Does trader leverage drive equity market liquidity? We use the unique features of the margin trading system in India to identify a causal relationship between traders’ ability to borrow and a stock's market liquidity. To quantify the impact of trader leverage, we employ a regression discontinuity design that exploits threshold rules that determine a stock's margin trading eligibility. We find that liquidity is higher when stocks become eligible for margin trading and that this liquidity enhancement is driven by margin traders’ contrarian strategies. Consistent with downward liquidity spirals due to deleveraging, we also find that this effect reverses during crises.

Reverse mortgage loans (RMLs) allow older homeowners to borrow against housing wealth without moving. Despite rapid growth in this market, only 1.9% of eligible homeowners had RMLs in 2013. In this paper, we analyze reverse mortgages in a calibrated life‐cycle model of retirement. The average welfare gain from RMLs is $252 per homeowner, and $1,770 per RML borrower. Bequest motives, uncertainty about health and expenses, and loan costs account for low demand. According to the model, the Great Recession's impact differs across age, income, and wealth distributions, with a threefold increase in RML demand for lowest income and oldest households.

Persistent interest rate differentials account for much of the currency carry trade profitability. “Commodity currencies” offer high interest rates on average, while countries that export finished goods tend to have low interest rates. We develop a general equilibrium model of international trade and currency pricing where countries have an advantage in producing either basic inputs or final goods. In the model, domestic production insulates commodity‐producing countries from global productivity shocks, forcing final‐good producers to absorb them. Commodity‐currency exchange rates and risk premia increase with productivity differentials and trade frictions. These predictions are strongly supported in the data.

To understand why investors hold socially responsible mutual funds, we link administrative data to survey responses and behavior in incentivized experiments. We find that both social preferences and social signaling explain socially responsible investment (SRI) decisions. Financial motives play less of a role. Socially responsible investors in our sample expect to earn lower returns on SRI funds than on conventional funds and pay higher management fees. This suggests that investors are willing to forgo financial performance in order to invest in accordance with their social preferences.

From 2007 to 2009 U.S. house prices plunged and mortgage defaults surged. While ostensibly consistent with widespread “ruthless default,” analysis of detailed mortgage and house price data indicates that borrowers do not walk away until they are deeply underwater—far deeper than traditional models predict. The evidence suggests that lender recourse is not the major driver of this result. We argue that emotional and behavioral factors play an important role in decisions to continue paying. Borrower reluctance to walk away implies that the moral hazard cost of default as a form of social insurance may be lower than suspected.

Given the competition for top journal space, there is an incentive to produce “significant” results. With the combination of unreported tests, lack of adjustment for multiple tests, and direct and indirect p‐ hacking, many of the results being published will fail to hold up in the future. In addition, there are basic issues with the interpretation of statistical significance. Increasing thresholds may be necessary, but still may not be sufficient: if the effect being studied is rare, even t > 3 will produce a large number of false positives. Here I explore the meaning and limitations of a p‐ value. I offer a simple alternative (the minimum Bayes factor). I present guidelines for a robust, transparent research culture in financial economics. Finally, I offer some thoughts on the importance of risk‐taking (from the perspective of authors and editors) to advance our field. SUMMARY Empirical research in financial economics relies too much on p ‐values, which are poorly understood in the first place. Journals want to publish papers with positive results and this incentivizes researchers to engage in data mining and “ p ‐hacking.” The outcome will likely be an embarrassing number of false positives—effects that will not be repeated in the future. The minimum Bayes factor (which is a function of the p ‐value) combined with prior odds provides a simple solution that can be reported alongside the usual p ‐value. The Bayesianized p ‐value answers the question: What is the probability that the null is true? The same technique can be used to answer: What threshold of t ‐statistic do I need so that there is only a 5% chance that the null is true? The threshold depends on the economic plausibility of the hypothesis.

Contrary to conventional wisdom, growth stocks (i.e., low book‐to‐market stocks) do not have substantially higher future cash‐flow growth rates than value stocks, in both rebalanced and buy‐and‐hold portfolios. Efficiency growth, survivorship and look‐back biases, and the rebalancing effect help explain the results. These findings suggest that duration alone is unlikely to explain the value premium.

Why do negative credit events lead to long‐term borrowing constraints? Exploiting banking regulations in Peru and utilizing currency movements, we show that consumers who face a credit rating downgrade due to bad luck experience a three‐year reduction in financing. Consumers respond to the shock by paying down their most troubled loans, but nonetheless end up more likely to exit the credit market. For a set of borrowers who experience severe delinquency, we find that the associated credit reporting downgrade itself accounts for 25% to 65% of their observed decline in borrowing at various horizons over the following several years.