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## Abstract

The authors examine whether technical analysis has predictive power in the case of the OMX Iceland All-Share Index. If trading rules have predictive power, could a trader design a strategy to beat the profitability of the buy-and-hold strategy, considering transaction costs and risk? The authors use four trading rules and test their profitability in the case of the OMX Iceland All-Share Index for the period from April 30, 1999 through December 30, 2016. They first conclude that trading rules have predictive power. They then design four strategies for each trading rule and conclude that it is possible to exploit the predictive power of these rules. Finally, they consider both risk and transaction costs of various trading rules and find three rules—MA200, MA150, and MACD—that can beat the buy-and-hold strategy for the entire period and each subperiod, even considering risk and transaction costs.

The efficient market hypothesis (EMH) has been a major proposition of finance since its conception in the 1960s. Fama (1970), one of the earlier proponents of the EMH, defined an efficient market as one in which security prices fully reflect all publicly available information. EMH rules out the possibility of developing a trading system to beat the buy and hold (B&H) strategy. Many financial economists have developed theoretical reasons why the EMH should hold. In addition, a vast array of empirical research including Kendall and Hill (1953), Larson (1960), Osborne (1962), Alexander (1964), Granger and Morgenstern (1963), Mandelbrot (1963), Fama and Blume (1966), Fama (1965), Van Horn and Parker (1967), and Jensen and Benington (1970) conclude that trading rules are not useful in predicting price changes. Jensen (1978, p. 95), one of the creators of the EMH, declares that “*there is no other proposition in economics which has more solid empirical evidence supporting it than the Efficient Market hypothesis*.” Shleifer (2000) declares that EMH has been the central proposition in the theory of finance over the past thirty years.

In a survey article, Yen and Lee (2008) provide a chronological review of empirical evidence on the EMH over the last fifty years. They conclude that the EMH no longer enjoys the level of strong support it received during the last decades of the 20th century; however, they believe that the EMH is here to stay and will continue to play an important role in financial theory. Recently, the EMH has also come under relentless criticism from the school of behavioral finance. Since the early 1990s, technical trading and behavioral finance have been enjoying a renaissance both on Wall Street and in academic circles. Behavioral finance is a theoretical challenge to the EMH, and technical analysis is an empirical challenge to the EMH.

The purpose of this article is twofold. We examine whether technical analysis has predictive power in the case of the OMX Iceland All-Share Index. If trading rules have predictive power, could a trader design a strategy to beat the profitability of the B&H strategy considering transaction costs and risk? In this study, we use four trading rules and test their profitability in the case of the OMX Iceland All-Share Index for the period of April 30, 1999 to December 30, 2016. We first conclude that trading rules have predictive power. We then design four strategies for each trading rule and conclude that it is possible to exploit the predictive power of these rules. Finally, we consider both risk and transaction costs of various trading rules and find three rules, MA200, MA150, and MACD, can beat the Buy and Hold strategy for the entire period and each subperiod, even considering risk and transaction costs.

Technical analysis assumes that the current security price discounts all information available in the market, that price movements are not random, and that there are patterns in price movements that repeat themselves or trend in some direction. Technical analysts study a stock’s price patterns through the use of charts, trend lines, support and resistance levels, and many other indicators based on past prices and volumes in order to predict future security price movements. Before the 1980s, as we have mentioned above, most financial economists believed that technical analysis is useless for return predictability. However, seminal research studies on technical analysis by Sweeney (1986), Lukac, Brorsen, and Irwin (1988), and Brock, Lakonishok, and LeBaron (1992) show that trading rules have predictive power. Sweeney applies filter rules for trading 10 currencies and finds that these trading rules have predictive power. Lukac, Brorsen, and Irwin (1988) apply moving averages and a few well-known technical rules to twelve U.S. futures markets and conclude that technical analysis has predictive power. Brock, Lakonishok, and LeBaron (1992, p. 1,758) analyze moving averages and trading range breakouts on the Dow Jones Industrial Index for a period of 89 years and conclude “results are consistent with technical rules having predictive power.”

Since these three influential papers were published, a significant number of research studies have been published on technical trading predictability power. Park and Irwin (2007) provide an excellent survey of the technical analysis literature up to 2004, and they conclude that early studies do not support the predictive power of technical analysis, but that later research results are mixed. Ülkü and Prodan (2013), Pätäri and Vilska (2014), Metghalchi, Chen, and Hayes (2015), and Gerritsen (2016) provide additional literature reviews concerning recent research on technical trading.

## DATA AND METHODOLOGY

Iceland is a developed country with a GDP per capita reaching 53,000 USD in 2016 (OECD 2017). The OMX Iceland All-Share Index is a value-weighted index including all the shares listed on the OMX Iceland Exchange. The base date for the OMX Iceland All-Share Index is December 31, 1997, with a base value of 1,000.^{1}

We use Datastream’s daily closing level of the OMX Iceland All-Share Index over the period of April 30, 1999 to November 30, 2016. The daily returns are calculated by taking the daily difference in the natural logarithms of the stock index. For the interest rate, we use the daily Iceland interbank middle rate. To estimate the daily money market return, we use Lucke’s (2002) technique of dividing the annual interbank rates by 260. Both the index price and interest rates are expressed in local currency, Krona.

In this article, we test the performance of trading rules based on two well-known (moving average and relative strength index) and two lesser known (moving average convergence/divergence and momentum) technical indicators. One of the most important trend determinations is the moving average (MA) technique. The moving average will smooth price fluctuations. In general, longer duration moving averages are smoother than shorter duration moving averages. We use simple moving averages of 20, 50, 100, 150, and 200 days. A buy signal is emitted when the price index moves from below to above these MAs, and a sell signal is emitted when the price index penetrates these MAs from above to below.

The second well-known indicator used in this article is the relative strength index (RSI), a popular indicator developed by Wilder (1978). This indicator measures the velocity of directional movement. RSI is a ratio of the upward price movement to the total price movement over a given period of days (Wilder suggested using 14 days). The calculation of RSI is described as:

RSI equals , which is a number between 0 and 100.

The third indicator is Gerald Appel’s (1974) moving average convergence/divergence (MACD). The MACD is calculated by subtracting the value of a 26-period exponential moving average from a 12-period exponential MA. We use two variants of MACD in this study.

The fourth indicator used is the rate of change (ROC) momentum. ROC, similar to a group of momentum oscillators, involves the analysis of the rate of price change rather than the price level. The speed of price movement and the rate at which prices are moving up or down provide clues to the amount of strength the bulls or bears have at a given point in time. ROC can be calculated by dividing the change in the days’ closing price by the closing price X number of days or weeks ago and then multiplying the quotient by 100.

1Some technicians simply use the difference of prices as a measure of momentum:

2We use both versions of momentum. We call the first version “ROC” and the second version simply Momentum. If ROC is positive, we will be in the market (buy signal) and, if it is negative, then it is a sell signal. The same trading rule applies to Momentum; if it is positive, we will be in the market and, if negative, we will be out of the market.

For all of the above trading rules, we will be either in the market (buy days) or out of the market (sell days). Following Metghalchi, Chen, and Hayes (2015), we assume all trades can be executed at the end of the day at the close of the market. A trader can presumably estimate the index price that would trigger the buy and sell signal just before the day’s close and initiate a conditional limit order at the close of the market to follow various trading rules. For example, if the MA of 50 days of the index price is 1,200 and the index price is trading near 1,200, a trade can have a limit order at the close to buy the index if the index is above the 1,200 price level at the close. If the conditional limit is filled at the close of the market, then the trader will be in the market the next day and, if it is not filled, then the trader is out of the market the next day. This eliminates non-synchronicity bias. We define mean buy and mean sell returns as follow:

3 4where, *N*_{(b)} and *N*_{(s)} are the total number of buy and sell days, and *R _{b}* and

*R*are daily returns of buy and sell days. We test whether the mean buy and the mean sell returns are different than the mean B&H and whether the mean buy returns are different than the mean sell returns. The three null and alternative hypotheses can be written as follow:

_{s}where X(b) is the mean buy returns, X(s) is the mean sell returns and X(h) is the mean B&H or unconditional mean return. From Brock, Lakonishok, and LeBaron (1992), we use the following statistic for the first test:

5where *Var*(*b*) and *Var*(*h*) are the variance of buy and B&H returns, respectively. The above formula also is used for the second and third tests replacing the appropriate variables in the *t*-statistic formula.

## EMPIRICAL RESULTS

For the total period examined and two subperiods of April 30, 1999 to December 31, 2007 and January 1, 2008 to November 30, 2016, Exhibit 1 presents the summary statistics of returns for the B&H strategy. In each cell of Exhibit 1 we show two numbers: The first number includes all returns, and the second number excludes the five highest negative returns that happened during the subprime crisis of 2008–2009. The five excluded dates are October 14, 2008; December 9, 2008; September 30, 2008; March 9, 2009; and March 24, 2009, with five negative returns of 66.58%, 30.67%, 14.08%, 12.73%, and 6.58% respectively; all of these large negative returns happened in the second subperiod in our sample. The daily average of the B&H strategy is 0.00001 (0.001 percent per day), with a standard deviation of 0.01945; the *t*-value for the B&H strategy over 4,588 days is 0.05 (0.00001 divided by 0.0195/√4588). We cannot reject the equality with zero. In this article we compare all *t*-statistics with 1.96, which is a critical *t*-value at 5% for large numbers of observations. The annual average return over the entire period is 0.37%.

As can be seen from Exhibit 1, when we include the outliers of the subprime crisis, the skewness of the entire period and the second subperiod become very high and negative implying the returns are not symmetric, but rather skewed left, or the left tail is long relative to the right tail. Also when including outliers of the subprime crisis, the kurtosis of the entire period and the second subperiod become very large, implying a high probability of extremely large positive or negative returns. When we exclude the five outliers of the subprime crisis, then the skewness is close to zero and the kurtosis is close to 3, implying a normal distribution. From Exhibit 1 we conclude that the return series is asymmetric if we include the five highest negative returns of the subprime crisis; however, when excluding the five outliers, the return series approaches a normal distribution.

Although we present the second numbers (excluding the five highest negative returns) to more clearly understand the distribution of the returns, we need to include all returns when comparing various trading strategies with the B&H strategy, since the B&H strategy means an investor holds the investment for the entire period including all negative return days. However, what will be seen in the analysis of this article is that various trading strategies could avoid some of the high negative returns.

In Exhibit 2 we present the chart of the OMX Iceland All-Share price index taken from Datastream. As can be seen, there is a break in the index prices around the sub-prime crisis of 2008–2009.

In Exhibit 3 we present the trading rules data for 10 trading rules. The first column represents each trading rule, namely, MA of 20, 50, 100, 150, and 200 days. RSI > 50 represents a trading rule such that if RSI is greater than 50, the trader will be in the market, otherwise out of the market. MACD > 0 represents the rule such that if MACD is greater than zero, the trader is in the market, otherwise out of the market. MACD > Signal line (MACD > SL) is the trading rule such that, if MACD is above the signal line, then a trader will be in the market, otherwise out of the market.

A less researched indicator used in this study is Rate of Change. ROC-30 is the 30-day rate of change of the price index presented by Equation (1); a trader will be in the market if ROC-30 is positive, otherwise she will be out of the market. The last trading rule is the price momentum rule represented by Equation (2); a trader will be in the market if the 30-day price momentum (Momentum 30) is positive, otherwise out of the market.

The results of Exhibit 3 are very strong in favor of technical trading rules having predictive power. As can be seen from Column 4, all the buy–sell differences (except MACD > SL) are positive and the *t*-statistics for these differences are highly significant, rejecting the null hypothesis of equality of the mean buy days with the mean sell days. The mean buy and sell returns are shown in Columns 2 and 3. All mean buy returns (except MACD > SL and Momentum 30) are positive, with significant *t*-statistics, rejecting the null hypothesis that the mean buy returns equal the mean B&H return. For sell day returns, six mean sell returns are negative with significant *t*-statistics, rejecting equality with the unconditional mean return. The results of Exhibit 3 cannot be explained by the EMH but instead support the predictive power of technical trading rules. As Brock, Lakonishok, and LeBaron (1992) point out, this predictability of returns can reflect either (1) changes in expected returns generated from an equilibrium model, or (2) market inefficiency. Although changes in expected returns are possible, it is hard to imagine an equilibrium model that predicts negative returns over such a large fraction of trading days.

The standard deviations of buy days and sell days are reported in Columns 5 and 6. For all 10 trading rules, the standard deviations for buy days are smaller than sell days, which implies that the market is less volatile for buy periods than sell periods. Columns 7 and 8 report the number of days a trader will be in the market and out of the market. On average for our 10 rules, 57% of the time a trader will be in the market (buy days) and 43% out of the market (sell days). Finally, Column 9 reports the total number of signals for going in and out of the market. ROC and Momentum rules have very high numbers of total trades resulting in high transaction costs. As can be seen from Exhibit 3, our best results (highest buy–sell) appears to be the moving average trading rules. However, we need to consider various trading strategies and account for both transaction costs and risk before selecting the best trading rule.

## PREDICTIVE POWER AND STRATEGIC DESIGNS

As we have seen from Exhibit 3, trading rules have predictive power. In this section we investigate whether it is possible to use this predictive power to beat the profitability of the B&H strategy considering both risk and transaction costs. As the results of Exhibit 3 imply, on average, for the 10 trading rules, a trader will be out of the market 43% of the time. What would this trader do when out of the market? Following Metghalchi, Chang, and Marcucci (2015), we design four strategies as follow: (1) the trader will be in the market when a trading rule emits buy signals and be in the money market when a trading rule emits sell signals (long/money), (2) the trader will be in the market when a trading rule emits buy signals and would short the market when the rule emits sell signals (long/short), (3) the trader will borrow at the money market rate and double stock investment when a trading rule emits buy signals and be in the money market when the rule emits sell signals (leverage/money), and (4) the trader will borrow at the money market rate and double stock investment when a trading rule emits buy signals and would short the market when the rule emits sell signals (leverage/short). In case of leverage, the total return on buy days is estimated as twice the market return minus the daily money market return.

For the above four strategies, a trader will have a return each day, and we then subtract from each strategy’s daily return the return of the B&H strategy to obtain the daily difference return. We then use the following hypothesis and test statistic to test whether the average daily difference is different than zero:

The *t-*statistic for the above test is:

where *X*(*ddif*) is the average daily difference of returns of each strategy over the B&H, *Var*(*ddif*) is the variance of daily difference returns, and *N* is the total number of days. Exhibit 4 reports the results of the above test for various strategies.

The annual return for each strategy is estimated by adding the daily returns produced by each strategy over the 4,588 days and then dividing this total by 17.7 years. The results of Exhibit 4 are very strong in favor of the effectiveness of trading rules to beat the B&H strategy. For Strategy 1, the average daily difference returns for all trading rules (except MACD > signal line and ROC-30) are positive with significant *t*-statistics rejecting the null hypothesis of equality of the mean daily difference returns with the mean B&H return. The average of all 10 trading rules for Strategy 1 have a mean daily return of 0.0067 and annual return of 16.03%, compared with 0.00001 and 0.37% of the B&H strategy.

For Strategy 2, the first seven trading rules have positive mean ddif returns with significant *t*-statistics but not the last three trading rules. Strategy 2 has higher daily and annual returns than Strategy 1, but we will see in the next section that it has higher risk too. For Strategy 3, all mean ddif returns (except MACD > signal line) are positive with significant *t*-statistics, and they have higher daily and annual returns than Strategy 1. This implies that leveraging on buy days improves returns. In the next section we will see that leveraging also increases risk. For Strategy 4, all trading rules (except MACD > signal line and ROC-30) have positive mean ddif returns with significant *t*-statistics, implying that the mean ddif return is greater than zero.

As we can see from Exhibit 4, most of the 10 trading rules for Strategies 1, 3, and 4 and seven trading rules for Strategy 2 can beat the profitability of the B&H strategy, with much higher annual returns than the B&H strategy. However, these trading rules imply many times in and out of the market resulting in transaction costs that will reduce net returns, and the various strategies have different levels of risk than the B&H strategy. The next question is which of these 10 trading rules and which strategy can beat the profitability of the B&H strategy considering both risk and transaction costs?

## RISK AND TRANSACTION COSTS

In the above sections, we have shown the predictive power of trading rules and the possibility of designing various strategies to beat the profitability of the B&H strategy. However, this knowledge may not be exploitable without considering transaction costs and risk. In order to see whether a trader can exploit the predictive power of a trading rule, we estimate the one-way “break-even” transaction costs (BEC) and compare them with actual transaction costs in Iceland. If the one-way BEC for a trading rule is higher than actual transaction costs and the risk associated with that rule is comparable or lower than the risk of the B&H strategy, then a trader can beat the profitability of B&H considering both transaction costs and risk. One-way break-even transaction costs can eliminate the additional return from trading rules. Exhibit 5 presents risk and BEC for our 10 trading rules and for each strategy.

The estimation of one-way break-even transaction costs is similar to Bessembinder and Chan (1998). We estimate BEC by adding the daily excess returns (beyond the B&H) produced by each trading rule and strategy over the 4,588 days and then divide it by the number of trades (in and out of the market) over the entire period. For Strategies 1 and 3, when a trader is out of the market and investing in the money market, he or she does not incur any transaction costs whereas, for Strategies 2 and 4, when a rule emits a sell signal, a trader will short the market resulting in transaction costs. Therefore, to estimate the one-way BEC for Strategies 2 and 4, we divide the sum of daily excess returns by 2 times the number of total trades. The risk of each strategy is estimated by the standard deviation of daily returns. This risk should be compared with the daily standard deviation of the B&H strategy or 1.95% as shown in Exhibit 1 for the entire period.

The results shown in Exhibit 5 are very strong for the moving averages and MACD > 0 trading rules. The best rules are those with high BEC and low risk. The last column of Exhibit 5 provides the average of the BEC and risk for each trading rule. Reviewing this column, we can determine that MA 200 is the best trading rule with the highest BECs. The next best trading rules are MA150, MA100, and MACD > 0. These BECs should be compared with actual one-way equity costs in Iceland but, to our knowledge, no one has published estimated equity transaction costs in Iceland.^{2} ,Domowitz, Glen, and Madhaven (2001) has estimated total equity transaction costs inclusive of implicit costs for 42 countries with the lowest transaction cost of 0.295% for France and highest of 1.975% for Korea. The mean of 42 countries is 0.71% with the mean of emerging markets of 0.95%. Thapa and Poshakwale (2010) have also estimated the equity transaction costs for 37 countries, and they include both explicit and implicit costs such as commissions, fees, market impact, and bid-ask spread. They find the lowest transaction cost of 0.20% for Japan and the highest transaction cost of 0.90% for the Philippines. Our best trading rules have much higher one-way BEC costs than the highest actual transaction costs estimated by Domowitz, Glen, and Madhaven (2001) and Thapa and Poshakwale (2010). For our best trading rules, we have very high BECs implying an excellent opportunity to beat the B&H strategy even considering transaction costs. For example, for MA 200 with Strategy 1, the annual extra return above the B&H is about 17.56% and average number of trades per year is 54/17.7 = 3.05, which yields a BEC of 17.56/3.05 = 5.75%.

If, for example, the actual one-way transaction cost in Iceland is 2%, then the MA200 trading rule and Strategy 1 beat the B&H strategy by 11.46% annually (17.56 − 3.05 * 2%). In summary, our results support the possibility of beating the profitability of the B&H strategy even considering transaction costs.

Finally, we need to look at the risks of these trading rules and strategies and compare them with the risk of the B&H strategy. Evaluating the various strategies, we can see that the BECs of Strategies 1 and 3 are higher than the BECs of Strategies 2 and 4. This implies that when a trading rule emits sell signals, a trader should be in the money market rather than short the market, since investing in the money market does not incur any transaction costs; whereas, shorting the market results in many ins and outs of the market and incurring large transaction costs. On the other hand, the BECs of Strategy 3 are higher than Strategy 1, which implies that leverage increases profitability of a trading rule. But when comparing the risk of Strategies 1 and 3, the risk of Strategy 3 is almost twice the risk of Strategy 1. As we can see from Exhibit 5, the risk of Strategy 1 for trading rules of MA 200, MA150, and MACD > 0 are 0.68%, 0.66%, and 60%, respectively, and these risks are much lower than the 1.95% risk of B&H strategy reported in Exhibit 1. Therefore, we have higher returns and lower risks than B&H for these three trading rules for Strategy 1. The risk of Strategy 2 (1.94%) is similar to the risk of the B&H (1.95%), since it is the same return distribution as the B&H. The risk of Strategy 3 for MA200, MA150, and MACD > 0 are 1.35%, 1.32%, and 1.20%, respectively, and these risks are lower than the 1.95% risk of B&H strategy reported in Exhibit 1. Therefore, again, we have higher leveraged returns and lower risks than the B&H for these three trading rules for Strategy 3. Finally, the risk of Strategy 4 of about 2.20% which is the highest and above the risk of the B&H.

A surprising result is that the risk of Strategy 3 is lower than the risk of the B&H. Usually this should not be case, since leveraging increases the risk. However in case of Iceland, the reason that we have much lower risk for Strategy 1 and even lower risk for Strategy 3 than the B&H risk is that for our best three trading rules, a trader will be in the money market (since the rule generated sell signals) during the subprime period, and a trader would have avoided the five large negative returns discussed above. Advocates of technical trading argue that a benefit of trading rules is the possibility of avoiding big downturns.

In summary, several of our trading rules, especially the moving average rules 200 and 150 days and MACD > 0 can beat the profitability of the B&H strategy considering both risk and transaction costs. All these three trading rules that have lower risk than the B&H strategy beat the profitability of the B&H; Strategy 1 has much lower risk and higher profit, and Strategy 3 has a bit lower risk than the B&H, but much higher profit. We have also estimated the BECs and risk of these three trading rules for Strategies 1 and 3 for Subperiod 1 and Subperiod 2, and we reach the same conclusions as for the entire period, namely lower risk but higher profitability. We conclude that, for three trading rules for the entire period and for each subperiod, a trader has the opportunity to beat the B&H strategy, even taking risk and transaction costs into account.

Exhibit 6 confirms our conclusion. We report the annual Sharpe ratios for Strategies 1 and 3 for our best trading rules; we also report the Sharpe ratio of the B&H on the top part of Exhibit 6 for comparison. We estimate the Sharpe ratio based on daily data as follow:

The numerator, ADER, is the average daily excess return (index return minus money market return) and the denominator, σ, is a daily measure of the standard deviation of excess returns. For example the annualized Sharpe ratio of the B&H strategy for the entire period is (−0.00030/0.01946) * √252 = −0.25. In the same way, the annualized Sharpe ratios have been estimated for our best three trading rules for Strategies 1 and 3. As can be seen from Exhibit 6, the Sharpe ratios of the three trading rules are much higher than the Sharpe ratio of the B&H strategy for the entire period and each sub-period, therefore, confirming that our trading rules beat the B&H strategy based on risk adjusted performance.

## CONCLUSION

When considering a long-term investment approach, advocates of the efficient market hypothesis believe that investors should buy and hold a broad diversified index and that the trading rules approach to investment is a futile exercise. In this article we investigate whether the investment approach of some trading rules could be more profitable than the Buy and Hold approach. Four trading rules used in this study are moving averages, relative strength index, moving average convergence divergence, and momentum rate of change. We first show in Exhibit 3 that trading rules have predictive power, and then we ask whether it is possible to design a strategy to exploit this predictive power of trading rules. We consider four strategies and conclude that Strategies 1 and 3 can be used to exploit the predictive power of trading rules.

In the last part of the article, we consider transaction costs and risk of various trading rules for Strategies 1 and 3. We conclude that three trading rules, MA200, MA150, and MACD can beat the B&H strategy considering both risk and transaction costs. A trader has a choice of one of these rules with Strategy 1 that has lower risk than the B&H and beats the profitability of the index strategy. If a trader wants to take a bit more risk than Strategy 1, then he or she can choose one of the trading rules with Strategy 3 that will beat the profitability of B&H handsomely and still with lower risk than the B&H strategy. The reason we have lower risk in the case of Iceland is mainly due to technical trading rules avoiding some downturns in the stock market. Finally, the results of Sharpe ratios confirm our conclusion. We recognize that because of the lack of liquidity for the Iceland ETF, at this time, it may be difficult for retail investors to implement these strategies. However, institutional investors have the opportunity to implement these strategies, possibly beating the B&H strategy.

## ENDNOTES

↵

^{1}In August 2013, an ETF mimicking the OMX All-Share index was launched by the Icelandic fund management company Landsbréf. The new ETF, “LEQ” (Landsbréf Equity), is a proxy for the OMX All-Share index, which a trader can use to mimic the Iceland Stock Index performance. Sigthor Jonson, CEO of Landsbréf, commented “The new LEQ ETF is a suitable trading product for investors who want to have an easy access to the most traded companies on the Icelandic market at low cost. It is our hope that the fund will be welcomed by investors in Iceland as well as abroad” (Nasdaq Nordic 2013).↵

^{2}According to Egill D. Brynjólfsson, senior fund manager at Landsbréf, for LEQ ETF trading “professional investors would usually pay about 20 basis points, or if they have DMA (direct market access), 10 basis points. However, retail investors would pay 1% with a minimum fee of 3.500 ISK, but usually if they are a client of a financial institution they can get a discount, in which case they would pay 0.75%, with a minimum fee of 1.950 ISK.” Another issue, according to Brynjólfsson, is the bid–ask spread for the LEQ, which is a bit high. “Currently, it is 1.36%, but if I take the average spread over the last year, it’s 1.14%.” (personal communication, August 25, 2017). Therefore, at this time, it may be somewhat difficult to implement these trading rules for retail investors but possible for institutional investors. If the Icelandic market becomes more accessible to world investors and more brokers include LEQ in their trading platform, then retail investors could perform trading rule strategies.

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