Analysing performance of stock returns for firms moving from the
Main Market to the Alternative Investment Market (a submarket) of
London Stock Exchange and vice versa
Master Thesis
Master in International Finance, Amsterdam Business School
Prepared by: Zaur Yusubov
Thesis Supervisor: Stefan Arping
Date: August 2016Page 2 of 35
Abstract
In recent years the regulation has been tightened and many firms
made a choice to switch to different stock exchanges with lower
regulatory requirements. This Master thesis examines the
consequences of switching between Main Market (MM) and
Alternative Investment Market (AIM) which have different regulatory
regimes but the same trading technology and both markets are
located in the same country (UK).
Companies switching to lighter regulation (AIM) from stricter
regulated environment (MM) experience negative returns. On the
other hand, these initial price reactions are reversed after the actual
switch. The analysis also reveals that stock returns show an intriguing
upward drift for longer-term, which might be a result of improved
operating performance and cost advantages associated with AIM.
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Table of Contents
Introduction ... 4
Literature Review ... 8
Descriptive Statistics and Sample ... 11
Hypothesis, Methodology and Analysis ... 13
Conclusion ... 18
References ... 19
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Introduction
A firm planning to go public in the UK may choose the Official Listing (also referred to as the “Main Market”) or Alternative Investment Market (further “AIM”). The two markets that I consider in this paper are both part of the London Stock Exchange (further ‘LSE’). The LSE’s Main Market is one of the world’s most international and diverse stock markets for the admission and trading of equity, debt and other securities. It hosts over 2,600 companies from 60 countries across 40 sectors, including many of the world’s largest, successful and dynamic companies.
In the meantime, the AIM has been a successful sub-market for the Main Market allowing small and medium sized firms to float shares with less regulatory requirements compared to the LSE’s Main Market. The purpose of the market is to help smaller and growing companies to raise the capital they need for expansion. Since its establishment in 1995 it has grown steadily to reach over 3,600 companies that joined AIM from across the globe with a market value exceeding £95bn by the end of February 2016.
The Main Market comprises companies that have both satisfied the requirements of the UKLA1 and continue to abide by the additional rules imposed by the LSE. The regulatory environment for AIM involves a high degree of self-regulation by the company’s nominated advisor, which acts as the main quality control mechanism. Companies on AIM also face fewer continuing obligations in terms of reporting and corporate governance. However, the trading mechanisms used by AIM companies are identical to those used by listed companies on the MM, and both markets are subject to the same UK legal system that protects the rights of shareholders.
The De-listing and admission process
Moving to AIM
Switching from the Main Market to AIM is a two stage process involving de-listing and an admission. De-de-listing2 process requires the approval the company's shareholders at general meeting with min. 75% of shareholders having voting rights. Firstly, the company will need to call a general meeting on appropriate notice to pass this resolution approving the application for admission of its shares to AIM and any other necessary resolutions. At the time the notice of meeting with the accompanying circular is sent to shareholders, the company will still be subject to the Listing Rules of Main Market. This process completes when the
1 the UK Listing Authority (UKLA), part of the UK Financial Services Authority (FSA)
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circular is approved by the stock exchange body (FSA) and provision has to be made for this in the timetable.
If a company is in a process of placement it will still be subject to the Main Market Listing Rules. These rules include a prohibition on issuing shares at a discount of greater than 10 per cent. to the middle market price at the time of announcing the placing. Admission for fully listed applicants takes place under the fast track procedure for "quoted companies". These are companies that have traded on a designated market, for at least 18 months prior to applying to AIM, including the Main Market of the UKLA, NASDAQ, NYSE, Deutsche Börse and the Australian Securities Exchange amongst others. Moving to AIM from the Main Market is a relatively well-worn path with fewer hurdles than for a new applicant – principally there being no need to produce a full admission document. The timetable is fairly lengthy given the need for a general meeting notice period and the 20 days between announcement and admission, as well as the UKLA's time to approve the circular.
Moving to the Main Market
The departure from AIM can often be part of a bigger success story where fast growing companies seek to move to the Main Market in order to access a greater pool of capital, attract a wider investor base and improve their image as a more established investment proposition.
The process is not dissimilar to that which the company will have gone through when seeking admission to AIM, although the disclosure requirements are more stringent. The cost and expense of preparing for a listing on the Official List and the need to comply with strict corporate governance principles is high but may be mitigated by the benefit of attracting new investors and the prospect of increased liquidity.
Thus, these markets are differently organised:
1) The AIM is operating as an “Exchange Regulated Market”;
2) Admission to the AIM is organised by the nominated adviser (a financial intermediary) who is advising on appropriateness of a company for the stock market. However, in the Main Market the companies should get confirmation of market authority for admission to LSE.
The comparison of main differences between MM and AIM in terms of requirements imposed on the companies is presented in Appendix I.
No research has examined the consequences and performance of firms shifting between these markets during the last 11 calendar years (2005-2015)
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applying several risk-adjusted models. Hence, this study will analyse performance of stocks by studying the stock price reaction of quoted firms to the switching between two markets with different regulatory regimes but the same trading technology of the LSE:
Switching from the Official List (referred to as the Main Market) to AIM; Vice versa, switching from AIM to MM.
The second-tier markets are presented in many countries, but a number of features of the UK markets make them particularly suitable for my purposes:
The Main Market and AIM are well-established and have large numbers of companies trading on them: at the end of June 2016, there were around 1240 companies on the MM, and just over 1000 on AIM. What is important for my research is that there has also been a significant flow of companies moving between these markets. Perhaps surprisingly, the net flow of companies moving between these markets has been very heavily towards AIM. My sample comprises 126 companies that have moved down to AIM, and 66 companies that moved up to the Main Market. Unlike some other stock exchange markets where the regulator can
“de-list” a company for failing to comply with one or more of its requirements, the LSE has few ongoing standards (e.g., minimum market capitalisation requirements) that leads companies towards moving down to AIM. The main exception is the requirement that at least 25% of the share capital of a firm should be in public hands. Excluding a few number of the companies that moved from the Main Market to AIM, the switch was a deliberate choice made by the management of the companies, rather than a condition imposed upon them by the LSE. Such decision to switch markets was at the discretion of the management, and did not require shareholder approval.
Since the date of establishment up to 2008 the growth and popularity of AIM was remarkable. Although still AIM is a market that mainly attracts smaller companies, over a quarter of AIM companies have market valuations above £50m (~$70m). AIM has attracted a growing number of overseas companies onto its market: nearly 350 foreign companies were quoted on AIM as of the year-end of 2007. In contrast to the ~500 new companies joining AIM per year during 2005 and 2006, the Main Market attracted only ~19 IPOs per year during this period. However, the growth trend of a number of companies listed in AIM started to decline after 2008, as a result of financial crises, e.g. many companies applied to bankruptcy or were acquired by other companies and delisted subsequently. As additional benefit to the existing studies made prior my research I am shedding light on the factors that have made AIM so attractive to companies moving from the Main Market.
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Trading information on switching firms is published regularly on the website of London Stock Exchange. Comparing market liquidity with other markets, like the US market, there is no big difference as companies move between AIM and the MM. In the U.S. market when the companies are moving off the major exchanges it often leads to a collapse in trading activity. Furthermore, while U.S. companies often stop reporting accounting and other information following SEC deregistration, there is no such issue in the U.K. markets, because all companies are required to publish this information regardless of whether they are subject to regulation of UKLA or, for that matter, traded on a stock exchange. This creates unique data availabilities that allows to analyse the immediate impact of switching markets and regulatory standards on share prices. It is also worth to note that these transactions are economically significant: the total market value of firms moving up to the Official List over the study period from January 2005 to December 2015 was £38.5bn while the corresponding figure for firms moving down to the AIM was £14.8bn.
In addition to the existing evidence from studies of cross-listings and de-registrations this study is going to examine the effect of the stock returns of firms that change their regulatory and governance requirements without changing their country of operation and jurisdiction.
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Literature Review
The stock exchange regulations in different countries were studied in various literatures, for instance, in financial reporting and disclosure rules (see Leuz and Wysocki (2008)). Changes in regulatory rules were the focus of such research, for instance the Securities Acts Amendments of 1964: Selective Provisions and Legislative Deficiencies (Ferrell (2004), Oyer, Greenstone and Vissing-Jorgenson (2006)) and the more recent adoption of Regulation Fair Disclosure (REG FD) in 2000, and the Sarbanes-Oxley Act of 2002 (Coates and Leuz (2007)). In conclusions of these researches generally the authors suggest that benefits from tighter regulatory standards are not necessarily outweigh the costs. Furthermore, some authors reveal that the optimal amount of disclosure and reporting is likely to be different across firms (see Bushee and Leuz (2005), Iliev (2007)), however, the others (Kong, Young, Duarte and Siegel (2008)) argue that there were no significant changes for small and large firms to the introduction of Sarbanes-Oxley Act.
In addition to the literature on the impact of regulation, a few previous studies have analysed the consequences of moving between markets for firm performance. For example, Angel and others (2004) focus on those companies that were listed on Nasdaq and were forced to move “down” on the Pink Sheets. They found that moving “down” to the Pink Sheets is quite costly for the shareholders. However, the Pink Sheets are essentially a quotation service where only dealers can apply to trade in the securities market, rather than a stock exchange. It should be noted that in some important aspects the quotation services offered by the Pink Sheets and the Nasdaq Over-the-Counter Bulletin Board (OTCBB) in the U.S. differ from AIM. As noted by Macey and O’Hara (2004), the Pink Sheets and OTCBB do not provide issuer listing services. Pink Sheets is neither an SEC registered exchange nor an NASD regulated broker/dealer3, and the OTCBB is a quotation service for equity securities that are regulated, but not listed or traded on NYSE, Nasdaq, or any other securities exchange. In contrast, AIM is a market run by one of the world’s leading stock exchanges - London Stock Exchange, where companies can conduct IPOs, can be included in benchmark indices and also can be traded using the trading technology provided by the LSEG. Moreover, AIM is a lightly regulated market segment that shares common trading technology with the Main Market.
Furthermore, agency cost and other motivations for cross listing, as well as the effect of a firm’s country of origin and its valuation, have been examined different studies (for instance, La Porta, Lopez-De-Silanes and Shleifer (1999);
3 source: www.pinksheets.com
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Foerster and Karolyi (1999), Roell, Pagano and Zechner (2002); Doige, Stulz and Karolyi (2007); Lel and Miller (2008) and Hail and Leuz (2009)).
The primary focus of existing cross-listing studies is on companies which seek an additional listing on a more regulated exchange rather than on companies that seek to switch to a less regulated exchange. The key motivations for these cross-listings include increased liquidity, and a variety of potential corporate governance benefits. These benefits are associated with more stringent legal, regulatory or disclosure rules and with a view to avoiding potential agency conflicts (see “Globalization, Corporate Finance and the Cost of Capital”, Stulz (1999)). Moving from a less-regulated market to a more regulated market has close similarities with cross-listings. In recent years a number of surveys have pointed to the potential disadvantages of cross-listing, as well as problems associated with information asymmetry and increased cost of compliance with legal systems and foreign corporate governance (see Karolyi (2006)). The dramatic decline in the number of cross-listed companies brings to the context for somewhat more sceptical view of the costs and benefits of cross-listings. For instance, in the reports of Karolyi (2006) revealed that between 1997 – 2002 the number of cross-listed stocks fell by more than 50% globally. Since the passing of Sarbanes-Oxley the reduction of IPOs and cross-listings of foreign companies has been particularly noticeable in the U.S. and opened an interesting debate about whether New York is losing its competitiveness to London (see, for instance, Zingales (2006), Doidge, Karolyi and Stulz (2007)). Despite this general trend AIM has been successful in attracting overseas listings in increasing numbers, although many of small- and medium-sized companies were not realistic candidates for a US cross-listing.
In contrast, Triantis, Leuz and Wang (2008) examine the motivation for voluntary SEC de-registrations and identify two categories of companies that “go private” and “go dark”. Both deregistration categories remove the obligation to comply with SEC reporting requirements. However, the securities of companies that go dark may continue to be traded on the OTC market whereas the securities of companies that go private are no longer traded after deregistration. In contrast to going dark transactions, companies that go private exhibit positive announcement returns, are larger and are less distressed, while companies that go dark exhibit negative announcement returns, are smaller and exhibit weaker corporate governance characteristics compared to firms going private and control firms (see Leuz, Triantis and Wang (2008)). In addition, Leuz, Triantis, and Wang suggest that firms going dark may be more likely to exploit minority shareholders, whereas firms going private may do so to achieve genuine cost savings, although they identify an overlap in motivation between the two
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categories of firms and suggest that going private is more costly than going dark. Thus, firms without the resources to go private may choose to go dark instead, even if their motives are similar, implying that it is difficult for minority investors to determine in advance whether a decision to go dark will benefit them or not.
The differences between the two market segments are well investigated in reports of Leitterstorf, S. Nicoletti, P. and Winkler (2008), Jenkinson, T. and T. Ramadorai (2009) and Gerakos, J. J., Lang, M. H. and M. G. Maffett (2011). Specifically, the majority of firms graduating from AIM section to the Main Market of the LSE generate positive average abnormal returns on the day the decision is announced and on the day when the switch is actually implemented; for the majority of firms moving from the Main Market to AIM, corresponding average abnormal returns are negative. After implementation of the switch, the pattern is reversed so that firms switching to the Main Market earn lower average abnormal returns while firms switching to AIM earn higher average abnormal returns.
In summary, this study adds to the existing evidence from studies of cross-listings and de-registrations by examining the consequences on the stock returns of firms that change their regulatory and governance obligations without changing their listing regime (i.e. geographic or legal jurisdiction), or the trading technology used in the quotation of stocks at the stock exchange market. This is achieved by studying firms which switch from the Main Market of the LSE to the more lightly regulated AIM section and vice versa. The companies in my sample chose to move between market segments, rather than being forced to move as a result of the imposition of more stringent rules or either violating existing rules.
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Descriptive Statistics and Sample
AIM is a much more recent stock exchange market (established in June 1995) comparing to The Main Market (MM) of London Stock Exchange (LSE) that was launched in the 17th century. However, the concept behind it is not really new: the LSE previously created a market segment for smaller companies, called the Unlisted Securities Market (USM), it was not successful. The companies that were on the USM were given a possibility of moving to AIM and many of them took this chance.
The growth of AIM can be seen in Appendix II, which shows the number of new admissions (including IPOs and switches from the MM), the number of international companies joining AIM and the trends in terms of money raised. As can be seen, the number of companies on AIM grew steadily until 1999, at which point the growth accelerated. From 347 companies trading on the market in 1999, the number at the end of 2007 reached 1694 companies. Of course, the market valuation of the MM companies still swamps that of AIM, which attracts mainly small, growing companies. But there has been an increase in the economic significance of AIM as a source of capital, with slightly above £16 billion being raised by AIM companies in 2007. Furthermore, the growth of international companies choosing AIM has been very impressive, especially since 2004 – 287 new overseas companies have joined the market between 2004 and 2007.
I use data from the LSE in order to construct a sample of firms that moved from the MM to AIM and vice versa. This data classifies all AIM admissions, and has done so from January 2005 to the end of 2015. During this sample period, 127 companies switched down to AIM and 68 switched up from AIM to the MM. Of the down switchers, I excluded two investment trusts and three companies whose primary listing was not in London. I then searched on Datastream for information on the remaining companies and could find data for all except five. This resulted in a base sample of 117 companies that moved down to AIM. The same approach was applied to companies switching up to MM, where some companies were de-listed after a short period of time, I excluded 19 companies resulting in a sample of 49 companies and excluded 5 more companies that had low trading activity (price remained unchanged for more than 50% of the sample period). Thus, as a base sample I used 44 companies switched from AIM to MM.
While the date of the actual switch is recorded in the LSE database, typically, the management of the firm announces the intention to switch a few weeks prior to the switch. For down switchers, provided the company has the support of its nominated advisor, AIM admission is assured. For switching up to the MM, the
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approval of the UKLA is required, although normally the company would not announce a switch without first having checked that they satisfied the UKLA requirements.
Summary statistics for the final sample are presented in Appendix III. The market capitalisation of firms at the point they switched to AIM varies a great deal. The largest company had a market value in excess of £500m (Peel Holdings, in real estate sector), and a few such large companies result in the average market capitalisation of £33,42m being considerably above the median of £9.37m (MM to AIM switch occurred in 2010). As noted above, there is no ongoing market capitalisation requirement for the MM, and so none of these companies were required to transfer to AIM.
Not surprisingly, the average size of those companies switching up is much larger than those switching down: median (mean) market capitalisation for the up switchers is around £406.8m (£590.65m).
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Hypothesis, Methodology and Analysis
As argued by Foerster, S.R. and Karolyi, G.A. (1999) in the context of firms seeking U.S. listings from less regulated markets, firms graduating to the Official Listing from the AIM is outweighed by a lower cost of capital. Asset pricing theory suggests that stock prices of firms moving up should rise and subsequent expected returns should fall on the switch date as the risk premium declines to reflect the lower agency costs of the Main Market. Thus, in my research I have defined following hypothesis to be examined:
Hypothesis 1. Firms experience negative stock returns immediately following the
transfer of firms from the Main Market to the AIM, followed by subsequent higher returns.
Firms transferring down from the Main Market to the AIM might be expected to initially suffer a stock price fall, but eventually the additional risk premium required to compensate for the higher agency costs of the less regulated market should result in higher returns, in equilibrium. This reasoning generates my second hypothesis:
Hypothesis 2. Firms experience positive stock returns immediately following the
transfer of firms from the AIM to the Main Market, followed by subsequent lower returns.
A logical motivation for a promotion to the Main Market is to raise the value of the firm’s equity for the benefit of existing investors and to make it easier for firms to raise capital to exploit additional investment opportunities.
Methodology
In terms of technique employed to analyse the effect of event I consider the event-study methodology pioneered by Fama, Fisher, Jensen and Roll (1969). Generally, researchers investigate return effects for both the announcement date and the implementation date (so called the ‘switch date’). In my research using the switch dates, I will line stocks up in event time, and analyze their average abnormal returns over a 180-days window surrounding the event. I will divide up this event window into various blocks of days prior to, during and after the event day: [-180, -98], [-97, -16], [-15, -8], [-7, 0, +7], [+8, +15], [+16, +97] and [+98, +180]. Four Factor Model
These abnormal returns are constructed in two steps. First, I estimate event parameters over an estimation window using a factor model. I employ Carhart’s
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(1997) four factor model4, employing the FTSE All-Share index (as market return), and risk free rate, SMB, HML and Momentum (UMD) factors constituents over the sample period. FTSE All-Share index data is obtained from Reuters Datastream platform and other time series data (risk free rate, SMB, HML and UMD based on the largest 350 firms) are available on the website of University Exeter Business School.
Using these data, I regressed each firm’s stock returns on the four factors in a 319-days estimation window prior to the event window ([-500, -181] 319-days in Estimation Window). I employed the Dimson (1979) correction in my regressions, to mitigate potential biases arising from non-synchronous trading. Writing for the returns of firm
i at date t in the estimation window, I used:
𝑟𝑖,𝑡 = 𝛼𝑖+ ∑ 𝛽𝑖,𝐹𝑇𝑆𝐸𝑘 1 𝑘=−1 𝑟𝐹𝑇𝑆𝐸,𝑡+𝑘+ 𝛽𝑖,𝑆𝑀𝐵𝑘 𝑟𝑆𝑀𝐵,𝑡+𝑘+ 𝛽𝑖,𝐻𝑀𝐿𝑘 𝑟𝐻𝑀𝐿,𝑡+𝑘+ 𝛽𝑖,𝑈𝑀𝐷𝑘 𝑟𝑈𝑀𝐷,𝑡+𝑘 + 𝜀𝑖,𝑡 𝛽𝑖,𝐹𝑇𝑆𝐸𝐷𝐼𝑀 = 𝛽𝑖,𝐹𝑇𝑆𝐸−1 + 𝛽𝑖,𝐹𝑇𝑆𝐸0 + 𝛽𝑖,𝐹𝑇𝑆𝐸+1 𝛽𝑖,𝑆𝑀𝐵𝐷𝐼𝑀 = 𝛽𝑖,𝑆𝑀𝐵−1 + 𝛽𝑖,𝑆𝑀𝐵0 + 𝛽𝑖,𝑆𝑀𝐵+1 𝛽𝑖,𝐻𝑀𝐿𝐷𝐼𝑀 = 𝛽𝑖,𝐻𝑀𝐿−1 + 𝛽𝑖,𝐻𝑀𝐿0 + 𝛽𝑖,𝐻𝑀𝐿+1 𝛽𝑖,𝑈𝑀𝐷𝐷𝐼𝑀= 𝛽𝑖,𝑈𝑀𝐷−1 + 𝛽𝑖,𝑈𝑀𝐷0 + 𝛽𝑖,𝑈𝑀𝐷+1
Then I saved estimated parameters 𝛼̂𝑖, 𝛽̂𝑖,𝐹𝑇𝑆𝐸𝐷𝐼𝑀, 𝛽̂𝑖,𝑆𝑀𝐵𝐷𝐼𝑀, 𝛽̂𝑖,𝐻𝑀𝐿𝐷𝐼𝑀, 𝛽̂𝑖,𝑈𝑀𝐷𝐷𝐼𝑀 for each firm. In order to create abnormal returns, I subtracted the estimated fitted value from realized firm returns for dates t in the event window:
𝑟𝑖,𝑡𝑎𝑏𝑛𝑜𝑟𝑚𝑎𝑙= 𝑟𝑖,𝑡 − 𝛼̂𝑖− 𝛽̂𝑖,𝐹𝑇𝑆𝐸𝐷𝐼𝑀𝑟𝐹𝑇𝑆𝐸,𝑡− 𝛽̂𝑖,𝑆𝑀𝐵𝐷𝐼𝑀𝑟𝑆𝑀𝐵,𝑡− 𝛽̂𝑖,𝐻𝑀𝐿𝐷𝐼𝑀𝑟𝐻𝑀𝐿,𝑡 − 𝛽̂𝑖,𝑈𝑀𝐷𝐷𝐼𝑀𝑟𝑈𝑀𝐷,𝑡 I then summed computed abnormal returns over the event window, to create cumulative average abnormal returns (CAARs). These CAARs are a measure of abnormal price increases.
In addition to Four Factor model I have implemented Three Factor model, Market Model, CAPM and Constant Mean Return models using a software ‘Event Study Metrics’. This software produces straightforward output and do not cost a fortune,
4This model expands on Fama-French Three Factor Model by adding one additional factor: momentum (UMD). This factor was added, because many studies, like Jegadeesh and Titman (1993), Fama and French (1996) and again Jegadeesh and Titman (2001) found that it was possible to increase your earnings by buying stock that was doing well over the last 1-6 months and selling stocks that were doing badly over the last 1-1-6 months. This strategy is often used in cases when you have to decide in a couple of minutes which stocks you wish to buy. Buying stocks that just lost a lot of value and selling stocks that increased in value tends to give good results. The reason behind this is that the market always corrects itself. After a large gain in value, there are always people that wish to cash out their profit and sell their stock for the high price, decreasing the value of the stock in the process. Another theory says that after a (large) increase in value, the stock may be overpriced and will quickly return to its real value. UMD is short for Up Minus Down, it measures the (historical) excess returns of the „winners‟ that went up minus the „losers‟ that lost value. HML should capture the excess return of stock with a high market-to-book ratio over stocks with a low market-to-book ratio and SMB is the monthly premium of the size factor, it should capture the excess return of small over big stocks (measured by market cap).
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the results for the down and up switchers are presented in Appendix IV and V respectively.
Three Factor Model
3 Factor model simply excludes the 4th factor, e.g. momentum (UMD) from above described 4 Factor model.
Market Model
The Market Model is based on the assumption of a constant and linear relation between individual asset returns and the return of a market index (FTSE All-Share index):
I estimate the model parameters by ordinary least squares regressions based on estimation-window observations.
CAPM
According the CAPM (Capital asset pricing model), the expected excess return of asset i is given by:
Where rf is the risk-free return. I estimate the model parameters of the CAPM by a time-series regression based on realized returns:
Constant Mean Return
Assume that expected asset returns can differ by company, but are constant over time. Then the constant mean return model is:
The parameter
µ
i is estimated by the arithmetic average of estimation windowPage 16 of 35
where Mi is the number of non-missing returns over the estimation window. Even
though the constant mean return model is simple and highly restrictive compared to other models, Brown and Warner (1980, 1985) show that results based on this model do not systematically deviated from results based on more sophisticated models.
The summary charts of all models reflecting down and up switchers are presented in Appendix VI and Appendix VII for the event window of 360 days and 30 days around the event day respectively.
General Analysis
Tables in Appendix VIII present the CAARs set to zero on the event day for the switching firms estimated in the 360 days around the event. Panel A (down switchers) reveals that on average, relative to the implemented risk-adjustment models, e.g. four factor, three factor, constant mean return and market models, the firms that switched down experienced positive return in the six months following the switch, with CAARs of over 9% in the event windows of [+16, +97] and [+98, +180]. However, immediately after the event day the CAARs are negative.
Summary charts in Figure 1A and 1B in Appendix VIII reproduces these results including CAPM and no risk-adjustment, e.g. ‘No adjustment’, for down and up switchers respectively. In the six months following the switch day ‘No adjustment’ reveals even a negative result for the firms switched from MM to AIM, e.g. resulting in total -0.58% in the event windows of [+16, +97] and [+98, +180].
The first model, i.e. ‘four factor model’ is reflecting relatively close results with the ‘three factor model’ in Panel A. This is explained by the fact that ‘three factor model’ employs same risk-adjustments factors except momentum factor, which has minor impact in my results. As the simplest method for constructing expected returns I used constant mean return model, where basically the expected return is the average return for each stock. Probably the most common approach to constructing expected returns is to use market model where the market adjusts the returns, subtracting off the FTSE All-share index returns in event time. This will certainly overcome the impact of general market movements in a rudimentary way, and is equivalent to the assumption that the stock’s beta in the market model or the CAPM is unity.
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The figures 1A (for the period of 180, 0, +180] days) and 2A (for the period of [-15, 0, +15] days) show that regardless of the method of risk adjustment that is employed, the pattern of negative announcement effect and subsequent strong bounce following the switch is clearly evident. On average, after a few days the bounce cancels out any negative return impact of the switch to the AIM market. Panel B of Appendix VIII shows the corresponding results for firms that switched in the opposite direction, up to the MM from the AIM market. The up switchers have slightly positive CAARs in the event windows of [-98, -180] and [-97, -16] in total for three factor, constant mean return and market models. However, four factor models show slightly negative result before the switch in [-97, -16] and [-15, -8] event windows and positive return in [-180, -98] event window. Once the switch takes place, however, CAARs are reflecting negative drift especially in the last window [+98, +180] reaching approx. -25%. A visual representation of these results (which also shows the results using all other alternative risk-adjustment techniques), is presented in Figures 1B and 2B. My conclusions regarding the up-switchers are inevitably more tentative, given the relatively small sample. But, on average, the positive return is followed by a slightly negative drift in the days before the actual switch occurs and then broadly downward drift in the subsequent six months.
In summary, a switch down to AIM results, on average, in a CAAR of around -2.5% as per all risk-adjusted models except CAPM. These negative returns raise the question as to why management decided to switch market segments. The returns continue to be negative during switch period of [-15, +15] days until 2 weeks after the switch day, but these negative CAARs are not generally significant. These results square with the intuition that the switch conveys negative information about the future earnings prospects of switching firms. However, the immediate and sustained reversal in performance in the period after the actual switch is surprising. The results I obtain for the up switchers are broadly the opposite, although, given the smaller sample, the results are not as well determined. I find that companies switch up after strong performance, and that the announcement effect reduces negative CAARs until actual switch takes place. Then, this is followed by continued negative drift in all the risk-adjusted models.
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Page 18 of 35
Conclusion
In this research I examine the effect of moving between markets with different regulatory standards and markets that use the same trading technology. A large sample of companies moved between the Official Listing and AIM provides me with an excellent opportunity for analysing the impact of high versus low levels of regulation on firm stock value.
Summarizing my main results, I find large and significant effects associated with the decision to change market segments. Firms moving from AIM to the MM experience significant switch effects, resulting in a negative drift in CAARs above -30% after the switch, which is visible from the charts in Appendix VIII. Firms moving from the MM to AIM experience negative announcement effects before switch happens. However, once these companies actually start trading on AIM, average returns are strongly positive, with CAARs standing at approx. +10% six months following the switch. Therefore, the net result viewed over this longer event window is strongly positive.
It is quite difficult to make precise conclusion about the reasons of such changes in returns of switchers. I presume that, in regard to up-switchers, investors tend to have more confidence in companies listed in a market with higher regulatory standards. On the other hand, the investors in small- and medium-caps that move down to AIM could benefit from compliance cost savings and tax advantages. These benefits might result in improved profitability and overall performance of the companies. Although, the more relaxed regulatory environment of AIM has its advantages comparing to the Main Market, the AIM also carries an extra risk for private investors, which is more likely to be subject to volatility in such shares.
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Page 19 of 35
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Page 20 of 35 Jain, P.K., 2005. Financial Market Design and the Equity Premium: Electronic vs. Floor Trading. Journal of Finance 60, 6, 2955-2985.
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Page 21 of 35 Websites:
LSE website: http://www.londonstockexchange.com/ Fama-French and Momentum Factors:
http://business-school.exeter.ac.uk/research/areas/centres/xfi/research/famafrench/files/ Database:
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Page 22 of 35
Appendixes
Appendix I – Summary of main differences between MM and AIM
Comparison of the Main Market and the AIM in terms of the requirements imposed on the firms – both on admission and on an ongoing basis is presented below:
MAIN MARKET ALTERNATIVE INVESTMENT MARKET
Admission requirements
Minimum 25% shares in public hands Normally 3 year trading record required Pre-vetting of admission documents by the UKLA, or another recognised EU authority Admission takes several months
Minimum market capitalisation on entry (£700K)
Sliding scale admission fees: e.g. £8.2K, £56.8K, £137.8K, £171.55k and £475k respectively for £5m, £50m, £250m and £500m and above market cap at issue
No minimum shares in public hands No trading record requirement
Admission documents not pre-vetted by Exchange or any listing authority
Admission can be achieved within 2 weeks No minimum market capitalisation.
Nominated adviser required at all times Sliding scale admission fees: e.g. £7.9K, £67.29K, £89.18K respectively for less than or equal to £5m, £250m and above market cap at issue
Continuing obligations
Prior shareholder approval required for substantial acquisitions and disposals Sponsors needed for certain transactions Companies are subject to extensive continuing obligations as required by the UKLA
Sliding scale annual fees: e.g. £5.4K, £12.69K and £54K respectively for up to £50m, above £50m and above £500m market capitalization stocks
No prior shareholder approval for transactions
Flat rate annual fee: £6.25K
Other Costs and Benefits
Sliding scale further issuance fees, starting from £5 m and above with minimum fee of £4.11k up to £46.25k
Sliding scale further issuance fees, starting from £5 m and above with minimum fee of £3.45k up to £44.59k
Aim companies enjoy some tax benefits – since UK tax authorities treat most AIM companies as unquoted “business” assets
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Page 23 of 35 Appendix II - the AIM Market Growth
The table below reflects the development of the AIM market since its establishment. Ten companies previously on the Unlisted Securities Market were transferred to AIM when it opened in June 1995. The first block of columns shows the total number of companies quoted at the year-end; international companies are those from outside the UK. New admissions comprise IPOs and transfers from the Main Market (MM). Money raised is split into funds raised at the time of the IPO or on transfer from the MM (Initial Issues), and further issues by AIM quoted companies.
Source: London Stock Exchange AIM market statistics, December 2015.
Year
Total
… of which
International Total
… of which
International Initial Issues Further Issues Total
19/06/1995 10 1995 121 3 123 3 71.2 25.3 96.5 1996 252 17 145 14 521.3 302.3 823.6 1997 308 22 107 7 341.5 350.2 691.7 1998 312 21 75 7 267.5 317.7 585.2 1999 347 22 102 6 333.7 600.2 933.9 2000 524 31 277 12 1,754.1 1,338.3 3,092.4 2001 629 42 177 15 593.1 535.3 1,128.4 2002 704 50 160 13 490.1 485.8 975.8 2003 754 60 162 16 1,095.4 999.7 2,095.2 2004 1021 116 355 61 2,775.9 1,880.3 4,656.1 2005 1,399 220 519 120 6,461.2 2,481.2 8,942.4 2006 1,634 304 462 124 9,943.8 5,734.3 15,678.1 2007 1,694 347 284 87 6,581.1 9,602.8 16,183.9 2008 1,550 317 114 27 1,107.8 3,214.5 4,322.3 2009 1,293 241 36 6 740.4 4,861.1 5,601.6 2010 1,195 228 102 26 1,219.4 5,738.1 6,957.6 2011 1,143 225 90 23 608.8 3,660.3 4,269.1 2012 1,096 226 71 24 712.1 2,448.7 3,160.8 2013 1,087 226 99 22 1,187.2 2,728.1 3,915.4 2014 1,104 219 118 23 2,599.2 3,269.2 5,868.4 2015 1,044 199 61 14 1,158.0 4,304.8 5,462.8 Total 3,639 650 40,562.8 54,878.3 95,441.1 Quoted Companies New Admissions Money Raised (£m)
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Page 24 of 35 Appendix III – Sample statistics
The table below reflects the number of companies with Market Cap that switched from the Main Market to AIM (left panel), and from AIM to the Main Market (right panel), between 1997 and the end of 2015. The sample used in this paper for testing hypothesis consists of companies that switched between 2005 and 2015. Companies are classified by year according to the date of the actual switch.
Source: London Stock Exchange, New issues and IPOs statistics, June 2016.
Note: A hike in Market Cap from AIM to MM in 2012 is due to Polyus Gold Intl Ltd the largest Russian gold producer with Market Cap of £5.97bn.
Year N (Firms) Mean (£m) Median (£m) Year N (Firms) Mean (£m) Median (£m)
1997 7 12.58 10.70 1997 0 - -1998 5 16.47 3.28 1998 24 74.96 72.05 1999 11 5.19 3.57 1999 14 166.90 88.15 2000 25 33.42 9.37 2000 18 217.98 104.05 2001 36 12.67 5.94 2001 6 87.66 88.15 2002 41 9.50 5.85 2002 5 150.61 128.48 2003 48 14.63 8.25 2003 3 261.07 163.42 2004 22 23.63 10.07 2004 2 145.02 145.02 2005 40 34.03 17.58 2005 2 268.60 268.60 2006 31 25.55 18.61 2006 3 283.67 274.20 2007 6 50.33 4.67 2007 12 829.67 531.17 2008 10 12.30 11.87 2008 11 282.78 307.84 2009 3 51.03 28.96 2009 9 506.76 406.80 2010 7 20.85 16.41 2010 9 268.33 227.46 2011 6 53.03 52.74 2011 9 358.50 407.50 2012 3 57.81 38.47 2012 3 2,437.82 1,011.06 2013 8 28.01 28.88 2013 0 - -2014 8 63.27 30.67 2014 4 530.36 563.65 2015 5 25.87 12.02 2015 6 730.70 527.70 Total (2005-2015) 127 38.37 18.61 Total (2005-2015) 68 590.65 406.80
Grand Total 322 28.96 11.87 Grand Total 140 400.07 227.46
Market Cap on Transfer Market Cap on Transfer
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Page 25 of 35 Appendix IV – Event study results - MM TO AIM
Four Factor Model (MM to AIM)
Table 1 presents cumulative average abnormal returns. The sample consists of 115 observations. The selected normal return model is '4 Factor Model' with the estimation window (-500; -181) and the maximum event window (-180;180).
Table 1
Three Factor Model (MM to AIM)
Table 2 presents cumulative average abnormal returns. The sample consists of 115 observations. The selected normal return model is '3 Factor Model' with the estimation window (-500; -181) and the maximum event window (-180; 180).
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Page 26 of 35 Constant Mean Return Model (MM to AIM)
Table 3 presents cumulative average abnormal returns. The sample consists of 117 observations. The selected normal return model is 'Constant Mean Return' with the estimation window (-500; -181) and the maximum event window (-180;180).
Table 3
Market Model (MM to AIM)
Table 4 presents cumulative average abnormal returns. The sample consists of 115 observations. The selected normal return model is 'Market Model' with the estimation window (-500;-181) and the maximum event window (-180;180).
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Page 27 of 35 CAPM Model (MM to AIM)
Table 5 presents cumulative average abnormal returns. The sample consists of 115 observations. The selected normal return model is 'CAPM' with the estimation window (-500; -181) and the maximum event window (-180; 180).
Table 5
No Adjustment (MM to AIM)
Table 6 presents cumulative average abnormal returns. The sample consists of 117 observations. The selected normal return model is 'No Adjustment' with the maximum event window (-180;180).
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Page 28 of 35 Appendix V – Event study results - AIM TO MM
Four Factor Model (AIM to MM)
Table 1 presents cumulative average abnormal returns. The sample consists of 42 observations. The selected normal return model is '4 Factor Model' with the estimation window (-500;-181) and the maximum event window (-180;180).
Table 1 c
Three Factor Model (AIM to MM)
Table 2 presents cumulative average abnormal returns. The sample consists of 42 observations. The selected normal return model is '3 Factor Model' with the estimation window (-500;-181) and the maximum event window (-180;180).
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Page 29 of 35 Constant Mean Return Model (AIM to MM)
Table 1 presents cumulative average abnormal returns. The sample consists of 44 observations. The selected normal return model is 'Constant Mean Return' with the estimation window (-500;-181) and the maximum event window (-180;180).
Table 3
Market Model (AIM to MM)
Table 4 presents cumulative average abnormal returns. The sample consists of 42 observations. The selected normal return model is 'Market Model' with the estimation window (-500;-181) and the maximum event window (-180;180).
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Page 30 of 35 CAPM Model (AIM to MM)
Table 5 presents cumulative average abnormal returns. The sample consists of 42 observations. The selected normal return model is 'CAPM' with the estimation window (-500; -181) and the maximum event window (-180;180).
Table 5
No Adjustment (AIM to MM)
Table 6 presents cumulative average abnormal returns. The sample consists of 44 observations. The selected normal return model is 'No Adjustment' with the maximum event window (-180;180).
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Page 31 of 35 Appendix VI - Summary Graph of All Models (360 days Event Window)
Cumulative Average Abnormal Returns: Down Switchers (MM to AIM) [-180, 0, +180] Days in Event Time
Cumulative Average Abnormal Returns: Up Switchers (AIM to MM) [-180, 0, +180] Days in Event Time
-35% -30% -25% -20% -15% -10% -5% 0% 5% -1 80 -1 70 -1 60 -1 50 -1 40 -1 30 -1 20 -1 10 -1 00 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 Ret u rn s (C AAR % ) Event Days
4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS) CAPM No Adjustement
-50% -40% -30% -20% -10% 0% 10% 20% -1 80 -1 70 -1 60 -1 50 -1 40 -1 30 -1 20 -1 10 -1 00 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 Ret u rn s (C AAR % ) Event Days
4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS) CAPM No Adjustements
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Page 32 of 35 Appendix VII - Summary Graph of All Models (30 days Event Window)
CAARs are calculated on basis of [-180, 180] event window, the charts reflect closer look at the period of 30 days around event time.
Cumulative Average Abnormal Returns: Down Switchers (MM to AIM) [-15, 0, +15] Days in Event Time
Cumulative Average Abnormal Returns: Up Switchers (AIM to MM) [-15, 0, +15] Days in Event Time
-15% -14% -13% -12% -11% -10% -9% -8% -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 Ret u rn s (C AAR % ) Event Days 4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS) -10% -9% -8% -7% -6% -5% -4% -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 Ret u rn s (C AAR % ) Event Days 4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS)
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Page 33 of 35 Appendix VIII – CAARs Set to zero for All models
This table presents four factor model-, three factor model-, constant mean return-, market model-, CAPM-adjusted and no-adjusted cumulative average abnormal returns (CAARs) in event time over [- 180, +180] days surrounding the switch days. Estimated alpha from the related models is set to zero when computing CAARs. The data comprise 117 firms which switched down from the London Stock Exchange Main Market (MM) to the Alternative Investment Market (AIM) between 2005 and 2015; and 44 firms which switched up from AIM to the MM between the same period. The rows show, in order, the cross-sectional mean factor-models-adjusted average abnormal return in percent accruing in each of the return windows, the CAARs on switch day are set to zero. All intercepts and betas for related models are estimated over the 319-days period ([-500, -181]) prior to the beginning of the event window. Panel A presents these data for the Down switchers, and Panel B for the Up switchers.
The next two page reflects graphical illustration of these results. Figure 1A(2A) and Figure 1B(2B) reflects Panel A and Panel B above respectively.
CAARs set to 0 [-180, -98] [-97, -16] [-15, -8] [-7, 0, +7] [+8, +15] [+16, +97] [+98, +180] Switch is Day 0
Four Factor Model 6.73% 2.98% -1.25% -1.38% -0.82% 1.92% 8.30%
Three Factor Model 6.51% 2.71% -1.19% -1.38% -0.88% 2.03% 8.09%
Constant Mean Return 7.33% 3.31% -0.99% -1.32% -1.01% 2.24% 9.34%
Market Model 7.50% 3.44% -1.13% -1.47% -1.26% 1.65% 7.70%
CAPM 18.70% 8.03% -0.20% -1.47% -2.20% -2.97% -3.41%
No Adjustment 15.96% 6.82% -0.28% -1.32% -1.72% -1.27% 0.69%
CAARs set to 0 [-180, -98] [-97, -16] [-15, -8] [-7, 0, +7] [+8, +15] [+16, +97] [+98, +180] Switch is Day 0
Four Factor Model 1.44% -2.71% -1.34% -1.13% -3.16% -8.01% -24.86%
Three Factor Model 1.93% -1.70% -1.22% -1.10% -3.15% -7.72% -22.99%
Constant Mean Return 3.24% -0.36% -0.76% -0.99% -3.46% -9.09% -26.24%
Market Model 3.57% -0.49% -0.81% -1.06% -3.53% -9.42% -27.06%
CAPM -6.82% -4.70% -1.62% -1.06% -2.89% -5.34% -16.73%
No Adjustment -7.49% -3.62% -1.44% -0.84% -2.33% -2.79% -10.48%
Panel A: Down Switchers (MM to AIM)
Days in Event Time
Panel B: Up Switchers (AIM to MM)
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Page 34 of 35 Cumulative Average Abnormal Returns are set to zero on switch day, [-180, 0, +180]
Days in Event Time.
Figure 1A(B) plots cumulative average abnormal returns for the firms switching from MM to AIM (AIM to Main) in my sample around the switch dates for the period of [-180, 0, +180] days. CAARs are set equal to 0 on the switch day and constructed for five different models: the four-factor model; a three-factor model comprising the FTSE All Share index; a constant mean return model and simple market adjustment using the FTSE All Share index (market model) and CAPM model. No adjustment reflects no-risk adjusted results.
Figure 1A: Down Switchers (MM to AIM), [-180, 0, +180] Days in Event Time
Figure 1B: Up Switchers (AIM to MM), [-180, 0, +180] Days in Event Time
-10% -5% 0% 5% 10% 15% 20% 25% 30% -1 80 -1 70 -1 60 -1 50 -1 40 -1 30 -1 20 -1 10 -1 00 -9 0 -8 0 -7 0 -6 0 -5 0 -4 0 -3 0 -2 0 -1 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 Ret u rn s (C AAR % ) Event Days
4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS) CAPM No Adjustement
-45% -40% -35% -30% -25% -20% -15% -10% -5% 0% 5% 10% -1 80 -1 70 -1 60 -1 50 -1 40 -1 30 -1 20 -1 10 -1 00 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 Re tur ns (C A A R % ) Event Days
4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS) CAPM No Adjustements
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Page 35 of 35 Cumulative Average Abnormal Returns are set to zero on switch day, [-15, 0, +15] Days
in Event Time.
Figure 2A(B) plots cumulative average abnormal returns based on [-180, 180] event window for the firms switching from MM to AIM (AIM to Main) in my sample for the period of 30 days around the switch dates (i.e. zoom in of Figures 1A and 1B). CAARs are set equal to 0 on the switch day. The CAARs are constructed four different models: the four-factor model; a three-four-factor model comprising the FTSE All Share index; a constant mean return model and simple market adjustment using the FTSE All Share index (market model).
Figure 2A: Down Switchers (MM to AIM), [-15, 0, +15] Days in Event Time
Figure 2B: Up Switchers (AIM to MM), [-15, 0, +15] Days in Event Time
-3% -2% -2% -1% -1% 0% -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 Ret u rn s (C AAR % ) Event Days 4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS) -5% -4% -3% -2% -1% 1% -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 Ret u rn s (C AAR % ) Event Days 4 Factor Model 3 Factor Model Constant Mean Return Market Model (OLS)
