In an efficient market, the no-arbitrage condition implies that the price difference between any two assets must be the market value of all differences in their cash flows. We use this logic to deduce the price of the prepayment option embedded in fixed-rate Government National Mortgage Association (GNMA) mortgage-backed securities. The option price equals the difference between an observed GNMA price and the cost of a synthetic, nonprepayable GNMA constructed from the least expensive portfolio of Treasury securities that exactly replicates the promised GNMA cash flow stream, assuming prepayment is precluded. We regress the option prices on variables found significant in previous prepayment studies, finding that five key regressors explain more than 90% of the prepayment option value in pooled time-series cross-sectional analysis. We also show that the time value of the prepayment option calculated by our method displays a pattern similar to that produced by the Black-Scholes (1973) option pricing model. An additional empirical result is the existence of negative option prices and negative time value of the option prices. We attribute these to the fact that homeowners sometimes exercise their prepayment options when they are out-of-the-money, and to refinancing transaction costs. Our method is independent of assumptions regarding interest rate processes and the homeowner's prepayment behavior, and it provides a benchmark for testing theoretical prepayment models. Copyright American Real Estate and Urban Economics Association.

This paper studies the relationship between working capital management and corporate profitability. We use the cash conversion cycle (CCC) as a measure of working capital management inefficiency and return on total operating assets (ROA) as the profitability indicator. Our empirical findings suggest that there is a strong negative relationship between CCC and ROA for U.S. corporations during the 1983 to 2012 period. However, this negative relationship does not apply to all of the companies across industries. In our industry analysis, we find that there is a strong negative relationship between CCC and ROA for six industry sectors based on the Global Industry Classification Standard (GICS) codes. On the other hand, there is no significant relationship for the Materials sector, and the relationship is significantly positive for the Consumer Staples industry.

Our paper analyzes the effects of supply and demand on the affordability of house prices in the San Francisco Bay Area housing market. We use all cities within this market. Based on the 1990 and 2000 census data, we run the ordinary least squares linear regression to examine these effects as well as investigate changes over a ten-year period. As expected, our key demand variable, median household income, significantly affects house prices in this market for both periods. Also, the changes of the effects between periods represent housing appreciation, which in turn may affect affordability. However, not all results in this study are typical due to unique supply and demand effects for this housing market.