EL CONTRATO MERCANTIL INTERNACIONAL
10. Lex Mercatoria
Medium/ Source
Importance of Information Type
(n= valid responses in both categories)
Corre- lation Signif Company fundamentals ( n= 102 ) .441 .000 Company technicals ( n= 103) .459 .000 General interest newspapers Non-market news ( n= 103 ) .416** .000 Specific industry fundamentals ( n= 102 ) .337 .001 Specific industry technicals ( n= 103 ) .337* .001 Company fundamentals ( n= 102 ) .319 .001 Company technicals ( n= 103 ) .346 .001 Finance/Business
newspapers
Non-market news ( n= 103 ) .390** .001
Specific industry fundamentals ( n=102) .212 .032
Specific industry technicals ( n=103) .293 .003
Company fundamentals ( n= 102 ) .314 .001 Company technicals ( n= 103) .412** .000 General interest magazines Non-market news ( n= 103 ) .448** .000 Company fundamentals (n= 102) .286 .004 Company technicals ( n= 103) .324 .001 Non-market news ( n= 103 ) .347 .000 Finance/Business magazines Information on media/sources ( n= 102 ) .346** .000 Company technicals ( n= 101 ) .286* .004
Historical market prices ( n= 101 ) .286** .004
Specialist Academic Journals
Non-market news ( n= 101 ) .279* .005
General market fundamentals ( n= 102 ) .387** .000 General Interest
Broadcast news Non-market news ( n= 103) .347* .000
General market fundamentals ( n= 101 ) .282* .002 Specific industry fundamentals ( n= 101) .270 .006
Company fundamentals ( n= 101 ) .238 .016
Company technicals ( n= 102) .261 .008
Current market prices (n= 102) .206 .038
Future market prices (negative) ( n= 101 ) -.259 .009
Non-market news ( n= 102 ) .293 .003
Finance/Business Broadcast news
Correlations of data/opinions ( n= 101 ) .287* .004
Current market prices ( n= 94 ) .425** .000 Future market prices (negative) ( n=93 ) -.427** .000 Specialist finance/
business channels
Non-market news ( n= 94 ) .331* .001 General market fundamentals ( n= 93 ) .341 .000 Specific industry fundamentals ( n= 93) .528** .000 Specific industry technicals ( n= 94 ) .414** .000 Company fundamentals ( n= 93) .530 .000 Company technicals ( n= 94) .438* .000 General Internet sites Non-market news ( n= 94 ) .311 .001 Company fundamentals ( n= 102) .287 .003
Specific industry fundamentals (n= 102) .241 .015
Financial/Business Internet sites
Correlations of data/opinions ( n= 102 ) .316* .001
General Market fundamentals (negative) ( n= 99 )
-.300 .003
Correlation of data/ opinions ( n= 99 ) .466** .000 Subscriber finance/
business Internet sites
Specific industry fundamentals
(negative) ( n= 100 )
-.277 .005
Company fundamentals (negative)
( n= 100 )
-.427 .000
Company technicals (negative) ( n= 101 ) -.322* .001 Specialist financial
information/wire
Other institutions’ opinions/ positions ( n= 101 )
.280 .005
Company fundamentals ( n= 102 ) .323** .001 Company technicals ( n= 103) .309* .001
Other institutions’ opinions/positions ( n= 103) .235 .017 Non-market news ( n= 103 ) .388* .000 Specialist industry reports
Correlation of data/ opinions ( n= 102 ) .215 .030
Specific industry fundamentals ( n= 102 ) .437** .000 Specific industry technicals ( n= 103) .373** .000 Company fundamentals ( n=102 ) .732** .000 Company technicals ( n= 103) .638** .000 Company reports/
announcements
Information of media/ sources ( n= 102 ) .408* .000 General market fundamentals ( n=102 ) .348* .000 General market mood/trends ( n= 103 ) .340* .000 Rival Investor opinions/positions
( n= 103 )
.371** .000
Current market prices ( n= 103) .287** .003
Non-market news ( n= 103) .422** .000 Informal work
discussion
Correlations of data/ opinions ( n= 102 ) .279 .005
General market mood/trends ( n= 102 ) .364* .000 Rival investor opinions/ positions
( n= 102 )
.409** .000 Non-market news ( n= 102 ) .378** .000 Specific work
discussion
Correlations of data/ opinions ( n= 101 ) .398* .000 General market mood/trends ( n= 98 ) .324* .000 Specific work instructions
Information on media/sources ( n= 97 ) .481** .000 General market mood/ trends (n= 102) .373* .000 Informal discussion with
external contacts Non-market news ( n= 102 ) .310* .002
General market mood/ trends ( n= 100 ) .205* .041
Current market prices ( n= 100 ) .240* .016
Non-market news ( n= 100 ) .260* .009
Specific work discussion with external contacts
Information on media/sources ( n= 99 ) .290* .004
Company fundamentals ( n= 101) .298 .003
Company technicals ( n= 102 ) .291** .003
Non-market news ( n= 102 ) .320* .001 Public Analyst investment
advice / recommendations
Information on media/sources ( n= 101 ) .340** .000
Company fundamentals ( n= 95 ) .280 .006
Company technicals ( n= 96) .231** .024
Correlations of data/opinions ( n= 95 ) .241 .019
Private Analyst investment advice/ recommendations
Information on media/sources ( n= 95 ) .286 .005
General market mood/ trends ( n= 99 ) .335* .000 Rival investor opinion/position ( n=99 ) .356** .001
Historical market prices ( n= 99 ) .212 .035
Current market prices ( n= 99 ) .288** .004
Specialist systems/ algorithms
Future market prices ( n= 99 ) .305** .002
Own Professional analysis Information on media/sources ( n= 101) .364* .000 Personal intuition/
feelings/ hunches
Correlation of data/ opinions (negative)
( n= 101)
The above table presents the overall correlations between importance ratings for media/sources and importance ratings for information types: The correlations matrix retained a default significance threshold of 0.05 (Spearman’s r), but the primary focus will be on the stronger and most significant correlations (> 0.3 and significance 0.001 or lower, in bold). .The double asterisked correlations indicate that there are multiple correlations across different market subsectors, suggesting that the relation between the variables is consistent throughout the sample, not an emergent characteristic or a product of a single correlation within one subsector (single asterisk).
There is no simple way to summarise all the data in the table, so the discussion will focus initially on identifying correlations between some of the media/sources and information types where both variables are rated as important or higher (rating >5), while working in commentary on related media/source and information forms where this aids narration. In some places, interview findings will be introduced where it is helpful in explaining the patterns of data. Again, for the sake of simplicity, in the ensuing discussion references to correlations will use the following terminology: < 0.4 = weak; < 0.6 = moderate; < 0.8 = strong; >0.8 = very strong. References to significance will be referred to thus: >0.05 insignificant; < 0.05 =marginally significant; <0.01 = significant; < 0.001 = highly significant.
Current market price data
Current market price data was the information type ranked most important. It is therefore interesting that relatively few importance ratings for media/sources correlate with current market price data. There is a weak (and only marginally significant) positive correlation with finance/business broadcast news, a moderate (highly significant) positive correlation with specialist financial business channels (such as CNBC156) and weak (although significant) correlations with informal discussion both with colleagues and specific work discussion with external contacts in the market. Surprisingly, there is no correlation at all with specialist financial wire services (such as Thomson-Reuters/ Bloomberg). This seems anomalous because it is evident both from the literature and the periods of trading room observation/on-site interviews that real-time ask-bid spreads and price-changes are closely monitored on these electronic platforms157. Indeed, if the required price information was readily available through the regular business news broadcasts or specialist channels, the need for the expensive, high-end screen arrays would be much reduced. The fact that there are multiple weak correlations between current price data and several different may indicate that this information is accessed through multiple sources. Another explanation might be that the availability of price data is ubiquitous and taken-for granted, so the importance rating of the wire services has to do with other specialist functions that they offer158.
The importance ratings for specialist financial wire services also exhibit several intriguing correlations. These include a weak (but significant) positive correlation with other institutions’ opinions/ positions (e.g. ratings agencies, central banks) and three negative correlations: One of these is a weakly negative (but significant) relation with specific industry fundamentals, one is a
156 It should be noted that during the period of data collection, CNBC was not being carried by the major
subscription provider, Sky, although it had been previously and the service is now available again.
157 This was double-checked by excluding stocks/equities (the ratings for which varied in relation to other
subsectors) and breaking down the analysis in other market subsectors. This still identified no correlation between wire services and current market price. Correlations were also checked excluding any responses rating the importance of financial wire services < 6, to check if those who rated this medium as most important also rated current price data as important. Again, no correlation was found.
158
Other interviews suggested that one of the key functions of the wire-service platforms was the ability to send and receive messages (both private and publicly-visible) to/from other market agents in order to share news, check rumours, and maintain a ‘finger on the pulse’ of the market, so to speak. Bloomberg in particular was often cited as providing useful instant messaging updates. This is consistent with the notion that the significance of the data on trading screens stems in part from the need for constant validation of trading frames/schemata (Beunza & Stark, 2005b; Beunza & Garud, 2005). This may suggest that media/source variable of specialist financial wire services is too broad to encapsulate all its functions.
weak (highly significant) relation with company technicals, and one is a moderately negative (highly significant) relation with company fundamentals. These negative correlations arise in relation to information types that are most important to stocks/equities markets. Coupled with aforementioned absence of any significant relation with price data, it counter-intuitive that the media/source rated most important to investment decisions is negatively correlated with data that would be normally considered core to financial market activity. The explanation that best fits the data requires a modification of the conception of trading screen data as reflexively constituting or ‘appresenting’ market reality. As noted earlier, Knorr-Cetina & Bruegger (2002a, 2002b) and Knorr-Cetina & Preda (2007) differentiate between three dimensions of screen data: current market prices: ask-bid spreads (along with any interaction/ conversation related to the execution of trades) and other market data or news-feeds deemed relevant to the securities being traded. Trading screens certainly display constant updates of prices as the transactions which crystallize those changes are registered through exchanges or trading systems and constantly update ask-bid spreads (see example of a Reuters Kobra screen for bond markets and currency futures below, courtesy of Deutsche Bank159).
Fig. 28 Reuters Kobra Screen
In several of the extended interviews, the researcher asked respondents to comment on the notion that the screen displays actually constituted the market reality with which they engaged when making decisions. Although several replies focused on technical details of the screen displays, two respondents (interviews #19 and #28) recognised the theoretical issue but explicitly disagreed with the proposition. Both pointed out that the prices and ask-bid spreads on the screen were regarded as indexical of potential trades with counterparties, emphasising that, while the data showed the presence of external market agencies with the inclination to deal, confirmation of prices and
159 The screen display includes a range of ask-bid spreads and quantities for various bonds (top row), Kiwi
dollar futures and Aussie dollar futures are highlighted in the red boxes and other international currencies in the green box
transaction volume (particularly for higher volume trades) often still required interaction with the counterparty, either through an electronic medium or (in some instances) through a brokerage agent. Interviewee #28 explained that although the trading screens would indicate price levels, these were often indicative of the mid-point between ask-bid spreads, and so the actual transaction price remained contingent until a trade itself was conducted. This is consistent with the explicit/ transactional form of informational reflexivity, although it also suggests that the contingent/game form of reflexivity (i.e. monitoring of other actors’ dispositions) plays a role here too. The discovery of current market prices therefore involves more than the appresentation of discrete values on screens. Institutional investors engage with each other through these electronic interfaces, but they are aware that the displays are not ontologically independent of positions of other agents whose positions they represent.
Specific work-related discussion
Overall importance ratings for specific work-related discussion were among the highest (>6) but as with financial wire services, this exhibits no correlation with the importance ratings for the most important information types. There are however, weak (but highly significant) positive correlations with market mood/trends, non-market news, and correlations of data/opinions, and also a moderate (highly significant) positive correlation with rival investor opinions/positions. These information types relate more to the context of market events and their underlying causes (such as political developments, bullish/bearish market sentiment, or particular strategies of other investors). The importance ratings for informal work discussion also exhibit several correlations with market mood/trends (weak/ highly significant), rival investor opinions/positions (weak/highly significant), non-market news (moderate/ highly significant) and correlations of data/opinions (very weak/significant). This suggests there is minimal distinction between informal and specific/work- related discussion with colleagues. However, the ratings for informal discussion are positively correlated with general market fundamentals (weak/highly significant), and current market prices (weak/significant). From the researcher’s on-site observation of the trading floors at Deutsche Bank and ANZ, numerous instances arose where colleagues consulted each other in response to a piece of news or unexplained price movement. The resident analysts appeared to play a significant role here, and in two observed cases, they were called over by trading desk colleagues in response to a particular instance of market activity where price movements were either unexpected or anomalous to provide contextual commentary to help rationalise the events in terms of market events and provide a frame for a trading response. Consistent with Smart’s (1999) emphasis on the narrative process underpinning financial practices, the analyst’s function appeared to be the articulation of an unfamiliar phenomenon in such a way as to render it intelligible/meaningful within a frame/schema that made sense in relation to the traders’ intersubjective codifications. Indeed, two of the analysts interviewed (interviewees #5 and #10), identified the importance of ‘selling a story’ to help rationalise investment decisions, either in the trading room or in regard to guiding external clients. As one commented, ‘What you’ve got to try and do is, from all the information that’s available, try
and create a story […] We try to distill ideas, so the information that’s available on the electronic networks is really a primary source- it’s your raw material that you’ve got to deal with. You know, after that, you’re still at work with the data.’ (interviewee #10). This reinforces the contention that in
a financial market environment characterised by informational abundance and real-time symmetry, the trading advantage will often derive from meta-information, including the recognition of what data is significant and which frames need to be employed to render it meaningful and salient to trading decisions. However, insofar as these processes need to take into account factors such as market psychology and the consensus views of market opinion leaders, they cannot be based solely on a mechanical response to fundamentals. Interestingly, the importance of specific work-related instructions and specialist systems/algorithms (both rated >5) also exhibit correlations that tend to support this differentiation. The former is correlated with market moods/trends (weak/highly significant) and information on media/sources themselves (moderate/highly significant). Specialist systems/ algorithms, meanwhile, are correlated with historical, current and future price data (all weak but significant correlations), general market mood/trends (weak/ highly significant) and rival investor opinion/position (weak/highly significant). The correlation with rival investor opinions may be indicative of the process Beunza & Garud (2005) point to, whereby the validity of investor’s own frames/schemata is regularly checked for correspondence with those of others (which is logical given that the institution’s own systems/algorithms are unlikely to reveal this data).
Informal and specific work-related discussion with external contacts
Informal discussion with external contacts exhibits a weak (significant) correlation with non-market news and weak (highly significant) correlation with general market mood/trends, which is perhaps indicative of the way even general conversation with external colleagues may still constitute a source of salient information or sensitise traders/analysts to particular themes/ issues in the news. Importance ratings for specific work-related discussion with external contacts also correlates with several information types: There is a weak (marginally significant) relation to general market mood/trends and a weak (significant) relation with non-market news. Furthermore, work-related discussion with external contacts has a weak (significant) correlation with information on media/sources. This could suggest that this information is sourced from external contacts, but a more compelling interpretation would be that respondents who regard external contacts as important also value information about which sources/contacts are reliable.
It is worth noting that the discussion of media/source and information type correlations so far has covered many of the institutional network-related media/sources to which access would primarily be limited to professional institutional investors. Interestingly, with the exception of some forms of price data, many of the informational correlations exhibited involve factors related to market psychology, opinions/positions of rival investors or other key institutions, correlations of data, information on media/sources and even non-market news. The absence of any broad pattern of correlations between these institutional/ network sources and information about market fundamentals, technical data and prices (and in some instances, negative correlations with these information types) is indicative that their importance stems from providing other forms of significant information. This would be consistent with the contention that markets are susceptible to influence from reflexive communication/ information processes, including investors’ need for meta-information concerning what kind of data is driving other investors’ decisions and the validation of prevailing trading frames /schemata. Nevertheless, as the data presented earlier indicated, information about market fundamentals, technicals and prices still remains important, and these exhibit correlations with ratings for several other media/source types.
Specialist industry reports
Specialist industry reports from central banks, ratings agencies and other influential financial institutions exhibit weak (highly significant) correlations with fundamentals and technical data related to companies, but not on an industry or general market level. This could be explained by ratings agency reports examining company credit ratings, (although interestingly, the main subsector correlations here arose in relation to derivatives and currencies, and some caution in needed in this interpretation). Interestingly, specialist industry reports are correlated with non- market news (weak/highly significant), other institutions’ opinions/positions (weak/marginal significance) and correlations of data/opinions (weak/marginal significance). The relation to non- market news could be explained by the way financial publications of this nature analyse financial markets in the context of broader geopolitical/ strategic/ historical trends, although this cannot be definitively verified from the data here. Otherwise, the relations to other institutions’ opinions includes the specification of stock exchanges, central banks and ratings agencies (on the survey form) and thus mirrors the media/source variable. Thus the relation to correlations of opinions/data is a logical extension of this. It is perhaps surprising that these latter correlations are not stronger, because official reports by these institutions are potentially market-moving, but this may be a result of using a relatively generic variable category.
Public and private analyst advice
Given the evidence presented in the review of literature that financial analyst recommendations and comments can trigger bouts of trading activity, it was interesting that both public and private analyst advice were rated as only moderately important (> 4) and partly objective (> 4), and that there was minimal difference between their ratings. This may reflect the recognition of the potential for vested interests to influence recommendations (notably among sell-side bank analysts working with client companies; see Golding, 2003). Another consideration here, though, is that most traders working for large financial institutions will have access to their own in-house analysts, and sometimes even a global network of such experts in the case of the largest institutions. The ratings for public and
private analyst sources are both correlated with information on media sources (weak/significant for private; weak/highly significant for public) which, again, is probably indicative that respondents who make use of analysts as sources also seek information about their reliability/independence. Public analyst advice is also positively correlated with non-market news (weak/ highly significant), possibly suggesting that public analysts are helpful in placing financial events in the context of other events and drawing attention to new frames (see earlier point regarding the narrative/ story-telling function of analysts). Private analyst advice, meanwhile, is correlated with correlations of data/sources (weak/highly significant) which would suggest that paid-for analyst advice is helpful in confirming the perceptions of market opinion leaders (even if one places more confidence in the advice of in-house analysts).
Print news media
General interest newspapers show a moderate (highly significant) positive correlation with non- market news (self-explanatory) and also company technical and fundamentals. The same positive correlations (weak/highly significant) also appear in the case of financial/business newspapers, but in the latter case there are also weak (highly significant) correlations with specific industry fundamentals and technicals. Insofar as these correlations are indicative of the types of information that might be sought from them, this would suggest a wider range of information is sought from the more specialised financial press (which have a higher overall rating than general newspapers). Interestingly, despite general interest magazine having the lowest overall importance rating (< 3 ),