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Cambridge Finance coordinates the programmes of research and study in all areas of finance across the University of Cambridge. Its members are grouped into seven research centres: 3CL, CCFin, CFR, CIMF, JBSF, REF, CFH and CEAM.
Updated: 19 min 46 sec ago

Thu 16 May 12:30: Title to be confirmed

Wed, 27/03/2024 - 14:12
Title to be confirmed

Abstract not available

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Thu 16 May 12:30: The Legal Framework for Venture Capital in Ukraine

Wed, 27/03/2024 - 09:36
The Legal Framework for Venture Capital in Ukraine

With reference to the role venture capital (VC) can play in Ukraine’s post-war reconstruction, we explore how far Ukraine’s legal framework supports VC funds and start ups. We use a multi-methods approach, combining a quantitative (‘leximetric’) review of the operation of legal norms, with qualitative evidence based on interviews with industry participants. In the leximetric part of our study, we observe a recent strengthening of formal legal protection for shareholders and creditors, of the kind which is consistent with VC, and brings Ukrainian law into line with the norms for other developed countries. Our interviews suggest that Ukraine-based funds and start ups are able to use overseas legal systems (mainly Delaware law and English law) to structure entities and transactions, and that this can be important for maintaining investor confidence. Reliance on foreign law may not, however, be a long-term solution if the aim is to build an indigenous VC ecosystem. The example of other countries in East Central Europe, including Estonia and Romania, suggests that civil law legal origin need not be an obstacle to the use of domestic laws to support VC. Our interviews also stress the importance of reducing corruption and building trust in public institutions as elements in Ukraine’s sustainable development.

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Thu 02 May 13:00: Title to be confirmed

Mon, 25/03/2024 - 08:57
Title to be confirmed

Abstract not available

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Thu 13 Jun 13:00: Estimating the Private Value of Financial Statement Statistics; the abstract is below. I hope to have a revised version ready closer to the actual presentation date.

Mon, 04/03/2024 - 14:25
Estimating the Private Value of Financial Statement Statistics; the abstract is below. I hope to have a revised version ready closer to the actual presentation date.

We develop a method for estimating the private value of knowing the future realization of some financial statistic and then apply the measure to the familiar ratios arising from the Dupont decomposition of return on equity. The estimation is grounded in the standard rational expectations model, adapted to accommodate relative risk aversion, and produces an investor’s willingness to pay for the signal. The method can accommodate different levels of investable wealth, multiple assets, and any information system that produces signals about those assets. To illustrate the use of this measure, we show that knowing next year’s return on equity, given that the investor already knows the current value, is worth six times more than knowing the value of next year’s sales growth. And, as predicted by the Dupont model, we find the value of knowing next year’s operating asset turnover depends crucially on the level of the operating profit margin. Finally, we show that knowing next year’s leverage is practically worthless. Given that investors face trade-offs when deciding where to expend effort in financial statement analysis, these estimates can help them to know where to allocate their time.

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Thu 16 May 12:30: Title to be confirmed

Mon, 04/03/2024 - 14:09
Title to be confirmed

Abstract not available

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Thu 07 Mar 13:00: The Origins of Random Choice

Thu, 22/02/2024 - 11:25
The Origins of Random Choice

Using lab data on both choices and eye-movements we exam the rela*tionship between randomness in choice and attention. We bring in 50 subjects and have each make 180 choices, involving repeated choices from the same choice sets, while tracking their eye movements. Our approach allows us to consider attention as a multi-dimensional object and link different aspects of attention to distinct patterns in choice. We show that although the monotonicity condition that underlies random utility models is frequently violated, the monotonicity condition on attention sets considered by Cattaneo et al., 2020 is satisfies by almost all observations. Despite this, attention explains at most around a third of the randomness in choice. Although randomness in choice is much larger at the aggregate compared to the individual level, attention explains randomness in choice to the same degree in both. In ongoing work we conduct revealed preference tests of both random utility and random attention models.

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Thu 22 Feb 12:00: Nonparametric conditional factors for unbalanced panels

Thu, 25/01/2024 - 12:16
Nonparametric conditional factors for unbalanced panels

We introduce a nonparametric estimator for conditional covariance matrices of unbalanced panels. Our approach naturally accommodates a low-dimensional nonlinear factor structure that ensures all structural relations between moments. In high-dimensional large-data applications, we investigate various conditional return expectation and covariance models that depend on asset characteristics. The empirically successful models imply substantial conditional Sharpe ratios, along with respectable ordinal and point predictions. Our approach can easily be extended to accommodate higher-order moments and comes with asymptotic theory that can be used with large unbalanced panels.

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Thu 25 Jan 12:30: How to discipline financial markets: reputation is not enough

Thu, 25/01/2024 - 12:06
How to discipline financial markets: reputation is not enough

Historically, shocks originating in the financial sector often spilled over into the real sector with dramatic consequences. We study in the lab how interventions targeting disclosure and capital requirements of financial intermediaries can reduce insolvencies or prevent their negative effects from propagating to the broader economy. In our two-sector economy, con*sumers and producers can fund financial intermediaries, who in turn provide them with liquidity to settle trades. However, intermediaries may undertake risky investments and become insolvent, which depresses real economic activity. In the experiment, insolvencies were frequent. As a consequence, consumers and producers often refused to fund intermediaries, which lowered the trade volume. Imposing the disclosure of risky investments did not reduce risk-taking and insolvencies. Instead, imposing capital requirements prevented insolvencies from disrupting real economic activity, thus boosting financial intermediation and trade.

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Thu 22 Feb 13:00: Nonparametric conditional factors for unbalanced panels

Thu, 18/01/2024 - 14:58
Nonparametric conditional factors for unbalanced panels

We introduce a nonparametric estimator for conditional covariance matrices of unbalanced panels. Our approach naturally accommodates a low-dimensional nonlinear factor structure that ensures all structural relations between moments. In high-dimensional large-data applications, we investigate various conditional return expectation and covariance models that depend on asset characteristics. The empirically successful models imply substantial conditional Sharpe ratios, along with respectable ordinal and point predictions. Our approach can easily be extended to accommodate higher-order moments and comes with asymptotic theory that can be used with large unbalanced panels.

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Thu 22 Feb 13:00: Kernel Conditional Covariance

Fri, 12/01/2024 - 09:33
Kernel Conditional Covariance

We introduce a nonparametric estimator for conditional covariance matrices of unbalanced panels. Our approach naturally accommodates a low-dimensional conditional nonlinear factor structure that ensures all structural relations between moments. In high-dimensional large-data applications, we investigate various conditional return expectation and covariance models that depend on asset characteristics. The empirically successful models imply substantial conditional Sharpe ratios, along with respectable ordinal and point predictions. Our approach can easily be extended to accommodate higher-order moments and comes with asymptotic theory that can be used with large unbalanced panels.

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