Columbia Business School Research

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.

Equity investors have been shifting away from actively managed funds to passive strategies for decades. Passification, if that is a word, has been slower to take off in fixed-income strategies, though.

Many organizations rely on formal management control systems that align employee values with organizational values (i.e., culture-fit) to shape organizational culture. Using proprietary data from a highly-decentralized organization, I examine the employee performance consequences of adopting a formal culture-fit measurement system in employee selection. I exploit the staggered feature of the adoption of the system, and find that employees selected with the system perform significantly better than those without the system.

We revisit the firm value and pricing implications of the negative screening of sin stocks. Unlike prior work, we find that institutional ownership and valuations related to sin stocks are not different from those of other stocks after controlling for differences in fundamentals between sin and non-sin stocks. Sin stocks do not differ in the likelihood of exiting the public market, the cost of raising new equity, and in the announcement returns around negative ESG news relative to non-sin stocks, casting further doubt on whether negative screening hurts sin stocks.

The crash of some of the flagbearers of the equity bubble in recent years has been painful for investors. We have seen “pandemic winner” Netflix dive 75 per cent from 2021 peaks, crypto exchange operator Coinbase plunge 86 per cent and the one-time meme stock and cinema chain AMC lose 80 per cent.

In modern asset markets, man and machine compete for profits. How does each fare? I build a learning model in which quantitative investors (reliant on computer models) have more learning capacity but less flexibility to adapt to market conditions than discretionary investors (reliant on human judgment). I use machine learning to categorize US active equity mutual funds as quantitative or discretionary. Consistent with the model's predictions, I find that quantitative funds hold more stocks, specialize in stock picking, and engage in more overcrowded trades.

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 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.

It will take an evolution of fixed-income managers’ approach to make a difference to corporate behaviour.

Not to be left out of the ESG gold rush, a growing number of bond firms now offer environmental, social and governance funds. ESG integration has become a standard box to be checked (or not) on bond clients’ Requests for Proposals. Investment banks have created tools to help fixed income managers ESG-ify their portfolios.

We investigate the impact of uneven transparency regulation across countries and industries on the location of economic activity. Using two distinct sources of regulatory variation—the varying extent of financial-reporting requirements and the staggered introduction of electronic business registers in Europe—, we consistently document that direct exposure to transparency regulation is negatively associated with the focal industry’s economic activity in terms of inputs (e.g., employment) and outputs (e.g., production).

We show that dealer market power impedes the pass-through of monetary policy in repo markets, which is an important first stage of monetary policy transmission. In the European repo market, most participants do not have access to trade on centralized exchanges. Rather, they rely on OTC intermediation by a small number of dealers that exhibit significant market power. As a result, the passthrough of the ECB's policy rate to repo markets is inefficient and unequal.

How should an investor value financial data? The answer is complicated as it not only depends on the investor himself but also on the characteristics of all other investors. Portfolio size, risk aversions, trading horizon, and investment style affect an investor's willingness to pay for data and the equilibrium value of data. Directly measuring all these characteristics of all investors is hopeless. Thus, we outline a simple model that gives rise to sufficient statistics that make an investor's private value of data measurable.