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The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.

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In this special issue , we present 9 articles that study the allocation of capital following the onset of the COVID-19 pandemic. These articles study a broad range of economic settings and agents, including financial technology (fintech) lenders, retail investors, venture capitalists, small businesses, public firms, and the government. Together, they provide a comprehensive, detailed account of the simultaneous allocative shock waves that the pandemic has sent throughout the economy.

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How do cash windfalls affect entrepreneurship evidence from the spanish christmas lottery.

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JFQ volume 59 issue 3 Cover and Front matter

• Journal of Financial and Quantitative Analysis , Volume 59 , Issue 3

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Predictability puzzles.

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Protecting Your Friends: The Role of Connections in Division Manager Careers

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ETFs, Creation and Redemption Processes, and Bond Liquidity

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The Capital Structure Puzzle: What Are We Missing?

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The Determinants of Firms' Hedging Policies

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• Journal of Financial and Quantitative Analysis , Volume 20 , Issue 4

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Finance articles from across Nature Portfolio

Latest research and reviews, speculative culture and corporate high-quality development in china: mediating effect of corporate innovation.

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Impact of CEO’s scientific research background on the enterprise digital level

The impact of religiosity and financial literacy on financial management behavior and well-being among Indonesian Muslims

• Haykal Rafif Wijaya
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Transitioning to low-carbon agriculture: the non-linear role of digital inclusive finance in China’s agricultural carbon emissions

• Jiansheng You

Impact of COVID-19 on jump occurrence in capital markets

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Dynamic analysis of the relationship between exchange rates and oil prices: a comparison between oil exporting and oil importing countries

• Shiying Chen
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News and Comment

Hunger, debt and interest rates

Financial imperatives to food system transformation

Finance is a critical catalyst of food systems transformation. At the 2021 United Nations Food Systems Summit, the Financial Lever Group suggested five imperatives to tap into new financial resources while making better use of existing ones. These imperatives are yet to garner greater traction to instigate meaningful change.

• Eugenio Diaz-Bonilla
• Brian McNamara

Central bank digital currencies risk becoming a digital Leviathan

Central bank digital currencies (CBDCs) already exist in several countries, with many more on the way. But although CBDCs can promote financial inclusivity by offering convenience and low transaction costs, their adoption must not lead to the loss of privacy and erosion of civil liberties.

• Andrea Baronchelli
• Hanna Halaburda
• Alexander Teytelboym

ESG performance of ports

An article in Case Studies on Transport Policy quantifies the environmental, social, and governance performances of three ports.

• Laura Zinke

Venture capital accelerates food technology innovation

Start-ups are now the predominant source of innovation in all categories of food technology. Venture capital can accelerate innovation by enabling start-ups to pursue niche areas, iterate more rapidly and take more risks than larger companies, writes Samir Kaul.

Challenges for a climate risk disclosure mandate

The United States and other G7 countries are considering a framework for mandatory climate risk disclosure by companies. However, unless a globally acceptable hybrid corporate governance model can be forged to address the disparities among different countries’ governance systems, the proposed framework may not succeed.

• Paul Griffin
• Amy Myers Jaffe

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Annals of Finance is a comprehensive resource for original research across all areas of finance.

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• Encompasses a wide range of areas, from financial economics to corporate finance.
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Science or scientism on the momentum illusion.

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Option pricing in a sentiment-biased stochastic volatility model

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The profitability of interacting trading strategies from an ecological perspective

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Natural disasters, public attention and changes in capital structure: international evidence

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Strict certainty preference in the predictive brain: a new perspective on financial innovations and their role in the real economy

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Financial Economics concentrates on monetary activities such as the study of financial variables including prices, interest rates, and shares, as well as risk and other variables related to financial decisions. The Financial Economics Network on SSRN is an open access preprint server that provides a venue for authors to showcase their research papers in our digital library, speeding up the dissemination and providing the scholarly community access to groundbreaking working papers and early-stage research. SSRN provides the opportunity to share different outputs of research such as preliminary or exploratory investigations, book chapters, PhD dissertations, course and teaching materials, presentations, and posters among others. SSRN also helps finance scholars discover the latest research in their own and other fields of interest, while providing a platform for the early sharing of their own work, making it available for subsequent work to be built upon more quickly.

The Financial Economics Network (FEN) helps promote new scholarship in finance and fosters interdisciplinary discovery in areas that intersect with it including accounting, economics, marketing, psychology, and management. On FEN finance researchers can learn what the best minds in the field are working on as well as find the latest and cutting-edge research that may take years before it is published. This not only helps researchers decide what the contemporary thought-provoking questions are but also helps them decipher the openings in and possible extension to the extant literature.

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Household Climate Finance: Theory and Survey Data on Safe and Risky Green Assets

This paper studies green investing in a quantitative asset pricing model with heterogeneous investors calibrated using high-quality, representative survey data of German households. We find substantial heterogeneity in green taste for both safe and risky green assets throughout the wealth distribution. Model counterfactuals show nonpecuniary benefits and hedging demands currently make green equity more expensive for firms. Yet, these taste effects are dominated by optimistic expectations about green equity returns, lowering firms' cost of green equity to a greenium of 1%. Looking ahead, we use our model to trace out the aggregate effects of information provision in an RCT and find green equity investment could potentially double when information about green finance spreads across the population. Regarding safe green assets, our model counterfactuals show that if green deposits could be offered at a 50 basis point interest rate spread, aggregate green investments in the economy could quadruple in the medium run.

We thank Thomas Jansson, Hanno Lustig, Johannes Stroebel, Luke Taylor, Chris Tonetti, Mirko Wiederholt, Ye Zhang, and many seminar and conference participants for their valuable comments. Disclaimers: This paper uses data from the Bundesbank Survey on Consumer Expectations. The results published and the related observations and analysis may not correspond to results or analysis of the data producers. The views expressed in this paper are those of the authors and do not necessarily coincide with the views of the Deutsche Bundesbank, the Eurosystem, or the National Bureau of Economic Research.

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• DOI: 10.1080/20430795.2024.2370325
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Where and how machine learning plays a role in climate finance research

• Andres Alonso-Robisco , Javier Bas , +2 authors J. M. Marques
• Published in Journal of Sustainable… 30 June 2024
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Transition versus physical climate risk pricing in european financial markets: a text-based approach, the double materiality of climate physical and transition risks in the euro area, scope 3 emissions: data quality and machine learning prediction accuracy, climate finance: what we know and what we should know, sustainability, technology, and finance, a bibliometric-qualitative literature review of green finance gap and future research directions, towards climate awareness in nlp research, the role of artificial intelligence in sustainable finance, the pertinence of incorporating esg ratings to make investment decisions: a quantitative analysis using machine learning, application of machine learning models for predictions on cross-border merger and acquisition decisions with esg characteristics from an ecosystem and sustainable development perspective, related papers.

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Dynamics of Demand for Index Insurance: Evidence from a Long-Run Field Experiment

This paper estimates how experimentally-manipulated experiences with a novel financial product, rainfall index insurance, affect subsequent insurance demand. Using a seven-year panel, we develop three main findings. First, recent experience matters for demand, consistent with overinference from small samples. Second, spillovers also matter, in the sense that the recent payout experience of village co-residents affects insurance demand about as much as one's own recent payout experience. Third, the spillover effect decays as time passes while the effect of one's own experience does not. We discuss implications of this analysis for commercial sustainability of this complicated but promising risk management technology.

This paper estimates how experimentally-manipulated experiences with a novel financial product, rainfall index insurance, affect subsequent insurance demand. Using a seven-year panel, we develop three main findings. First, recent experience matters for demand, consistent with overinference from small samples. Second, spillovers also matter, in the...

Expectations of Returns and Expected Returns

We analyze time-series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The evidence is not consistent with rational expectations representative investor models of returns.

We analyze time-series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated...

• August 2014

Mortgage Convexity

Most home mortgages in the United States are fixed-rate loans with an embedded prepayment option. When long-term rates decline, the effective duration of mortgage-backed securities (MBS) falls due to heightened refinancing expectations. I show that these changes in MBS duration function as large-scale shocks to the quantity of interest rate risk that must be borne by professional bond investors. I develop a simple model in which the risk tolerance of bond investors is limited in the short run, so these fluctuations in MBS duration generate significant variation in bond risk premia. Specifically, bond risk premia are high when aggregate MBS duration is high. The model offers an explanation for why long-term rates could appear to be excessively sensitive to movements in short rates and explains how changes in MBS duration act as a positive-feedback mechanism that amplifies interest rate volatility. I find strong support for these predictions in the time series of US government bond returns.

Most home mortgages in the United States are fixed-rate loans with an embedded prepayment option. When long-term rates decline, the effective duration of mortgage-backed securities (MBS) falls due to heightened refinancing expectations. I show that these changes in MBS duration function as large-scale shocks to the quantity of interest rate risk...

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Financial Repression in the European Sovereign Debt Crisis

By the end of 2013, the share of government debt held by the domestic banking sectors of Eurozone countries was more than twice its 2007 level. We show that this type of increasing reliance on the domestic banking sector for absorbing government bonds generates a crowding out of corporate lending. For a given domestic firm, new debt is less likely to be a loan—i.e., the loan supply contracts—when local banks have purchased more domestic sovereign debt and when that debt is risky (as measured by CDS spreads). These effects are most pronounced in the period following the second Greek bailout in early 2010.

By the end of 2013, the share of government debt held by the domestic banking sectors of Eurozone countries was more than twice its 2007 level. We show that this type of increasing reliance on the domestic banking sector for absorbing government bonds generates a crowding out of corporate lending. For a given domestic firm, new debt is less likely...

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Our intellectual roots are based in a long line of scholars from Robert Merton whose collaborative work on risk management and option pricing won him the Nobel Prize in Economics in 1997, to John Lintner who co-created the Capital Asset Pricing Model and made significant contributions to dividend policy , and Gordon Donaldson whose work helped shape the field of corporate finance . We strive to understand how managers and firms make value-enhancing decisions; and how financial institutions, markets, and instruments contribute to this process. Our approach to research is distinguished by its unique combination of theory, empirical analysis, mathematical modeling, and field observations at companies.

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Using data representing one-third of the world’s population, we find that extreme hot and cold days cause substantial labor supply declines for weather-exposed workers, but not for weather-protected workers. With these results and a simple theoretical framework, we calculate that...

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We develop an empirical Bayes ranking procedure that assigns ordinal grades to noisy measurements, balancing the information content of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the race and gender contact gaps of 97 U.S. employers, the identities of which we disclose for the first time. The grades are presented alongside measures of uncertainty about each firm’s contact gap in an accessible report card that is easily adaptable to other settings where ranks and levels are of simultaneous interest.

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Much maligned Google Flu Trends service gets an AI reboot — new AI-infused approach appears in research paper

Google may be considering rebooting its much-maligned Google Flu Trends (GFT) service, which died of embarrassment in 2015. First launched in 2008, GFT overestimated, underestimated, and failed to predict several major flu-related events during its seven-year existence. However, Google researchers recently published a paper outlining a new and improved flu rate prediction model that uses modern artificial intelligence (AI) methodology.

To put it mildly, the original GFT service wasn’t a roaring success. Google’s own AI summation (image above) of GFT highlights several studies that found it inaccurate and thus unusable. It failed to predict the 2009 spring pandemic and also “consistently overestimated the relative incidence of flu” in both 2011 and 2013.

GFT launched in 2008 based upon quite a simple and logical premise – people search Google for flu symptoms when they get ill, and the trending flu symptom searches across regions could be used to pre-warn health agencies that a wave of flu infections was likely so precautions/preparations could be implemented. Thus, GFT relied on a kind of Collective Intelligence (CI), which was shoved into a linear model and would be tweaked across the lifetime of the service, but to little worthwhile effect. Hence, Google killed off GFT estimates in August 2015.

The new Google research paper outlines two key techniques that it is hoped will get better results from analyzing and modeling the huge swathes of user data that Google is privy to. These are outlined as follows:

we introduce SLaM Compression, a way to quantify search terms using pre-trained language models and create a representation of search data that has low dimensionality, is memory efficient, and effectively acts as a summary of search, and

we present CoSMo, a Constrained Search Model for estimating real-world events using only search data. We demonstrate the efficacy of our contributions by estimating with high accuracy U.S. automobile sales and U.S. flu rates using only Google Search data.

Image 1 of 2

Image 2 of 2

You might have heard of similar tech before, as SLaM (Search Language Model Compression) is used for machine learning tasks and has been especially useful in automotive AI. Meanwhile, CoSMo is a new language model (LM) approach that uses around 512 dimensions to predict real-world events.

We must note that Google also seemed quite confident in its science/methods when it originally launched GFT back in 2008. However, this time, we have new AI-related science and even tighter correlations between what the new model would have predicted and what actually happened in history.

Google seems to have found its new approach to be successful, noting, "We also introduce CoSMo, a constrained search model, which has inductive biases that greatly improve the accuracy of our models built on search data. For estimating the flu rates, we show our simple approach is on par or better than the existing complex ensemble methods. [...] Finally, we demonstrate that our models, despite being highly non-linear neural networks, offer interpretability that explains what terms are related to the variables of interest."

Whether the heralded new flu rate modeling research leads to Google resurrecting GFT remains to be seen. However, it demonstrates that Google is still interested in perfecting its protection tech, which could be applied to a wide range of potential uses that would eventually make the company yet more money.

Three big moves that can decide a financial institution’s future in the cloud

Most financial institutions today have a presence in the cloud, but adoption in the financial-services sector is still at a relatively early stage. Among the financial-services leaders who took part in a recent McKinsey survey, only 13 percent had half or more of their IT footprint in the cloud. But migration to the cloud is gathering momentum. More than half of the survey respondents—54 percent—said they expect to shift at least half of their workloads to the public cloud over the next five years. 1 McKinsey survey of 120 C-level executives from regional banks, large commercial banks, payments providers, and other financial institutions, conducted in 2020; McKinsey Infrastructure and Cloud webinar 2020.

Given the value at stake, this sense of urgency is hardly surprising. A McKinsey analysis found that Fortune 500 financial institutions alone could generate as much as $60 billion to $80 billion in run-rate EBITDA in 2030 by making the most of the cost-optimization levers and business use cases unlocked by cloud.

Some early adopters are already making inroads into this pool of value. One European bank was able to deliver the same output with 20 to 30 percent smaller teams, after onboarding them on DevSecOps and cloud. Another bank in Asia that migrated more than half of its workloads to the cloud can now develop and launch multiple new products rapidly and at scale in international markets. And another European bank has partnered with a leading cloud service provider (CSP) to develop AI-based cyber-defense capabilities to improve security for its customers.

These examples are still outliers in the financial sector, where most companies have been tentative about moving to cloud at scale. There is good reason for this hesitancy, since cloud migration is uniquely complex for financial institutions. Furthermore, the IT landscape at financial institutions is particularly varied, with 40-year-old applications running alongside more modern systems.

These challenges and others have led financial institutions to move in a more incremental fashion when it comes to cloud, running limited experiments, for example, or targeting a subset of applications based on the ease of migrating them, or phasing their efforts to coincide with a planned exit from a particular data center. Focusing on a few of these kinds of high-impact “lighthouses” can be effective in creating early momentum. However, institutions that do not define an overall aspiration and put in place the right success factors to achieve it often fail to capture value from the cloud.

Three shifts to accelerate your cloud migration

Working with dozens of financial institutions on their cloud migrations, we have found that those seeking to evolve beyond nascent cloud programs need to make critical shifts across three dimensions: strategy and management, business-domain adoption, and foundational capabilities (exhibit). Which dimensions they choose to prioritize or emphasize will depend on their particular needs and the stage they have reached in their cloud journey.

1. Strategy and management

From ‘we need to experiment’ to ‘cloud is in our future’.

The most important step a financial institution can take in capturing cloud’s value is building awareness across the organization about the practical value of cloud as distinct from the exciting marketing material from vendors.

One route is to use lighthouses to demonstrate the future value potential and make them truly scalable. We find, however, that many institutions use what they call lighthouses as limited-life-span experiments. Persuading them to change that mindset and to treat them instead as “incubators”—which, with the right support and capability building, could be practical at-scale destinations themselves—is a big unlock. The best way to convince a CFO that cloud can reduce total cost of ownership or a business leader that cloud can speed innovation is to demonstrate that it does.

Another route is to work with CSPs as partners rather than vendors. Striking strategic deals can lower barriers to entry, especially costs, and signal full-scale commitment to cloud across the organization. This happened at one North American bank, which had been struggling to make much progress in its cloud migration. Technology was leading the charge, but it lacked sufficient investment and a plan for scaling, primarily because it couldn’t win the business’s support.

Realizing that this “slow-roll” approach would not scale, the CEO and business leadership got involved. They approached several CSPs to structure a strategic partnership with a primary provider. Not only did they manage to secure significant discounts to offset initial “bubble” costs, but the process also forced the bank to take a more comprehensive approach to its cloud migration in order to take advantage of all the services that the CSP offered.

The process also led them to secure a commitment from the CSP to train the bank’s staff on key tools and capabilities and co-invest in innovative propositions that could take advantage of the assets of the CSP’s parent company, such as its ecosystems and marketplaces. When the deal was announced, it was a clear signal internally and externally of the bank’s commitment to the migration, which quickly led many of those who had been resistant to get on board. The bank is now on track to migrate 70 percent of its applications to the cloud within three years.

Still another approach is to develop a comprehensive business case built around specific levers and use cases. Those break down into technology benefits in the form of better resiliency, lower maintenance and operations costs, and elastic infrastructure to meet varying demand, as well as business benefits, such as speedier innovation, lowered costs to experiment, and the ability to scale up advanced analytics. In this way, institutions can place less emphasis on the theoretical value of cloud and use the business case as a practical guide to real value, which makes it easier for the organization to understand and support the goals of a migration.

Would you like to learn more about Cloud by McKinsey ?

2. business-domain adoption, from ‘make it run better’ to ‘make our business more valuable’.

If an institution treats cloud migration as a way to improve IT, it will struggle to capture the cloud’s full value. Moving cloud out of the realm of an IT project to a business-backed initiative requires two things:

• First, change the operating model. Companies that have the most success have a working model where technology and business work together in cross-functional teams . This approach orients the entire cloud migration toward the business value it might generate.
• Second, start your migration at the domain level—a complete product, service, or function, such as the checking suite or security foundation—rather than by opportunistically moving disparate applications. Migrate one business domain and use it to build a repeatable approach, complete with support skills, that can be rolled out domain by domain across the institution. In the interest of practicality, companies sometimes choose to start with applications, which are easier to migrate, as a way to build skills and experience. But the full value of migration comes when those applications are mutually reinforcing within a domain. One institution calls this effect “app magnetism.” Within these parameters, joint teams calibrate the level of application modernization needed to capture business benefits and then build a pipeline of business use cases that can be enabled in the cloud, such as advanced analytics use cases, AI-enabled process automation, and innovative customer journeys.

One leading payments company initially struggled to make much progress on its cloud aspirations because it was limited to an “IT initiative.” That changed when it needed to integrate a major acquisition. Successful integration required closer collaboration between the business and technology groups, which allowed the company to shift its cloud strategy to a top business priority, a significant unlock.

In addition, the deal enabled the company to pilot new products on cloud as well as to modernize its IT’s core transaction-processing system. The company has since mandated that all new development will occur only in the cloud platform.

Over and above the benefits to IT, such as the consolidation of data centers and greater cost efficiency, the business benefits have been significant. The company has increased the velocity of its application modernization by 300 percent, improved data integration between the parent company and the acquisition, and established protocols that support the easy reuse of applications or features developed for different use cases. This has both decreased the time required to launch new products and increased customer satisfaction.

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3. foundational capabilities, from ‘migrate apps but keep the same processes in place’ to ‘automate as much as possible and install a hybrid ops foundation’.

The short-term, incremental approach to cloud migration creates significant barriers that make it all but impossible to scale. For instance, defaulting to on-premises security controls, which are not well suited to the cloud, leads to delays or, worse, breaches. Investing in migrating apps without investing in a strong cloud foundation creates an economic reality where each successive app costs at least as much if not more to migrate than the first one. That’s because this approach doesn’t address the underlying infrastructure, security, and governance processes and merely transfers to the cloud existing process and operational issues that increase the “ tech debt ” the cloud is creating for management.

Building out an effective cloud foundation requires doing a variety of things, such as setting up the right number of isolation zones to limit fallout from issues affecting any one application. One of the most important actions is to automate everything that is possible to automate. Successful cloud innovators do the following:

• automate infrastructure processes through infrastructure as code (IaC)
• implement end-to-end application patterns that can be consumed as code by developers to enable a frictionless, self-serve experience
• use automated continuous integration/continuous delivery (CI/CD) pipelines
• adopt “policy as code” (PaC) and “ security as code ” (SaC)

SaC essentially automates the testing of application and infrastructure code to ensure that it meets security, resiliency, and compliance needs using policies that are instantiated as code rather than in word-processing documents. Any code that doesn’t meet these policy requirements is automatically rejected before it’s deployed. What needs to be corrected is clearly articulated so that the code can come into compliance.

When properly implemented, this SaC approach can also allow companies to more easily and clearly meet regulatory requirements and satisfy audit needs without significant disruptions. To define how the new foundations will improve the institution’s compliance, security, and resilience, top institutions integrate their risk functions across all three lines of defense.

This focus on automation also extends to FinOps (financial operations), the process of dynamically managing application costs in the cloud. Because the cloud is so dynamic—new servers can come online as needed, and capacity can be extended to meet unforeseen spikes in usage—automating finances can help to flag or adjust financial issues to keep costs in line with the business’s goals.

Lastly, leading institutions also rewire their operating model across application development, infrastructure, risk, and security to take full advantage of the automation enabled by cloud, particularly during the period before the full cloud migration is complete. This requires DevOps and site-reliability-engineering (SRE) practices, productized infrastructure services, outcome-driven governance, and engineering-centric capabilities. This “hybrid ops” management of both on-premises and cloud operations—for incident management, as an example—can set the stage for the institution’s eventual cloud destination while making sure nothing slips during the extended period when it is operating in more than one environment.

When one US regional bank began its migration, it planned to move 40–50 percent of its IT workloads to the cloud within three years, with the rest following in the next few years. But halfway through the first phase of the effort, it found that provisioning cloud infrastructure, such as environments, network changes, and access and identity management, still took three to four months—nowhere near the target of less than 24 hours. Only some aspects of the process had been automated, forcing application teams to continue using manual security controls and ticket-based requests, which introduced significant delays, limited the agility that cloud was expected to bring, and created additional risk.

Leaders decided to hit pause in order to build technical tools and capabilities that would enable them to make faster progress in the future. To lay a firm foundation, they committed to fully automating their cloud foundation and security controls. Another key step was to streamline policies and governance processes to take advantage of automation and minimize manual handoffs. Thanks to these and other efforts, the bank now expects to complete its cloud migration and exit its data centers ahead of its original schedule.

While most financial institutions are still early in their cloud journey, we are already seeing a widening gap in success between those taking a tentative, experimentation approach and those working backwards from a well-defined destination to architect lighthouses and a plan characterized by the three shifts outlined here. We believe this offers financial institutions their best chance of capturing the significant business value cloud can offer.

Chhavi Arora is a partner in McKinsey’s Seattle office, Aaron Bawcom is a distinguished cloud architect in the Atlanta office, Xavier Lhuer is a partner in the New York office, and Vik Sohoni is a senior partner in the Chicago office.

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