|By Constantinides, Panos|
The introduction of mortgage securitization in the
Keywords: Information Infrastructures, Mortgage Securitization, Calculation, Risk, Legacy Assets.
The practice of mortgage securitization, which constitutes the main innovation in mortgage financing since the 1970s (Fligstein & Goldstein, 2010; Quinn, 2009), was heavily implicated in the emergence of the 2008 financial crisis. In the US, securitization emerged in the 1970s as a political campaign of the Federal Government to seek additional means to finance the housing needs of the American people. In other words, securitization was an invention of the
Although securitization emerged as a "social policy innovation" as part of the
By the 1990s, mortgage securitization practices spread to the
The transfer of securitization in
This process of developing information infrastructures to support this type of financial innovation remains relatively unexplored in the literature, with only a few shinning exceptions (Markus, Dutta, Steinfield & Wigand, 2008; Poon, 2009). In this paper, by combining insights from existing research in information infrastructures and economic sociology, we explore how financial information infrastructure (FII) innovation mediates the transfer of legacy mortgage assets into emerging securitization markets. We also explore the implications of this process for the assessment of loan quality and the calculation of financial risk. We do so in the context of a
Our contribution is twofold. First, by drawing on Callon's (2007) concept of performativity, we argue that the role of FII is to perform the financial function prescribed by the economist (as economic agent or practitioner rather than as scientist) and not to facilitate capital flow in the generic way. The significance of this renewed understading is that FII are built with specific financial functions in mind that are formulated in the context of managerial and political decisions. Second, we discuss the implications of our findings for practices of risk calculation. More specifically, drawing on Kalthoff(2005), we show that data validation, although not a direct calculation of risk, constitutes an effort to order risk calculation practices. This is important in understanding the role of FII innovation as a mechanism by which FII become an integrating and standardising force in securitization markets.
In Section 2, we discuss the logic of securitization, the practices of risk calculation involved in it, and the relevant role of financial information infrastructures. In Section 3, we discuss the significance of the particular case, and our methods of data collection and analysis. In Sections 4 and 5, we present and discuss our findings, and, in Section 6, we conclude the paper.
2. Securitization and Financial Information Infrastructures
2.1. The Logic of Securitization and Practices of Risk Calculation
The main difference of securitization markets from other types of markets is that, unlike other products, securitization assets constitute "promises to pay" within a given period of time and therefore their value depends on the "credibility of the promisor" (Carruthers & Stinchcombe, 1999) and not on certain intrinsic characteristics related to their form and functionality. It is this credibility that realizes the value of the asset and helps it to be bought and sold easily in the market (Carruthers & Stinchcombe, 1999). Thus, to a large extent, the making of securitization markets is synonymous with the making of financial credibility, while the absence of it is understood as risk.
Mortgage finance can be distinguished into "primary" and "secondary" mortgage markets (Cummings & DiPasquale, 1997). In the primary markets, individual borrowers obtain loans from lenders, while, in the secondary markets, lenders sell these mortgages to investors (Cummings & DiPasquale, 1997; Markus et al., 2008). Mortgage securitization refers to the secondary mortgage markets and it can be understood as a practice of pooling and bundling together a stream of "promises to pay" arising from mortgage repayments that are repackaged and sold to investors. Investors however, as new securitization market participants, also need reassurance and evidence of credibility in the products that they invest in. To produce such knowledge, market participants (lenders, rating agencies) collectively engage in a series of risk-calculating practices that produce this credibility. Such practices include mortgage underwriting, pooling, and credit rating.
Underwriting is a loan-origination process (Markus et al., 2008), part of the primary mortgage lending practices, by which a lender calculates the risk and decides whether to offer a mortgage loan to an individual borrower. Underwriting of individual loans is implicated in securitization markets in the sense that it may be used to reassure investors in the secondary markets about the quality of the underlying loans via the consistency of the underwriting standards (Akhavein, Frame, & White, 2005). These standards are represented in credit scores (i.e., "assessments of the odds that a consumer might default on a loan expressed as a probability") (Poon, 2009, p. 657). Credit scores and data generated during the underwriting process are then made available to securitizers in the secondary markets who use them to make decisions on which loans to select for securitization (i.e., pooling). In this sense, pooling is the practice of transferring assets (i.e., individual mortgage loans) from the context of primary mortgage market to the secondary securitization market. It has been shown, however, that pooling large numbers of home mortgages reduces the amount of information needed to understand their value, so instead of compiling idiosyncratic information about each individual home and borrower, a lender need only use aggregate information about means and variables of pool mortgages (Carruthers & Stinchcombe, 1999). Pooling, then, renders the value of a loan more generally knowable and encourages those with little particular knowledge of the individual mortgage to invest their money (Carruthers & Stinchcombe, 1999). Finally, credit rating is the process by which the rating agencies2 produce opinions on the risk of securitization products that become publicly available and creates public consensus about value and reassurance to market participants (Carruthers & Stinchcombe, 1999; MacKenzie, 2011). Credit ratings are usually expressed through a system of "tranching" (see Figure 1).
To summarize, underwriting, pooling, and credit rating are practices that help to calculate risk and construct credibility in mortgage markets. This credibility is essential for securitization as a funding tool to work because it opens the avenue toward capital markets. Potential investors, then, would be convinced to invest in these products and help the originating bank to raise the desired funds. In Section 2.2, we discuss how the development of financial information infrastructures may act as platforms on which to integrate primary and secondary mortgage markets and help banks adapt their existing practices with the emerging capital markets (i.e., emerging
2.2. The Role of Financial Information Infrastructures in Mortgage Securitization
Financial information infrastructures are ubiquitous, mediating information and communication technologies (ICT) that:
provide the adequate information flows and databases to "create, set up, control and maintain the network of exchanges and relevant contracts"... that build up the fabric of economic institutions such as markets and firms. (Ciborra, 2007, p. 36)
Recent information systems research understands FII innovation as an integrative and standardiszing force in the mortgage markets. More specifically, the development of FII in mortgage finance and in financial markets more generally has been described as a collective action (Markus, Steinfield, Wigand & Minton, 2006) or a computerization movement whereby technical apparatuses are adopted and reframed according to different contexts and actors' interests (Markus et al., 2008). Poon (2009), for instance, shows the successive "movements" and "translations" that took place as the FICO infrastructure spread through the U.S. mortgage industry. Scott and Zachariadis (2012), in their historical analysis of the evolution of SWIFT, also show that, because the SWIFT infrastructure offered such benefits as "speed of messaging, lower costs, increased volumes, more secure transactions and standardisation", interests in establishing common standards began to spread across banks and types of services, including securities.
By means of a process of innovation-during-diffusion (or "innofusion" (Fleck, 1988)), the spreading of FII across different levels and practices of mortgage finance constituted a political, social, and technical force toward the infrastructural integration of primary and secondary markets. This impacted the practices of risk calculation. Indeed, primary and secondary mortgage markets were placed on the same platform of risk calculation (Poon, 2009, p. 665). That is, FII contributed to a vertical integration that allowed single loans and pools of loans to be represented by the same metric (Poon, 2009, p. 671). As part of the same process, underwriting tools that produced credit scores for individual consumers expanded to become a central technology of securitization as they were diffused and adopted by securitizers and rating agencies (Poon, 2009). The expanding and evolving FII that were integrating primary and secondary mortgage markets created an avenue for individual banks to adapt their business models and techno-organizational practices and thus gain access to the capital markets that would become available through mortgage securitization. In other words, a way for banks to make the transition to securitization was through FII innovation, which meant to find a way to successfully plug into the expanding FII in the securitization markets.
Recent research in economic sociology and more specifically in the social studies of finance has provided insights and useful concepts that enhance understanding of what type of information infrastructure innovation would be necessary in order for the transition to securitization to be effective. More specifically, more detailed attention is paid to the role of financial data and their dissociation from the "reality" of the assets they refer to. This means that FII enabled the integration of primary and secondary mortgage markets by standardizing financial data, which, in turn, enable the rationalization of decision-making (e.g., decision to lend or decision to invest or decision on selecting loans for securitization) as separated from broader social processes and the reality they represent (Preda, 2006). Indeed, trust and authority in financial decisions are dissociated from individuals and transferred to technology; trustworthy data in securitization, therefore, are data produced or recorded by an authoritative technology, which enables them to be transferred from primary to secondary markets without losing their properties (Preda, 2006, p. 756). To be able to plug into part of a securitization chain and make securitization work as a funding mechanism, therefore, banks would need to build authoritative FII.
In information infrastructure research, trust and authority is reflected in the notion of transparency (Bowker & Star, 1999; Rolland & Monteiro, 2002; Star & Ruhleder, 1996; Timmermans, Bowker, & Star, 1996). Star and Ruhleder (1996, p. 113) argue that an "infrastructure is transparent to use in the sense that it does not have to be reinvented each time or assembled for each task, but invisibly supports those tasks" (emphasis added). They add: an "infrastructure takes on transparency by plugging into other infrastructures and tools in a standardized fashion"(Star & Ruhleder, 1996, p. 113). Such efforts towards more transparency can be understood, in the context of a long tradition in information infrastructure research, as an attempt to better align and integrate disparate systems and institute more control over organizational processes (e.g., Henderson & Venkatraman, 1993; Weill &
These infrastructures can be seen as constituting a distributed socio-technical calculative apparatus that can be theoretically understood as a set of rules, conventions, and tools (e.g., Bowker & Star, 1999; MacKenzie & Wajcman, 1999; Preda, 2006). Indeed, technical tools (e.g., ICTs) configured in financial practices (i.e., underwriting, credit rating, pooling) and in broader institutional relations between rating agencies and lenders constitute "machineries of knowing" (Knorr-Cetina, 1999) that generate knowledge on financial products for the benefit of market participants and help them calculate the relevant risk.
The literature conceptualizes FII innovation as an integrating force in securitization whereby authoritative and transparent FII constitute a platform for a distributed effort to calculate risk and install credibility. Banks that wish to enter the securitization markets are faced with challenges related to how to innovate their information infrastructures in order to plug into the broader FII more effectively and achieve a transparency that would create credibility and trust in the market. Building on literature from information systems and economic sociology, we further explore FII innovation as an integrative force. More specifically, in Section 4, we present new evidence on the role of FII innovation to enable the transition from primary mortgage lending practices to securitization markets.
3.1. Research Site/Case Study Selection
To answer our key research question, we selected one of the
Finally, apart from being pioneers in European securitization and from offering opportunities to examine the way in which FII mediate securitization, the selected bank was a successful and infamous institution that managed to do quite well even in the period of the 2007-2009 crisis and while the securitization markets were more or less inactive. Indeed, as the financial crisis was affecting
3.2. Exploring the Field and Gaining Access
To secure access to the specific institution, we had to overcome certain challenges. First of all, there was the challenge of identifying the appropriate institution with the appropriate characteristics on which to build our case study around because our research was not particularly targeting this specific bank from the research project's inception. Second, we had to gain access to relevant rich qualitative data covering at least the last 15 years, about the time that securitization was transferred to the
The time frame in which the fieldwork was carried out (between 2010 and 2012) was rather peculiar in the financial sector. Soon after the 2008 collapse of the subprime mortgage market in the US, the problem started to spread beyond the U.S. subprime mortgage sector in other markets and geographic locations (BIS, 2008). By the time the credit crisis hit the
Additionally, although securitization was indeed implicated in the financial crisis, it had become a very popular topic with policy makers and professional associations.
3.3. Data Collection and Analysis
We follow an interpretive case study approach (Walsham, 2006; Yin, 1994). The unit of analysis (the case) is largely defined in the context of a specific firm (
Overall, our data includes 35 interviews, 20 of which are with individuals from different historical periods of the
To analyze the data from the case study, we followed a process-based approach by paying attention to key events in the evolution of the securitization sector (especially the key role of FII in innovations of practice) (Langley, 1999). Our analytical strategy included a combination of theory application and analysis of theoretical themes emerging from the data (Langley, 1999).
Specifically, we initially coded the data by drawing on our theorization of FII as standardizers and integrators of financial markets. We allowed the theory to "speak" before the data was a conscious strategy because we were drawing on legitimate research on the financial industry that had already established strong links between theoretical constructs and actual phenomena (e.g., Markus et al., 2006; Markus et al., 2008; Poon, 2009; Preda, 2006, 2009). This helped us to develop a more critical appreciation of the concepts we were using. We then allowed the data to "speak" on behalf of the theoretical constructs in an effort to build on and extend the existing literature. We discuss theoretical implications of our findings after the case.
4. Empirical Case
4.1. Transition of Legacy Assets to Securitization via FII Innovation: Evidence From a
In this section, we present our empirical case study by focusing on how the
4.1.1. Securitization as a
As we mention throughout the paper, in its early days in the
A team of in-house experts, along with the
There was the vision within [
The same interviewee explains how this team of individuals within the bank were perceiving securitization as something innovative and new: "I might have a slightly biased view on that but I think that that's where the real innovation was taking place; that sort of thinking within the organization at the time".
He also explains how the scepticism that surrounded the
There weren't anybody at the time that thought that this [securitization] was the way forward but obviously in subsequent years we saw a number of other entrants in the market and were focused on the fact that securitised funding was probably a way that was going to make sense for them.
There was the bet "let's try to prove that this works" and if it does we have made available to the group another stream of funding for mortgage assets. Given that there was a lot of hunger for mortgage market share and the ability to grow mortgage assets...There was a recognition there [by the BoD]...
So, in these early days, when there was uncertainty around the usefulness of securitization as a funding tool, the
4.1.2. Technical Integration of FII
The first securitization of about £250 million was issued in early 1998 and was followed by others in the sector. In the following years, securitization transactions in
The treasury had a vision that wasn't necessarily shared across the organization. It took an awful lot of work for the board to get comfortable with expanding the capability to do this type of transaction [...] politically, early on in the process of acquiring this platform, its performance, its ability to write business was overshadowing what the [
The expansion and integration of FII in the
Figure 2 shows how the platforms were integrated and how the securitization functionality, extending from their existing automated underwriting tool, was replicated to the larger mortgage system. They created a centralized database (OCDB), which all of the underlying systems that were likely to become subject of securitization fed data into. The automated securitization functionality was subsequently overlaid on top of that database.
The technical integration of the existing platforms of legacy mortgage assets and the extension of their underwriting tools from calculating the risk of individual loans into selecting those loans for securitization did not automatically mean that the
4.1.3. The Issue of Legacy Assets
A key problem with the development of FII for securitization was how to select and transfer loans already on the bank's books in the securitization market and start moving them onto investors. This was challenging because the legacy assets residing in the bank's platforms were not underwritten in the same timeframe and under the same criteria as loans underwritten according to the rules and requirements set by the emerging securitization market. Indeed, the
In banks like the [
Different underwriting criteria produced different kind of data that were not standardized and homogenized. This would create problems when the bank would want to select which assets to securitize. One of our interviewees offered an example:
If I have a loan where the borrower's annual income is stated as £100, that is more likely to be a mistake than it is to be a genuine case of "we have an advanced mortgage of many thousands of pounds based on an income of £100". That is going to be an error. However, if you have a situation that is flagged as a first time buyer but the information you have tells you that they are not a first time buyer, that's less likely to be a manifest data mistake in the way the understated income is. So, one of them falls into this category of an error of input, and the other one falls into the category of "this is flagged in a way that would make me look at it from a credit point of view slightly differently". This is a case where you'd want to ask an "are you sure" kind of question.
The technical integration of the bank's existing mortgage platforms made available a variety of legacy assets that had been underwritten in different timeframes and under different criteria. The heterogeneity of the legacy assets made it difficult for the bank to successfully plug into the securitization chain because the underwriting criteria of the market did not match with the bank's internal criteria of loan quality. This was the main problem that the FII developers tried to tackle.
4.1.4. Validating Data and Plugging into the Securitization Chain
To deal with these issues, the
We used to take each individual file and we had a team of people who would go through a series of blank screens and re-key some elements of the data from the paper file where the mortgage had been underwritten on... Once they'd done that they went through a process of re-populating screens that were blank and then they would hit a button and the system would make a comparison of what they had input, to what had been input when the loan was written and it would throw out exception reports of where the difference is, and that was our way of ensuring that there was a high degree of data integrity and quality.
This way, underwriting data both from old and new loans were validated in the emerging context of securitization. In order to successfully plug into the securitization chain and homogenize internal lending criteria with those accepted in the market, the FII developers in the
We weren't looking at this [validation] blind, we basically looked at their [rating agencies'] template of data requirements for rating a securitization and we identified if there were any gaps in our system...And then we would focus our energy on capturing the key elements of data that could have an impact on the rating process... those that were more likely to give you a less efficient transaction.
Therefore, the validation process, which sought to validate whether the data on the system were accurate, was filtered through the rating agencies' templates for rating transactions. This way, the bank's new FII was interfacing between internal data about loan quality and external criteria set by the rating agencies about what constitutes a loan that meets the criteria for securitization. Aligning internal criteria with market criteria would help reassure market participants that, as data were transferred into securitization, they had not lost their properties. This way, the
4.1.5. Data Validation and Risk Calculation
A major finding of this research concerns the relationship between the process of data validation described in Section 4.1.4 and the practices of risk calculation involved in securitization. Our data show that the transition from the internal (legacy) criteria of loan quality to the external (market) was simply a question of re-validating data rather than re-calculating the risk or the price of individual loans. Indeed, the validation process was done independently from any risk calculation practices involved in the decisions to lend individual loans. As an interviewee explained:
Let's be clear, this validation was not about the lending decision. The lending decision had already been made at this point. The validation was purely about the integrity of the data. And it was never intended to be something that its primary purpose was to question the lending decision of the individuals. It was about how good is my data on this system. Am I recording the right information the right way? The purpose of the team was to validate data on a case that had already been approved.
The separation of the validation process from risk calculation practices was also mirrored in the organizational structure of the new securitization unit. Organizationally, the securitization unit in the
We had a diverse group of people doing this validation. When we set this team up, we didn't want everyone to have a detailed lending background because we wanted them to understand what it was we were trying to achieve, which was all about data validation and integrity.
The practices involved in the securitization of mortgages were considered in and of themselves as a risk calculation process. An interviewee involved in the marketing of securitization deals stated that "the whole process [originating and marketing a deal] is a risk analysis process. And at the end of the day you're presenting the facts of your deal for the investor to determine if it's appropriate risk for them to take". However, the validation process, the main issue that the FII developers in the bank focused on, organizationally and in practice was not even about securitization (i.e., producing a risk analysis) but rather about enabling the transition to securitization. Another interviewee explained:
Although this [validation] was a securitization function, that part of validation process was as far as they [validators] went. They weren't in control of the process to actually securitise the assets. They were simply part of that process to get there.
In Section 5, we discuss the significance of the data validation process for transferring legacy assets into securitization markets and for calculating risk.
5. Discussion: The Role of FII Innovation in Mortgage Securitization
The paper provides new empirical evidence to the understanding of the role of information infrastructure innovation in mortgage securitization. Our findings show that the specific purpose of the infrastructure developed by the
FII in securitization are arranged as a socio-technical agencement (i.e., assemblage), a combination of heterogeneous elements (i.e., human actors, technical devices, algorithms, criteria) that have carefully adjusted to one another (Callon, 2007). In the agencement concept, there is no divide between human agents (those who arrange or assemble) and things that have been arranged: material objects and technologies co-exist with human agents and one cannot exist or be understood without the other. However, there are multiple agencements in relation to securitization. Their form and shape depends on the socio-economic, political, and technical circumstances and on the particular phase of the history of securitization. In the US, for instance, the socio-technical agencement of securitization initially took the form of a political program of the Federal Government and later on it became a collective process of standardization to establish market criteria for risk calculation (Markus et al., 2008; Poon, 2009). In the
The role and purpose of FII innovation in this process was to enable certain mechanisms. In our case, this translated in helping transfer existing legacy assets to the securitization chain in order for them to be sold to investors for the purpose of raising funds. The transfer of assets across institutions via FII innovation is an elegant example of Callon's (2007) concept of performativity, which takes a more literal, rather than a linguistic, sense in this case. Performativity, as a concept, emerged through contributions in linguistics to show how language can be a constructive force in society. Later on, the concept was adopted by economic sociologists to explain how economics as a field not only describe financial markets but also play a big role in performing them. Thus, the concept took a more literal meaning. Indeed, performativity in economics is much more than an economist's speech act or a "performative utterance" (
This is significant because it shows that FII are designed with specific functions in mind. Our data show that these functions were the outcome of managerial and political negotiations. Indeed, "prescribing" these functions on the FII was based on broader socio-economic and political circumstances, but was also the result of internal organizational politics. In the
Beyond these observations, the process of making the transition to securitization posed more specific challenges to the FII developers in the
The intervention of FII as a tool for risk calculation marks the shiftin the notion of calculation from "calculation of something" to "calculation with something", (i.e., to take into consideration, to count on something, but also to form a judgement with something upon something) (Kalthoff, 2005 quoting Heidegger, 1954). This denotes the inseparability of calculation practices from the tools used for their performance, as emphasised in the concept of socio-technical agencement discussed above. Calculation, however, is not a one-offindependent process. It happens within certain economic, market, and techno-organizational contexts, each of which represents a different level of "calculation with something". In our case study, for example, as legacy assets were transferred from the context of primary mortgage markets onto the context of secondary capital markets, there were two different levels of "calculation with something" corresponding to these different contexts. One was the level of calculating the risk of an individual mortgage loan using certain underwriting tools, while the other level was the one of calculating the risk involved in a securitization deal where multiple such loans were bundled together. Because securitization calculative practices emerged as an extension of the mortgage lending calculative practices, they formed a "chain" of calculation (cf. Kalthoff, 2005) where one level set the ground for the next via FII innovation.
Indeed, one level of calculation (individual mortgage underwriting) sets up another level of calculation (risk calculation in securitization); one economic representation (existing data on legacy assets) sets another economic representation (validated data according to rating agencies' templates for securitization); one interpretation (decision to lend individual loans) sets another interpretation (decision on which of these loans to securitize). As we moved from one point on the chain of calculation to another the role of FII was to frame the performance of risk calculation as an epistemic activity (cf. Kalthoff, 2005), toquestion the objects under focus and frame these objects' limits. In our case study, therefore, FII innovation was not directed toward calculating the risk of the underlying mortgage assets in light of securitization, but rather toward enacting a system for ordering the risk calculation of assets and toward revealing those assets as objects that are calculable in a securitization context.
Ordering the risk calculation of assets and revealing them as calculable objects means that FII transforms them into uniform materialities that can be measured and calculated by other agents, potentially in other contexts. In turn, this sets in motion a wide set of risk calculation practices, all of which are represented by the FII and their ability to integrate data about those practices. In other words, FII become the means by which representation of securitization assets is framed and is expected to be framed; FII become authoritative (cf. Preda, 2006) and transparent (cf. Star & Ruhleder, 1996). Data validation then, in the context of our case study, although it was not a calculation in the direct sense of the operation with things already in place, it was a process of setting the boundaries of calculable objects and bringing them into existence. The boundaries were decided based on the market's criteria (rating agencies' templates). Validation was not the end of the story, but the starting point of of a different level of "calculating with something"; it was a process by which new economic representations were established (cf. Kalthoff, 2005). Validation reframed existing data and made them meaningful (made them matter) for risk calculation in securitization. An interviewee offered an example of this transition:
A decision to lend and the quality of the information recorded in an information system are two very different things. Let's say for example that a borrower's income gets mistaken. There's a zero missing at the end so a 100,000 salary is keyed as 10,000. This may not necessarily matter in terms of the lending decision, but it does matter in terms of the quality of that data when you go to subsequently securitise the asset.
This again points to the role of FII as an authoritative technology that informs decisions in the newly formulated context of securitization and the importance of transferring trustworthy data from pre-existing contexts (Preda, 2006). Indeed, securitization constitutes a new "cognitive frame" (Beunza & Stark, 2004) in which legacy data take on new meaning. In conclusion, the transfer of legacy assets into the securitization markets by means of FII innovation in the
With the advent of securitization in the
The empirical research for this paper was funded by the
1 The financial press uses the metaphor "plumbing" to talk about this infrastructure (Economist, 2012).
2 The role of rating agencies as market makers is highly controversial, especially after the crisis when they received heavy criticism in relation to their conflicting interests and role (
3 Master trusts were first introduced by the Bank of
4 The mortgage platform consisted of the
5 In the same system, therefore, one could see information from before the mortgage was granted to the administration of a mortgage on an ongoing basis and whether it was securitized or not.
Akhavein, J., Frame, W. S., & White, L. J. (2005). The diffusion of financial innovations: An examination of the adoption of small business credit scoring by large banking organizations.
Beunza, D., & Stark, D. (2004). Tools of the trade: The socio-technology of arbitrage in a
BIS (2008). 78th annual report, chapter VI: Financial Markets
Bowker, G., &
Callon, M. (2007). What does it mean to say that economics is performative? In
Carruthers, B., & Stinchcombe, A. (1999). The social structure of liquidity: Flexibility, markets, and states.
Ciborra, C. (2007). Digital technologies and risk: A critical review. In O. Hanseth, & C. Ciborra (Eds.), Risk, complexity and ICT (pp. 23-45.
Cummings, J., & DiPasquale, J. (1997). A primer on the secondary mortgage market.
Economist (2012). Of plumbing and promises: the back office moves centre stage. The Economist. Special Report on Financial Innovation. Retrieved
ESF (2006). European Securitization Forum Data report, Autumn 2006. Available at: http://www.securitization.net/pdf/Publications/ESFDataRprt1006.pdf
Fincham, R., Fleck, J., Proctor, R.,
Fleck, J. (1988). Innofusion or diffusation?: The nature of technological development in robotics (Edinburgh PICT Working Paper No. 4).
Fligstein, N., & Goldstein, A. (2010). The anatomy of the mortgage securitization crisis. In
Kalthoff, H. (2005). Practices of calculation: Economic representations and risk management. Theory,
Knorr-Cetina, K. (1999). Epistemic Cultures: How the sciences make knowledge.
Langley, A. (1999). Strategies for theorising from process data.
MacKenzie, D. (2011). The credit crisis as a problem in the sociology of knowledge.
MacKenzie, D., & Wajcman J. (eds) (1999). The social shaping of technology (2nd ed.)
Markus, M. L., Steinfield, C. W., Wigand, R. T. & Minton, G. (2006). Industry-wide IS standardization as collective action: The case of the US residential mortgage industry. MIS Quarterly, 30(Special Issue), 439-465.
Markus, M. L., Dutta, A., Steinfield, C. W. & Wigand, R. T. (2008). The computerization movement in the US home mortgage industry: Automated underwriting from 1980 to 2004. In
Preda, A. (2006). Sociotechnical agency in financial markets: The case of the stock ticker. Social Studies of Science, 36(5), 753-782.
Preda, A. (2009). Framing finance: The boundaries of markets and modern capitalism.
Poon, M. (2009). From new deal institutions to capital markets: Commercial consumer risk scores and the making of subprime mortgage finance. Accounting, Organization and Society, 34(5), 654-674.
Quinn, S. (2009). Towards a market for mortgage-backed securities: Credit lending, the federal budget, and the politics of debt management (Working paper).
Rolland, K. H., & Monteiro, E. (2002). Balancing the local and the global in infrastructural information systems. Information Society, 18(2), 87-100.
Rosenthal, J. A., & Ocampo, J. M. (1988). Securitization of credit: Inside the new technology of finance.
Scott, S. V., & Zachariadis, M. (2012). Origins and development of SWIFT, 1973-2009. Business History, 54(3), 462-482.
SIFMA. (2013). Bond basics. Retrieved
Star, S. L., & Ruhleder, K. (1996). Steps toward an ecology of infrastructure: Design and access for large information spaces.
Schwarcz, S. L. (2007). Structured finance: A guide to the principles of asset securitization (3rd ed.).
Transparency Initiative (2008). Ten industry initiatives to increase transparency in the securitization market. Retrieved
Tett, G. (2009). Fool's Gold. Little
Timmermans, S., Bowker, G., & Star, S. L. (1996). Infrastructure and organizational transformation: classifying nurses' work. In W. Orlikowski, G. Walsham,
Wainwright, T. (2009). Laying the foundations for a crisis: Mapping the historico-geographical construction of residential mortgage-backed securitization in the
Walsham, G. (2006). Doing interpretive research.
Weill, P. & Broadbent, M. (1998). Leveraging the new infrastructure.
Weill, P. &
Yin, R. (1994). Case study research: Design and methods, (2nd ed).
About the Authors
Antonios KANIADAKIS is a Lecturer in Information Systems and Management at the
Panos CONSTANTINIDES is an Associate Professor of Information Systems and Director of the MSc Information Systems Management & Innovation (ISMI) at the
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