Market Timing with Moving Averages

sustainability Article Market Timing with Moving Averages Jukka Ilomäki 1, Hannu Laurila 1,* and Michael McAleer 2,3,4,5,6 1Faculty of Management, University of Tampere, FI-33014 Tampere, Finland; [email protected] 2Department of Finance, Asia University, Taichung City 413, Taiwan; [email protected] 3Discipline of Business Analytics University of Sydney Business School, Sydney NSW 2006, Australia 4Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands 5Department of Economic Analysis and ICAE, Complutense University of Madrid, 28040 Madrid, Spain 6Institute of Advanced Sciences Yokohama National University, Yokohama 240-8501, Japan *Correspondence: [email protected]; Tel.: +358-50-318-5998 Received: 7 June 2018; Accepted: 20 June 2018; Published: 22 June 2018   Abstract: Consider using the simple moving average (MA) rule of Gartley to determine when to buy stocks, and when to sell them and switch to the risk-free rate. In comparison, how might the performance be affected if the frequency is changed to the use of MA calculations? The empirical results show that, on average, the lower is the frequency, the higher are average daily returns, even though the volatility is virtually unchanged when the frequency is lower. The volatility from the highest to the lowest frequency is about 30% lower as compared with the buy-and-hold strategy volatility, but the average returns approach the buy-and-hold returns when frequency is lower. The 30% reduction in volatility appears if we invest randomly half the time in stock markets and half in the risk-free rate. Keywords: market timing; moving averages; risk-free rate; returns and volatility JEL Classification: G32; C58; C22; C41; D23 1. Introduction According to the standard investing separation theorem of Tobin [ 1 ], investors allocate investments between risk-free and risky assets. If the risk-free rate is low (high), the investors shift their wealth to (from) the risky assets. Fama [ 2 ] divided forecasters into two categories, namely macro forecasters (or market timers) and micro forecasters (or security analysts), who try to forecast individual stock returns relative to the market returns. Merton [ 3 ] defined a market timer to forecast when stocks will outperform (underperform) the risk-free asset, indicating that, when rm t>rf t(rm t<rf t) , where rm t is average stock market returns, rf t is the risk-free asset, ri t=rf t+βi(rm t−rf t) + εi t , ri t is the return for individual stock i included in the market portfolio m , βi is a positive parameter, and E[εi trm t] = E[εi t] . That is, a market timer only forecasts the statistical properties of rm t−rf t , indicating that their forecasts contain only the differential performance among individual stocks arising from systematic risk in the markets. Merton [ 3 ] showed theoretically that, when investors have heterogeneous beliefs and imperfect information, the value of a random market timing forecast is zero, and if the forecast variable is distributed independently or the forecast is based on public information, its value is zero, too. In fact, Merton showed that the maximum value of skilled market timing is the value of the protective put against buy-and-hold strategy. Sustainability 2018,10, 2125; doi:10.3390/su10072125 www.mdpi.com/journal/sustainability Sustainability 2018,10, 2125 2 of 25 Henriksson and Merton [ 4 ] presented an empirical procedure whereby correct forecasts can be analyzed statistically. However, if it is assumed that εi t follows an approximate normal distribution, this leads to the Capital Asset Pricing Model (CAPM) of Sharpe [5], and Lintner [6]. The purpose of the paper is to detect whether the frequency used in calculating the MA affects the performance of the trading rule. We use a large sample with more than eight million observations for robustness of the empirical results, and a simple MA rule for the timing aspect for individual Dow Jones Industrial Average (DJIA) stocks with different frequencies. We use a simple MA rule for the timing aspect for individual Dow Jones Industrial Average (DJIA) stocks with different frequencies. Zhu and Zhou [ 7 ] showed analytically that MA trading rules, as a part of asset allocation rules, can outperform standard allocation rules when stock returns are partly forecastable. The standard rule means investing a fixed proportion of wealth in risky assets and the rest in risk-free assets, with the ratio determined by the risk tolerance of an investor. It is well known that MA is a widely used technical trading rule, which adds value for a risk averse investor if returns are predictable. This is the well-known reward/risk (or mean-variance) principle in the spirit of Markowitz [ 8 ], Tobin [ 1 ], and Sharpe [ 5 ]. Zhu and Zhou [ 7 ] argued that the fixed allocation rule is not optimal if returns are forecastable by using the MA rule. Therefore, assuming that risk tolerance and the forecast performance of stock market returns are constant, the linear combination rule means that, when the MA rule suggests an uptrend (downtrend), the rule suggests that the total weight should be allocated to stock markets (the risk-free rate). The empirical findings suggest a low volatility anomaly that might be explained by investors’ affection to high volatility, as suggested by Baker et al. [ 9 ], and noted in Ang et al. [ 10 ]. On the other hand, the reported predictability of risk premia (see, for example, Cochrane [ 11 ], and Fama [ 12 ]) can explain why, for instance, MA rules forecast better than using random highs and lows in the stock market (as noted in Jagannathan and Korajczyck [ 13 ]). The topic is important, as Friesen and Sapp [ 14 ], among others, reported that mutual fund investors had negative outcomes, on average, in their timing to invest and withdraw cash from US mutual funds from 1991 to 2004. Munoz and Vicente [ 15 ] reported similar results with more recent data in US markets. The remainder of the paper is organized as follows. Section 2provides a literature review, and alternative model specifications are presented in Section 3. The empirical analysis is conducted in Section 4, while Section 5gives some concluding comments. 2. Literature Review In efficient markets, investors earn above average returns only by taking above average risks (Malkiel [ 16 ]). Samuelson [ 17 ] conformed with Fama [ 2 ] by noting that market efficiency can be divided into micro and macro efficiency. The former concerns the relative pricing of individual stocks, and the latter, for markets as a whole. The CAPM by Sharpe [ 5 ], and Lintner [ 6 ] argues that beta is a proper definition for systematic risk for stock i, if unexplained changes in risk adjusted returns for the stock follow approximately normal distribution with zero mean. Black [ 18 ] stated that the slope of the security market line (SML) is flatter if there exist restrictions in borrowing, that is, leverage constraints in the model. Starting from Black et al. [ 19 ], many studies have reported that the security market line is too flat in US stocks compared with the SML suggested by the CAPM version of Sharpe and Lintner. Ang et al. [ 10 ], Baker et al. [ 20 ], and Frazzini and Pedersen [ 21 ] found that low-beta stocks outperform high-beta stocks statistically significantly. In fact, Frazzini and Pedersen reported that significant excess profits in US stocks can be achieved by shorting high-beta stocks and buying low-beta stocks with leverage, but that leverage constraints make them disappear. Using Black [ 18 ], investors often have leverage constraints, thereby making them place too much weight on risky stocks, which results in lower required return for high-beta stocks than would be justified by the Sharpe–Lintner CAPM. Sustainability 2018,10, 2125 3 of 25 Markowitz [ 8 ] defined portfolio risk simply as the volatility of portfolio returns. Clarke et al. [ 22 ] found that the volatility of stock returns contains potentially an additional risk factor with respect to systematic risk that can be defined in the betas of CAPM by Sharpe and Lintner. Moreover, Ang et al. [ 10 ] reported that the total volatility of international stock market returns is highly correlated with US stock returns, thereby suggesting a common risk factor for US stocks. Baker et al. [ 9 ] suggested that the low-volatility anomaly is due to investor irrational behavior, mainly because an average fund manager seeks to beat the buy-and hold strategy by overinvesting in high-beta stocks. The explanations include preference for lotteries (Barberis and Huang [ 23 ]; Kumar [ 24 ]; Bali et al. [ 25 ]), overconfidence (Ben-David et al. [ 26 ]), and representativeness (Daniel and Titman [ 27 ]), which means that people assess the probability of a state of the world based on how typical of that state the evidence seems to be (Kahneman and Tversky [28]). Baker et al. [ 9 ] argued that the anomality is also related to the limits of arbitrage (see also Baker and Wurgler [ 29 ]). In fact, the extra costs of shorting prevent taking advantage of overpricing (Hong and Sraer [ 30 ]). More importantly, Li et al. [ 31 ] reported that the excess returns of low-beta portfolios are due to mispricing in US stocks, indicating that the low-volatility anomaly does not exist because of systematic risk by some rational, stock specific volatility risk factor. They tested the low-volatility anomaly with monthly data from January 1963 to December 2011 in NYSE, NASDAQ, and AMEX stocks. Market timing is closely related to technical trading rules. Brown and Jennings [ 32 ] showed theoretically that using past prices (e.g., the MA rule of Gartley [ 33 ]) has value for investors, if equilibrium prices are not fully revealing, and signals from past prices have some forecasting qualities. More importantly, Zhu and Zhou [ 7 ] indicated that the MA rules are particularly useful for asset allocation purposes among risk averse investors, when markets are forecastable (quality of signal). Moskowitz et al. [ 34 ] argued that there are significant time series momentum (TSM) effects in financial markets that are not related to the cross-sectional momentum effect (Jegadeesh and Titman [ 35 ]). However, TSM is closely related to MA rules, since it gives a buy (sell) signal according to some historical price reference points, whereas MA rules give a buy (sell) signal, when the current price moves above (below) the historical average of the chosen calculated rolling window measure. Starting from LeRoy [ 36 ] and Lucas [ 37 ], the literature in financial economics states that financial markets returns in efficient markets are partly forecastable, when investors are risk averse. This leads to the time-varying risk premia of investors, as noted by Fama [ 12 ]. For example, Campbell and Cochrane [ 38 ] presented a consumption-based model, which indicates that when the markets are in recession (boom), risk averse investors require larger (smaller) risk premium for risky assets. More importantly, Cochrane [ 11 ] noted that the forecastability of excess returns may lead to successful market timing rules. Brock et al. [ 39 ] tested different MA lag rules for US stock markets, and found that they gain profits compared with holding cash. On the other hand, Sullivan et al. [ 40 ] found that MA rules do not outperform the buy-and-hold strategy, if transaction costs are accounted for. Allen and Karjalainen [ 41 ] used a genetic algorithm to develop the best ex-ante technical trading rule model using US data, and found some evidence of outperforming the buy-and-hold strategy. Lo et al. [ 42 ] found that risk averse investors benefit from technical trading rules because they reduce volatility of the portfolio without giving up much returns when compared against the buy-and-hold strategy. More recently, Neely et al. [ 43 ] used monthly data from January 1951 to December 2011, and reported that MA rules forecast the risk premia in US stock markets statistically significantly. Marshall et al. [44] found that MA rules give an earlier signal than TSM, suggesting better returns for MA rules, but they both work best with outside of large market value stocks. Moskowitz et al. [ 34 ] used monthly data from January 1965 to December 2009, and reported that TSM provides significant positive excess returns in futures markets. However, Kim et al. [ 45 ] reported Sustainability 2018,10, 2125 4 of 25 that these positive excess returns produced by TSM are due to the volatility scaling factor used by Moskowitz et al. 3. Model Specification Consider an overlapping generation economy with a continuum of young and old investors [0, 1] . A young risk-averse investor j invests their initial wealth, wj t , in infinitely lived risky assets i= 1, 2, . . . I , and in risk-free assets that produce the risk-free rate of return, r f . A risky asset ipays dividend Di t , and has xs i outstanding. Assuming exogenous processes throughout, the aggregate dividend is Dt. A young investor j maximizes their utility from old time consumption through optimal allocation of initial resources wj t, between risky and risk-free assets: maxxj tEt(Pt+1+Dt+1) Pt−(1+rf)−νj 2xj2σ2 s.t. xj tPt≤wj t where Et is the expectations operator, Pt is the price of one share of aggregate stock, νj is a constant risk-aversion parameter for investor j , σ2 is the variance of returns for the aggregate stock, and xj t is the demand of risky assets for an investor j. The first-order condition is: Et(Pt+1+Dt+1) Pt−(1+rf)−νjxj tσ2=0, which results in optimal demand for the risky assets: xj t=Et((Pt+1+Dt+1)/Pt)−(1+rf) νjσ2(1) Suppose that an investor j is a macro forecaster who allocates their initial wealth, wj t , between risky stocks and risk-free assets according to their forecast about the return of the risky alternative. Then, Equation (1) says that the investor invests in the risky stocks only if the numerator on the right hand side is positive. 4. Empirical Analysis This section presents the empirical results from seven frequencies for the (MA) trend-chasing rules. The data consist of 29 companies included in the Dow Jones Industrial Average (DJIA) index in January 2018. The trading data (daily closing prices) cover 30 years from 1 January 1988 to 31 December 2017. Choosing the current DJIA companies for the last 30 years creates a “survivor bias” in the buy-and-hold results. However, this should not be an issue, as we intend to compare the performance of the alternative MA frequency rules. The rolling window is 200 trading days. The first rule is to calculate MA in every trading day; the second frequency takes into account every 5th trading day (thereby providing a proxy for the weekly rule); the third frequency takes into account every 22th trading day (proxy for the monthly rule); the fourth rule is to calculate MA for every 44th trading day (proxy for every other month); the fifth rule takes into account every 66th trading day (proxy for every third month); the sixth rule takes into account every 88th trading day (proxy for every fourth month); and the seventh rule takes into account every 100th trading day (proxy for every fifth month). For the 29 DJIA companies, 26 of them have daily stock data available from 27 March 1987, thereby giving 4 January 1988 as the first trading day. The data for Cisco are available from 12 February 1990, for Goldman Sachs from 4 May 1999, and for Visa from 19 March 2008. There are 217,569 observations of daily returns from DJIA stocks. Thus, there are 217,569 × 9 = 1,958,121 daily returns for the first Sustainability 2018,10, 2125 5 of 25 three frequencies (rules), 217,569 × 4 = 870,276 daily returns for the fourth rule, 217,569 × 3 = 652,707 daily returns for the fifth rule, 217,569 × 2 = 435,138 daily returns for the sixth rule, and 217,569 daily returns for the seventh rule. The trading rule for all cases is to use a simple crossover rule. When the trend-chasing MA turns lower (higher) than the current daily closing price, we invest the stock (three-month US Treasury Bills) at the closing price of the next trading day. Thus, the trading rule provides a market timing strategy where we invest all wealth either in stocks (separately, every stock included in DJIA), or to the risk-free asset (three-month U.S. Treasury bill), where the moving average rule advices the timing. At the first frequency (every trading day), we calculate daily returns for MA200, MA180, MA160, MA140, MA120, MA100, MA80, MA60, and MA40. For example, MA200 is calculated as: Pt−1+Pt−2+. . . +Pt−200 200 =Xt−1 At the lowest frequency, where every 100th daily observation is counted, MAC2 is calculated as: Pt−1+Pt−100 2=Xt−1 If Xt−1<Pt−1, we buy the stock at the closing price, Pt, thereby giving daily returns as Rt+1=lnPt+1 Pt Tables A1–A7 in Appendix Ashow that the annualized average log returns of MA200 − MA40 are +0.053 after transaction costs (with 0.1% per change of position). Recall that there are 200 closing day prices in the rolling window MA200, whereas MA40 means that there are 44 closing day prices in the window. The respective log returns for MAW40 − MAW8 (weekly) are +0.063 ; for MA10 − MA2 (monthly) +0.071 ; for MAD5 − MAD2 (every other month) +0.078 ; for MAT4 − MAT2 (every third month) +0.084 ; for MAQ3 − MAQ2 (every fourth month) +0.094 ; and for MAC2 (every fifth month) +0.088 after transaction costs. Tables A1–A7 show that, as the frequency decreases until every fourth month frequency (MAQ3 − MAQ2), average returns tend to increase, and decrease thereafter. In comparison, the biased buy-and-hold strategy produces +0.117 with equal weights among all DJIA stocks, and with 0.295 annual volatility. A random investment (half the time in the risk-free rate, and half in the equally weighted portfolio from 4 January 1988) produces ( 0.117 × 0.5 + 0.022 × 0.5 ) = +0.070 annually, on average, with ( 1 −√0.5 = 0.293 ) = 29.3% reduction in volatility, indicating 0.209 annual volatility for that portfolio. The data are dividend excluded, but the average annual dividend yield in DJIA stocks over the last thirty years has been +0.026, so that the biased buy and hold strategy produces +0.143 annually with equal weights among DJIA stocks before taxes. Thus, the random investment strategy produces +0.083 annually, with survivor bias. Appendix A(namely the second column of Tables A1–A7) also reports the annualized average log returns calculated in the largest sample (full 200 observations) in every category: MA200 +0.065 ; MAW40 +0.073 ; MA10 +0.079 ; MAD5 +0.083 ; MAT4 +0.089 ; MAQ3 +0.091 ; and MAC2 +0.088 after transaction costs and before dividends. Adding +0.013 produces after dividends and before taxes: MA200 +0.078 ; MAW40 +0.086 ; MA10 +0.092 ; MAD5 +0.096 ; MAT4 +0.102 ; MAQ3 +0.104; and MAC2 +0.101 . These results imply that starting from every fifth trading day frequency, a macro forecaster beats the buy and hold strategy in returns. Figure 1illustrates the effects of frequency on the returns to volatility ratio (the second column in Appendix A, Tables A1–A7). Sustainability 2018,10, 2125 6 of 25 Sustainability 2018, 10, x FOR PEER REVIEW 6 of 25 Figure 1. Returns to volatility ratio in MA200, MAW40, MA10, MAD5, MAT4, MAQ3, MAC2, and the theoretical random timing efficient SML. In Figure 1, the straight line illustrates the return to volatility ratio of portfolios, where wealth is randomly invested in combinations of the three-month Treasury Bill (risk-free rate), with stocks included in the DJIA between 4 January 1988 and 31 December 2017. The red crosses represent the average return/volatility points calculated in the 200-day rolling window with the following frequencies: daily, every five days, every 22 days, every 44 days, every 66 days, every 88 days, and every 100 days (with only the most observations in each frequency giving 200, 40, 10, 5, 4, 3, and 2 observations). The red crosses plot a convex curve that deviates increasingly from the straight return to volatility ratio line, thereby symbolizing superior portfolio efficiency. Tables A8–A14 in Appendix B show that the annualized volatility of daily returns read, on average: MA200−MA40 0.2044; MAW40−MAW8 0.205; MA10−MA2 0.2091; MAD5−MAD2 0.213; MAT4−MAT2 0.219; MAQ3−MAQ2 0.221; and MAC2 0.218. Thus, there is virtually no difference between the MA frequencies, while the biased buy-and-hold strategy produces 0.295. Figure 1 presents the volatilities calculated in the largest sample (full 200 day rolling window in every category, the second column in Tables A8–A14). They read MA200 0.207; MAW40 0.208; MA10 0.211; MAD5 0.213; MAT4 0.218; MAQ3 0.215; and MAC2 0.218 after transaction costs. Investing randomly half of the time in the risk-free rate and the other half in the equally weighted portfolio, produces 0.209. Thus, the difference between the annual volatilities produced in profitable market timing MA rules (MA10−MAC2) and random market timing (half and half) ranges from 0.009 to 0.002. In Figure 2, the straight line again presents the return to volatility ratio of portfolios with random investment in the risk-free rate and the stocks in DJIA between 4 January 1988 and 31 December 2017. The red crosses plot the average return to volatility ratios, calculated by using a 200-day rolling window, with the following frequencies: daily, every five days, every 22 days, every 44 days, every 66 days, every 88 days, and every 100 days. The averages of every lag are reported in Tables A1–A14, Appendices A and B. Thus, all daily returns from Tables A1–A14 are included. Figure 1. Returns to volatility ratio in MA200, MAW40, MA10, MAD5, MAT4, MAQ3, MAC2, and the theoretical random timing efficient SML. In Figure 1, the straight line illustrates the return to volatility ratio of portfolios, where wealth is randomly invested in combinations of the three-month Treasury Bill (risk-free rate), with stocks included in the DJIA between 4 January 1988 and 31 December 2017. The red crosses represent the average return/volatility points calculated in the 200-day rolling window with the following frequencies: daily, every five days, every 22 days, every 44 days, every 66 days, every 88 days, and every 100 days (with only the most observations in each frequency giving 200, 40, 10, 5, 4, 3, and 2 observations). The red crosses plot a convex curve that deviates increasingly from the straight return to volatility ratio line, thereby symbolizing superior portfolio efficiency. Tables A8–A14 in Appendix Bshow that the annualized volatility of daily returns read, on average: MA200 − MA40 0.2044 ; MAW40 − MAW8 0.205 ; MA10 − MA2 0.2091 ; MAD5 − MAD2 0.213 ; MAT4 − MAT2 0.219 ; MAQ3 − MAQ2 0.221 ; and MAC2 0.218 . Thus, there is virtually no difference between the MA frequencies, while the biased buy-and-hold strategy produces 0.295. Figure 1presents the volatilities calculated in the largest sample (full 200 day rolling window in every category, the second column in Tables A8–A14). They read MA200 0.207; MAW40 0.208; MA10 0.211; MAD5 0.213 ; MAT4 0.218 ; MAQ3 0.215 ; and MAC2 0.218 after transaction costs. Investing randomly half of the time in the risk-free rate and the other half in the equally weighted portfolio, produces 0.209. Thus, the difference between the annual volatilities produced in profitable market timing MA rules (MA10 − MAC2) and random market timing (half and half) ranges from 0.009 to 0.002 . In Figure 2, the straight line again presents the return to volatility ratio of portfolios with random investment in the risk-free rate and the stocks in DJIA between 4 January 1988 and 31 December 2017. The red crosses plot the average return to volatility ratios, calculated by using a 200-day rolling window, with the following frequencies: daily, every five days, every 22 days, every 44 days, every 66 days, every 88 days, and every 100 days. The averages of every lag are reported in Tables A1–A14, and. Thus, all daily returns from Tables A1–A14 are included. Sustainability 2018,10, 2125 7 of 25 Sustainability 2018, 10, x FOR PEER REVIEW 7 of 25 Figure 2. Returns to volatility ratio in MA200 − MA40, MAW40 − MAW8, MA10 − MA2, MAD5 − MAD2, MAT4 − MAT2, MAQ3 − MAQ2, MAC2, and the theoretical random timing efficient SML. Comparing Figures 1 and 2, it is clear that using the whole 200 daily observation windows in the MA rules produces more efficient results in market timing. That is, comparing the products of shorter and longer MA rule rolling windows, e.g., the last two monthly observations compared with ten monthly observations, average realized returns drop from +0.079 to +0.059 before dividends, while volatility remains approximately unchanged (from 0.211 to 0.207). This suggests that, in both cases, about half and half is invested in the equally-weighted DJIA portfolios and in the risk-free rate, and the MA rules advise the timing. More importantly, Tables A8–A14 in Appendix B show that the range in volatilities with all MA rules varies between 0.202 and 0.227 (with 0.02 difference), whereas Tables A1–A7 in Appendix A show that realized returns vary between 0.096 and 0.033 before dividends (with 0.063 difference). These results indicate that a macro market timing with 200 days rolling window produces a reduction in volatility from 0.295 (the buy-and hold) to between 0.207 and 0.218, but the average annualized returns (dividends included) tend to rise as the MA frequency falls (+0.078 with all 200 observations to +0.104 with every fourth month observations). Thus, the results indicate that MA market timing finds long term stochastic trends more efficiently than short term stochastic trends. The Sharpe ratio of random market timing (half and half) with dividends is 0.292; for MA200 0.271; for MAW40 0.308; for MA10 0.332; for the MAD5 0.347; for MAT4 0.370; for MAQ3 0.381; and for MAC2 it is 0.362. Figure 3 shows that when the volatility changes 1% in the DJIA stocks, then the average returns change is 0.39%. Figures 1 and 2 suggest that the theoretical change should be such that, when the volatility changes 1%, the average returns change is 0.50%, suggesting a flatter SML line in the data. This suggests strongly that DJIA investors have overweight high-beta stocks in the last 30 years. Figure 2. Returns to volatility ratio in MA200 − MA40, MAW40 − MAW8, MA10 − MA2, MAD5 − MAD2, MAT4 −MAT2, MAQ3 −MAQ2, MAC2, and the theoretical random timing efficient SML. Comparing Figures 1and 2, it is clear that using the whole 200 daily observation windows in the MA rules produces more efficient results in market timing. That is, comparing the products of shorter and longer MA rule rolling windows, e.g., the last two monthly observations compared with ten monthly observations, average realized returns drop from +0.079 to +0.059 before dividends, while volatility remains approximately unchanged (from 0.211 to 0.207). This suggests that, in both cases, about half and half is invested in the equally-weighted DJIA portfolios and in the risk-free rate, and the MA rules advise the timing. More importantly, Tables A8–A14 in Appendix Bshow that the range in volatilities with all MA rules varies between 0.202 and 0.227 (with 0.02 difference), whereas Tables A1–A7 in Appendix Ashow that realized returns vary between 0.096 and 0.033 before dividends (with 0.063 difference). These results indicate that a macro market timing with 200 days rolling window produces a reduction in volatility from 0.295 (the buy-and hold) to between 0.207 and 0.218, but the average annualized returns (dividends included) tend to rise as the MA frequency falls (+0.078 with all 200 observations to +0.104 with every fourth month observations). Thus, the results indicate that MA market timing finds long term stochastic trends more efficiently than short term stochastic trends. The Sharpe ratio of random market timing (half and half) with dividends is 0.292 ; for MA200 0.271 ; for MAW40 0.308 ; for MA10 0.332 ; for the MAD5 0.347 ; for MAT4 0.370 ; for MAQ3 0.381 ; and for MAC2 it is 0.362. Figure 3shows that when the volatility changes 1% in the DJIA stocks, then the average returns change is 0.39%. Figures 1and 2suggest that the theoretical change should be such that, when the volatility changes 1%, the average returns change is 0.50%, suggesting a flatter SML line in the data. This suggests strongly that DJIA investors have overweight high-beta stocks in the last 30 years. Sustainability 2018,10, 2125 8 of 25 Sustainability 2018, 10, x FOR PEER REVIEW 8 of 25 Figure 3. Returns to volatility ratio in current DJIA stocks, annual averages from 4 January 1988 to 31 December 2017. It is obvious that transaction costs are crucial in MA performance. In the above calculations, the transaction costs are 0.1% per transaction from current wealth. Tables A15 and A16 in Appendix C report the transaction costs for the MA200−MA40 and MA10−MA2 rules. In the MA200−MA40 rules, the average annualized transaction costs are 0.0133, such that the rules have about 13 changes in positions per year. Meanwhile, for the MA10−MA2 rules, the average annualized transaction costs are 0.0032, suggesting about three changes in positions per year. Allen and Karjalainen [41] gave reasons for using a cost of 0.2% per transaction in their sample, but since technological progress has reduced transaction costs since the mid-1990s, 0.1% per transaction should be fair, on average. Nevertheless, a trial with 0.2% transaction costs shows that, for example, the average annualized daily returns become 0.0403 for the MA200−MA40 rules, and 0.0674 for the MA10−MA2 rules. Note that the returns grow 67%, on average, for the MA10−MA2 rules (with about the same volatility) compared with costs of 0.1% per transaction. Note that the model prohibits short selling since we only have long positions in stocks or investing in the risk-free rate. Then, the limits of arbitrage argument of Baker et al. [9] are consistent with our results. 5. Concluding Remarks The analysis suggests that a macro forecaster can obtain higher returns with equal volatility (30% below that of the buy-and-hold strategy) by reducing the frequency used in MA rules. The return to volatility ratio for risk-averse investors with MA market timing significantly outperforms the random benchmark strategy, when the frequency in the MA rules is reduced. This indicates that the forecasts become more accurate as the time frame becomes longer. The results suggest that a flatter SML in the CAPM can be followed by the irrational preference of investors in high-beta stocks, as suggested by Baker et al. (2011) and Li et al. (2016), since the empirically efficient frontier of portfolios becomes flatter than the theoretically efficient SML (random timing) (see Figure 1). In other words, the empirical results suggests\ that market timing with the few past observations (for example, every fourth month) in the past 200 rolling window daily prices, Figure 3. Returns to volatility ratio in current DJIA stocks, annual averages from 4 January 1988 to 31 December 2017. It is obvious that transaction costs are crucial in MA performance. In the above calculations, the transaction costs are 0.1% per transaction from current wealth. Tables A15 and A16 in Appendix C report the transaction costs for the MA200 − MA40 and MA10 − MA2 rules. In the MA200 − MA40 rules, the average annualized transaction costs are 0.0133 , such that the rules have about 13 changes in positions per year. Meanwhile, for the MA10 − MA2 rules, the average annualized transaction costs are 0.0032, suggesting about three changes in positions per year. Allen and Karjalainen [ 41 ] gave reasons for using a cost of 0.2% per transaction in their sample, but since technological progress has reduced transaction costs since the mid-1990s, 0.1% per transaction should be fair, on average. Nevertheless, a trial with 0.2% transaction costs shows that, for example, the average annualized daily returns become 0.0403 for the MA200 − MA40 rules, and 0.0674 for the MA10 − MA2 rules. Note that the returns grow 67%, on average, for the MA10 − MA2 rules (with about the same volatility) compared with costs of 0.1% per transaction. Note that the model prohibits short selling since we only have long positions in stocks or investing in the risk-free rate. Then, the limits of arbitrage argument of Baker et al. [ 9 ] are consistent with our results. 5. Concluding Remarks The analysis suggests that a macro forecaster can obtain higher returns with equal volatility (30% below that of the buy-and-hold strategy) by reducing the frequency used in MA rules. The return to volatility ratio for risk-averse investors with MA market timing significantly outperforms the random benchmark strategy, when the frequency in the MA rules is reduced. This indicates that the forecasts become more accurate as the time frame becomes longer. The results suggest that a flatter SML in the CAPM can be followed by the irrational preference of investors in high-beta stocks, as suggested by Baker et al. (2011) and Li et al. (2016), since the Sustainability 2018,10, 2125 9 of 25 empirically efficient frontier of portfolios becomes flatter than the theoretically efficient SML (random timing) (see Figure 1). In other words, the empirical results suggests \ that market timing with the few past observations (for example, every fourth month) in the past 200 rolling window daily prices, have produced significantly better returns to risk ratio for the portfolio of DJIA equally weighted stocks in the past 30 years than random timing. The finding points to the low-volatility anomaly. One explanation for the results is that they are due to time-varying risk premiums. This is emphasized by Neely et al. (2014), who claimed that MA rules, in effect, forecast changes in the risk premium. If the results are rational products of time-varying risk premiums, the results suggest that investor sensitivity to risk must be extremely high, and their risk premium is larger (smaller) in downs (ups), as suggested by Campbell and Cochrane (1999). As volatility rises (decreases), usually in downs (ups), the results suggest that, when volatility is high, investors as a group tolerate significantly more risk (that is, volatility) than in calmer periods. Consider the following numerical example: Assume that the risk premium is 0.08 in volatile downs, and 0.04 in calm ups, and the variance of returns is 0.09 in downs and 0.03 in ups. Then, the risk aversion coefficient must be 0.89 in volatile down periods, and 1.33 in calm up periods. As market timing with MA rules works better in longer periods with few observations, it seems to be more accurate in longer stochastic (up or down) trends. Author Contributions: This paper is to be attributed in equal parts to the authors. Funding: This research, for the part of the third author, was funded by the Australian Research Council and the Ministry of Science and Technology (MOST), Taiwan. Acknowledgments: The authors are most grateful for the helpful comments and suggestions of George Tauchen. The authors also thank the two anonymous referees for their helpful comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2018,10, 2125 16 of 25 Appendix B Table A8. Annualized daily volatility of MA40–MA200, average annualized volatility. Buy and Hold MA200 MA180 MA160 MA140 MA120 MA100 MA80 MA60 MA40 3M 0.225 0.164 0.165 0.161 0.161 0.159 0.159 0.158 0.158 0.157 American Express 0.345 0.227 0.228 0.221 0.225 0.224 0.225 0.224 0.228 0.229 Apple 0.451 0.317 0.321 0.315 0.315 0.313 0.315 0.315 0.310 0.305 Boeing 0.294 0.201 0.203 0.199 0.201 0.199 0.198 0.198 0.201 0.204 Caterpillar 0.311 0.216 0.218 0.216 0.216 0.214 0.215 0.214 0.213 0.215 Chevron 0.244 0.167 0.168 0.166 0.166 0.166 0.165 0.164 0.167 0.168 Coca-Cola 0.225 0.164 0.166 0.161 0.160 0.159 0.158 0.158 0.156 0.155 Walt Disney 0.291 0.196 0.201 0.199 0.200 0.199 0.198 0.203 0.204 0.203 Exxon 0.230 0.162 0.163 0.159 0.159 0.159 0.157 0.156 0.155 0.157 GE 0.275 0.174 0.175 0.172 0.173 0.173 0.171 0.168 0.168 0.168 Home Depot 0.314 0.226 0.228 0.223 0.221 0.221 0.219 0.217 0.217 0.214 IBM 0.271 0.187 0.189 0.185 0.184 0.181 0.179 0.177 0.176 0.174 Intel 0.382 0.273 0.275 0.267 0.265 0.263 0.260 0.257 0.256 0.254 Johnson & Johnson 0.215 0.163 0.164 0.161 0.159 0.157 0.155 0.153 0.152 0.149 JP Morgan 0.375 0.223 0.226 0.223 0.224 0.227 0.237 0.242 0.245 0.248 McDonalds 0.240 0.183 0.184 0.18 0.178 0.177 0.176 0.176 0.175 0.174 Merck 0.269 0.177 0.179 0.173 0.173 0.174 0.172 0.174 0.174 0.177 Microsoft 0.323 0.248 0.249 0.243 0.241 0.237 0.236 0.233 0.232 0.231 Nike 0.327 0.243 0.245 0.238 0.236 0.235 0.235 0.232 0.232 0.233 Pfizer 0.266 0.188 0.19 0.187 0.186 0.187 0.186 0.187 0.187 0.187 Procter & Gamble 0.225 0.169 0.169 0.164 0.163 0.161 0.158 0.157 0.156 0.156 Travellers 0.268 0.174 0.175 0.174 0.175 0.178 0.180 0.184 0.182 0.185 United Technologies 0.261 0.179 0.181 0.179 0.178 0.177 0.177 0.177 0.176 0.173 United Health Group 0.386 0.290 0.293 0.290 0.290 0.283 0.282 0.282 0.280 0.273 Verizon 0.246 0.163 0.165 0.164 0.163 0.163 0.163 0.161 0.161 0.163 Wal-Mart 0.263 0.203 0.204 0.200 0.198 0.195 0.191 0.19 0.189 0.191 Cisco 0.415 0.300 0.302 0.297 0.295 0.291 0.290 0.285 0.282 0.275 Goldman Sachs 0.373 0.222 0.226 0.22 0.222 0.223 0.228 0.230 0.227 0.229 Visa 0.260 0.209 0.212 0.209 0.208 0.212 0.208 0.206 0.205 0.197 Average 0.295 0.207 0.209 0.205 0.205 0.204 0.203 0.203 0.202 0.202 0.204 Sustainability 2018,10, 2125 17 of 25 Table A9. Annualized daily (every fifth trading day) volatility of MAW8–MAW40 (W = number of weeks), average annualized volatility. Buy and Hold MAW40 MAW36 MAW32 MAW28 MAW24 MAW20 MAW16 MAW12 MAW8 3M 0.225 0.165 0.165 0.163 0.163 0.16 0.159 0.157 0.157 0.159 American Express 0.345 0.227 0.224 0.224 0.227 0.225 0.223 0.228 0.232 0.234 Apple 0.451 0.316 0.316 0.313 0.318 0.316 0.343 0.317 0.312 0.309 Boeing 0.294 0.204 0.203 0.204 0.204 0.203 0.203 0.201 0.201 0.206 Caterpillar 0.311 0.216 0.215 0.215 0.217 0.214 0.215 0.215 0.213 0.214 Chevron 0.244 0.169 0.168 0.169 0.168 0.168 0.167 0.166 0.168 0.172 Coca-Cola 0.225 0.165 0.165 0.164 0.162 0.160 0.159 0.159 0.157 0.155 Walt Disney 0.291 0.195 0.198 0.197 0.197 0.199 0.200 0.202 0.203 0.204 Exxon 0.230 0.163 0.161 0.160 0.161 0.160 0.157 0.156 0.153 0.158 GE 0.275 0.174 0.174 0.174 0.175 0.174 0.170 0.169 0.171 0.166 Home Depot 0.314 0.228 0.228 0.226 0.225 0.222 0.224 0.219 0.219 0.214 IBM 0.271 0.190 0.188 0.185 0.184 0.183 0.178 0.177 0.178 0.177 Intel 0.382 0.267 0.267 0.268 0.264 0.263 0.259 0.256 0.259 0.259 Johnson & Johnson 0.215 0.164 0.163 0.162 0.160 0.158 0.156 0.156 0.152 0.15 JP Morgan 0.375 0.222 0.225 0.224 0.230 0.236 0.239 0.243 0.241 0.252 McDonalds 0.240 0.185 0.182 0.181 0.179 0.177 0.177 0.176 0.174 0.171 Merck 0.269 0.179 0.175 0.174 0.173 0.173 0.172 0.175 0.176 0.175 Microsoft 0.323 0.250 0.247 0.245 0.244 0.24 0.236 0.236 0.230 0.232 Nike 0.327 0.244 0.241 0.239 0.240 0.241 0.238 0.235 0.232 0.232 Pfizer 0.266 0.189 0.187 0.186 0.187 0.188 0.190 0.189 0.189 0.184 Procter & Gamble 0.225 0.170 0.168 0.167 0.165 0.164 0.161 0.158 0.160 0.156 Travellers 0.268 0.175 0.175 0.175 0.178 0.177 0.177 0.184 0.184 0.185 United Technologies 0.261 0.181 0.179 0.178 0.177 0.177 0.177 0.177 0.176 0.172 United Health Group 0.386 0.292 0.291 0.292 0.291 0.290 0.289 0.287 0.282 0.278 Verizon 0.246 0.163 0.162 0.162 0.162 0.164 0.162 0.161 0.160 0.159 Wal-Mart 0.263 0.205 0.202 0.201 0.198 0.194 0.191 0.191 0.190 0.192 Cisco 0.415 0.307 0.305 0.300 0.296 0.292 0.293 0.288 0.285 0.281 Goldman Sachs 0.373 0.225 0.223 0.221 0.221 0.220 0.230 0.233 0.241 0.241 Visa 0.260 0.203 0.210 0.209 0.208 0.210 0.208 0.206 0.203 0.195 Average 0.295 0.208 0.207 0.206 0.206 0.205 0.205 0.204 0.203 0.203 0.205 Sustainability 2018,10, 2125 18 of 25 Table A10. Annualized daily (rule in every 22th trading day) volatility of MA2–MA10, average annualized volatility. Buy and Hold MA10 MA9 MA8 MA7 MA6 MA5 MA4 MA3 MA2 3M 0.225 0.167 0.169 0.162 0.163 0.161 0.161 0.157 0.156 0.156 American Express 0.345 0.232 0.235 0.222 0.218 0.22 0.219 0.22 0.243 0.235 Apple 0.451 0.343 0.347 0.342 0.339 0.339 0.338 0.342 0.335 0.331 Boeing 0.294 0.207 0.210 0.202 0.202 0.199 0.200 0.197 0.207 0.205 Caterpillar 0.311 0.216 0.220 0.217 0.215 0.214 0.217 0.218 0.221 0.224 Chevron 0.244 0.169 0.171 0.172 0.17 0.169 0.169 0.167 0.181 0.171 Coca-Cola 0.225 0.168 0.171 0.169 0.168 0.166 0.161 0.161 0.161 0.156 Walt Disney 0.291 0.203 0.207 0.202 0.203 0.203 0.210 0.212 0.215 0.211 Exxon 0.230 0.166 0.167 0.165 0.164 0.163 0.162 0.157 0.161 0.160 GE 0.275 0.177 0.177 0.175 0.175 0.175 0.172 0.169 0.172 0.180 Home Depot 0.314 0.234 0.235 0.228 0.221 0.230 0.228 0.233 0.225 0.219 IBM 0.271 0.194 0.196 0.196 0.196 0.196 0.19 0.194 0.195 0.190 Intel 0.382 0.273 0.277 0.272 0.272 0.268 0.266 0.266 0.264 0.259 Johnson & Johnson 0.215 0.168 0.169 0.167 0.167 0.162 0.158 0.158 0.154 0.150 JP Morgan 0.375 0.222 0.223 0.217 0.220 0.230 0.233 0.234 0.244 0.234 McDonalds 0.240 0.189 0.189 0.186 0.185 0.185 0.179 0.170 0.171 0.180 Merck 0.269 0.177 0.178 0.173 0.173 0.174 0.173 0.181 0.182 0.192 Microsoft 0.323 0.250 0.251 0.247 0.239 0.233 0.235 0.237 0.233 0.234 Nike 0.327 0.247 0.248 0.244 0.241 0.240 0.235 0.236 0.238 0.248 Pfizer 0.266 0.188 0.190 0.186 0.186 0.186 0.187 0.187 0.191 0.189 Procter & Gamble 0.225 0.173 0.174 0.171 0.167 0.165 0.163 0.164 0.158 0.155 Travellers 0.268 0.171 0.172 0.17 0.169 0.171 0.191 0.186 0.192 0.198 United Technologies 0.261 0.178 0.179 0.178 0.177 0.177 0.175 0.178 0.176 0.173 United Health Group 0.386 0.300 0.302 0.299 0.298 0.294 0.289 0.280 0.283 0.275 Verizon 0.246 0.167 0.167 0.164 0.162 0.160 0.164 0.157 0.160 0.163 Wal-Mart 0.263 0.208 0.210 0.205 0.199 0.196 0.197 0.198 0.198 0.189 Cisco 0.415 0.304 0.307 0.301 0.298 0.300 0.292 0.290 0.281 0.278 Goldman Sachs 0.373 0.230 0.232 0.225 0.232 0.245 0.239 0.253 0.268 0.256 Visa 0.260 0.204 0.203 0.212 0.225 0.221 0.219 0.217 0.217 0.196 Average 0.295 0.211 0.213 0.209 0.208 0.208 0.208 0.208 0.210 0.207 0.209 Sustainability 2018,10, 2125 19 of 25 Table A11. Annualized daily (every other month) volatility of MAD2–MAD5 (D = every other month, and 5, 4, 3, 2 are the numbers of observations in the rolling window), average annualized volatility. Buy and Hold MAD5 MAD4 MAD3 MAD2 3M 0.225 0.168 0.169 0.162 0.159 American Express 0.344 0.222 0.226 0.216 0.211 Apple 0.450 0.351 0.363 0.357 0.338 Boeing 0.294 0.210 0.216 0.211 0.208 Caterpillar 0.311 0.218 0.229 0.215 0.211 Chevron 0.244 0.168 0.175 0.166 0.165 Coca-Cola 0.225 0.168 0.173 0.165 0.158 Walt Disney 0.291 0.197 0.200 0.198 0.203 Exxon 0.230 0.172 0.174 0.159 0.156 GE 0.274 0.175 0.181 0.176 0.182 Home Depot 0.314 0.229 0.230 0.221 0.237 IBM 0.271 0.196 0.199 0.200 0.200 Intel 0.382 0.274 0.286 0.267 0.265 Johnson & Johnson 0.215 0.173 0.175 0.165 0.154 JP Morgan 0.375 0.236 0.241 0.246 0.237 McDonalds 0.240 0.182 0.186 0.178 0.169 Merck 0.269 0.185 0.196 0.188 0.199 Microsoft 0.323 0.245 0.249 0.238 0.250 Nike 0.327 0.252 0.258 0.253 0.253 Pfizer 0.266 0.199 0.203 0.191 0.189 Procter & Gamble 0.225 0.173 0.177 0.169 0.166 Travellers 0.268 0.176 0.178 0.183 0.191 United Technologies 0.261 0.182 0.187 0.178 0.177 United Health Group 0.386 0.313 0.313 0.299 0.305 Verizon 0.246 0.163 0.171 0.165 0.153 Wal-Mart 0.263 0.197 0.199 0.194 0.193 Cisco 0.415 0.312 0.317 0.315 0.285 Goldman Sachs 0.373 0.229 0.245 0.239 0.265 Visa 0.260 0.215 0.215 0.225 0.222 Average 0.295 0.213 0.218 0.212 0.210 0.213 Table A12. Annualized daily (every third month) volatility of MAT2–MAT4 (T = every third month, and 4, 3, 2 are the numbers of observations in the rolling window), average annualized volatility. Buy and Hold MAT4 MAT3 MAT2 3M 0.225 0.172 0.174 0.171 American Express 0.344 0.230 0.237 0.206 Apple 0.450 0.345 0.357 0.349 Boeing 0.294 0.206 0.219 0.200 Caterpillar 0.311 0.219 0.223 0.214 Chevron 0.244 0.176 0.182 0.170 Coca-Cola 0.225 0.177 0.179 0.181 Walt Disney 0.291 0.220 0.228 0.205 Exxon 0.230 0.168 0.176 0.158 GE 0.274 0.178 0.185 0.177 Home Depot 0.314 0.236 0.251 0.241 IBM 0.271 0.205 0.209 0.193 Intel 0.382 0.285 0.296 0.274 Johnson & Johnson 0.215 0.185 0.188 0.165 JP Morgan 0.375 0.242 0.248 0.240 McDonalds 0.240 0.198 0.204 0.192 Merck 0.269 0.191 0.191 0.180 Microsoft 0.323 0.257 0.267 0.258 Nike 0.327 0.264 0.265 0.258 Sustainability 2018,10, 2125 20 of 25 Table A12. Cont. Buy and Hold MAT4 MAT3 MAT2 Pfizer 0.266 0.195 0.206 0.208 Procter & Gamble 0.225 0.177 0.181 0.168 Travellers 0.268 0.187 0.188 0.198 United Technologies 0.261 0.192 0.199 0.187 United Health Group 0.386 0.300 0.308 0.315 Verizon 0.246 0.176 0.176 0.160 Wal-Mart 0.263 0.202 0.208 0.208 Cisco 0.415 0.310 0.311 0.303 Goldman Sachs 0.373 0.226 0.232 0.235 Visa 0.260 0.204 0.215 0.208 Average 0.295 0.218 0.224 0.214 0.219 Table A13. Annualized daily (every fourth month) volatility of MAQ2–MAQ3 (Q = every fourth month, 3 and 2 are the number of observations in the rolling window), average annualized volatility. Buy and Hold MAQ3 MAQ3 3M 0.225 0.168 0.176 American Express 0.344 0.220 0.226 Apple 0.450 0.360 0.373 Boeing 0.294 0.213 0.224 Caterpillar 0.311 0.222 0.239 Chevron 0.244 0.167 0.177 Coca-Cola 0.225 0.173 0.182 Walt Disney 0.291 0.206 0.218 Exxon 0.230 0.160 0.176 GE 0.274 0.180 0.195 Home Depot 0.314 0.237 0.242 IBM 0.271 0.194 0.218 Intel 0.382 0.274 0.293 Johnson & Johnson 0.215 0.181 0.186 JP Morgan 0.375 0.218 0.227 McDonalds 0.240 0.177 0.193 Merck 0.269 0.204 0.212 Microsoft 0.323 0.248 0.260 Nike 0.327 0.258 0.265 Pfizer 0.266 0.198 0.207 Procter & Gamble 0.225 0.173 0.174 Travellers 0.268 0.182 0.192 United Technologies 0.261 0.181 0.188 United Health Group 0.386 0.299 0.314 Verizon 0.246 0.167 0.177 Wal-Mart 0.263 0.194 0.207 Cisco 0.415 0.341 0.349 Goldman Sachs 0.373 0.240 0.260 Visa 0.260 0.212 0.225 Average 0.295 0.215 0.227 0.221 Sustainability 2018,10, 2125 21 of 25 Table A14. Annualized daily (every fifth month) volatility of MAC2 (C = every fifth month, 2 = observations in rolling window), average annualized volatility. Buy and Hold MAC2 3M 0.225 0.176 American Express 0.344 0.226 Apple 0.450 0.323 Boeing 0.294 0.218 Caterpillar 0.311 0.227 Chevron 0.244 0.165 Coca-Cola 0.225 0.168 Walt Disney 0.291 0.206 Exxon 0.230 0.166 GE 0.274 0.187 Home Depot 0.314 0.242 IBM 0.271 0.202 Intel 0.382 0.296 Johnson & Johnson 0.215 0.187 JP Morgan 0.375 0.244 McDonalds 0.240 0.182 Merck 0.269 0.194 Microsoft 0.323 0.250 Nike 0.327 0.249 Pfizer 0.266 0.191 Procter & Gamble 0.225 0.187 Travellers 0.268 0.183 United Technologies 0.261 0.204 United Health Group 0.386 0.298 Verizon 0.246 0.170 Wal-Mart 0.263 0.223 Cisco 0.415 0.333 Goldman Sachs 0.373 0.218 Visa 0.260 0.220 Average 0.295 0.218 Sustainability 2018,10, 2125 22 of 25 Appendix C Table A15. Transaction costs per year of MA40–MA200, with one transaction costing 0.1% of total wealth, average annualized transaction costs. MA200 MA180 MA160 MA140 MA120 MA100 MA80 MA60 MA40 3M 0.010 0.011 0.010 0.011 0.012 0.013 0.016 0.019 0.022 American Express 0.011 0.011 0.011 0.012 0.012 0.013 0.016 0.017 0.023 Apple 0.007 0.008 0.008 0.009 0.010 0.012 0.014 0.015 0.020 Boeing 0.008 0.009 0.010 0.011 0.011 0.012 0.014 0.015 0.020 Caterpillar 0.008 0.009 0.010 0.011 0.012 0.013 0.015 0.015 0.019 Chevron 0.011 0.012 0.012 0.013 0.014 0.016 0.018 0.020 0.024 Coca-Cola 0.009 0.010 0.011 0.011 0.011 0.012 0.015 0.018 0.022 Walt Disney 0.007 0.008 0.009 0.011 0.012 0.012 0.013 0.017 0.021 Exxon 0.011 0.013 0.016 0.017 0.017 0.018 0.019 0.023 0.028 GE 0.007 0.008 0.009 0.010 0.011 0.012 0.014 0.017 0.023 Home Depot 0.008 0.009 0.010 0.011 0.013 0.014 0.016 0.018 0.021 IBM 0.009 0.010 0.010 0.010 0.012 0.012 0.013 0.014 0.019 Intel 0.007 0.009 0.010 0.010 0.012 0.014 0.014 0.016 0.019 Johnson & Johnson 0.009 0.008 0.009 0.010 0.012 0.014 0.016 0.020 0.024 JP Morgan 0.010 0.010 0.011 0.012 0.012 0.014 0.015 0.016 0.020 McDonalds 0.010 0.011 0.011 0.013 0.012 0.014 0.016 0.018 0.023 Merck 0.008 0.009 0.009 0.011 0.011 0.013 0.015 0.017 0.022 Microsoft 0.008 0.009 0.010 0.010 0.011 0.013 0.015 0.015 0.020 Nike 0.009 0.009 0.010 0.010 0.011 0.012 0.013 0.014 0.019 Pfizer 0.008 0.010 0.010 0.011 0.011 0.012 0.014 0.017 0.021 Procter & Gamble 0.010 0.010 0.010 0.011 0.012 0.014 0.016 0.019 0.022 Travellers 0.010 0.011 0.012 0.012 0.013 0.015 0.016 0.018 0.024 United Technologies 0.009 0.010 0.011 0.011 0.012 0.014 0.015 0.018 0.021 United Health Group 0.008 0.008 0.010 0.010 0.011 0.012 0.014 0.017 0.021 Verizon 0.011 0.011 0.011 0.011 0.013 0.014 0.017 0.018 0.023 Wal-Mart 0.010 0.010 0.012 0.013 0.013 0.014 0.015 0.019 0.022 Cisco 0.006 0.006 0.008 0.010 0.009 0.010 0.014 0.017 0.023 Goldman Sachs 0.008 0.010 0.012 0.012 0.014 0.015 0.022 0.026 0.035 Visa 0.008 0.008 0.009 0.009 0.008 0.010 0.011 0.014 0.022 Average 0.009 0.0010 0.010 0.011 0.012 0.013 0.015 0.018 0.022 0.013 Sustainability 2018,10, 2125 23 of 25 Table A16. Transaction costs per year of MA2–MA10, average annualized transaction costs. MA10 MA9 MA8 MA7 MA6 MA5 MA4 MA3 MA2 3M 0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.005 0.006 American Express 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.006 Apple 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.005 0.006 Boeing 0.002 0.002 0.002 0.002 0.002 0.003 0.004 0.004 0.006 Caterpillar 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.005 0.006 Chevron 0.002 0.003 0.003 0.003 0.003 0.003 0.004 0.005 0.007 Coca-Cola 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.006 Walt Disney 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.006 Exxon 0.002 0.002 0.003 0.003 0.003 0.004 0.004 0.005 0.006 GE 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.004 0.006 Home Depot 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.006 IBM 0.003 0.002 0.003 0.002 0.003 0.003 0.004 0.004 0.006 Intel 0.002 0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.006 Johnson & Johnson 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.005 0.006 JP Morgan 0.002 0.003 0.003 0.003 0.003 0.003 0.003 0.004 0.006 McDonalds 0.002 0.002 0.003 0.003 0.003 0.003 0.004 0.005 0.006 Merck 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.005 0.006 Microsoft 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.004 0.006 Nike 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.004 0.006 Pfizer 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.004 0.006 Procter & Gamble 0.002 0.002 0.003 0.003 0.003 0.004 0.004 0.005 0.006 Travellers 0.003 0.002 0.003 0.003 0.003 0.004 0.004 0.005 0.007 United Technologies 0.002 0.002 0.002 0.002 0.003 0.003 0.004 0.004 0.006 United Health Group 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.004 0.006 Verizon 0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.005 0.006 Wal-Mart 0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.005 0.006 Cisco 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.005 0.006 Goldman Sachs 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.004 0.005 Visa 0.002 0.001 0.002 0.002 0.002 0.003 0.003 0.003 0.005 Average 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.004 0.006 0.003 References 1. 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