specified theory and then tested this theory with statewide aggregate data, our analysis is based on a more tightly specified model for individual loans which is then tested using statewide disaggregated data. The empirical results indicate that the regulatory effects are not only significant but consistent with our theoretical microeconomic model.     

SOME PUBLIC POLICY ISSUES RAISED BY THE DEREGULATION OF FINANCIAL INSTITUTIONS

Successful deregulation of financial institutions promotes flexibility and effeciency while avoiding the instabilities and abuses that characterized the financial system prior to the reform legislation of the 1930s. It is essential that deregulation nut undermine the efficacy of deposit insurance. The growth of money market funds has already resulted in a shift from insured to uninsured accounts. With deregulation there will be much larger shifts of funds to uninsured transactios accounts. In a deregulated world, it is essential that insurance be extended to cover transactions accounts, no matter who issues them, The paper also asserts that existing statutes are inadequate to deal with the conflicts of interest and anti-competitive practices that are likely to occur with deregulation. Safeguards should be established before deregulation goes any further. Finally, it is argued that in a deregulated environment, most forms of money will pay interest, and required reserves will disappear. While these developments will not destroy the efficacy of monetary policy, they will affect it. Little work has been done an the issue of monetary policy in a deregulated world. The paper discusses existing studies and suggests some topics for future research.     

Return,Risk, and Yield: Evidence from Ex Ante Data

The purpose of this study is to investigate the relationship between return and yield in the context of ex ante data from The Value Line Investment Survey and by examining the role of dividends as a proxy for risk. The use of ex ante data should substantially reduce the confounding of tax and information effects that has affected earlier studies. Heteroscedasticity is detected in the after-tax CAPM and found to be negatively related to yield and positively related to beta. Maximum likelihood methods are used to correct for heteroscedasticity and generate efficient coefficient estimates. Using data for each of the years 1973 through 1983, there is an overall positive relationship between expected return and yield. However, coefficient estimates of yield are highly variable from year to year.     

Reinforcement Learning and Savings Behavior

We show that individual investors over‐extrapolate from their personal experience when making savings decisions. Investors who experience particularly rewarding outcomes from 401(k) saving—a high average and/or low variance return—increase their 401(k) savings rate more than investors who have less rewarding experiences. This finding is not driven by aggregate time‐series shocks, income effects, rational learning about investing skill, investor fixed effects, or time‐varying investor‐level heterogeneity that is correlated with portfolio allocations to stock, bond, and cash asset classes. We discuss implications for the equity premium puzzle and interventions aimed at improving household financial outcomes.