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Project Summary
Prosjekttittel:
Kortsiktig og Spekulativ Kapital
Prosjektnummer:
189010/i99
Prosjektleder:
Professor Øyvind Norli
Prosjektdeltagere:
Professor Øyvind Norli
Professor Richard Priestley
Associate Professor Geir Bjønnes
Phd. student Limei Che
Phd. student Kjell Jørgensen
Phd. student Siri Valseth
14. januar 2011
Contents
1 Project I: Access to Capital and Cost of Capital
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2 Project II: Algorithmic Trading on the Oslo Stock Exchange
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3 Project III: Information Spillovers
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Preface
Dette dokumentet er en oppsummering av prosjektet “Kortsiktig og Spekulativ Kapital,” finansiert gjennom en bevilgning fra FinansmarkedsFondet. Prosjektet inkludere bidrag fra professor
Øyvind Norli, professor Richard Priestley, frsteamanuensis Geir Bjønnes, Phd studenter Limei
Che, Kjell Jørgensen, and Siri Valseth.
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Project I: Access to Capital and Cost of Capital
The goal of project I (Chapters 2 through 4 in the full project report) is to improve our understanding of how foreign investors influence the Norwegian stock market. There are two
competing views regarding the potential impact of foreign investors. On the one hand, there is
the “dark side” of foreign investment where it is argued that foreign investors are return chasing
speculators whose decisions can create excess volatility in the local stock market. This type of
behaviour has a detrimental impact on the local stock market. In particular, the excess volatility caused by return chasing could destabilize the local equity market which affects the cost of
capital. The mechanism through which this can work is that the excess volatility requires local
investors to demand a higher return to hold equity to compensate them for the additional risk
that foreign speculators cause. This will in turn increase the cost of equity capital, make future
investment opportunities unprofitable, and reduce economic growth.
Concerns about foreign speculators around the time of the Asian crisis led the Malaysian
regulator to impose capital controls in 1998. Recently, there have been calls from political
parties in France, Germany and Norway for the imposition of a Tobin tax on foreign currency
transaction. The aim of such policy recommendations is to reduce what is perceived to be
speculation by foreign investors. The idea is that increasing the cost of trading will curtail
speculation and reduce the volatility of flows both in and out of the equity market. In turn, this
should reduce stock market volatility and hence risk.
On the other hand, the counter argument to the so called dark side of foreign investment
is based on the notion that foreign investors provide real economic benefits to the local market
through risk sharing between local and foreign investors. This risk sharing leads to a reduction
in the equity cost of capital of local firms since foreign investors are willing to pay a premium
in order to obtain the diversification benefit. Many countries liberalized their equity markets in
the 1980s and 1990s to allow for the free flow of capital. The European Union and the creation
of the single market and the single currency are testament to the belief that free movement of
capital is an important tenant of wealth creation. Another potential benefit of foreign investors
is improved liquidity. If liquidity is a factor that commands a risk premium, then improved
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liquidity, from increased foreign liquidity provision, provides another channel for reducing the
cost of equity capital in the local market.
An assessment of the benefits or costs of foreign investment is ultimately an empirical issue.
Part I of this report provides an thorough investigation of questions related to how foreign
investors impact the Norwegian stock market.
In Chapter 2, our analysis focusses on the aggregate stock market as well as examining
the impact of foreign investors at the industry level. We aim to assess empirically whether the
trading patterns of foreign investors has an effect on the Norwegian stock market. The empirical
results are based on an sample that covers the period 1995 to 2007. However, it is natural to
split the sample into two separate periods around the adoption of the SAXESS trading system
in June 2002. The reason for this is that the SAXESS trading environment led to a significant
increase in the ease foreigners had in trading on the OSE. For example, the number of foreign
members on the OSE increased from an average of five from the period 1996 to 2002 to twenty
two in 2005. This shift in the trading system has a substantial impact on our findings.
During the pre-SAXESS period, foreign investors’ flows into the Norwegian stock market
premanently reduced stock prices. This suggests an increase the cost of equity capital. We
provide evidence that foreign investors’ flows increase after prices increases. This suggests return
chasing. Finally, stock market liquidity falls after an increase in foreign investors’ flows. Overall,
in the period where the OSE had few foreign members, the impact of foreign investors’ flows
was detrimental to the well functioning of the stock market.
During the post-SAXESS period, foreign investors’ flows into the Norwegian stock market
increase stock prices and the impact is permanent. Our calculations indicate that the cost of
equity falls by around 1%. There is no evidence that foreign investors’ flows increase after prices
increases. This would suggest the reason for foreign investors flows are for diversification benefits
rather than return chasing speculation. All measures of stock market liquidity increase after
an increase in foreign investors’ flows. Overall, in the period where the OSE had a substantial
increase in foreign members, the impact of foreign investors’ flows was extremely beneficial to
the well functioning of the stock market.
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In Chapter 3, we study how domestic individual investors, financial investors and foreign
investors affect stock return volatility at the stock level. We find that foreign investors and
domestic financial investors have opposite impacts on stock return volatility. While the former
increase return volatility, the latter decrease volatility. This indicates that institutional investors,
from different geographical regions, could have different behavior and impacts on stock return
volatility. This paper also finds that both domestic individual investors and domestic financial
investors reduce stock return volatility. We provide three explanations: trading style, trading
volume and investment horizons, for the results of different types of investors’ impacts on stock
return volatility. The results show that foreign investors, who exacerbate stock return volatility,
are momentum traders, trade the most and have the shortest investment horizons; individual
investors, who have the strongest negative impacts on return volatility, are contrarian traders,
trade the least and have the largest return volatility; and financial investors fall somewhere
in-between.
In Chapter 4, we study whether or not the stock market performance of foreigners is related
to how far away from Norway they are located. The question is motivated by studies finding
that investors perform better when they invest in stocks that are geographically close. In the
context of foreigners investing in Norwegian stocks, one would therefore expect that foreigners
located far away from Norway would have an informational disadvantage relative to Norwegian
domestic investors and that this would impact their performance. If this is the case, one could
easily imagine that foreign investors would be reluctant to invest in the Norwegian market and
that this would hamper the ability to harness the positive effects of foreign investments that we
document in Chapter 2. To investigate this, we look at the performance of portfolios formed by
aggregating the stock holdings of investors within a certain distance from Oslo Stock Exchange.
This gives us portfolios representing the investment choices of investors located close to Norway
as well as portfolios representing investors far away from Norway.
The main finding is that we are unable to document any performance differences related to
distance. This is true both when distance is measured using the number of kilometers between
the capital of each investor’s home country and Oslo and when distance is measured using
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time-zones. The implication of the finding is not necessarily that foreigners does not have
an informational disadvantage. They may very well have an informational disadvantage, but,
independent of where they are located, foreigners seems to keep broadly diversified portfolios
with relatively low turnover. In other words, foreigners act as if they are tracking an index
of Norwegian stocks. Thus, when we are using the Oslo Stock Exchange market index as a
benchmark, foreigners abnormal performance will be close to zero.
Chapter 4 also briefly document the extent to which foreign investors participate in equity
offerings on the Oslo Stock Exchange. As one of the key roles of a stock exchange is to provide
access to capital for the firms listed on the exchange, it is interesting to uncover whether or
not foreign investors contribute in allowing the exchange to serve this important role. The
main conclusion is that foreigners are active in all type of equity offerings and, hence, serve as
important contributors to the Oslo Stock Exchange’s ability to allow Norwegian firms to raise
new equity capital.
Policy Implications
Our findings illustrate the importance of allowing foreign investors access to the Norwegian
stock market. Even though we show that foreigners tend to increase the volatility of individual
stocks (Chapter 3), the cost of capital is reduced when foreigners are getting better access to
the Norwegian market: In the Post-SAXESS period, foreign investors had far easier access to
the Norwegian equity market. This led to a substantial increase in foreign investors’ flows.
Therefore, if regulators are concerned with reducing the cost of capital for Norwegian firms and
hence increasing investment, improving liquidity and, in general, allowing for a more efficient
capital market, they should be looking at further ways of attracting foreign investment. Clearly,
increasing the costs of trading, through, for example, a Tobin tax on foreign transactions, with
the aim of reducing foreign investment will be counterproductive. Political rhetoric (see for
example Sande (2010)) calling for a Tobin tax, is clearly not optimal and is not based on
empirical facts. It should be disregarded. If anything, costs should be lowered to attract further
foreign investment which benefits all firms. At the individual firm level, our findings should
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encourage firms to attract foreign investors. Firms that are closely held by Norwegian investors
would benefit most from this.
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Project II: Algorithmic Trading on the Oslo Stock Exchange
This project is covered in Chapter 5. The study is motivated by the fact that technological
changes have contributed to significant changes in the way financial markets function and the
way financial assets are traded. Many markets have been reorganized into electronic limit order
books. Electronic limit order markets will usually allow investors to use computer algorithms to
directly manage the trading process at high frequency. In algorithmic trading, computers directly
interface with trading platforms, and orders are placed without any human intervention. The
computers are fed with market data, and possibly other data such as input from press releases
on earnings announcements. The speed and quality of electronic limit order markets encourages
the use of algorithmic trading. During the last 15 years the share of trades involving algorithms
has grown from zero to be responsible for more than 50% in several markets. For instance, it is
assumed that algorithms was responsible for more than 70% of trading volume in U.S. in 2009.
Market participants use algorithms to execute orders in a single stock, in pairs of stocks, or in
baskets of stocks. The algorithms determine timing, price, and quantity of orders. Furthermore,
such models will dynamically monitor market conditions across different securities and trading
venues. Algorithmic models will typically use a combination of active and passive strategies.
By using algorithms, market participants try to reduce market impact by optimally breaking
large orders into smaller pieces. Typical users of algorithms are institutional investors such as
dealers and hedge funds. Algorithms can be used to provide liquidity (market making), to detect
arbitrage opportunities, to adjust portfolio positions, or to initiate speculative positions.
Proponents for algorithmic trading claim that it may reduce frictions and the cost of trading,
and furthermore facilitate more efficient risk sharing, improve liquidity, and also lead to more
efficient prices. The ultimate consequence may be lower cost of capital. However, it has also been
claimed that algorithmic trading may lead to reduced liquidity and increased volatility, especially
at times of market stress. It is thus very important to learn more about algorithmic trading
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strategies and its role in the price formation process. Despite the importance and interest, there
is not much empirical research on algorithmic trading. This is primarily due to the lack of data
where algorithmic trades are clearly identified.
Hendershott, Jones, and Menkveld (2009) use the electronic flow of messages on the NYSE
as a proxy for algorithmic trading. They conclude that algorithmic trading likely causes an
improvement in market liquidity and that it makes quotes more informative. Hendershott and
Riordan (2009) uses data for stocks traded on the Deutche Boerse. An advantage with their
data is that they can directly identify algorithmic trading. Although using different data, their
results are consistent with the results of Hendershott, Jones, and Menkveld (2009). Their
evidence suggests that algorithmic trades are more informative than other trades. This would be
a natural mechanism by which algorithmic trading would lead to more informationally efficient
prices. Consistent with Hendershott and Riordan (2009), Chaboud et al. (2009) find that
algorithmic traders seem to follow correlated strategies. In contrast with the other two studies,
however, Chaboud et al. (2009) find that algorithmic trades in FX markets are less informative
than other trades.
In this report, Chapter 5 examines algorithmic trading strategies with respect to liquidity
provision using a unique data set from Oslo Stock Exchange. The data allow a separation of the
orders/trades from algorithmic models from other orders/trades at the individual dealer level.
In addition to being able to separate algorithmic trades from non-algorithmic trades, one major
advantage with these data is that we compare algorithmic trades with non-algorithmic trades by
similar market participants. This can be very important because institutional investors may be
different from other investors. Institutional investors are in many studies found to be informed.
Hence, it can be expected that algorithmic trading by institutional investors will also be informed
if the models utilize the institutional traders private information (e.g. from customer flows).
We find that the typical order from algorithmic models is smaller and less aggressive than
orders others. This is also the case when comparing orders from algorithmic models and other
orders from institutional investors only. Furthermore, we find no evidence that algorithmic
strategies are correlated. There is also little evidence that the fraction of algorithmic trades and
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volatility is correlated. Compared with other investors, algorithmic models are more frequently
providing liquidity by submitting limit orders. However, algorithmic models are more often
consuming liquidity close to the end of the trading day. Our results may indicate that the most
common use of algorithms at OSE is to unwind positions with as little price impact as possible.
This would be consistent with e.g. Bertsimas and Lo (1998) who find that, in the presence
of temporary price impacts and a trade completion deadline, orders are optimally broken into
pieces so as to minimize cost.
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Project III: Information Spillovers
This project (Chapter 6) introduces a framework that directly quantifies information spillovers
between markets. Information spillovers occur when market specific information, defined as
information that directly affects the return and volatility in one market only, indirectly affects
returns and volatility in other markets. By using a market microstructure variable, market
specific order flow, as a proxy for market specific information we estimate the spillover effects
from the stock market to the bond market and vice versa. We find evidence of spillovers, both
in means and volatility, across the two markets. Negative stock market specific order flow spills
over to long and medium term maturity bonds by increasing both returns and volatility. Our
findings are consistent with portfolio rebalancing as a channel of transmission. In line with the
literature on cross-market linkages we find that the spillover effects are strongest in periods of
increasing negative correlation, which characterizes episodes of ”flight-to-quality”.
The nature of the market specific information that spills over across markets can be illustrated by two examples from our sample period. An example of negative stock market specific
information was the sharp appreciation of the Norwegian krone in the first half of 2002. The
appreciation of the exchange rate was interpreted by many market participants to be detrimental for the export and import-competing sectors. A stronger currency reduced the expected
future earnings for internationally exposed firms, which are a majority of the firms listed at
OSE. On the other hand, the exchange rate appreciation was not interpreted to influence the
bond market. The Central Bank did not signal any reductions in the policy rate to weaken the
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currency. Rather, the policy rate was increased in June 2002. The exchange rate appreciation
had therefore no effect on expected returns in the bond market, but was specific for the stock
market. The negative stock market specific information spilled over to the bond market as
investors sold stocks and bought bonds.
An an example of positive bond market specific information, consider the decreasing consumer price index in 2003. Low inflation numbers were interpreted by some market participants
to eventually lead to lower bond yields. They assumed that the Central Bank would have to
reduce the policy rate in the future in order to meet the inflation target and expected an increase
in bond returns. This information on inflation did not directly affect expected stock returns as
inflation is assumed to have a neutral effect on firm profits. The positive bond market specific
information induced financially constrained traders to sell stocks in order to finance an increase
in their bond holdings.
Overall, the analysis and results of Chapter 6 indicate that linkages between the stock and
bond markets are at least partly due to information spillovers. Rebalancing by constrained
investors appear to be a channel of transmission. Spillovers are strongest in periods with high
levels of correlation. This implies that increases in cross-market linkages to some extent can
be contained by impeding the channels of transmission. For example, government authorities
can impose a temporary ban on short sales or introduce a transactions tax on trades in one or
several asset markets. By limiting trading activity for a certain period, and thereby portfolio
rebalancing, spillover effects may be smaller than without such measures. However, given the
analysis and conclusion from Chapters 2 through 4, this hardly seems optimal.
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