We use mutual fund manager data from the technology bubble to examine the hypothesis that inexperienced investors play a role in the formation of asset price bubbles. Using age as a proxy for managers’ investment experience, we find that around the peak of the technology bubble, mutual funds run by younger managers are more heavily invested in technology stocks, relative to their style benchmarks, than their older colleagues. Furthermore, young managers, but not old managers, exhibit trend-chasing behavior in their technology stock investments. As a result, young managers increase their technology holdings during the run-up, and decrease them during the downturn. Both results are in line with the behavior of inexperienced investors in experimental asset markets. The economic significance of young managers’ actions is amplified by large inflows into their funds prior to the peak in technology stock prices.

This paper documents that hedge funds did not exert a correcting force on stock prices during the technology bubble. Instead, they were heavily invested in technology stocks. This does not seem to be the result of unawareness of the bubble: Hedge funds captured the upturn, but, by reducing their positions in stocks that were about to decline, avoided much of the downturn. Our findings question the efficient markets notion that rational speculators always stabilize prices. They are consistent with models in which rational investors may prefer to ride bubbles because of predictable investor sentiment and limits to arbitrage.

We evaluate the effects of three European Central Bank (ECB) policies (the Securities Markets Programme (SMP), the Outright Monetary Transactions (OMT), and the Long-Term Refinancing Operations (LTROs)) on government bond yields. We use a novel Kalman-filter augmented event-study approach and yields on euro-denominated sovereign bonds, dollar-denominated sovereign bonds, corporate bonds, and corporate credit default swap (CDS) rates to understand the channels through which policies reduced sovereign bond yields. On average across Italy, Spain and Portugal, considering both the SMP and the OMT, yields fall considerably. Decomposing this fall, default risk accounts for 37% of the reduction in yields, reduced redenomination risk for 13%, and reduced market segmentation effects for 50%. Stock price increases in distressed and core countries suggest that these policies also had beneficial macro-spillovers.

We examine subjective risk premia implied by return expectations of individual investors and professionals for portfolios of stocks, bonds, currencies, and commodity futures. While in-sample predictive regressions with realized excess returns suggest that objective risk premia vary countercyclically with business-cycle and asset-valuation measures, subjective risk premia extracted from survey data are largely acyclical. Out-of-sample forecasts of excess returns exhibit a similar lack of cyclicality, which suggests that investors’ learning of forecasting relationships in real time may help explain the cyclicality gap. There is a subjective risk-return tradeoff, with subjective risk premia increasing in subjective perceptions of risk quantity.

We investigate whether individual experiences of macroeconomic shocks affect financial risk taking, as often suggested for the generation that experienced the Great Depression. Using data from the Survey of Consumer Finances from 1960 to 2007, we find that individuals who have experienced low stock market returns throughout their lives so far report lower willingness to take financial risk, are less likely to participate in the stock market, invest a lower fraction of their liquid assets in stocks if they participate, and are more pessimistic about future stock returns. Those who have experienced low bond returns are less likely to own bonds. Results are estimated controlling for age, year effects, and household characteristics. More recent return experiences have stronger effects, particularly on younger people.

Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our equilibrium model, N assets have cash flows that are linear in J characteristics, with unknown coefficients. Risk-neutral Bayesian investors learn these coefficients and determine market prices. If J and N are comparable in size, returns are cross-sectionally predictable ex post. In-sample tests of market efficiency reject the no-predictability null with high probability, even though investors use information optimally in real time. In contrast, out-of-sample tests retain their economic meaning.

We adapt stochastic discount factor (SDF) valuation methods for venture capital (VC) performance evaluation. Our approach generalizes the popular Public Market Equivalent (PME) method and allows statistical inference in the presence of cross‐sectionally dependent, skewed VC payoffs. We relax SDF restrictions implicit in the PME so that the SDF can accurately reflect risk‐free rates and returns of public equity markets during the sample period. This generalized PME yields substantially different abnormal performance estimates for VC funds and start‐up investments, especially in times of strongly rising public equity markets and for investments with betas far from one.

In sharp contrast to most previous crisis episodes, the Treasury market experienced severe stress and illiquidity during the COVID-19 crisis, raising concerns that the safe-haven status of US Treasuries may be eroding. We document large shifts in Treasury ownership and temporary accumulation of Treasury and reverse repo positions on dealer balance sheets during this period. We build a dynamic equilibrium asset pricing model in which dealers subject to regulatory balance sheet constraints intermediate demand/supply shocks from habitat agents and provide repo financing to levered investors. The model predicts that Treasury inconvenience yields, measured as the spread between Treasuries and overnight-index swap rates (OIS), as well as spreads between dealers’ reverse repo and repo rates, should be highly positive during the COVID-19 crisis, as is confirmed in the data. The same model framework, adapted to the institutional setting in 2007–2009, can also explain the negative Treasury-OIS spread observed during the Great Recession.

The field of asset pricing is a rich and diverse discipline that has contributed to many areas of discourse, including those of fundamental importance to policy makers, investors, and households.1 As we look ahead during a time of substantial economic and political change, it is apparent that society faces many pressing questions, both new and old, that the field is uniquely suited to informing.To contribute to this conversation, the NBER Asset Pricing program convened a panel discussion on “Perspectives on the Future of Asset Pricing” at its November 8, 2019, meeting that took place at Stanford University. The objective of the panel was to identify some of the important questions the field could productively address in the next five to 10 years. The panelists, consisting of experts in several subfields of asset pricing, were invited to share their views on these questions with an eye toward innovative research topics that are ripe for exploring, and the metrics the field could be using to gauge progress.