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Finance articles from across Nature Portfolio

Fair energy finance increases global equity in the green energy transition

Five climate–energy–economy models are used to explore the effect of reducing the cost gap in energy financing between developed and developing countries through fair-finance. Such convergence is projected to increase energy availability, affordability, and sustainability in developing countries, thereby improving energy justice.

Latest Research and Reviews

Qualitative insights into a scholarship scheme designed to optimise test scores and expedite sdg-4 actualisation in nigeria.

• Chris Ifediora
• Karen Trimmer
• Etomike Obianyo

The impact of social media discourse on financial performance of e-commerce companies listed on Borsa Istanbul

• Larissa M. Batrancea
• Mehmet Ali Balcı
• Anca Nichita

Financial and market risks of bitcoin adoption as legal tender: evidence from El Salvador

• Griffin Msefula
• Tony Chieh-Tse Hou
• Tina Lemesi

Enterprise digital transformation, managerial myopia and cost stickiness

• Panpan Feng
• Yongjian Huang

Foreign direct investment, total factor productivity, and economic growth: evidence in middle-income countries

• Hoa Thanh Phan Le
• Khoa Dang Duong

Communication dynamics: Fintech’s role in promoting sustainable cashless transactions

• Weidong Huo
• Wang Xiohui
• Muhammad Rizwan Ullah

News and Comment

Exploring the multifaceted impacts of artificial intelligence on public organizations, business, and society.

Along with the rapid development of information and communication technology (ICT), artificial intelligence (AI) has increasingly become a core component of the daily activities of businesses and society in general. AI is having multifaceted impact across public organisations, businesses, and societies, and as such merits scrutinty and ongoing scholarship. There are many opportunities that can be gained from the implementation of AI, from reduced production or lower labour costs to more efficient and affordable automation solutions. However, the extent to which AI has the potential to take over key tasks and the decision-making process at the individual, organisational, or societal level remains to be seen.

• Muhammad Anshari

Sustainable cost recovery principles can drive equitable, ongoing funding of critical urban sanitation services

This Comment critiques current urban sanitation financing discourse and proposes sustainable cost recovery principles as a framework for more constructive conversations. The way we talk about financing matters, and a better conversation can lead to better outcomes. We contend that framing discussions around sustainable cost recovery principles can foster fairer, more sustainable financing arrangements that acknowledge sanitation as a critical public good while ensuring service provider viability and user affordability.

• Naomi Carrard
• Juliet Willetts
• Rajeev Munankami

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

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Article Contents

1. the “big data” revolution, 2. what is big data in finance research, 3. what is included in this special issue, 4. where does big data research go from here, acknowledgement, big data in finance.

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• Supplementary Data

Itay Goldstein, Chester S Spatt, Mao Ye, Big Data in Finance, The Review of Financial Studies , Volume 34, Issue 7, July 2021, Pages 3213–3225, https://doi.org/10.1093/rfs/hhab038

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Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

The digital age has created mountains of data that continue to grow exponentially. The International Data Corporation estimates that the world generates more data every two days than all of humanity generated from the dawn of time to the year 2003. This “big data” revolution is reshaping the financial industry. As the Wall Street Journal wrote, “Today, the ultimate Wall Street status symbol is a trading floor comprising Carnegie Mellon Ph.D.s, not Wharton M.B.A.s.” 1 This industry transition has already started to affect the way we teach students. Along with the drop in the number of Master of Business Administration (MBA) programs, as well as the decline in applications and enrollment in MBA programs, 2 we see a surge of new programs such as Master of Business Analytics (also MBA).

The impact of big data on academic research in finance is also starting to reveal itself, but with it many questions emerge. The classical definition of big data as encompassing three V’s (volume, velocity, and variety) has a strong relation to engineering and computer science, but it does not fully reflect the opportunities and challenges that big data poses to research and practice in finance. What does big data in finance actually mean? How can financial economists benefit from the big data revolution? Does big data open new research topics for financial economists or allow us to answer traditional questions in novel and more revealing ways? Is this really a revolution for finance research or just a continuation of a gradual change? After all, large datasets always have been a feature of research in finance.

In October 2018, the National Science Foundation (NSF) provided a joint grant to the National Bureau of Economic Research (NBER) and the National Center for Supercomputing Application (NCSA) at the University of Illinois at Urbana–Champaign that aimed to explore answers to these questions. Part of the grant is dedicated to education and outreach and support a series of NBER conferences to explore the future of big data research in finance. The summer conferences, organized by Toni Whited and Mao Ye, focus on tutorial sessions on big data techniques and presentations of early ideas on big data. The winter conferences, organized by Itay Goldstein, Chester Spatt, and Mao Ye, focus on completed papers using big data and related methodologies.

This special issue of the Review of Financial Studies (RFS) on big data in finance includes four papers from the first NBER-RFS Winter Conference on Big Data held on March 8, 2019, and two other papers that are closely related to this theme. The RFS has the tradition of encouraging scholars to pursue risky projects that have the potential to push the frontiers of research in finance. The NBER-RFS Conferences on Big Data and this special issue reflect the RFS’s efforts to encourage the use of big data in finance studies and provide a natural complement to the RFS FinTech initiative that was featured in the May 2019 special issue (see Goldstein, Jiang, and Karolyi 2019 for an introduction).

In this introduction, we try to define what “big data” encompasses in the context of finance research. We then review the six papers included in the special issue, discussing how they are related to each other and to the general theme. Finally, we provide some thoughts for future research directions.

It is fairly clear that a definition of big data in finance research should be different from ones that are used in engineering and statistics. Researchers in these disciplines focus on providing facilities and tools to capture, curate, manage, and process data. Financial economists, on the other hand, focus on applying these tools to address interesting economic questions. While it is risky to give a broad-based definition at this stage, we think it is important to try. The definition may be imprecise or incomplete, but it will provide a starting point for future iterations and corrections.

We thus propose three properties that together can potentially define big data in finance research: large size, high dimension, and complex structure. This definition combines the characteristics of the data with possible new research questions that cannot be addressed using “small data.” We used this definition in our call for papers for the 2019 NBER-RFS Winter Conference. “Big data” papers can feature different combinations of these three properties. We now elaborate on what each of these properties captures.

Large size: As the term “big data” suggests, it would be impossible to avoid a reference to size. This feature means that data are large in an absolute or relative sense. A natural example for absolute size is transaction-level market microstructure data. 3 In a relative sense, big data is defined relative to the best existing “small data.” Many datasets are small simply because they are a subset of a larger dataset. By subsampling or aggregating observations into categories or taking snapshots of activities in time series, large datasets are made smaller. Using the underlying larger dataset is important if it overcomes the sample selection bias in the small dataset, or if it captures important economic activities not depicted in the small dataset.

High dimension: “Big data” is not just about size. The second feature means that the data have many variables relative to the sample size. Machine learning, which is often thought of as a hallmark of big data research, is a common solution to the dimension challenge, and it is increasingly used in finance research. Machine learning techniques become economically meaningful if they satisfy, but are not limited to, the following criteria: (i) the actual economic problem involves lots of variables; (ii) the impact of the variables is highly nonlinear or involves interaction terms among the variables (high dimensionality of function class); and (iii) prediction is more important economically than statistical inference. The most natural research questions occur when the decision-makers are machines, such as algorithmic traders or robo-advisors.

Complex structure: Finally, another important feature is that data are not in the traditional row-column format. Unstructured data include text, pictures, videos, audio, and voice. Unstructured data create value if they can measure economic activities that cannot be captured using structured data. Unstructured data are often high-dimensional by nature. The first step to analyze the data is usually to extract features from the unstructured data, often with help from deep learning and computer science. For example, researchers may extract semantic information from text using natural language processing (NLP), identify tone information from voice and audio using speech recognition, and recognize geographic or facial information from images and videos using computer vision (CV).

Overall, as these features reveal, big data is not only about the size of the data, but also about other characteristics. Developments in all three categories—increased availability and capability of handling large datasets, developments in methodologies to deal with high dimensionality, and the emergence of complex datasets with new methods for processing them—have led to the increased prominence of big data in finance research.

Each of the six papers in this special issue fits into one or more of these three categories. Anand et al. (2021) analyze the agency conflicts between brokers and their customers using a particularly large dataset established by the Financial Industry Regulatory Authority (FINRA) called the Order Audit Trail System (OATS). The dataset is big also in the relative sense because the OATS data include publicly unavailable information on broker identity and do not suffer from attrition and sample selection bias from self-reported data. Easley et al. (2021) also analyze large market microstructure data and, due to high dimensionality, apply machine-learning techniques to evaluate the effectiveness of traditional market microstructure measures after machines started dominating trading. The dataset in Giglio, Liao, and Xiu (2021) is distinctive not for its size, but for its high dimensionality. They also use machine-learning techniques to develop a new framework to deal with data snooping, a major concern in empirical asset pricing. Unlike the study by Giglio, Liao, and Xiu (2021) , where high dimensionality comes from a large number of hypothesis tests that may lead to false positives in multiple testing, the high dimensionality in the paper by Erel et al. (2021) comes from the interaction terms and nonlinearity. Erel et al. (2021) show that machines can dominate humans in choosing directors, perhaps because machines suffer less from biases or agency conflicts. Papers by Benamar, Foucault, and Vega (2021) and Li et al. (2021) both use unstructured data. Benamar, Foucault, and Vega (2021) measure information demand and uncertainty using clickstream data provided by a vendor that transforms unstructured data into structured data. Li et al. (2021) transform unstructured data themselves and develop a measure of corporate culture from textual data based on earnings calls.

These six papers cover topics in asset pricing, corporate finance, and market microstructure, demonstrating the broad scope of big data techniques in finance research. We now turn to describe these papers in more detail, their relation to one another, and to the broader theme.

In the first paper in the special issue, Erel et al. (2021) show that machine learning can outperform the actual selection of new board members, currently done by humans. They demonstrate that directors who algorithms predict will perform poorly indeed do, compared to a realistic pool of candidates in out-of-sample tests. 4 Relative to algorithm-selected directors, management-selected directors who later receive predictably low shareholder approval are more likely to be male, have larger networks, and sit on more boards. One possibility is that firms that nominate predictably unpopular directors tend to be subject to homophily, while the algorithm selects a more diverse board. The authors also find that firms that nominate predictably poor directors suffer from worse corporate governance structures, which suggests that agency conflicts could be a driver for the distortion in selecting directors.

The analysis in this paper is among the first applications of machine-learning methods in corporate finance, demonstrating the broad appeal of these methods across areas of finance. The authors demonstrate the usefulness of these methods by showing that traditional OLS results are unable to adequately predict director performance. They attribute these findings to nonlinearity and interactions among variables being key in predicting future performance. These results raise interesting questions for future research, trying to understand why the interaction among variables and/or the nonlinearity in the effects of different variables are so important.

Machine learning in a corporate finance context is a key characteristic of the second paper in the special issue, written by Li et al. (2021) . The authors try to quantify the notion of corporate culture and understand its implications across firms. Corporate culture is important because it is perceived to be a key factor behind many business successes and failures ( Graham et al. 2018 ), and it is thought to be able to solve problems that cannot be regulated properly ex ante ( Guiso, Sapienza, and Zingales 2015 ). Data challenges have always made studying corporate culture a formidable task. Despite the boom in empirical studies since the mid-1980s, 5 variables of economic interest may not be measured perfectly with structured data. Indeed, in the interview evidence by Graham et al. (2018) , corporate executives suggest 11 sources of data to measure corporate culture, most of which are unstructured data.

Li et al. (2021) make progress by using NLP models to extract key features of corporate culture from earnings call transcripts, which is one source of data suggested by corporate executives. They use a semi-supervised machine-learning approach with word embedding for textual analysis instead of the traditional “bag of words” approach ( Loughran and McDonald 2011 ). The “bag of words” approach is good at predicting the tone of a document by counting positive or negative words, but it is hard to capture important semantic information in an earnings call. The authors provide a method to decompose corporate culture onto a five-dimensional space of innovation, integrity, quality, respect, and teamwork, which are the five most-often mentioned values by the S&P 500 firms (see Guiso, Sapienza, and Zingales 2015 ). Guiso, Sapienza, and Zingales (2015) find that the culture-performance link is more significant during periods of distress, and that corporate culture is shaped by major corporate events, such as mergers and acquisitions. They show that firms scoring high on the cultural values of innovation and respect are more likely to be acquirers, and firms closer in cultural value are more likely to merge.

Another area where machine-learning methods have much unexploited potential is market microstructure. The third paper in the special issue, by Easley et al. (2021) , explores an application for analyzing whether machine-based trading affects the efficacy of market microstructure measures that were developed before machines dominated trading volume. Specifically, Easley et al. (2021) examine whether six extant market microstructure measures—the Roll measure, the Roll impact, 6 volatility (VIX), Kyle’s |$\lambda$|⁠ , the Amihud measure, and the volume-synchronized probability of informed trading (VPIN)—can still predict the future values of price and liquidity.

The authors find that the answer is still positive after the rise of high-frequency and machine-based trading. The functional form to make such predictions, however, depends on the application. For example, for making predictions within the same asset, a simple logistic regression performs almost as well as complex machine-learning techniques. One explanation is that there is already a deep understanding of the market structure for a single asset. For making predictions across assets, however, machine learning strictly dominates simple logistic regression. 7 Although the rise of high-frequency and machine-based trading has made cross-asset trading more the norm, few market microstructure theories show how these cross-asset effects should, or even could, occur. Easley et al. (2021) provide strong evidence that the interactions among assets can predict market outcomes and that machine learning helps address challenges from high dimensions in cross-asset market microstructure.

Thinking about big data in the context of market microstructure research more broadly, it is often noted that large datasets were the norm in this literature for a long time. Yet, the fourth article in the special issue, by Anand et al. (2021) , pushes the boundary in this sense, analyzing a particularly large dataset to identify agency conflicts between institutional traders and their brokers. To find such agency conflicts, it is very instructive to know the brokers’ identities, which are missing in the publicly available TAQ data. Self-reported data would suffer from attrition or sample selection bias issues. Anand et al. (2021) use OATS data to surmount these two challenges, as it is comprehensive regulatory data from FINRA.

The authors find that brokers, who route more orders to affiliated alternative trading systems (ATSs), offer lower execution quality (lower fill rates and higher implementation shortfall costs) for their customers. Therefore, these brokers take the private benefit by increasing the market share and fee revenues of their own ATSs, but do not necessarily satisfy their fiduciary responsibilities to achieve the best execution for their customers. As Anand et al. (2021) use a large and comprehensive dataset, a subsample of the dataset can still generate enough statistical power, which allows the authors to establish causality using a unique controlled experiment that overlaps with their sample period: the SEC Tick Size Pilot (TSP). Based on a triple-difference analysis, the authors find that execution quality improves for TSP-treated stocks for orders handled by brokers who prefer affiliated ATSs since the TSP imposes constraints on brokers to route orders to ATS venues.

The fifth paper in the special issue, written by Benamar, Foucault, and Vega (2021) , also analyzes a large dataset in the context of trading in financial markets. Another important feature of this paper is the processing of unstructured data. Here, unlike in Li et al. (2021) , who process such data themselves, Benamar, Foucault, and Vega rely on commercial data vendors that preprocessed the raw and unstructured data into structured data. This is part of the trend in the era of big data: along with the boom of data availability, the data vending industry has grown as well. J. P. Morgan’s Big Data and AI Strategies report provides a 78-page summary of available data vendors. 8 Benamar, Foucault, and Vega (2021) measure information demand with webpage clickstream statistics from Bitly, a URL-shortening service provider. 9 They use this to understand the role of uncertainty in financial market trading, a topic that has long occupied academics studying financial markets.

Benamar, Foucault, and Vega (2021) show that information demand is a good proxy for uncertainty because, based on their theory, an exogenous increase in an asset’s uncertainty motivates investors to search for more information on it. The search for information, however, cannot fully neutralize the increase in uncertainty. Thus, a stronger information demand about future interest rates ahead of macroeconomic and monetary policy announcements (MMPAs) implies that U.S. Treasury yields exhibit both higher uncertainty and stronger sensitivity to MMPAs. They find that a one-standard-deviation increase in the number of Bitly clicks on the news related to nonfarm payroll (NFP) in the two hours preceding NFP announcements raises the sensitivity of U.S. Treasury note yields by 4 to 6 basis points (bps), depending on maturity. The increase is economically significant because the unconditional sensitivity of U.S. Treasury note yields to NFP announcements varies between 3. 5 bps and 7 bps (depending on maturity) during their sample period. They also find that such predictability mostly comes from clicks within two hours before the announcement, which highlights the usefulness of high-frequency data for measuring information demand and uncertainty.

Finally, closing the special issue is the paper by Giglio, Liao, and Xiu (2021) . This paper belongs to the asset pricing literature, in which machine-learning methods have already been explored in some depth. A recent special issue of the Review of Financial Studies featured some of this research in the context of new methods for the cross-section of returns (see Karolyi and Van Nieuwerburgh 2020 for an introduction). Giglio, Liao, and Xiu (2021) show how machine learning can be applied by proposing a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, with a focus on addressing data snooping.

The dimension challenge in Giglio, Liao, and Xiu (2021) comes from multiple testing—that is, when trying to identify which factors in the “factor zoo” add explanatory power for the cross-section of returns or to identify which funds among thousands of funds can produce positive alpha. If the number of tests is high due to a large number of factors or funds, a potentially large fraction of the tests will be positive purely by chance and lead to a high false discovery rate. Giglio, Liao, and Xiu (2021) solve data snooping and false positives using a combination of matrix completion, wild bootstrap, screening, and false discovery control. Matrix completion, a machine-learning technique, helps them to interpolate missing data and latent factors. The latent factors constructed from machine learning correct correlation among alpha test statistics. Bootstrap and screening improve the robustness of multiple testing in a finite and skewed sample. The authors illustrate their framework using a hedge fund dataset, but their toolbox can be applied in other asset pricing research as well.

The six papers in this special issue can provide a starting point for discussing big data in finance. As a burgeoning field, big data and machine learning raise many new questions. We discuss several promising lines of research. We believe the list will continue to grow and be refined over time.

4.1 Machine learning and learning machines

To date, most research using machine learning, including papers in this special issue, use machine learning to understand human behavior. One promising area of machine learning in finance is when the decision-makers are machines. For example, most existing machine-learning research in asset pricing uses monthly return data from CRSP or quarterly holding data from 13F filings. Yet traders who apply machine learning techniques often operate at a horizon that is much less than a month. Hedge funds such as Renaissance, Two Sigma Investments, D. E. Shaw Group, PDT Partners, and TGS Management Company make thousands of trades and manage tens of billions of dollars in investor assets. 10 These firms, which are faster than most traditional funds but slower than high-frequency traders, are largely outside the radar of the academic finance literature. One exception is Chinco, Clark-Joseph, and Ye (2019) , who find that machine learning aims to predict news at the minute-by-minute horizon. A promising new line of research is to bridge the gap between studies that focus on the monthly horizon or above and the studies on high-frequency traders, which focus on horizons below a second. In this underexplored territory, applying machine learning is not only natural but also necessary. Just as insights into human behavior from the psychology literature spawned the field of behavioral finance, so can insights into algorithmic behavior (or the psychology of machines) spawn an analogous blossoming of research in algorithmic behavioral finance.

4.2 Feedback effects of the big data revolution

Once machines become decision-makers, will corporations change their behavior? The widespread application of machine learning in the investment community and the feedback effects between the secondary market and corporate decisions ( Bond, Edmans, and Goldstein 2012 ) imply that firms should respond to the big data revolution. While no papers in this special issue examine feedback effects, we saw related studies at the 2020 NBER-RFS Winter Conference on Big Data. Cao et al. (2020) find that firms adjust their 10-Ks and 10-Qs to cater to machine readers. The next step following their research is perhaps to examine whether firms react to the big data revolution when making real decisions. For example, as investors increasingly become machines, will firms increasingly pursue shorter-term projects? Does the advent of “big data” reduce managers’ incentives to learn from market prices because firms now have more information sources, or does it increase incentives because prices aggregate more information from the “big data” collected by investors?

4.3 Heterogeneous impact of the big data revolution

Although big data provides more information for sophisticated players such as institutional investors and firms, the impact of big data may not always be positive. Chawla et al. (2019) show that social media, which allows enthusiasm for the market to spread much more widely than it would have otherwise ( Shiller (2015) ), can push price away from fundamentals. In Chawla et al. (2019) , the price pressures led by retail traders quickly revert, probably because sophisticated arbitragers rapidly jump in and trade against retail behavioral bias. We witnessed a much more significant impact of social media during the GameStop episode in January 2021. Retail traders coordinated using social media, resulting in the hedge fund Melvin Capital losing 53%. 11 The interaction between retail and sophisticated investors leads to extreme market volatility. The impact of big data on different types of agents and its aggregated effect on society will be an interesting new direction to explore.

4.4 More complex data

Big data in finance starts from analyzing large-size data such as trades and quotes. More recent development allows researchers to use natural language processing (NLP) to extract information from unstructured data such as text ( Gentzkow, Kelly, and Taddy 2019 ). A promising research line is to analyze data of more complex structures, such as audio, video, and images if these more complex data provide additional insights. For example, Li et al. (2021) use the transcripts of earnings call as input for their analysis in this special issue. The earnings call transcripts are small data when we compare them with the audio file that generates the transcripts. Mayew and Venkatachalam (2012) show that managerial vocal cues contain information about a firm’s fundamentals, incremental to information conveyed by linguistic content. As the NBER-RFS Big Data Conference evolves, we see submissions using more complex datasets, such as satellite images ( Gerken and Painter 2020 ). More complex datasets create value for finance researchers if they measure economic activities that cannot be captured using simpler data.

4.5 Regulations

As machines start to be major players in many areas such as trading ( Angel, Harris, and Spatt 2015 ), it will be interesting to examine whether existing regulations, which are designed mostly for humans, need to be adapted to an environment with machines. O’Hara, Yao, and Ye (2014) provide one example for such need. Regulators used to consider trades of less than 100 shares to come from retail traders, and would exempt these odd lots from the reporting requirement. Yet informed traders later became major sources of odd lots by using algorithms to slice and dice their orders to less than 100 shares to escape the reporting requirement. While much of our financial regulatory system focuses on actual realized transactions, assessing problematic aspects of the underlying algorithms is arguably more fundamental and cuts to the heart of such issues as the possibility of front running by market markers, whether brokers have satisfied their best execution responsibilities, and whether insiders are exploiting informational advantages. Spatt (2020) discusses how regulations designed years ago need to be adapted to modern reality. The traditional focus of regulators has not emphasized biases in specific algorithms.

The other promising line of research on big data will be on privacy regulations and the fairness of algorithms and data (e. g., Kearns and Roth 2020 ). The question becomes extremely important because algorithms and data increasingly became a major resource for the economy, particularly for finance. Back in 2017, the Economist published a story titled “The World’s Most Valuable Resource Is No Longer Oil, but Data,” which called for new regulations for the data economy. 12 Who owns the data, what is the price of the data, and what is the impact of unfair access to data? Easley, O’Hara, and Yang (2016) provide a theoretical analysis of the issue. It would be interesting to explore this topic empirically.

The papers in this special issue are predominantly empirical, but theoretical work is also important for big data in finance. Although high-dimensional data are often defined as when the number of variables is larger than the number of observations ( Martin and Nagel 2019 ), the dataset frequently used in finance research is typically large enough to cover the number of variables. The success of machine learning often comes from high-order interaction terms between variables ( Mullainathan and Spiess 2017 ). Indeed, the success of machine learning for the papers in this issue also comes from nonlinear terms and interactions between variables. Such high-order interactions are a natural place to develop new theoretical models to explain why one economic variable’s impact depends on its interaction with another variable. The nonlinearity also motivates theory models to explain why a variable’s impact depends largely on its value. Machine learning is one way to describe the world, and we also need theory to explain the world.

Theory may become more important in the era of machine learning and artificial intelligence for one simple reason. Human judgment can be inconsistent, whereas machines tend to make consistent decisions based on their model. Li and Ye (2020) find that their theory model can generate quantitatively accurate predictions for market liquidity in cross-section and after corporate events such as stock splits, probably because liquidity providers are now algorithms, and these algorithms probably make decisions using similar models to the theoretical models in Li and Ye (2020) .

4.7 Interdisciplinary collaborations

Future work on big data in finance may involve more scholars from other fields. We believe such collaborations will expand the tools and scope of research in finance and economics and help researchers overcome big data challenges.

Researchers can overcome the large-size challenge by collaborating with supercomputing centers. The NSF’s Extreme Science and Engineering Discovery Environment Project (XSEDE) provides computing resources and staff support to manage and store large datasets free of charge. NBER has posted videotaped lectures for researchers in economics and finance on the application process for such free resources on the webpage for the 2018 Summer Conference on Big Data. 13

Researchers can overcome the high-dimension challenge and the complex-structure challenge by collaborating with scholars from the fields of math, statistics, and computer science. The recent development in deep-learning models like natural language processing (NLP), speech recognition, and computer vision (CV) helps researchers parse textual, verbal, and visual data. Researchers can also choose to work with data vendors. J. P. Morgan’s Big Data and AI Strategies report provides a list of vendors for alternative data, such as satellite photos, sentiment measures, and credit card usages.

The NSF lists big data as one of its 10 big ideas and provides funding to support innovative, interdisciplinary research in data science. We hope this special issue is only a starting point, and that we will see more research at the intersection of big data, finance, and public policy for many years.

This introduction is written for a special issue of the Review of Financial Studies focused on big data in finance. The authors thank Ken French, Harrison Hong, Wei Jiang, Andrew Karolyi, and Jim Poterba for comments. We thank Jim Poterba and Carl Beck for help with the NBER Workshops on Big Data. Ye acknowledges support from National Science Foundation grant 1838183 and the Extreme Science and Engineering Discovery Environment (XSEDE).

1 G. Rogow, “Meet the New Kings of Wall Street,” Wall Street Journal , May 21, 2017, https://www.wsj.com/articles/the-quants-meet-the-new-kings-of-wall-street-1495389163 .

2 C. Cutter, “Elite MBA Programs Report Steep Drop in Applications,” Wall Street Journal , October 15, 2019, https://www.wsj.com/articles/elite-m-b-a-programs-report-steep-drop-in-applications-11571130001 .

3 One day of current option trading data alone is roughly two terabytes. In the 2019 NBER-RFS Summer Conference on Big Data supported by the same NSF grant, the chief economist of the U.S. Securities and Exchange Commission (SEC), S. P. Kothari, pointed out that one of the biggest data collection efforts in finance is the Consolidated Audit Trial (CAT), which provides a single, comprehensive database enabling regulators to track more efficiently and thoroughly all trading activity in equities and options throughout the U.S. markets. https://www.sec.gov/news/speech/policy-challenges-research-opportunities-era-big-data .

4 The task of measuring the performance of an individual director is challenging because directors generally act collectively on the board. The authors’ main measure of director performance is the level of shareholder support in annual director reelections, because Hart and Zingales (2017) emphasize that directors’ fiduciary duty is to represent the interests of the firm’s shareholders.

5 See Einav and Levin (2014) .

6 Roll impact is the Roll measure divided by the dollar value traded over a certain period.

7 The cross-asset effects in their paper mean using market microstructure measures in one asset, such as equity futures, to predict price and liquidity dynamics of another asset, such as fixed-income futures.

8 Kolanovic and Krishnamachari (2017) .

9 A shortened URL is a compressed link to certain webpages. For example, https://academic.oup.com/rfs/advance-articles can be shortened to https://bit.ly/3mS7yDv .

10 G. Zuckerman and B. Hope, “The Quants Run Wall Street Now,” Wall Street Journal , May 21, 2017, https://www.wsj.com/articles/the-quants-run-wall-street-now-1495389108 .

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Top 25 Cited Recent Articles

A collection of the most cited articles published in the Journal of Finance over the last 5 years.

Taming the Factor Zoo: A Test of New Factors

Published: 2/2020,   Volume: 75,   Issue: 3  |   DOI: 10.1111/jofi.12883  |   Cited by: 410

GUANHAO FENG, STEFANO GIGLIO, DACHENG XIU

We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high‐dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.

Tracking Retail Investor Activity

Published: 5/2021,   Volume: 76,   Issue: 5  |   DOI: 10.1111/jofi.13033  |   Cited by: 344

EKKEHART BOEHMER, CHARLES M. JONES, XIAOYAN ZHANG, XINRAN ZHANG

We provide an easy method to identify marketable retail purchases and sales using recent, publicly available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 bps over the following week. Less than half of the predictive power of marketable retail order imbalance is attributable to order flow persistence, while the rest cannot be explained by contrarian trading (proxy for liquidity provision) or public news sentiment. There is suggestive, but only suggestive, evidence that retail marketable orders might contain firm‐level information that is not yet incorporated into prices.

Is Bitcoin Really Untethered?

Published: 6/2020,   Volume: 75,   Issue: 4  |   DOI: 10.1111/jofi.12903  |   Cited by: 328

JOHN M. GRIFFIN, AMIN SHAMS

This paper investigates whether Tether, a digital currency pegged to the U.S. dollar, influenced Bitcoin and other cryptocurrency prices during the 2017 boom. Using algorithms to analyze blockchain data, we find that purchases with Tether are timed following market downturns and result in sizable increases in Bitcoin prices. The flow is attributable to one entity, clusters below round prices, induces asymmetric autocorrelations in Bitcoin, and suggests insufficient Tether reserves before month‐ends. Rather than demand from cash investors, these patterns are most consistent with the supply‐based hypothesis of unbacked digital money inflating cryptocurrency prices.

Firm‐Level Climate Change Exposure

Published: 3/2023,   Volume: 78,   Issue: 3  |   DOI: 10.1111/jofi.13219  |   Cited by: 310

ZACHARIAS SAUTNER, LAURENCE VAN LENT, GRIGORY VILKOV, RUISHEN ZHANG

We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net‐zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.

Declining Labor and Capital Shares

Published: 5/2020,   Volume: 75,   Issue: 5  |   DOI: 10.1111/jofi.12909  |   Cited by: 289

SIMCHA BARKAI

This paper presents direct measures of capital costs, equal to the product of the required rate of return on capital and the value of the capital stock. The capital share, equal to the ratio of capital costs and gross value added, does not offset the decline in the labor share. Instead, a large increase in the share of pure profits offsets declines in the shares of both labor and capital. Industry data show that increases in concentration are associated with declines in the labor share.

Local Crowding‐Out in China

Published: 8/2020,   Volume: 75,   Issue: 6  |   DOI: 10.1111/jofi.12966  |   Cited by: 268

YI HUANG, MARCO PAGANO, UGO PANIZZA

In China, between 2006 and 2013, local public debt crowded out the investment of private firms by tightening their funding constraints while leaving state‐owned firms' investment unaffected. We establish this result using a purpose‐built data set for Chinese local public debt. Private firms invest less in cities with more public debt, with the reduction in investment larger for firms located farther from banks in other cities or more dependent on external funding. Moreover, in cities where public debt is high, private firms' investment is more sensitive to internal cash flow.

Common Risk Factors in Cryptocurrency

Published: 2/2022,   Volume: 77,   Issue: 2  |   DOI: 10.1111/jofi.13119  |   Cited by: 263

YUKUN LIU, ALEH TSYVINSKI, XI WU

We find that three factors—cryptocurrency market, size, and momentum—capture the cross‐sectional expected cryptocurrency returns. We consider a comprehensive list of price‐ and market‐related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long‐short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three‐factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.

Why Don't We Agree? Evidence from a Social Network of Investors

Published: 11/2019,   Volume: 75,   Issue: 1  |   DOI: 10.1111/jofi.12852  |   Cited by: 213

J. ANTHONY COOKSON, MARINA NIESSNER

We study sources of investor disagreement using sentiment of investors from a social media investing platform, combined with information on the users' investment approaches (e.g., technical, fundamental). We examine how much of overall disagreement is driven by different information sets versus differential interpretation of information by studying disagreement within and across investment approaches. Overall disagreement is evenly split between both sources of disagreement, but within‐group disagreement is more tightly related to trading volume than cross‐group disagreement. Although both sources of disagreement are important, our findings suggest that information differences are more important for trading than differences across market approaches.

Lazy Prices

Published: 2/2020,   Volume: 75,   Issue: 3  |   DOI: 10.1111/jofi.12885  |   Cited by: 206

LAUREN COHEN, CHRISTOPHER MALLOY, QUOC NGUYEN

Using the complete history of regular quarterly and annual filings by U.S. corporations, we show that changes to the language and construction of financial reports have strong implications for firms’ future returns and operations. A portfolio that shorts “changers” and buys “nonchangers” earns up to 188 basis points per month in alpha (over 22% per year) in the future. Moreover, changes to 10‐Ks predict future earnings, profitability, future news announcements, and even future firm‐level bankruptcies. Unlike typical underreaction patterns, we find no announcement effect, suggesting that investors are inattentive to these simple changes across the universe of public firms.

Attention‐Induced Trading and Returns: Evidence from Robinhood Users

Published: 10/2022,   Volume: 77,   Issue: 6  |   DOI: 10.1111/jofi.13183  |   Cited by: 200

BRAD M. BARBER, XING HUANG, TERRANCE ODEAN, CHRISTOPHER SCHWARZ

We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention‐induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high‐attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention‐induced trading, intense buying by Robinhood users forecasts negative returns. Average 20‐day abnormal returns are −4.7% for the top stocks purchased each day.

The Pollution Premium

Published: 4/2023,   Volume: 78,   Issue: 3  |   DOI: 10.1111/jofi.13217  |   Cited by: 185

PO‐HSUAN HSU, KAI LI, CHI‐YANG TSOU

This paper studies the asset pricing implications of industrial pollution. A long‐short portfolio constructed from firms with high versus low toxic emission intensity within an industry generates an average annual return of 4.42%, which remains significant after controlling for risk factors. This pollution premium cannot be explained by existing systematic risks, investor preferences, market sentiment, political connections, or corporate governance. We propose and model a new systematic risk related to environmental policy uncertainty. We use the growth in environmental litigation penalties to measure regime change risk and find that it helps price the cross section of emission portfolios' returns.

Banking on Deposits: Maturity Transformation without Interest Rate Risk

Published: 4/2021,   Volume: 76,   Issue: 3  |   DOI: 10.1111/jofi.13013  |   Cited by: 181

ITAMAR DRECHSLER, ALEXI SAVOV, PHILIPP SCHNABL

We show that maturity transformation does not expose banks to interest rate risk—it hedges it. The reason is the deposit franchise, which allows banks to pay deposit rates that are low and insensitive to market interest rates. Hedging the deposit franchise requires banks to earn income that is also insensitive, that is, to lend long term at fixed rates. As predicted by this theory, we show that banks closely match the interest rate sensitivities of their interest income and expense, and that this insulates their equity from interest rate shocks. Our results explain why banks supply long‐term credit.

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Published: 12/2021,   Volume: 77,   Issue: 1  |   DOI: 10.1111/jofi.13090  |   Cited by: 176

ANDREAS FUSTER, PAUL GOLDSMITH‐PINKHAM, TARUN RAMADORAI, ANSGAR WALTHER

Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.

Presidential Address: Social Transmission Bias in Economics and Finance

Published: 5/2020,   Volume: 75,   Issue: 4  |   DOI: 10.1111/jofi.12906  |   Cited by: 160

DAVID HIRSHLEIFER

I discuss a new intellectual paradigm, social economics and finance—the study of the social processes that shape economic thinking and behavior. This emerging field recognizes that people observe and talk to each other. A key, underexploited building block of social economics and finance is social transmission bias: systematic directional shift in signals or ideas induced by social transactions. I use five “fables” (models) to illustrate the novelty and scope of the transmission bias approach, and offer several emergent themes. For example, social transmission bias compounds recursively, which can help explain booms, bubbles, return anomalies, and swings in economic sentiment.

Foreign Safe Asset Demand and the Dollar Exchange Rate

Published: 3/2021,   Volume: 76,   Issue: 3  |   DOI: 10.1111/jofi.13003  |   Cited by: 151

ZHENGYANG JIANG, ARVIND KRISHNAMURTHY, HANNO LUSTIG

We develop a theory that links the U.S. dollar's valuation in FX markets to the convenience yield that foreign investors derive from holding U.S. safe assets. We show that this convenience yield can be inferred from the Treasury basis, the yield gap between U.S. government and currency‐hedged foreign government bonds. Consistent with the theory, a widening of the basis coincides with an immediate appreciation and a subsequent depreciation of the dollar. Our results lend empirical support to models that impute a special role to the United States as the world's provider of safe assets and the dollar as the world's reserve currency.

What Is a Patent Worth? Evidence from the U.S. Patent “Lottery”

Published: 12/2019,   Volume: 75,   Issue: 2  |   DOI: 10.1111/jofi.12867  |   Cited by: 148

JOAN FARRE‐MENSA, DEEPAK HEGDE, ALEXANDER LJUNGQVIST

We provide evidence on the value of patents to startups by leveraging the quasi‐random assignment of applications to examiners with different propensities to grant patents. Using unique data on all first‐time applications filed at the U.S. Patent Office since 2001, we find that startups that win the patent “lottery” by drawing lenient examiners have, on average, 55% higher employment growth and 80% higher sales growth five years later. Patent winners also pursue more, and higher quality, follow‐on innovation. Winning a first patent boosts a startup’s subsequent growth and innovation by facilitating access to funding from venture capitalists, banks, and public investors.

The Limits of Limited Liability: Evidence from Industrial Pollution

Published: 10/2020,   Volume: 76,   Issue: 1  |   DOI: 10.1111/jofi.12978  |   Cited by: 141

PAT AKEY, IAN APPEL

We study how parent liability for subsidiaries' environmental cleanup costs affects industrial pollution and production. Our empirical setting exploits a Supreme Court decision that strengthened parent limited liability protection for some subsidiaries. Using a difference‐in‐differences framework, we find that stronger liability protection for parents leads to a 5% to 9% increase in toxic emissions by subsidiaries. Evidence suggests the increase in pollution is driven by lower investment in abatement technologies rather than increased production. Cross‐sectional tests suggest convexities associated with insolvency and executive compensation drive heterogeneous effects. Overall, our findings highlight the moral hazard problem associated with limited liability.

Weathering Cash Flow Shocks

Published: 5/2021,   Volume: 76,   Issue: 4  |   DOI: 10.1111/jofi.13024  |   Cited by: 140

JAMES R. BROWN, MATTHEW T. GUSTAFSON, IVAN T. IVANOV

Unexpectedly severe winter weather, which is arguably exogenous to firm and bank fundamentals, represents a significant cash flow shock for bank‐borrowing firms. Firms respond to these shocks by drawing on and increasing the size of their credit lines. Banks charge borrowers for this liquidity via increased interest rates and less borrower‐friendly loan provisions. Credit line adjustments occur within one calendar quarter of the shock and persist for at least nine months. Overall, we provide evidence that bank credit lines are an important tool for managing the nonfundamental component of cash flow volatility, especially for solvent, small bank borrowers.

Glued to the TV: Distracted Noise Traders and Stock Market Liquidity

Published: 2/2020,   Volume: 75,   Issue: 2  |   DOI: 10.1111/jofi.12863  |   Cited by: 138

JOEL PERESS, DANIEL SCHMIDT

In this paper, we study the impact of noise traders’ limited attention on financial markets. Specifically, we exploit episodes of sensational news (exogenous to the market) that distract noise traders. We find that on “distraction days,” trading activity, liquidity, and volatility decrease, and prices reverse less among stocks owned predominantly by noise traders. These outcomes contrast sharply with those due to the inattention of informed speculators and market makers, and are consistent with noise traders mitigating adverse selection risk. We discuss the evolution of these outcomes over time and the role of technological changes.

The Wisdom of the Robinhood Crowd

Published: 4/2022,   Volume: 77,   Issue: 3  |   DOI: 10.1111/jofi.13128  |   Cited by: 136

Robinhood investors increased their holdings in the March 2020 COVID bear market, indicating an absence of collective panic and margin calls. This steadfastness was rewarded in the subsequent bull market. Despite unusual interest in some “experience” stocks (e.g., cannabis stocks), they tilted primarily toward stocks with high past share volume and dollar‐trading volume (themselves mostly big stocks). From mid‐2018 to mid‐2020, an aggregated crowd consensus portfolio (a proxy for the household‐equal‐weighted portfolio) had both good timing and good alpha.

The Impact of Salience on Investor Behavior: Evidence from a Natural Experiment

Published: 11/2019,   Volume: 75,   Issue: 1  |   DOI: 10.1111/jofi.12851  |   Cited by: 136

CARY FRYDMAN, BAOLIAN WANG

We test whether the display of information causally affects investor behavior in a high‐stakes trading environment. Using investor‐level brokerage data from China and a natural experiment, we estimate the impact of a shock that increased the salience of a stock's purchase price but did not change the investor's information set. We employ a difference‐in‐differences approach and find that the salience shock causally increased the disposition effect by 17%. We use microdata to document substantial heterogeneity across investors in the treatment effect. A previously documented trading pattern, the “rank effect,” explains heterogeneity in the change in the disposition effect.

Do CEOs Matter? Evidence from Hospitalization Events

Published: 3/2020,   Volume: 75,   Issue: 4  |   DOI: 10.1111/jofi.12897  |   Cited by: 136

MORTEN BENNEDSEN, FRANCISCO PÉREZ‐GONZÁLEZ, DANIEL WOLFENZON

Using variation in firms’ exposure to their CEOs resulting from hospitalization, we estimate the effect of chief executive officers (CEOs) on firm policies, holding firm‐CEO matches constant. We document three main findings. First, CEOs have a significant effect on profitability and investment. Second, CEO effects are larger for younger CEOs, in growing and family‐controlled firms, and in human‐capital‐intensive industries. Third, CEOs are unique: the hospitalization of other senior executives does not have similar effects on the performance. Overall, our findings demonstrate that CEOs are a key driver of firm performance, which suggests that CEO contingency plans are valuable.

Is There a Replication Crisis in Finance?

Published: 6/2023,   Volume: 78,   Issue: 5  |   DOI: 10.1111/jofi.13249  |   Cited by: 134

THEIS INGERSLEV JENSEN, BRYAN KELLY, LASSE HEJE PEDERSEN

Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication that leads to different conclusions. The majority of asset pricing factors (i) can be replicated; (ii) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio; (iii) work out‐of‐sample in a new large data set covering 93 countries; and (iv) have evidence that is strengthened (not weakened) by the large number of observed factors.

Global Pricing of Carbon‐Transition Risk

Published: 8/2023,   Volume: 78,   Issue: 6  |   DOI: 10.1111/jofi.13272  |   Cited by: 133

PATRICK BOLTON, MARCIN KACPERCZYK

The energy transition away from fossil fuels exposes companies to carbon‐transition risk. Estimating the market‐based premium associated with carbon‐transition risk in a cross section of 14,400 firms in 77 countries, we find higher stock returns associated with higher levels and growth rates of carbon emissions in all sectors and most countries. Carbon premia related to emissions growth are greater for firms located in countries with lower economic development, larger energy sectors, and less inclusive political systems. Premia related to emission levels are higher in countries with stricter domestic climate policies. The latter have increased with investor awareness about climate change risk.

A Tale of Two Premiums: The Role of Hedgers and Speculators in Commodity Futures Markets

Published: 11/2019,   Volume: 75,   Issue: 1  |   DOI: 10.1111/jofi.12845  |   Cited by: 129

WENJIN KANG, K. GEERT ROUWENHORST, KE TANG

This paper studies the dynamic interaction between the net positions of traders and risk premiums in commodity futures markets. Short‐term position changes are driven mainly by the liquidity demands of noncommercial traders, while long‐term variation is driven primarily by the hedging demands of commercial traders. These two components influence expected futures returns with opposite signs. The gains from providing liquidity by commercials largely offset the premium they pay for obtaining price insurance.

Research / Media release

August 22, 2024

Latest MFAA research highlights picture of a strong industry

Today, the Mortgage & Finance Association of Australia released the latest edition of its sought-after Industry Intelligence Service (IIS) report .

Featuring data for the 1 April – 30 September 2023 period, the report provides insights on the mortgage and finance broking industry including the size of the mortgage broker population, the value of loans settled and lender segment market share.

MFAA CEO Anja Pannek said that despite the period covered in the report being one marked by continued high refinancing levels and borrower concern about interest rates, mortgage broker activity remained strong.

“Our industry is growing, with more mortgage brokers than ever before, and positive shifts recorded across a number of aspects of the industry during the period covered in the report,” she said.

“The choice and competition mortgage brokers have brought to the home lending market to the benefit of consumers shines through in this data.”

Settlement values for mortgage broker originated home loans surpassed $300 billion for a 12-month period for the second time, at $350.63 billion to September 2023.

The mortgage broker population grew 3.3% year-on-year to 19,872. Seven out of ten home loans were written by brokers during the six-month period with the September 2023 quarter recording a 71.5% market share.

However, the conversion rate of home loan applications to settlements has seen a decline, indicating that serviceability challenges are taking a toll on prospective homebuyers seeking finance.

“While overall home loan applications are up across most of the country, we hear consistently from our members that serviceability has been a challenge for their clients as they adjust to current interest rate levels," Ms Pannek explained.

Conversion rates recorded a second consecutive six-month period of decline, experiencing a 9.2 percentage point dip year-on-year and falling below 80% for the first time since 2021.

"The downward shift in conversion rates highlights this it's harder to get deals through, with much more work required on the part of mortgage brokers to find the right solution for their clients,” Ms Pannek said.

The report also covers the extent of commercial lending facilitated by mortgage brokers.

While the number of mortgage brokers who also settled commercial loans during the period declined, the value of those loans reached a record high at $17.29 billion.

For the first time since the measure has been tracked in the IIS, the market share of the major banks fell below 40% for the period covered in the report.

"This result indicates that borrowers are more confident to go through lenders outside the big four to secure a loan that meets their needs," said Ms Pannek.

"There are over 100 lenders in the market today, and it is because of brokers that Australian homebuyers have access to a wide range of lenders. It is also clear that this choice is a valued and important part of the market."

The IIS report draws on data supplied by the industry’s leading aggregator brands to provide mortgage broker, industry performance and demographic data.

The IIS was first published in 2015, this is the 17th edition.

Download the Industry Intelligence Service 17th edition .

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