Columbia Business School Research

How do venture capital investors react to social movements, especially those that relate to historical underrepresentation in their funding decisions? We use image and name algorithms combined with clerical review to classify race for 150,000 founders and 30,000 investors. Our new data allow us to assess the impact of George Floyd's murder on VC funding of Black entrepreneurs and identify which VCs were most responsive. Although VCs responded swiftly, investment in Black-owned startups reverted to prior levels within two years.

One measure of the ex ante cost of disasters is the welfare gain from shorten-ing their expected duration. We introduce a stochastic clock into a standard disaster model that summarizes information about progress (positive or negative) toward disaster resolution. We show that the stock market response to duration news is essentially a sufficient statistic to identify the welfare gain to interventions that alter the state.

How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information
about others’ characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data

Big data technologies change the way in which data and human labor combine to create knowledge. Is this a modest technological advance or a data revolution? Using hiring and wage data, we show how to estimate firms' data stocks and the shape of their knowledge production functions. Knowing how much production functions have changed informs us about the likely long-run changes in output, in factor shares, and in the distribution of income, due to the new, big data technologies.

Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean generates overreaction to forecast revisions and a modest response of forecasts to lagged realizations. Heterogeneity in signals and priors about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral biases helps improve the model's predictions for short-horizon overreaction and dispersion.

The speed of stock price reaction to news exhibits substantial time variation. Higher risk-bearing capacity of financial intermediaries, lower passive ownership of stocks, and more informative news increase price responses to contemporaneous news; surprisingly, these interaction variables also increase price responses to lagged news (underreaction). A simple model with limited attention and three investor types (institutional, noninstitutional, and passive) predicts the observed variation in news responses.

This paper theoretically and empirically investigates the effects of liquidity regulation on the banking system. We document that the current quantity-based liquidity rule has reduced banks’ liquidity risks. However, the mandated liquidity buffer appears to crowd out bank lending and lead to a migration of liquidity risks to banks that are not subject to liquidity regulation. These findings motivate a model of liquidity regulation with endogenous liquidity premiums and heterogeneous banks.

We propose a tractable model of dynamic investment, spinoffs, financing, and risk management for a multi-division firm facing costly external finance. Our analysis formalizes

The onset of COVID-19 was characterized by voluminous, negative news. Higher narrativity news topics (measured by textual proximity to articles describing the 1987 stock market crash and textual distance from Federal Reserve communications) were systematically associated with contemporaneous market responses, which were larger on high volatility days (hypersensitivity), and with markets–news feedback. Hypersensitive news topic-market pairs were associated with next-day reversals.

We develop a model of investor information choices and asset prices in which the availability of information about fundamentals is time-varying and responds to investor demand for information. A competitive research sector produces more information when more investors are willing to pay for that research. This feedback, from investor willingness to pay for information to more information production, generates two regimes in equilibrium, one having high prices and low volatility, the other the opposite.

We propose a theory of banking in which banks cannot perfectly control deposit flows. Facing uninsurable loan and deposit shocks, banks dynamically manage lending, wholesale funding, deposits, and equity. Deposits create value by lowering funding costs. However, when the bank is undercapitalized and at risk of breaching leverage requirements, the marginal value of deposits can turn negative as deposit inflows, by raising leverage, increase the likelihood of costly equity issuance.

In the twenty-first century, the most valuable firms in the world are valued primarily for their data. This makes data central to finance. Data are an important asset to price; they change firm valuation and are a key consideration for an entrepreneur starting a new firm.

Big data is changing every corner of economics and finance. The largest firms in the US economy are valued chiefly for their data. Yet, these data are largely excluded from macroeconomic and finance research. We review work and relevant tools for measuring economic activity, market power, data markets, and the role of data in financial markets. We also highlight areas where future work is needed.

After the global financial crisis, the yields of U.S. Treasury bills frequently exceed other risk-free rate benchmarks, thereby pointing to a diminishing convenience premium. Constructing a new measure of dealers’ balance sheet constraints for providing intermediation in U.S. Treasury markets, we trace these diminishing convenience premiums to primary dealers’ ability to act as intermediaries.

Banks strategically choose and dynamically restructure deposits and nondeposit debt in response to the minimum requirements on total capital and tangible equity. We derive the optimal strategic liability structure and show that it minimizes the protection for deposits conditional on capital requirements. Although, given any liability structure, regulators can set capital requirements high enough to remove the incentive for risk substitution, the strategic response to the capital requirements always preserves this incentive.

Working from home resulted in a sharp contraction in office demand. We built a valuation model to find that the office stock lost about 45% in value. More for low-quality buildings and in cities with a larger IT sector and less for trophy buildings. We discuss the implications for mortgage lenders and the vitality of cities.

This article analyzes the hedging potential of real estate and especially looks at the impact of lease contracts in various countries around the world on the inflation hedge capability for both expected and unexpected inflation. The dataset consists of direct real estate rent and capital value data for 59 cities/MSAs in 25 countries between 1991 and 2020 to make international comparison over a long time period possible. The results indicate that real estate is a good hedge against inflation, and especially against unexpected inflation.

As financial technology improves and data becomes more abundant, do market prices reflect this growing information and allocate capital more efficiently? While a number of recent studies have documented rises in aggregate price efficiency, we show that there are large cross-sectional differences. The previously-documented increases are driven by a rise in the informativeness of large, growth stocks. The informational efficiency of smaller assets' prices or prices of assets with less growth potential are either flat or declining.

Housing affordability has become the main policy challenge for most large cities in the world. Zoning, rent control, housing vouchers, and tax credits are the main levers employed by policy makers. We build a new dynamic stochastic spatial equilibrium model to evaluate the effect of these policies on house prices, rents, residential construction, labor supply, output, income and wealth inequality, as well as the location decision of households within the city. The analysis incorporates risk, wealth effects, and resident landlords.

We use supervisory loan-level data to document that small firms (SMEs) obtain shorter maturity credit lines than large firms, post more collateral, have higher utilization rates, and pay higher spreads. We rationalize these facts as the equilibrium outcome of a trade-off between lender commitment and discretion. Using the COVID recession, we test the prediction that SMEs are subject to greater lender discretion. Consistent with this hypothesis, SMEs did not draw down whereas large firms did, even in response to similar demand shocks.

We examine the ability of existing and new factor models to explain the comovements of G10-currency changes. Extant currency factors include the carry, volatility, value, and momentum factors. Using a new clustering technique, we find a clear two-block structure in currency comovements with the first block containing mostly the dollar currencies, and the other the European currencies.

Lenders (loan originators) frequently sell the right to service loans to other intermediaries (loan servicers). It is loan servicers rather than originators who are responsible for resolving borrowers’ financial distress. They are also required to make payment advances to investors on behalf of delinquent borrowers until the distress resolution process is complete. We begin with the observation that at the start of the COVID-19 pandemic, shadow banks— nondepository financial institutions—serviced approximately half of the total mortgage debt in the United States (Cherry et al.

We analyze the impact of the Second Avenue Subway (Q-train) construction on local real estate prices, which capitalize the benefits of transit spillovers. We find evidence of higher real estate prices in the vicinity of areas served by the new Q-train, relative to other areas in Manhattan's Upper East Side. Only 30% of the private value created by the subway leads is captured through property taxes, and is insufficient to cover the cost of the subway. Value capture through targeted property tax increases can help close the funding gap.

We develop a model of information and portfolio choice in which ex ante identical investors choose to specialize because of fixed attention costs required in learning about securities. Without this friction, investors would invest in all securities and would be indifferent across a wide range of information choices. When securities' dividends depend on an aggregate (macro) risk factor and an idiosyncratic (micro) shocks, fixed attention costs lead investors to specialize in either macro or micro information.

This article uses advanced natural language processing (NLP) methods to identify companies that are aligned with the UN Sustainable Development Goals (SDGs) based on the text in their sustainability disclosures. Using the Corporate Social Responsibility (CSR) reports of Russell 1000 companies between 2010–2019, we apply a logistic classifier, support vector machines (SVM), and a fully-connected neural network to predict alignment with the SDGs.

The covid-19 crisis has led to a sharp deterioration in firm and bank balance sheets. The government has responded with a massive intervention in corporate credit markets. We study equilibrium dynamics of macroeconomic quantities and prices, and how they are affected by government intervention in the corporate debt markets. We find that the interventions should be highly effective at preventing a much deeper crisis by reducing corporate bankruptcies by about half, and short-circuiting the doom loop between corporate and financial sector fragility.

This article empirically examines the magnitude of risk premiums for direct real estate investments on a global basis. As this article analyzes ex-ante risk premiums over more than 25 years consistently across the world, it enhances current knowledge about the regional differences between risk premiums and helps long-term investors with their global portfolio allocation over time. On a global level, the authors find a risk premium of 4.1% for Gordon’s growth and 3.7% for two-stage growth model. The periodic growth model shows a slightly lower risk premium of 3.1%.

We follow a representative panel of millions of consumers in the U.S. from 2007 to 2017 and document several facts on the long-term effects of the Great Recession. There were about six million foreclosures in the ten-year period after Lehman's collapse. Owners of multiple homes accounted for 25% of these foreclosures, while comprising only 13% of the market. Foreclosures displaced homeowners, with most of them moving at least once. Only a quarter of foreclosed households regained homeownership, taking an average four years to do so.

We propose a new valuation method for private equity investments. First, we construct a cash-flow replicating portfolio for the private investment, using cash-flows on various listed equity and fixed income instruments. The second step values the replicating portfolio using a flexible asset pricing model that accurately prices the systematic risk in listed equity and fixed income instruments of different horizons.

We extract aggregate supply and aggregate demand shocks for the US economy from macroeconomic data on inflation, real GDP growth, core inflation and the unemployment gap. We first use unconditional non-Gaussian features in the data to achieve identification of these structural shocks while imposing minimal economic assumptions. We find that recessions in the 1970s and 1980s are better characterized as driven by supply shocks while later recessions were driven primarily by demand shocks. The Great Recession exhibited large negative shocks to both demand and supply.

We formulate a dynamic no-arbitrage asset pricing model for equities and corporate bonds, featuring time variation in both risk aversion and economic uncertainty. The joint dynamics among cash flows, macroeconomic fundamentals and risk aversion accommodate both heteroskedasticity and non-Gaussianity. The model delivers measures of risk aversion and uncertainty at the daily frequency. We verify that equity variance risk premiums are very informative about risk aversion, whereas credit spreads and corporate bond volatility are highly correlated with economic uncertainty.

The major cities of the world have attracted a flurry of out-of-town (OOT) home buyers. Such capital inflows affect housing affordability, the spatial distribution of residents, construction, labor income, wealth, and ultimately welfare. We develop a spatial equilibrium model of a city with substantial heterogeneity among residents. We calibrate the model to the New York and Vancouver metro areas. The observed increase in OOT purchases is associated with 1.1% (5.0%) higher house prices and a 0.1% (0.34%) welfare loss in New York (Vancouver).

This paper studies systemic risk in the interbank market. We first establish that in the German interbank lending market, a few large banks intermediate funding flows between many smaller periphery banks. We then develop a network model in which banks trade off the costs and benefits of link formation to explain these patterns. The model is structurally estimated using banks' preferences as revealed by the observed network structure before the 2008 financial crisis.

How much capital should financial intermediaries hold? We propose a general equilibrium model with a financial sector that makes risky long-term loans to firms, funded by deposits from savers. Government guarantees create a role for bank capital regulation. The model captures the sharp and persistent drop in macro-economic aggregates and credit provision as well as the sharp change in credit spreads observed during the Great Recession.

Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do differences in these patterns come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks on growth, the effect can be positive, negative, or zero.

We study how small and medium enterprise (SME) lenders react to information about their competitors’ contracting decisions. To isolate this learning from lenders’ common reactions to unobserved shocks to fundamentals, we exploit the staggered entry of lenders into an information-sharing platform. Upon entering, lenders adjust their contract terms toward what others offer. This reaction is mediated by the distribution of market shares: lenders with higher shares or that operate in concentrated markets react less.

We investigate risk and return in the major corporate bond markets of the developed world. We find that average returns increase with maturity and ratings class (where ratings go from high to low) and that this pattern is fit well by a global CAPM model, where the market consists of equity, sovereign and corporate bonds. Nonetheless, we strongly reject "asset class integration," finding a model which separates the market portfolio into its three components to fit much more of the corporate bond return variation.

Shared Appreciation Mortgages (SAMs) feature mortgage payments that adjust with house prices. These mortgage contracts are designed to stave off home owner default by providing payment relief in the wake of a large house price shock. SAMs have been hailed as an innovative solution that could prevent the next foreclosure crisis, act as a work-out tool during a crisis, and alleviate fiscal pressure during a downturn. They have inspired Fintech companies to offer home equity contracts. However, the home owner's gains are the mortgage lender's losses.

We examine the statistical term structure model of cochrane and Piazzesi (2005) and its affine counterpart, developed in cochrane and Piazzesi (2008) in several out-of-sample analyzes. The model’s one-factor forecasting structure characterizes the term structures of additional currencies in samples ending in 2003. In post-2003 data one-factor structures again characterize each currency’s term structure, but we reject equality of the coefficients across the two samples.

In this paper, we examine a novel two-stage mechanism for selling government securities, wherein the dealers underwrite in the first stage the sale of securities, which are auctioned in stage 2 via either a discriminatory auction (DA) or a uniform price auction (UPA).