Thesis Ioannis Konstantopoulos Final

Illiquidity: A study on how investing in illiquid assets can be profitable

MSc Finance Ioannis Konstantopoulos 703791 Abstract The aim of this study is to examine whether illiquidity, introduced with some specific factors, could explain the asset returns in Lisbon stock exchange, France stock exchange and Tokyo stock exchange from May 1st, 2006 to May 1st 2016. The examination is annual in order to investigate for both positive and negative effects. Based on the research theory, the illiquidity should affect positively the assets’ returns in periods of normal growth and negatively during crisis. The results of this study are various and confirm that argument in some of the cases.

1 Introduction

Illiquidity is an alarming term for a lot of investors. It conceals the danger of a great loss in a sudden liquidation. The need of an easy and fast asset sale occurs from the uncertain economic cycles, the changes on asset trends and the new investment opportunities. Due to the aforementioned reasons, the majority of investors tend to have a short term investment horizon. This fact promotes the trading on assets that could be immediately sold without great losses, the liquid assets. Subsequently, some other assets that do not have these characteristics are being targeted by a smaller group, the long term investors. This group of investors is choosing these assets in order to benefit from the liquidity premium and achieve higher long term results. An example of long term investors are pension funds, whose liabilities (pensions) can have a very long horizon. Especially now, that the interest rates are very low and the present value of these liabilities is bigger, the need of high returns is vital. There is a plethora of factors that affect the liquidity of an asset, such as transaction costs, demand pressure, private information, search friction and funding constraints. Also, the academic research on illiquidity has resulted in many different methods and ways to measure it, some of these are the bid-ask spread, the Roll ratio, the market depth and the Amihud ratio. All these ways of measure of illiquidity, as well as the sources of illiquidity are analyzed extensively in the next section, the Literature review. The Amihud ratio and a modification of it are used in this study, in order the illiquidity to be quantified and its effects to be assessed. The period that is tested is from May 1st, 2006 to May 1st, 2016. The chosen period includes dates before, during and after the recent financial crisis. Also, the assets that were chosen for this study are equities, which are listed in Lisbon Stock exchange, Paris Stock exchange and Tokyo Stock exchange. Portugal was chosen as a country that was highly affected by the financial crisis due to its significant public debt and deficits. France had also great deficits during the crisis, but its relatively lower debt and its significant industrial base, reduced the effects. Finally, Japan was chosen as a more independent country, with great industrial base. These choices are targeting to the dissimilarity of the results and are analyzed in the third section, Sample description. The fourth section of this study focuses on the two illiquidity ratios that are used, some essential assumptions and the expected results depending of the time period and each point of the economic cycle that is assessed. The next section describes the regression process. In the main regressions of this study the annual returns are used as the dependent variable, the Market Risk and separately each illiquidity factors as the independents. This model is used for every year of our observations period. In order to avoid collinearity and prevent market risk from being an omitted variable, I used the Fama - Macbeth methodology. So the first part of my empirical test is a time series regression for each stock using excess returns as dependent and market excess returns as independent, in order to get the stocks’ betas. Then, these betas are used for the above regression as the market risk factor. After, the empirical results are interpreted and analysed, in order to determine when and why the illiquidity factor affects significantly the stock returns. In the final section the

results are summarized and their consistency with the theoretical framework is being examined.

2 Literature Review In this section I investigate the theoretical indication of liquidity premium, as it has been described in many research papers. Risk Neutral Investors First, it is considered that investors trade twice, one the date that they buy the asset and one the date that they sell it, without intermediate trades (De Jong and Driessen 2013). The illiquidity factor will be set as the transaction costs of these trades. Another assumption is the risk-neutrality of the investors. So, under these conditions, if all investors have the same horizon (h) (Beber, Driessen and Tuijp (2012)) we can conclude that

E R# = R% +1h c#

Where E R# is the expected return of stock i in for period h, R% is the return of the risk free asset for period h and c# is the transaction costs for the two aforementioned trades of stock i. This results to net expected returns equal to risk free returns for all stocks, as risk-neutrality is assumed. In this case +

,c#can be considered as liquidity premium, but it

cannot generate excess returns for the investors as it is an amount that they need to pay. The same assumptions can be applied to investors with different investment horizons. In that case the examination of borrowing constrains is needed. If borrowing constrains don’t exist, then the investor with the longest horizon would buy all the assets and the equation would be

𝐸 𝑅/ = 𝑅0 +1ℎ2𝑐/

Where ℎ2 is the investment horizon of the investor with the longest horizon. In that case, investors with shorter investment horizon (ℎ4 < ℎ2) would get less expected net returns than the risk free asset returns and due to the assumption of risk neutrality, they wouldn’t buy any stocks. Now, let’s consider a third case, where the short-term investors can only invest in liquid assets. (Amihud and Mendelson (1986)) and thus only the long-term investors can invest in illiquid assets. I set ℎ6 the horizon of the short-term investor, ℎ7 the horizon of the long term investor andℎ7 = 𝑘 ∗ ℎ6 the relation between the two horizons, where k>1.

𝐸 𝑅:/ = 𝑅0 +1ℎ6𝑐:/

Where 𝐸 𝑅:/ is the expected returns of the liquid asset and 𝑐:/ the transaction costs of the liquid asset.

𝐸 𝑅:/ −1ℎ6𝑐:/ = 𝑅0

So, 𝑅0 is the expected net return of the short term investor. Next, are examined the expected net returns of the long term investor on the liquid asset.

𝐸 𝑅:/ −1ℎ7𝑐:/ = 𝑅0 +

1ℎ6𝑐:/ −

1ℎ7𝑐:/

𝐸 𝑅:/ −1ℎ7𝑐:/ = 𝑅0 +

1ℎ6𝑐:/ −

1𝑘ℎ6

𝑐:/

𝐸 𝑅:/ −1ℎ7𝑐:/ = 𝑅0 +

𝑘 − 1𝑘ℎ6

𝑐:/

From the equation above can be retrieved that 4<+

4=>𝑐?@ is positive as k>1, so the expected

net returns of the long term investor on the liquid asset are more than 𝑅0 and thus more than the returns of the short term investor on the same asset. Because of the assumption of risk-neutrality the long term investor demands the same net expected returns on the illiquid asset so 4<+

4=>𝑐?@ is the liquidity premium and the sum of it

and the risk free asset’s return gives the net expected return for the illiquid asset. Risk Averse Investors In the case of absolute risk aversion, Acharya and Pedersen 2005 have used an extension of CAPM, with stochastic transaction costs and 1 period investors’ horizon. Their model was trying to investigate whether commonality of liquidity and liquidity risk could affect the returns of the assets. They used a factor that includes the covariance of stock return and market return (market beta), the covariance of stock return and the market transaction costs, the covariance of stock transaction costs and market return and the covariance of stock transaction costs and market transaction costs. All these were used with the suitable sign in order to have a positive effect on the stock returns. 𝐸 𝑅/ = 𝑅0 + 𝐸 𝑐/ + 𝜆{𝐶𝑜𝑣 𝑅/, 𝑅G − 𝐶𝑜𝑣 𝑅/, 𝑐G − 𝐶𝑜𝑣 𝑐/, 𝑅G + 𝐶𝑜𝑣 𝑐/, 𝑐G }

The λ coefficient depends on the investors risk aversion. This model had a good fit and was better than CAPM in terms of p-value and R-squared. A wider model was used by Beber, Driessen and Tuijp (2012), where heterogeneous horizons of the investors were included in the analysis. In this model, borrowing constrains don’t exist, in opposition to Acharya and Pedersen, so illiquidity premium cannot be produced by them. Also, the investors do not rebalance and the transaction costs are IID. In their case short term investors invest only in liquid assets, but long term investors can invest in both liquid and illiquid assets. So, optimal portfolios depend on the investors’ horizon. In the case that there are two investors with investment horizonsℎ+, ℎI

where ℎI>ℎ+, risk aversions𝛾+,𝛾I and liquid and illiquid assets are not correlated the model that describes the expected return of the liquid asset is:

𝐸 𝑅KL+:/M = 𝑅0 +

𝛾+ + 𝛾I𝛾+ℎ+ + 𝛾IℎI

𝐸 𝑐KL+:/M +

1𝛾+ℎ+ + 𝛾IℎI

𝐶𝑜𝑣(𝑅KL+:/M − 𝑐KL+

:/M , 𝑅KL+O − 𝑐KL+O )

and the expected return of the illiquid asset is:

𝐸 𝑅KL+/::/M = 𝑅0 +

1ℎI𝐸 𝑐KL+

/::/M + 1

𝛾+ℎ+ + 𝛾IℎI𝐶𝑜𝑣 𝑅KL+

/::/M − 𝑐KL+/::/M, 𝑅KL+O − 𝑐KL+O

+(1

𝛾IℎI−


)𝐶𝑜𝑣 𝑅KL+/::/M − 𝑐KL+

/::/M, 𝑅KL+O − 𝑐KL+O

The main difference with the liquid asset expected return is the third term, which is the segmentation risk premium. This risk of the illiquid asset is shared by only the long term investor and that is the reason that they demand a compensation for this. In the case that liquid and illiquid assets are correlated the expected return of illiquid asset is:

𝐸 𝑅KL+/::/M = 𝑅0 +

1ℎI𝐸 𝑐KL+

/::/M +ℎI−ℎ+ℎI

𝛾+𝛾+ℎ+ + 𝛾IℎI

𝛽𝐸 𝑐KL+:/M

+1

𝛾+ℎ+ + 𝛾IℎI𝐶𝑜𝑣 𝑅KL+

/::/M − 𝑐KL+/::/M, 𝑅KL+O − 𝑐KL+O

+(1

𝛾IℎI−


)𝐶𝑜𝑣 𝑅KL+/::/M − 𝑐KL+

/::/M, 𝑅KL+O − 𝑐KL+O

−(1

𝛾IℎI−


)𝛽𝐶𝑜𝑣 𝑅KL+:/M − 𝑐KL+

:/M , 𝑅KL+O − 𝑐KL+O

𝛽 =𝐶𝑜𝑣 𝑅KL+

/::/M − 𝑐KL+/::/M, 𝑅KL+

:/M − 𝑐KL+:/M

𝑉𝑎𝑟 𝑅KL+:/M − 𝑐KL+

:/M

By this equation can be retrieved that the liquidity effect on illiquid assets return is bigger when the liquidity and illiquid assets have positive correlation. Also, a positive correlation decreases the segmentation risk premium, mentioned above, as the illiquidity assets return can be replicated by the liquid assets. Furthermore, if investors are more risk averse, both the liquidity and segmentation premium increase. Finally, it was found that when the horizon of the long term investor increases, the liquidity premiums decrease.

Sources of illiquidity Now, in order to analyze the sources of liquidity and hence illiquidity, we need to take into consideration the market microstructure theoretical findings, which deal with issues of markets’ structure and trading mechanisms and concerns with how those can affect price formation and price discovery, market information, transaction and timing costs (O’ Hara, 1995). Much of the microstructure literature has been focused on discovering why illiquidity exists and which the factors that can determine it are. Below are listed and analyzed the main sources of illiquidity that as an investor would take into consideration before making any investment decision. Transaction costs (De Jong and Driessen, 2013): One of the most common sources of illiquidity is the exogenous transaction costs. It refers to fixed costs that are necessary for the transaction. These costs are insignificant for the institutional investors, in contrast to some small investors. But, for some assets these costs can be considerable even for the institutional investors. High costs make trading more expensive and difficult, so they are preferred by low frequency and long term traders. Brokerage fees, order-processing costs, or transaction taxes are common examples of transaction costs. Demand pressure: Demand pressure arises because of the danger of limited buyers, when the investor wants to sell the asset. In these occasions, a third party may exist (market maker) that buys the assets. The market maker is exposed to price changes risk but also to the inventory risk (risk due to the storing). These risks lead to a very big compensation that imposes a cost for the sell side. Private information (Vayanos and Wang, 2012): Many examples of the financial transactions have indicated an occasion in which the one side (seller) may have different or more information from the other side of the transaction (buyer). This occasion follows the creation of a wide Bid Ask spread. When the buyer faces the risk that the seller has more information about the product, he will ask for a lower price in order to be compensated for that risk. In the opposite direction the seller can ask a higher price if the buyer has been better informed. Private information can exist on the fundamentals of the security but also on the order flow. (for example size of transaction) Search friction: The danger of a non-centralized market (OTC Markets), where it is more difficult and costly to find counterparty for a transaction, may lead to delays and as a result to financing or opportunity costs. The other option that the counterparty has is to trade “faster than it should” with a dealer and face the illiquidity cost. Depending to market’s competition a wide Bid Ask spread again is created. Funding Constraints: A very common occasion in the financial world and hence in the financial transactions is the leverage investing. Borrowing money, in order for the investors to be able to invest in the desirable assets, can lead to the creation of a bid-ask spread. More specifically, particularly after the beginning of the financial crisis, lending requirements became stricter and stricter, and investors were often unable to borrow the desired amount especially due to the counterparty risk that lenders were afraid of. That is a common reason for the increase of the bid-ask spread especially the last years.

Liquidity measures There are numerous measures of illiquidity in the academic literature. Most of them focus on daily and monthly data, as the elaboration of intraday data is very difficult. Some of the most important and widely used measures are analysed below. Bid-ask spread: One of the most common indicators of market liquidity that analysts use is the bid-ask spread. This measure is based on transaction costs in dealer markets. Bid-ask spread can be distinguished between the dealer spread and the market spread (Cohen, Maier, Schwartz and Whitcomb 1986).Dealer spread is the simple and familiar to all, bid-ask spread that indicates the difference between the lowest ask price and the highest bid price (best market offer). The market spread is the difference between the lowest ask and the highest bid but across all dealers quoting the same stock at the same point of time. The dealer is the person that “decides” for these quotes therefore the spread that comes up is the price that the market pays for the liquidity services offered by him (Alexandros Gabrielsen, Massimiliano Marzo and Paolo Zagaglia 2011). Amihud measure: Amihud measure is a low frequency measure that gives the daily price impact of the order flow (Amihud, Y. 2002). Brennan and Subrahmanyam (1996) tested and concluded that this measure is strongly and positively related to microstructure estimates of illiquidity. The model is:

𝐼𝐿𝐿𝐼𝑄/,X =1𝐷/X

|𝑅/X[|𝑉𝑂𝐿𝐷/X[

]^_

/`+

Where 𝐷/X is the number of days for which data are available for stock I in year y,

𝑉𝑂𝐿𝐷/X[ is the trading volume in euros (currency) for stock i the day d of year y and

𝑅/X[ is the return of stock i the day of year y. So in cases that great price changes with small trading volume exist, this ratio increases, which means that the illiquidity of the stock increases. Amivest measure: (Dubofsky and Broth (1984)) This ratio is very similar to the Amihud ratio. It is the sum of the daily volume of trades to the sum of the absolute return during these days.

𝐴𝑚𝑖𝑣𝑒𝑠𝑡/,X =𝑉𝑂𝐿𝐷/X[

]^_/`+

|𝑅/X[|]^_/`+

Roll measure (1984): Roll introduced this measure under the assumptions that all the assets are traded internationally in efficient markets and also that the distribution of the observed price changes is considered to be stationary at least for short intervals. With efficient markets, if the unanticipated information is received by the participants then there will be a price change but there will be no dependence in successive price changes.

Nevertheless, when a market maker gets involved in the transactions it is expected that prices will have negative serial dependence. These are the possible paths of the market prices.

Each path is equally likely. So, the probability of successive price changes, depends on the previous trade, if it was at the bid price or at the ask price. The probabilities of price changes depending on where the price is the t-1 can be seen in the next table.

So the joint distribution of successive price changes is

The covariance between successive price changes is

𝐶𝑜𝑣 ∆𝑝K, ∆𝑝KL+ = −18 𝑠

I −18 𝑠

I = −𝑠I

4 Lesmond, Ogden and Trzcinka measure (1999): This measure is based on the opinion that assets with many transaction costs will have less frequent moves and as a result if an asset is illiquid it will have lots of zero return trading days. An advantage if this measure is that the only input that needs is the daily returns of an asset. Also empirical results have shown that zero returns are negatively related to the firm size and positive related to both the bid-ask spread and the roll measure. Easley, Kiefer and O’Hara (1997) measure: This is a high frequency measure. It is based on the assumption that in the market exist both informed and uninformed investors. If there were only informed investors every buy trade would be followed by a buy trade and every sell trade would be followed by a sell trade, depending on the information.

Also, it is assumed that informed investors are risk neutral and competitive. The arrival of informed investors depends on the news. The zero expected profit price is

And the ask price is

Where, 𝑃l, 𝑃m, 𝑃n are the probability of no news this day, the probability of bad and the probability of good news. Also, ε is the rate at which the uninformed sellers and buyers arrive per minute (for example no news day), µ is the rate at which the informed sellers and buyers arrive per minute. Pastor and Stambaugh measure (2003): For this measure it is assumed that if an asset is illiquid the sign of its previous day’s return will be opposite to the sign of next day’s return. For that reason is used the coefficient of trading volume with the sign of the previous day’s return.

Where 𝑟/,[,K is the return of stock i in the month t, 𝑟/,[,Ko = 𝑟/,[,K − 𝑟O,[,K where 𝑟O,[,K is the return on the CRSP value-weighted market return on day d, in month t and 𝑢/,[,K is the volume in dollars (currency) for stock i, the day d of month t. Market liquidity risk Another interesting factor that could affect the stock returns and depends on liquidity is the market liquidity risk. This factor depends on the total market liquidity level. Amihud (2002) calculates the liquidity level of the market, by taking the average illiquidity (Amihud ratio) of all the stocks. He does this in order to examine if the changes in market liquidity affect the stock returns. One of the findings is that when the market illiquidity increases the stock returns decrease. In general he concludes that market liquidity changes affect stock returns, so illiquidity should be considered as a risk factor. Also, Acharya and Pedersen (2005) examine whether there is liquidity and liquidity risk premium. Their model includes the covariance of stock returns with market liquidity, the market’s returns with stock liquidity and the stock liquidity with market liquidity. Korajczyk and Sadka (2008) try to categorise the effects of the liquidity socks by making 8 measures of illiquidity. By using liquidity socks that affect all the measures they found that high liquidity risk portfolios over perform the low liquidity risk portfolios.

3 Sample Description The DataStream was used to obtain the data. For France I downloaded daily data from May 1st 2006 to May 1st 2016 for 96 common stocks listed in Paris stock exchange. The variables that were used for these stocks are Share Price, Firm’s Market Value and

Volume of trades in the local currency. Also, CAC ALL-TRADEABLE price index was downloaded. For Portugal I downloaded daily data from May 1st 2006 to May 1st 2016 for 34 common stocks listed in Lisbon stock exchange. The variables that were used from these stocks are Share Price, Firm’s Market Value and Volume of trades in the local currency. The number of stocks for Portugal is much lower than that of the other countries, because of the lack of available data and especially the volume of trades’ variable. Also, PORTUGAL PSI ALL-SHARE price index was downloaded. For these European countries Euribor 1-month deposit middle rate was used as the risk free return. For Japan I downloaded daily data from May 1st 2006 to May 1st 2016 for 100 common stocks listed in Tokyo stock exchange. The variables that were used from these stocks are Share Price, Firm’s Market Value and Volume of trades in the local currency. The lack of available data for the volume of trades variable before 2009, shortened slightly the examined period for Japan. Also, TOKYO STOCK EXCHANGE TOPIC price index was downloaded. For Japan the Japanese Yen 1-month deposit – middle rate was used as the risk free return. After, I used Microsoft Excel for all the required calculations. For each stock I calculated the annual ln returns. I also calculated the annual ln returns for each stock index. Then I converted the risk free rates to monthly effective rates. Additionally, I calculated the monthly illiquidity factors and after I set their average as the annual illiquidity factors. The formula of the illiquidity factors and the calculations are described completely in the next section. In order to get better explanations from the results, when compared to financial events (financial crisis), I chose to set the Year, as the consecutive months from June to May, instead of January to December. So for example when referring to year 2010 the period used is from May 2009 to May 2010.

4 Illiquidity Ratios The main ratio that this study focuses on is the Amihud illiquidity ratio.

𝐴𝑀𝐼/,X =1𝐷/X

|𝑅/X[|𝑉𝑂𝐿𝐷/XK

]^_

/`+

Where 𝐷/X is the number of days for stock i in year y. |𝑅/XK| is the absolute return of

stock i for year y in day t. 𝑉𝑂𝐿𝐷/XK is the volume of trades of stock i for year y in day t.

This ratio provides only positive results. Also, the bigger the ratio is the more illiquid the asset is. Hence, assets with high AMI factor are considered relatively less liquid than assets with low AMI factor. In the next sections the term AMI will refer to Amihud illiquidity ratio. Furthermore, another ratio is used in order to achieve a better explanatory power and more significant results. Instead of the daily volume of trades in local currency, I use the daily volume of trades as a percentage of the daily market value of the firm. So the new ratio is:

𝐹𝑀𝑉/,X =1𝐷/X

|𝑅/X[|𝑉𝑂𝐿𝐷/XK𝑀𝑉/XK

]^_

/`+

Where 𝑀𝑉/XK is the market value of stock i for the year y in day t. This ratio is similar to Amihud ratio so again the assets with high FMV factor are considered relatively less liquid than assets with low FMV factor. In the next sections the term FMV will refer to this market value modified Amihud illiquidity ratio. The illiquidity is expected to affect positively the returns of a stock during a period of economic growth and negatively during a crisis, where a fast sale may be required in order to restrain the losses. Summary statistics on the data exist in the appendix Table 4, Table 8 and Table 12.

5 Empirical Analysis This section is the last part of data elaboration, contains all the regression models and explanation about the exact methodology. For all of the regressions STATA software was used. First of all, I run time series regressions using the excess stock returns as the dependent variable and the excess market returns as the independent.

𝑅/K − 𝑟0K = 𝛽/ ∗ 𝑅OK − 𝑟0 + 𝜀/K (1) Where, 𝑅/Kis the return of stock i the month t, 𝑟0K is the risk free return the month t, 𝛽/ is the beta (market effect) of stock i and 𝜀/K is the error term. This regression provides me the β of each asset, which is used in the next model. These betas can be found on Table 1, Table 2 and Table 3 in the appendix. The next regression is a cross sectional regression, which uses the stock returns as the dependent variable and the β as the independent. I used this regression in order to compare the results of this model, to the results of the models that include the illiquidity factors as well.

𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜀/(2) The results of this regression can be found on Table 5, Table 9 and Table 13 in the appendix. The next regression is a cross sectional regression, which uses the stock returns as the dependent variable and the β and the Amihud ratio as the independents.

𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐴𝑀𝐼/ + 𝜀/ (3)

Where, 𝐴𝑀𝐼/ is the Amihud ratio. The results of this regression can be found on Table 6, Table 10 and Table 14 in the appendix. The next regression is the same, but with the market value modified Amihud ratio instead of the Amihud ratio.

𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐹𝑀𝑉/ + 𝜀/ (3)

Where, 𝐹𝑀𝑉/ is the market value modified Amihud ratio. The results of this regression can be found on Table 7, Table 11 and Table 15 in the appendix. The regressions (1), (2) and (3) are repeated for every year.

The methodology that was used above is known as Fama-Macbeth methodology. This allowed me to generate the market risk factors (betas) and use them in my final regressions. The market risk factors explain the change of each stock’s price in every change of the market index price. By using this method, I was able to run a regression for each year by having a beta for each company, otherwise I would be unable to do that as the market excess returns are the same for each company every year and STATA would consider it as omitted variable.

6 Results

In this section are presented the results of the regressions. These results can be found on Appendix Tables 1-15. Betas First of all, the values of market return betas are reasonable, for France the minimum beta is 0,383 and the maximum 2,092. For Portugal the minimum beta is 0,484 and the maximum 2,068. For Japan the minimum beta is 0,339 and the maximum 2,116. Summary statistics are in Tables 4, 8 and 12. Returns and Betas regressions For France, the results of the first regressions (Table 5) with the stock returns as the dependent variable and the betas as independent, look to have a moderate explanatory power, with an average R-squared of 9,6%, but for 2008 and 2009 the average R-squared is 25,4% and the coefficient of beta gets more significant. For Portugal, the results of the same regression (Table 9), look to have a worse explanatory power than French result, with an R-squared of 8,1% and there is no change in the explanatory power for 2008 and 2009. For Japan, the results look better (Table 13), with a whole sample average R-squared of 11,96%, which gets better for the period 2014-2016 to 13,6%. The betas are still insignificant for the whole sample. In most of the examined years and especially during the crisis 2008 and 2009 the coefficients of beta are big and negative, which is an expected result as during that period the markets faced great losses. This negative coefficient during these periods can be explained as, the bigger the beta of the asset, the more negatively its returns were affected. Returns, Betas and Amihud regressions The results of the regressions, in which the stock returns were used as the dependent variable, the betas and the Amihud factor as the independent (Table 6), show that there is an increase in the explanatory power of the model in comparison to the simple model (without the Amihud factor). The new R-squared is 14,4% increased by 4,8% in absolute value. Also, the average R-squared for the 2008-2009 period is 31,5%, increased by 6,1%. The Amihud coefficients get very significant during the 2008-2009 period and they have negative sign. These negative significant coefficients are consistent with the theoretical research, which indicates that during illiquidity socks and crisis in the markets, the illiquid assets face great losses.

For Portugal there is an increase in the explanatory power of the model as well (Table 10). The average R-squared is 9,6%, increased by 1,5%, but the Amihud coefficients are not significant, not even during the 2008-2009 period. For Japan there is also an increase in R-squared (Table 14). The R-squared for this model is 14,6%, increased by 2,6%. Furthermore, the Amihud coefficient is significant and positive for the period 2014-2016, with the average R-squared for that period equal to 18,1%. Returns, Betas and FMV regression The FMV coefficients show similar results to the Amihud coefficients, as expected. First, the signs of the coefficient are the same for most of the years. Also, the explanatory power of that model is very similar to the Amihud model. (Tables 7, 11 and 15) For France, the average FMV R-squared is 14,2%, when the Amihud R-squared 14,4%. For Portugal, the average FMV R-squared is 11,4%, when the Amihud R-squared 9,6%. For Japan, the average FMV R-squared is 14,6%, exactly the same as the Amihud R-squared. Also, the FMV has a significant negative coefficient for France 2008-2009 period, as Amihud had, but for Japan 2014-2016 the FMV coefficient is not significant, in contrast to the Amihud coefficient. Overall Results In most of the studies and research papers where Amihud factor was used, the Amihud coefficient was positive and significant. In my study this happened only for Japan, where the Amihud coefficient is positive for the whole period and significant for 2014-2016. But, a good explanation for these results would be the one used by Acharya and Pedersen (2005). They support that liquidity predicts future returns and liquidity co-moves with contemporaneous returns. So, a positive shock in illiquidity predicts high future illiquidity and thus the demanded returns are increased and the contemporaneous prices are decreased. Also, they conclude that investors should worry about the securities’ performance and tradability, both in market downtrends and when illiquidity increases. In general, Amihud can be a very useful tool for liquidity risk measuring and pricing, but when the liquidity level and the returns of the market change massively every year, it is very difficult to get sound estimations and results.

7 Conclusion From this research can be concluded that is difficult to estimate the effects of illiquidity on the stock returns during economically unstable periods. The effects of illiquidity are clearer, when there is an obvious market trend and other factor remain constant (such as market total liquidity). An example is France during 2008-2009, when it was clear that the returns of the assets were negatively affected by the illiquidity. Also, the empirical results of this study showed that illiquidity factors are not enough to explain a great part of the movement of stock returns and other factors may need to be included. This study could be extended in the future with more factors (such as Book to Market ratio, or Market liquidity risk). Moreover, I trust that the results of this study would be very interesting if was examined a not so financially fluctuate period. Finally, I believe that a long term investor that would like to follow a strategy focusing on illiquid assets, should care about the point of the economic cycle that the country is. Also, this research showed that illiquidity cannot affect the asset returns in the same way for all the markets, even when if are very correlated, so the investor should understand the market characteristics and identify through an empirical analysis whether illiquidity is effective in his targeted market. In conclusion, even if illiquidity can sometimes be a good indicator for high, long-term returns, other macroeconomic factors are also needed for a completed long term investment strategy.

References

Glosten, L. and Milgrom, P., (1985), “Bid, Ask, and Transaction Prices in a Specialist Market With Heterogeneously Informed Traders”, Journal of Financial Economics 14, 71-100. Amihud, Y., Mendelson, H. (1991) Liquidity, Maturity, and Yields on the U.S. Treasury Securities, Journal of Finance, 46, 4, pp 1411-1425 Campbell, J.Y., Grossmann, S.J., Wang, J. (1993) Trading Volume and serial correlation in stock returns, Quarterly Journal of Economics, 65, pp 111-130 Chordia, T., Roll, R., Subrahmanyam, A. (2001) Market Liquidity and Trading Activity, Journal of Finance, 66, 2, pp 501-530. Amihud, Y. (2002), Illiquidity and stock returns: cross-section and time-series effects, Journal of Financial Markets 5, 31-56 Spiegel, M. (2008) Patterns in cross market liquidity; Finance Research Letters 5 2–10 Goyenko, R., Holden C., Trzcinka, C. (2009) Do liquidity measures measure liquidity? Journal of Financial Economics 92 153-181 Vayanos, Dimitri, and Jiang Wang (2012), Market Liquidity-Theory and Empirical Evidence, Handbook of Economics and Finance. De Jong, F., and J. Driessen, (2013), The Norwegian Government Pension Fund’s potential for capturing illiquidity premiums, Report to the Norwegian Ministry of Finance. Ang. Papanikolaou, Westerfield (2014), Portfolio Choice with Illiquid Assets Cohen, K. J., S. F. Maier, R. A. Schwartz, and D. K. Whitcomb, (1986), “Transaction Costs, Order Placement Strategy and the Existence of the bid-ask Spread”, Journal of Political Economy, Vol. 89, No. 2, 287-305. Alexandros Gabrielsen, Massimiliano Marzo and Paolo Zagaglia (2011), “Measuring market liquidity: An introductory survey” Yakov Amihud, Haim Mendelson and Lasse Heje Pedersen (2005), “Liquidity and Asset Prices” Roll R. (1984), “A Simple Implicit Measure of the Bid-Ask Spread in an Efficient Market”, Journal of Finance 39, 1127-1139. Ben-Raphael, A. O. Kadan and A. Wohl (2012), “The Diminishing Liquidity Premium”, working paper Tel Aviv University. David Easley, Nicholas M. Kiefer, Maureen O’ Hara and Joseph B. Paperman (1996) “Liquidity, Information and Infrequently traded stocks”,

David A. Lesmond, Joseph P. Ogden and Charles A. Trzcinka (1999), “A New Estimate of Transaction Costs”, Oxford Journals Lubos Pastor, Robert F. Stambaugh (2003), “Liquidity Risk and Expected Stock Returns”, The Journal of Political Economy Viral V. Acharya and Lasse Heje Pedersen (2005), “Asset Pricing with Liquidity Risk”, Brennan and Subrahmanyam (1996), “Market microstructure and asset pricing: On the compensation for illiquidity in stock returns”, Journal of Financial Economics Beber, Driessen and Tuijp (2012) “Pricing Liquidity Risk with Heterogeneous Investment Horizons”, Netspar Discussion Paper Korajczyk Robert and Sadka Ronnie (2008), “Pricing the Commonality Across Alternative Measures of Liquidity”, Journal of Financial Economics David A. Dubofsky, John C. Groth (1984), “Exchange Listing and Stock Liquidity”, The Journal of Financial Research Eugene F. Fama, James D. MacBeth (1973), “Risk, Return, and Equilibrium: Empirical Tests”, The Journal of Political Economy

Appendix Table 1

FrancebetasSANOFI 0,646 PEUGEOT 1,397 SARTORIUS 0,677LOREAL 0,681 KLEPIERRE 1,009 SEB 0,834LVMH 1,023 VALEO 1,538 STMICROELECTRONICS 1,373BNP 1,423 VEOLIA 1,311 EURAZEO 1,401AXA 1,737 ACCOR 1,070 EUTELSAT 0,472AIRBUS 0,912 BOUYGUES 0,977 IMERYS 1,180DANONE 0,548 ILIAD 0,630 IPSEN 0,661ORANGE 0,577 TECHNIP 1,200 ODET 0,858VINCI 1,063 BOLLORE 0,763 REMY 0,878AIR 0,707 CNP 1,134 TELEPERFORMANCE 0,633ENGIE 0,972 DASSAULT1 0,480 TF1 1,263HERMES 0,638 ATOS 0,956 UBISOFT 1,061SCHNEIDER 1,130 GECINA 1,415 COLAS 0,873SOCIETE 1,973 EIFFAGE 1,137 EULER 1,252CHRISTIAN 1,153 INGENICO 1,007 LAGARDERE 1,042CREDIT 1,727 BIC 0,440 NEXITY 1,247ESSILOR 0,535 CASINO 0,838 RUBIS 0,639RENAULT 2,092 CIC 0,791 VALLOUREC 1,688SAFRAN 0,909 JCDECAUX 0,950 ALTAREA 0,472VIVENDI 0,734 ZODIAC 0,859 ALTEN 1,082EDF 1,091 AIR2 1,406 ALTRAN 1,314SAINT 1,372 ALSTOM 1,137 FIMALAC 0,583CARREFOUR 0,876 EUROFINS 0,898 FONCIERE2 0,582KERING 1,108 FAURECIA 1,642 FROMAGERIES 0,348DASSAULT 0,669 SCOR 0,736 NEXANS 1,527MICHELIN 1,201 WENDEL 1,800 SOPRA 0,786THALES 0,679 BIOMERIEUX 0,405 AREVA 0,955SODEXO 0,568 FONCIERE 1,091 CGG 1,786CAP 1,118 HAVAS 1,111 EUROSIC 0,659PUBLICIS 0,817 ICADE 0,775 FAIVELEY 0,865LEGRAND 0,825 ORPEA 0,653 NATIXIS 1,812 PLASTIC 1,502

This table contains the results of the first first time series regression for France. The model is 𝑅/K − 𝑟0K = 𝛽/ ∗ t𝑅OK − 𝑟0u + 𝜀/K and the daily data for years 2007-2016 of the stock excess returns and the market excess returns were used. The table contains the β coefficient (the market beta) for every French stock of the sample. The stocks that their names are followed by a number are stocks of the same corporate group and this has been done, in order to avoid miscalculation of β coefficients.

Table 2

PortugalbetasEDP 0,641JERONIMO 1,209BANCO 1,408NOS 0,750BANCO1 1,408NAVIGATOR 0,750SONAE 1,606ALTRI 1,408CORTICEIRA 0,641MOTA 0,750SEMAPA 1,606SONAE1 1,606CIMENTOS 1,408CIPAN 1,408COFINA 1,408COMPTA 1,408ESTORIL 0,753FUTEBOL 0,522GI.GLB.INTEL.TECHS.SGPS 0,522IBERSOL 0,866IMPRESA 1,353INAPA 1,209LISGRAFICA 0,750NOVABASE 0,706OREY 0,460PHAROL 1,062REDITUS 0,559SAG 0,772SDC 1,606SONAE2 2,068SPORTING 0,388SUMOL 0,095TOYOTA 0,091VAA 0,498

This table contains the results of the first first time series regression for Portugal. The model is 𝑅/K − 𝑟0K = 𝛽/ ∗ t𝑅OK − 𝑟0u + 𝜀/K and the daily data for years 2007-2016 of the stock excess returns and the market excess returns were used. The table contains the β coefficient (the market beta) for every Portuguese stock of the sample. The stocks that their names are followed by a number are stocks of the same corporate group and this has been done, in order to avoid miscalculation of β coefficients.

Table 3 Japanbetas

TOYOTA 1,0286 PANASONIC 1,2714 DAITO 0,4786NTT 0,4556 CHUGAI 0,6306 KYOWA 0,8443NIPPON 0,5439 FUJIFILM 1,1507 NITORI 0,3481JAPAN 0,9940 NINTENDO 0,9809 NITTO 1,2035KDDI 0,6203 DAIWA 0,9617 TOKYU 0,7065MITSUBISHI 1,3787 EISAI 0,5612 WEST 0,5262SOFTBANK 1,1036 KUBOTA 1,1956 ANA 0,5031HONDA 1,1237 KYOCERA 1,0592 CHUBU 0,4927NISSAN 1,2992 NIPPON 1 1,2629 DAIWA 1 1,5266SUMITOMO 1,4436 SHIONOGI 1,0042 MAZDA 2,1157MIZUHO 1,5123 ORIX 1,5045 SHISEIDO 0,6772SEVEN 0,7305 SECOM 0,7489 SUMITOMO4 1,1462CANON 0,9838 KOMATSU 1,3273 ACOM 0,9031KEYENCE 0,8141 MSAD 1,2242 ASAHI1 0,8693CENTRAL 0,7664 ASAHI 0,5562 ISUZU 1,5533TAKEDA 0,5912 DAIICHI 0,8979 MITSUBISHI 4 1,2703EAST 0,7349 NOMURA 1,8510 NIPPON 2 0,8741SONY 1,6080 SMC 0,8163 TAISEI 0,7117DENSO 1,1749 RAKUTEN 0,6708 TOSHIBA 1,5296ASTELLAS 0,6013 DENTSU 1,1557 MAKITA 0,9715FANUC 0,8548 KIRIN 0,9590 UNI 0,4740FAST 0,8501 NTT 1 0,8938 AISIN 1,3294FUJI 1,3763 SYSMEX 0,8173 NIDEC 0,8211BRIDGESTONE 0,7270 TERUMO 0,9197 ITOCHU 1,0238KAO 0,5788 HOYA 0,8552 MITSUBISHI 1 1,1452 OLYMPUS 1,2860 ONO 0,5328 SHIMANO 0,8014 MITSUBISHI 2 1,2181 SUMITOMO 1 1,4465 MURATA 1,1571 SUZUKI 1,1032 MITSUBISHI 3 1,1625 TORAY 0,7136 TOKIO 1,0230 TOYOTA2 1,0852 YAHOO 0,9243 AEON 0,9195 SHINETSU 0,8250 AJINOMOTO 0,5107 DAIKIN 1,1872 MITSUBISHI 4 1,2703 MITSUI 1,1425 SEKISUI 0,7673 ORIENTAL 0,3178 SUMITOMO 2 0,7912 HITACHI 1,4477 SUMITOMO 3 1,3254 MITSUI1 1,0557 TOKYO 1,2843

This table contains the results of the first first time series regression for Japan. The model is 𝑅/K − 𝑟0K = 𝛽/ ∗ t𝑅OK − 𝑟0u + 𝜀/K and the daily data for years 2007-2016 of the stock excess returns and the market excess returns were used. The table contains the β coefficient (the market beta) for every Japanese stock of the sample. The stocks that their names are followed by a number are stocks of the same corporate group and this has been done, in order to avoid miscalculation of β coefficients.

Table 4

Variable Obs. Mean Std.Dev. Min MaxANNUALRETURNS 960 0,019 0,355 -1,408 1,269ANNUALMARKETRETURNS 960 -0,019 0,221 -0,490 0,209AVERAGEANNUALAMI 960 0,856 0,405 0,548 1,900AVERAGEANNUALFMV 960 1,456 0,348 0,991 2,335BETAS 940 1,015 0,383 0,348 2,092

This table contains the summary statistics for France. For every variable in this table, the number of observations, the average, the standard deviation, the minimum and the maximum values are included. The variables that are presented are: the annual stock returns, the annual market returns, the average annual Amihud factor for every stock of the sample multiplied by 10w, the average annual FMV factor multiplied by 10x and the market betas, calculated in the time series regression.

Table 5

YEAR BETAS R-SQUARED

2007 -0,04 0,6%

2008 -0,19 10,8%

2009 -0,50 40,0%

2010 0,15 4,7%

2011 0,07 1,7%

2012 -0,46 28,1%

2013 0,01 0,1%

2014 0,21 14,6%

2015 -0,16 7,2%

2016 -0,24 11,2%

AVERAGE -0,11 9,6%ST.ERROR 0,24

SIGNIFICANCE 63,39%

2008-2009

AVERAGE -0,35 25,4%STERROR 0,22

SIGNIFICANCE 11,06%

2010-2016


SIGNIFICANCE 80,44%

This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas for France.{𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜀/ }In the first column are the coefficients of the betas and in the second the R-squared. Below are presented the average of the betas coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2008 to 2009 and from 2010 to 2016.

Table 6

YEAR BETAS AMI R-SQUARED

2007 0,00 1,32 7,9%

2008 -0,22 -0,87 12,4%

2009 -0,57 -1,05 50,5%

2010 0,18 0,43 6,1%

2011 0,07 0,12 1,8%

2012 -0,49 -0,68 29,3%

2013 0,01 -0,41 0,3%

2014 0,25 0,81 16,1%

2015 -0,18 -0,42 8,0%

2016 -0,24 0,18 11,7%

AVERAGE -0,12 -0,06 14,4%ST.ERROR 0,27 0,76

SIGNIFICANCE 65,85% 94,14%

2008-2009

AVERAGE -0,39 -0,96 31,5%STERROR 0,25 0,13


2010-2016

AVERAGE -0,06 0,01 10,5%STERROR 0,26 0,53


This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas and Amihud factor for France. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐴𝑀𝐼/ + 𝜀/} In the first column are the coefficients of the betas, in the second the coefficients of Amihud factor and in the third the R-squared. Below are presented the average of the betas coefficients and Amihud coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2008 to 2009 and from 2010 to 2016.

Table 7

YEAR BETAS FMV R-SQUARED

2007 0,00 1,62 11,2%

2008 -0,22 -0,48 12,3%

2009 -0,57 -0,88 46,5%

2010 0,18 0,12 4,8%

2011 0,07 -0,13 1,7%

2012 -0,49 -0,61 28,7%

2013 0,01 -0,21 0,2%

2014 0,25 0,82 17,0%

2015 -0,18 -0,47 8,5%

2016 -0,23 0,23 11,5%

AVERAGE -0,12 0,00 14,2%ST.ERROR 0,27 0,75


2008-2009

AVERAGE -0,39 -0,68 29,4%STERROR 0,25 0,28


2010-2016

AVERAGE -0,06 -0,03 10,3%STERROR 0,26 0,48


This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas and FMV factor for France.{𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐹𝑀𝑉/ + 𝜀/} In the first column are the coefficients of the betas, in the second the coefficients of the FMV factor and in the third the R-squared. Below are presented the average of the betas coefficients and Amihud coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2008 to 2009 and from 2010 to 2016.

Table 8

Variable Obs. Mean Std.Dev. Min MaxANNUALRETURNS 340 -0,142 0,495 -2,379 1,403ANNUALMARKETRETURNS 340 -0,067 0,216 -0,492 0,179AVERAGEANNUALAMI 340 9,168 9,194 0,172 2,658AVERAGEANNUALFMV 340 1,237 9,611 0,507 3,130BETAS 340 0,991 0,484 0,091 2,068

This table contains the summary statistics for Portugal. For every variable in this table, the number of observations, the average, the standard deviation, the minimum and the maximum values are included. The variables that are presented are: the annual stock returns, the annual market returns, the average annual Amihud factor for every stock of the sample multiplied by 10w, the average annual FMV factor multiplied by 10x and the market betas, calculated in the time series regression.

Table 9 YEAR BETAS R-SQUARED

2007 0,03 0,1%

2008 -0,23 10,0%

2009 -0,14 6,2%

2010 0,26 18,4%

2011 -0,11 2,4%

2012 -0,18 4,5%

2013 0,29 8,7%

2014 -0,07 0,8%

2015 -0,37 10,6%

2016 -0,40 11,4%


SIGNIFICANCE 69,34%

2008-2009


SIGNIFICANCE 0,31%

2010-2016


SIGNIFICANCE 76,87%

This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas for Portugal. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜀/ }In the first column are the coefficients of the betas and in the second the R-squared. Below are presented the average of the betas coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2008 to 2009 and from 2010 to 2016.

Table 10


2007 0,03 -0,145 0,5%

2008 -0,23 -0,160 10,9%

2009 -0,14 0,003 6,6%

2010 0,27 -0,012 22,8%

2011 -0,10 -0,010 6,6%

2012 -0,18 0,005 5,5%

2013 0,30 -0,004 16,5%

2014 -0,06 -0,007 1,7%

2015 -0,37 -0,002 11,8%

2016 -0,41 0,004 12,8%

AVERAGE -0,09 -0,03 9,6%ST.ERROR 0,24 0,06


2008-2009

AVERAGE -0,19 -0,08 8,7%STERROR 0,06 0,12


2010-2016

AVERAGE -0,08 0,00 11,1%STERROR 0,28 0,01


This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas and Amihud factor for Portugal. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐴𝑀𝐼/ + 𝜀/} In the first column are the coefficients of the betas, in the second the coefficients of Amihud factor and in the third the R-squared. Below are presented the average of the betas coefficients and Amihud coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2008 to 2009 and from 2010 to 2016.

Table 11


2007 0,04 0,51 0,5%

2008 -0,24 -0,09 10,0%

2009 -0,17 -0,02 7,9%

2010 0,22 -0,08 23,4%

2011 -0,08 0,01 3,4%

2012 -0,20 -0,08 6,1%

2013 0,09 -0,07 32,1%

2014 -0,13 -0,11 5,8%

2015 -0,39 -0,03 12,3%

2016 -0,36 0,17 12,6%

AVERAGE -0,12 0,02 11,4%ST.ERROR 0,20 0,19


2008-2009

AVERAGE -0,20 -0,05 8,9%STERROR 0,05 0,05


2010-2016

AVERAGE -0,12 -0,03 13,7%STERROR 0,22 0,09


This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas and FMV factor for Portugal. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐹𝑀𝑉/ + 𝜀/} In the first column are the coefficients of the betas, in the second the coefficients of the FMV factor and in the third the R-squared. Below are presented the average of the betas coefficients and Amihud coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2008 to 2009 and from 2010 to 2016.

Table 12

Variable Obs. Mean Std.Dev. Min MaxANNUALRETURNS 700 0,100 0,294 -0,775 1,111ANNUALMARKETRETURNS 700 0,057 0,230 -0,214 0,456AVERAGEANNUALAMI 700 2,463 1,677 0,427 4,594AVERAGEANNUALFMV 700 2,043 1,020 8,937 3,688BETAS 700 0,985 0,339 0,318 2,116

This table contains the summary statistics for Japan. For every variable in this table, the number of observations, the average, the standard deviation, the minimum and the maximum values are included. The variables that are presented are: the annual stock returns, the annual market returns, the average annual Amihud factor for every stock of the sample multiplied by 10w, the average annual FMV factor multiplied by 10x and the market betas, calculated in the time series regression.

Table 13

YEAR BETASR-

SQUARED

2010 0,09 2,4%

2011 -0,12 5,1%

2012 -0,27 23,3%

2013 0,28 12,3%

2014 -0,06 1,0%

2015 -0,19 13,5%

2016 -0,36 26,1%


SIGNIFICANCE 67,91%

2010-2013

AVERAGE -0,01 10,8%

STERROR 0,24 SIGNIFICANCE 98,13%

2014-2016


SIGNIFICANCE 18,47%

This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas for Japan. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜀/ }In the first column are the coefficients of the betas and in the second the R-squared. Below are presented the average of the betas coefficients their standard error and the average R-squared. These are calculated also for two sub periods, from 2010 to 2013 and from 2014 to 2016.

Table 14


2010 0.11 0.15 3.94%

2011 -0.11 0.06 5.62%

2012 -0.26 0.10 25.11%

2013 0.28 0.02 13.10%

2014 -0.03 3.01 3.59%

2015 -0.14 8.84 20.54%

2016 -0.34 10.77 30.05%

AVERAGE -0.07 3.28 14.6%STERROR 0.21 4.62

SIGNIFICANCE 73.54% 47.77%

2010-2013

AVERAGE 0.00 0.08 11.9%STERROR 0.24 0.06


2014-2016

AVERAGE -0.17 7.54 18.1%STERROR 0.15 4.04


This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas and Amihud factor for Japan. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐴𝑀𝐼/ + 𝜀/} In the first column are the coefficients of the betas, in the second the coefficients of Amihud factor and in the third the R-squared. Below are presented the average of the betas coefficients and Amihud coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2010 to 2013 and from 2014 to 2016.

Table 15


2010 0.11 0.03 4.8%

2011 -0.11 -0.01 5.6%

2012 -0.26 -0.02 25.4%

2013 0.28 -0.01 13.1%

2014 -0.03 0.32 3.5%

2015 -0.14 1.17 22.2%

2016 -0.34 0.31 28.0%

AVERAGE -0.07 0.26 14.6%STERROR 0.21 0.43


2010-2013

AVERAGE 0.00 0.00 12.2%STERROR 0.24 0.02


2014-2016

AVERAGE -0.17 0.60 17.9%STERROR 0.15 0.49


This table contains the cross sectional regression results for the regression of the Annual Returns and the Betas and FMV factor for Japan. {𝑅/ − 𝑟0 = 𝜆+ ∗ 𝛽/ + 𝜆I ∗ 𝐹𝑀𝑉/ + 𝜀/} In the first column are the coefficients of the betas, in the second the coefficients of the FMV factor and in the third the R-squared. Below are presented the average of the betas coefficients and Amihud coefficients, their standard error and the average R-squared. These are calculated also for two sub periods, from 2010 to 2013 and from 2014 to 2016.

France Graph 1

This graph shows the average annual Amihud ratio for French stocks from 2006 to 2016. Graph 2

This graph shows the average annual FMV ratio for French stocks from 2006 to 2016.

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

AMIFactor

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

FMVFactor

Graph 3

This graph shows the Paris Stock Exchange – CAC all tradable annual returns from 2006 to 2016.

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

CAC- ALLTRADABLE

Portugal Graph4

This graph shows the average annual Amihud ratio for Portuguese stocks from 2009 to 2016. Graph 5

This graph shows the average annual FMV ratio for Portuguese stocks from 2009 to 2016.

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Amihud

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

FMVFactor

Graph 6

This graph shows the Lisbon Stock Exchange PSI all-share annual returns from 2009 to 2016.

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

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2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Portugal- PSIallshare

Japan Graph 7

This graph shows the average annual Amihud ratio for Japanese stocks from 2009 to 2016. Graph 8

This graph shows the average annual FMV ratio for Japanese stocks from 2009 to 2016.

2010 2011 2012 2013 2014 2015 2016

AmihudFactor

2010 2011 2012 2013 2014 2015 2016

MVFactor

Graph 9

This graph shows the Tokyo Stock Exchange – Topix annual returns from 2009 to 2016.

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0

0.1

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2010 2011 2012 2013 2014 2015 2016

Thesis Ioannis Konstantopoulos Final

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