Invest in low-volatility stocks with high-dividend returns

You want to have a global view  on the net dividends distributed by companies quoted on the Brussels’ Stock Exchange ? This post is for you.

You want to invest in the lowest volatile Belgian stocks? This post is also for you.

The first column represents the closed prices on August, 24 2015. The second column represents the downside risk. Moreover, I also compute some risk measures such as volatility, Value-at-Risk (1%) and Expected Shortfall (1%). These statistics, expressed in %, are computed on a 10-year basis. The tickers are Yahoo Finance’s tickers.

My favorite stocks are Befimmo, Cofinimmo, Engie and QRF: downside risk lowest than 25%, dividend return higher than 3.5% (net) and volatility lower than 2%.

Price return_min (%) % net dividend Volatility (%) VaR (1%) ES (1%)
^BFX 3265.59 -53.23 NA 1.31 -3.556 -4.972
ABI.BR 91.28 -87.46 2.465 1.978 -5.381 -9.117
ABLX.BR 11.5 -80.43 0 2.633 -7.308 -9.819
ACKB.BR 123 -74.45 1.189 1.631 -4.726 -5.923
AED.BR 49.5 -49.08 2.879 1.285 -3.637 -4.735
AGS.BR 34.51 -98.35 3.369 19.059 -9.121 -15.416
AGFB.BR 2.236 -53.94 0 3.133 -8.182 -13.122
ARGX.BR 9.62 -31.39 0 2.934 -7.991 -11.16
ASC.BR 55.01 -35.41 2.045 1.342 -4.025 -5.507
ATEB.BR 39.895 -46.66 3.76 1.907 -4.538 -7.538
BANI.BR 7.799 -16.37 0 1.807 -5.092 -6.282
BAR.BR 53.54 -81.7 2.241 2.34 -6.658 -10.297
BEFB.BR 54.07 -20.99 4.785 1.545 -4.091 -5.68
BEKB.BR 24.51 -28.56 2.601 2.935 -6.318 -11.289
BELG.BR 30.03 -45.65 3.75 1.372 -4.271 -5.447
BPOST.BR 21.185 -33.92 4 1.154 -2.786 -4.519
BREB.BR 36.91 -72.88 1.321 1.526 -4.241 -6.477
CPINV.BR 13.62 -1.54 3.469 1.429 -4.127 -6.212
CYAD.BR 40.84 -69.39 0 3.656 -6.216 -9.389
CFEB.BR 114 -85.2 1.316 2.312 -6.318 -8.108
CMB.BR 12.48 -3.73 0 2.26 -6.196 -8.76
COFB.BR 91.2 -22.17 4.523 1.232 -3.424 -5.116
COLR.BR 41.05 -33.7 1.827 2.052 -3.381 -8.113
COMB.BR 225 -41.33 2.453 1.571 -4.516 -7.207
DELB.BR 74.28 -65.52 1.181 1.772 -4.855 -6.892
DIE.BR 31.195 -12.29 1.443 2.636 -5.353 -10.184
ECONB.BR 7.315 -38.48 1.23 2.502 -5.385 -9.789
ELI.BR 35.765 -41.87 3.229 0.962 -2.622 -3.753
EURN.BR 10.885 -72.9 1.723 2.605 -6.849 -8.431
ENX.PA 37.1 -53.32 1.274 1.866 -4.624 -5.376
EVS.BR 21.595 -1.92 6.946 2.306 -6.082 -9.617
EVS.BR 21.595 -1.92 6.946 2.306 -6.082 -9.617
EXM.BR 9.06 -56.95 4.139 2.343 -6.596 -10.022
GIMB.BR 39.5 -30.35 4.652 1.811 -4.382 -8.139
GSZ.PA 15.06 -6.24 3.65 1.916 -4.927 -6.807
GRYFO.BR 13.65 -53.11 0 1.991 -4.672 -6.649
HAMO.BR 9.11 -56.09 0 3.275 -8.199 -10.465
HOMI.BR 82.5 -55.09 3.409 1.444 -4.059 -5.596
IBAB.BR 24.01 -84.05 0 2.523 -6.563 -7.9
IMMO.BR 43.51 -74.65 0 1.699 -4.505 -7.55
INTO.BR 21.14 -21.19 4.967 2.236 -5.627 -8.91
JEN.BR 19.51 -80.01 0 2.275 -6.106 -9.484
KBC.BR 54.38 -89.89 2.758 3.643 -10.155 -15.237
KIN.BR 35.025 -58.46 1.392 2.327 -4.378 -8.613
LEAS.BR 81.61 -45.59 4.136 1.798 -4.914 -7.089
LOTB.BR 1425 -92 0.653 1.444 -3.662 -4.939
MELE.BR 37.7 -91.17 0 2.11 -5.883 -7.34
MOBB.BR 17.64 -41.89 0 1.742 -4.615 -7.785
MONT.BR 33.08 -41.85 4.466 1.4 -3.885 -5.394
NYR.BR 1.936 -47.78 0 3.379 -8.839 -12.249
ONTEX.BR 25.3 -29.98 0.563 1.642 -3.964 -5.127
PIC.BR 44.5 -96.85 0 2.855 -8.864 -12.543
QFG.BR 9.29 -71.58 5.813 7.08 -17.659 -33.024
QRF.BR 25.495 -6.65 3.824 0.969 -2.727 -3.473
REC.BR 5 -67.97 3 2.148 -5.721 -7.722
BIL.BR 6.36 -39.62 0 1.79 -4.754 -6.61
RES.BR 146.5 -83.62 0.973 1.562 -3.829 -5.021
RET.BR 66.49 -56.18 3.497 1.233 -3.098 -5.392
ENGB.BR 195 -37.9 3.077 1.972 -5.496 -7.443
ROU.BR 14.2 -35.92 0 2.405 -6.468 -9.12
SAP.BR 28.7 -5.56 0 1.783 -4.429 -6.766
SIOE.BR 14.02 -80.88 1.979 2.179 -6.146 -8.293
SIP.BR 40.98 -56.32 2.288 2.599 -5.385 -10.62
SOF.BR 97.5 -54.48 1.315 1.41 -4.088 -5.538
SOFT.BR 1.93 0 0 2.146 -5.382 -8.632
SOLB.BR 100.1 -58.04 2.547 1.798 -4.999 -6.139
SOLV.BR 110 -52.27 3.218 1.67 -4.76 -6.615
SEV.PA 15.065 -47.73 2.427 1.916 -5.102 -6.532
TNET.BR 48.4 -79.57 0 1.818 -4.459 -7.911
TERB.BR 75.9 -55.07 2.47 1.392 -3.929 -5.149
TESB.BR 31.39 -50.09 0 1.975 -5.563 -7.253
THR.BR 4.36 0 0 2.528 -6.16 -9.965
TIG.BR 0.881 -77.3 0 3.913 -9.073 -14.396
TUB.BR 51.3 -78.44 0.702 1.924 -4.794 -7.561
UCB.BR 64.59 -72.85 1.231 1.78 -4.389 -6.309
UMI.BR 34.085 -69.87 2.2 2.779 -6.486 -11.246
VAN.BR 51.27 -59.12 5.12 1.498 -3.817 -5.182
WDP.BR 67.09 -52.66 3.801 1.348 -3.674 -5.187
WEHB.BR 94 -57.66 3.67 1.915 -5.013 -7.172
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The Effective Combination of Risk-Based Strategies with Momentum and Trend Following (Part 3)

In the first post, I explained the broad context, the underlying motivations as well as the main results of my paper (here). The second post aims to explain the methodology used in this article (here). This post is about the main findings of portfolio simulations conducted in the last chapter of my Ph.D. thesis. For the complete version of the paper, please see http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2556747 .

Data

The first table below summarizes results based on a constant dataset composed of 18 country MSCI stock market indices between 1975 and 2014. This dataset is composed of country MSCI Standard (Large and Mid cap) indices (price in USD) provided by MSCI. These indices have been available for 18 countries since December 31, 1969: Australia, Austria, Belgium, Canada, Denmark, France, Germany, Hong Kong, Italy, Japan, the Netherlands, Norway, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States. All observations are collected on a monthly basis (end-of-month).

Moreover, the second table presents results (also between 1975 and 2014) of an evolving dataset composed of the previous 18 country MSCI stock market indices plus country MSCI stock market indices with issuance dates later than the issuance date of the previous 18 country MSCI stock market indices for the following countries: Finland, Ireland, Israel, New Zealand, Portugal, Brazil, Chile, China, Columbia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Malaysia, Mexico, Peru, Philippines, Poland, Qatar, Russia, South Africa, South Korea, Taiwan, Thailand, Turkey, and the United Arab Emirates. This second “unrestricted” dataset aims to determine whether the two-step approach is suitable in an evolving environment in which we add the available MSCI indices throughout the estimation period. All observations are also collected on a monthly basis (end-of-month).

Main Findings

Trend following and high momentum strategies are effective for 1/N portfolio optimization but also for risk-based portfolios because they produce better annual returns compared with initial risk-based portfolios. With respect to risk measures such as volatility, Value-at-Risk (VaR) and Expected Shortfall (ES), risk-based portfolios that employ moving averages exhibit lower volatility than initial risk-based portfolios as well as lower VaR and ES. This finding is important because it enables investors to reduce the risk to which they are exposed. With higher returns and lower risk, risk-based portfolios that use moving averages have higher Sharpe ratios than initial risk-based portfolios. High momentum risk-based portfolios, by contrast, have higher risk, which is largely compensated for by higher returns. Therefore, such portfolios are characterized by higher Sharpe ratios than initial risk-based portfolios. This paper documents the effectiveness, in terms of risk and return, of the use of these relevant timing strategies combined with risk-based portfolio strategies.

Our results also reveal the outperformance of risk-based portfolios that use a relevant timing strategy (i.e., moving average or high momentum) relative to the 1/N portfolio. Risk-based portfolios that use timing strategies deliver better risk-adjusted returns than the 1/N portfolio using timing strategies, which implies that risk-based strategies coupled with a timing strategy are very effective in improving risk-adjusted portfolio performance. Moreover, risk-based strategies are characterized by lower risk in terms of VaR and ES than the traditional 1/N portfolio.

In addition to that, robustness checks were also conducted with different other datasets, different estimation periods as well as different parameters of the variance-covariance matrix. For more details, please see http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2556747 .

Capture d’écran 2015-08-10 à 19.05.36

Capture d’écran 2015-08-10 à 19.06.56

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The Effective Combination of Risk-Based Strategies with Momentum and Trend Following (Part 2)

In the first post, I explained the broad context, the underlying motivations as well as the main results of my paper (here). This post is the second part of a series of post related to the last chapter of my Ph.D. thesis. You can easily found the paper here (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2556747). It aims to explain the methodology used in this article.

Methodology

First step: Selection of the stock market indices

The methodology consists of two steps. In the first step, moving averages are used to select stock market indices that perform well (by exhibiting an upward trend) and that are used in the second step of our analysis (i.e., in the risk-based and 1/N portfolio optimization). Stock market indices exhibiting a negative trend are not selected as an input in the portfolio optimization procedure. We employ the same trend-following strategies as in Clare et al. (2014) and ap Gwilym et al. (2010), among others. If the price of the stock market index is above its x − month moving average, then this index is selected for the portfolio optimization procedure. Conversely, if it crosses below its x − month moving average, then the stock market index is not selected for the second step. As is usually done in the literature (see, among others, Faber, 2010, 2013 and Clare et al., 2014.), we use moving averages of varying lengths: 6, 8, 10, and 12 months.

To add an additional timing strategy, we also select stock market indices in accordance with the concept of momentum, in which a stock market’s performance relative to its peers predicts its future relative performance. In this paper, we do not use the traditional momentum definition because, as long-term investors, we wish to build long-only portfolios. We therefore follow Asness et al.(2013). The momentum strategy involves ranking stock market indices based on their past 12-month performance and splitting them into three subsets, depending on the value of their momentum compared with one of their peers. The three subsets are the low, medium and high momentum subsets, respectively.

In other words, moving averages allow us to select (and de-select) stock markets indices in an upward (downward) trend for the second step, while the ranking based on momentum is designed to form three groups of stock market indices composed of low, medium, and high momentum components. After running the different timing strategies separately at the end of each month, we compute risk-based strategies and the 1/N portfolio based on them.

Second step: Portfolio Optimization

After selecting stock market indices following the different timing strategies of the first step, the second stage consists in applying different portfolio optimization procedures to find the optimal weights of the selected stock market indices. Selection (1st step) and weighting (2nd step) are adjusted simultaneously, i.e., on a monthly basis (end of month). A monthly rebalancing is usually considered a good trade- off between a daily/weekly rebalancing (being too reactive, which may lead to high turnover) and a quarterly/yearly rebalancing, which does not take into account potential rapid changes in economic and financial market conditions.

We apply three risk-based portfolio strategies (Minimum Variance, Equal Risk Contribution and Maximum Diversification) as well as the 1/N benchmark portfolio strategy, usually considered a relevant benchmark in the literature.

First, the Minimum Variance (MV) portfolio aspires to minimize the global variance of the portfolio. This portfolio allocation principle does not take into account precise and complete return forecasts in the minimization process.

Second, the Equal Risk Contribution (ERC) portfolio is the portfolio in which the risk contribution is the same for all assets in the portfolio. As with the MV portfolio, the ERC portfolio relies only on the variance-covariance matrix of stock market index returns, and no asset contributes more to the total risk of the portfolio than its peers. By comparison with the MV portfolio that equalizes the marginal risk contributions, the ERC portfolio equalizes risk contributions of the assets composing the portfolio. The marginal risk contribution of asset i is the rate of change in portfolio risk as the weight of asset i increases, while the risk contribution of asset i simply represents how much an asset contributes to portfolio risk (Roncalli, 2013).

Third, the Maximum Diversification (MD) portfolio (also called the Most Diversified Portfolio), introduced by Choueifaty (2006), is the portfolio that maximizes diversification. Diversification is computed using the diversification ratio. As with the two other risk-based strategies, the MD portfolio does not rely on expected returns but only on the variance-covariance matrix of stock market index returns. The diversification ratio is defined as the ratio of its weighted average volatility to its portfolio volatility. The numerator is the weighted average volatility of single assets, and it is computed as the scalar product of the weight vector and the standard deviations of asset returns, while the denominator is the portfolio volatility assessed by the standard deviation of portfolio returns. By construction, if the portfolio owns only one asset, the diversification ratio is 1. In other cases, the diversification ratio of a long-only portfolio will be strictly greater than 1. The higher is this ratio, the greater is portfolio diversification. The MD portfolio approach aims to maximize the benefits of diversification, i.e., hold assets that are not perfectly correlated to maximize the diversification ratio.

For more details on the methodology used, please do not hesitate to download the paper: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2556747. The next post will relate to the results of portfolios simulations based on country MSCI stock market indices.

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The Effective Combination of Risk-Based Strategies with Momentum and Trend Following (Part 1)

This post is the first part of a series of post related to the last chapter of my Ph.D. thesis. This post is a part of a paper that you can easily found here (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2556747). It aims to explain the broad context in which I write this article, the underlying motivations of my research as well as the main findings.

General context

This paper addresses an important and fundamental question for investors: the allocation of total wealth among the different possible investment choices. This issue has bothered each investor for many centuries. Already in about the fourth century, a man named Rabbi Issac bar Aha proposed the following asset allocation rule: “One should always divide his wealth into three parts: a third in land, a third in merchandise, and a third ready to hand” (DeMiguel et al., 2009a). Portfolio optimization aims to develop and implement strategies to make tactical asset allocations. At the centre of portfolio optimization, the “idea of diversification is strongly connected with portfolio theory” (Scherer, 2010). The objective of diversification is to subdivide risks so that all your eggs are not in the same basket. Diversification is a risk management technique aiming to mix a wide variety of asset classes within a portfolio (through stocks, bonds, commodities, real estate, but also via an international diversification for example) in order to minimize portfolio risk.

The first major paper on portfolio optimization and diversification is Markowitz (1952)’s seminal paper, being one of the most important developments in finance. The theory popularised in his paper refers to modern portfolio theory, based on the concept of mean- variance. Markowitz shows that the allocation of funds among the different possible investment choices depends on a trade-off between the expected return and the level of risk. A mean-variance optimal portfolio either maximizes the expected mean return for a given level of risk or it minimizes the risk for a given expected return. This trade-off between risk and return was, in a sense, revolutionary for two main reasons (Kolm et al., 2014). First, Markowitz considered risk and return jointly based on a major principle, portfolio diversification. Portfolio risk indeed does not only depend on the risk of its components but also depends on their cross-correlations. Prior to him, the former classical financial analysis only focused on the returns of single investments with an important belief stating to invest in assets offering the highest future returns. The second revolutionary thing about Markowitz’s work relates to the formulation of an optimization problem. Among the large number of portfolios reaching a particular return (risk) objective, the investor should choose the portfolio with the smallest variance (highest return).

Nevertheless, the initial mean-variance framework of Markowitz (1952) is very challenging and presents several important shortcomings, one of which is the estimation of expected returns. They are indeed extremely hard to predict, and they are unstable because they depend on both idiosyncratic and systematic factors. A small example illustrates this statement. Regarding idiosyncratic factors, the success of a product (or service) of a company (and therefore, its potential positive or negative behaviour on financial markets) is usually very difficult to predict. It indeed depends on different factors such as the appropriateness, the quality, the usefulness of the product or the adoption by customers, but also competition, among others. Nevertheless, even if a product or service of a company has a great success, its share price does not necessarily evolve in line with the fundamentals of the company due to systematic factors such as a market bubble crash, a global slowdown or monetary policy actions taken by central banks, for example. In the same vein, expected returns are difficult to predict and several authors indicate the high sensitiveness of portfolio weights to the mean return estimates (Among others, see Best and Grauer, 1991, 1992; Chan et al., 1999; Chopra and Ziemba, 1993; Clarke et al., 2006; Merton, 1980; Michaud, 1989 and Jagannathan and Ma, 2003). Some errors may indeed occur in the estimation of expected returns, which creates a significant deviation from the optimal portfolio. Even small reductions in the mean return of a given asset may shift the asset from a large long position to a large short one (Levy and Levy, 2014). In other words, the differences between the true expected values and the fore- cast ones are true in practice (i.e. there exist some estimation errors), which leads to a large deviation between the optimal investment strategy and the one recommended based on the estimated sample parameters. Moreover, classical mean-variance portfolios have shown poor empirical performance (Among others, see DeMiguel et al., 2009a; Jorion, 1985; Qian, 2006 and Kolm et al., 2014).

There have therefore been important refinements and extensions of the Markowitz approach in the portfolio optimization literature to decrease estimation errors. Among others, a large part of the literature has focused on the Bayesian analysis with the “set up of prior knowledge of the parameters of the distribution of future security returns” (Barry, 1974), but other authors such as Jobson and Korkie (1980) and Jorion (1985) among others, also use Bayesian methods to shrink the estimate of the mean, as well as the estimation of the covariance matrix (Ledoit and Wolf, 2003, 2004). Some authors, such as Michaud (1989), propose to use resampling methods, while others propose a robust portfolio optimization in terms of utility (Goldfarb and Iyengar, 2003; Tutuncu ̈ and Koenig, 2004), or robust portfolio optimization based on additional portfolio risk measures such as the Value-at-Risk or the conditional Value-at-Risk (Garlappi et al., 2007). Another important set of methods also deals with potential decreases of the estimation errors of the variance-covariance matrix (Among others, see Best and Grauer, 1992; Chan et al., 1999 and Ledoit and Wolf, 2004). Other methods also propose to combine different portfolios, such as Kan and Zhou (2007) and Tu and Zhou (2011), among others.

Even though the portfolio optimization literature is rich in refinements and extensions of the Markowitz approach, three main shortcomings need to be highlighted. First, these improvements keep the same framework, i.e. they try to minimize risk for a given targeted expected return (or they try to maximize the expected return for a given targeted risk). By keeping the same framework, estimation errors are still present through two main sources: return and risk estimates. Second, these different extensions may add additional computational burden because, in some cases, investors need to compute solutions across a large set of scenarios, which does not simplify their task (Scherer, 2010). Third, as described by Maillard et al. (2010), investors prefer more heuristic solutions, being computationally simple to develop and implement but also being more robust because they do not depend on expected returns.

In this context and due to their “return-agnostic and risk management features” (Jurczenko et al., 2013), risk-based portfolio strategies have gained in popularity in the asset management industry. The three most famous risk-based strategies are Minimum Variance (MV), Equal Risk Contribution (ERC) and Maximum Diversification (MD), and these strategies do not depend on asset returns’ forecasts and they are based on a single criterion: risk. The interest in estimation procedures relying on a risk measure could be explained by three major factors. First, in recent years, asset managers have been reconsidering the importance and the relevance of portfolio risk management. The different recent crises have indeed shown that asset managers being exposed to stock market indices may have been largely impacted if they did not implement effective risk management practices. Assets managers have had to face much more pressure for transparency and performance from their clients, which has renewed the interest in using risk management tools. Moreover, as pointed out by Bruder and Roncalli (2012), “the job of a fund manager is first of all to manage risk”. Second, security variance and covariance risks are persistent and much more predictable than expected returns. Return variances and covariances are indeed easier to estimate from historical data than stock returns (Among others, see Chan et al., 1999; Merton, 1980 and Nelson, 1992). The third major factor is the “low-volatility anomaly” in which the relationship between risk and return is flat or even inverted (Among others, see Baker et al., 2011; Haugen and Heins, 1975 and Baker and Haugen, 2012). These risk-based strategies have been shown to have preferences for low-volatility and low-beta assets (Jurczenko et al., 2013), which captures one of the most important features in finance, the low-volatility anomaly (Baker et al., 2011). Under the Efficient Market Hypothesis (EMH), investors may only have returns above the average if they take higher risks. Nevertheless, this theory has not been supported by empirical facts because low risk assets usually outperform high risk ones over a long time horizon. This low-volatility anomaly is consistent over time and across different markets (Among others, see Ang et al., 2006, 2009; Baker et al., 2011; Black, 1972; Haugen and Heins, 1975 and Baker and Haugen, 2012). The low-volatility anomaly is usually explained by several behavioural biases: (i) preference for lotteries, i.e., investors prefer investing in high-volatility stocks with a small chance of high gain and a large probability of small loss; (ii) overconfidence, i.e., investors tend to be overconfident regarding their own beliefs; and (iii) representativeness bias, i.e., a bias in favour of historically successful investments. In addition to these behavioural biases, Baker and Haugen (2012) argue that the low-volatility anomaly is also explained by the nature of manager compensation and agency issues (i) between professional investment managers within an organization and (ii) between investment professionals and their clients. More recently, Buffa et al. (2014) examine the nature of fund managers’ contracts and whether these contracts may lead fund managers to become less willing to deviate from certain benchmarks. Their compensation is usually highly sensitive to their performance relative to a benchmark, which creates a situation in which managers do not wish to deviate from the benchmark. Due to these behavioural biases, the nature of manager compensation and agency issues, among other factors, investors strongly favour high-volatility stocks without having undertaken a sufficiently deep analysis of their fundamentals, leading to over-pricing of these stocks and inferior future returns (Baker and Haugen, 2012). In other words, undervalued (overvalued) assets become cheaper (more expensive), which continues to bias the aggregate market upwards and its expected return downwards (Buffa et al., 2014).

Added to this low-volatility anomaly, a large number of authors have also highlighted another major anomaly calling into question the Efficient Market Hypothesis. This anomaly is the “momentum anomaly”. The momentum effect has been emphasized by Jegadeesh and Titman (1993) and is usually considered as one of the most important financial anomalies. Jegadeesh and Titman (1993) indeed find that trading strategies buying past winners and selling past losers realized “significant abnormal returns”, while the inverse strategy (i.e. buying past losers and selling past winners, which is a contrarian strategy) showed the worst performance. Momentum represents the phenomenon that securities having performed well relative to peers continue on average to outperform, and that securities having poorly performed go on underperforming. This momentum effect reflects the relationship between the return of an asset and its recent relative performance history (Asness et al., 2013). Momentum strategies are profitable in most major stock markets worldwide and this outperformance of momentum strategies is consistent over time (Among others, see Jegadeesh and Titman, 1993, 2001; Rouwenhorst, 1998 and Chui et al., 2010). Linked to this concept of momentum, trend following strategies consist of applying some indicators (e.g. moving averages) to detect trading signals, which determines the trend of an asset (Clare et al., 2014). These trend following models have slowly gained recognition in the academic community even though the first major paper on trend following is Brock et al. (1992)’s paper. They show that moving average trading rules have predictive power for future returns, and trend following strategies with moving averages are effective in practice (Among others, see Clare et al., 2014; Faber, 2007, 2013; Hurst et al., 2010 and ap Gwilym et al., 2010).

To explain the predictive power of such timing strategies (i.e. momentum and trend following) on the future behaviour of stock markets, some behavioural effects, such as anchoring, herding, and disposition effects, among others, have been featured in the literature. The concept of anchoring simply means that investors are very slow to react to new information, so there is an underreaction on their part (see Barberis et al., 1998 and Hong and Stein, 1999, among others). “Investors underweight new information when they update their priors” (Jegadeesh and Titman, 2011). Thus, momentum occurs because investors are slow to revise their priors when new information arrives. Then, when they react, other investors simply follow the first movers and adopt herding behaviour – e.g., if people buy a stock, others will follow by buying the same stock, causing the stock price to move above its fundamental value (see, among others, Grinblatt et al., 1995). The disposition effect means that loss-averse investors tend to sell stocks too quickly when they are winners and, conversely, hold stocks too long when they are losers, which reinforces the tendency to anchor (Frazzini, 2006; Shefrin and Statman, 1985).

Motivations

In this context, in which risk-based strategies and timing strategies have been developed in the literature, the purpose of this paper is to combine the two strategies. This two-step approach consists in applying a timing strategy (either a moving average or a momentum strategy in the first step) followed by risk-based portfolio optimization procedures (second step). In other words, the objective of this paper is to use the predictive power of timing and risk-based strategies to deliver portfolios with better risk- adjusted returns than traditional risk-based portfolios. We compute risk-based and equally weighted (as a benchmark) portfolios with and without timing strategies in the first step for an empirical dataset composed of 18 country MSCI stock market indices. The estimation period ranges from January 1975 to December 2014.

This two-step approach is motivated by several factors. First, the first step allows us to make a prior selection of stock market indices that are likely to continue to outperform compared with stock market indices that exhibit negative trends or low momentum. Second, mixing the two approaches aims to benefit from the pro-cyclical behaviour of the timing strategies (because momentum and trend following strategies largely invest in assets with positive trends) as well as from the counter-cyclical behaviour of risk-based strategies (i.e., these risk-based strategies focus on assets whose returns are more stable over time to protect against the negative impact of short-term volatility). Third, coupling these strategies makes sense because each portfolio strategy works individually (see Section 2). With our two-step approach, we therefore seek to associate different sources of excess returns. Finally, a large part of the investment literature in recent years has focused on combining multiple advanced investment strategies that have proved their worth individually. Among others, Asness et al. (2013) and Blitz and Van Vliet (2008) use value and momentum strategies as complements. DeMiguel et al. (2009a), in one of their portfolios of interest, mix 1/N and Minimum Variance strategies, similarly to other authors, who also combine sophisticated strategies with “naive” ones (see, among others, Tu and Zhou, 2011 and Kan and Zhou, 2007). In the same vein, some authors couple timing strategies. For example, Antonacci (2013) combines relative and absolute momentum to form a dual momentum strategy, while ap Gwilym et al. (2010) investigate whether the use of momentum and trend following can be combined to deliver higher risk-adjusted returns than those of individual strategies. To the best of our knowledge, this paper is the first to shed light on the combination of timing and risk-based strategies.

Main Findings

This paper provides several contributions. First, risk-based strategies have higher returns when a relevant timing strategy (i.e., a moving average or high momentum) is applied than when such a strategy is not applied. The second important contribution of our analysis relies on the significantly lower standard deviations of risk-based portfolios that use a moving average in the first step compared with initial risk- based portfolios. With higher returns and lower volatility, risk-based portfolios have higher risk-adjusted returns when we apply a moving average in the first step of portfolio optimization. Regarding high momentum risk-based portfolios, they have higher volatility than initial risk-based portfolios, but this higher volatility is compensated for by much higher returns than those of initial risk-based portfolios. High momentum risk-based portfolios therefore have larger Sharpe ratios than initial risk-based portfolios. Third, risk-based portfolios coupled with a moving average strategy are characterized by much lower Value-at-Risk (VaR) and Expected Shortfall (ES) levels than initial risk-based portfolios. If we compare risk-based strategies with the 1/N benchmark portfolio within a framework in which a timing strategy is applied in the first step, risk-based portfolios appear to have greater risk-adjusted returns and lower VaR and ES than 1/N portfolios. This finding supports the effectiveness and relevance of such an approach, which suggests outperformance of risk-based portfolios using relevant timing strategies relative to traditional 1/N portfolios. Among these risk-based portfolios, the MD and MV allocation principles usually exhibit the best performance statistics in terms of risk-adjusted returns.

If the reader is interested by the literature review on risk-based strategies as well as on timing strategies, please do not hesitate to download the paper: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2556747. The next posts will relate to the general explanation of the methodology used for portfolios simulations (here), results of portfolios simulations based on country MSCI stock market indices as well as results of portfolios simulations based on Belgian stocks, respectively.

Publié dans Investments | Tagué , , , | 2 commentaires

Les petites banques, une solution pour une meilleure stabilité financière?

Depuis l’occurrence de la crise financière la plus importante depuis la grande récession des années 30, le paysage financier et bancaire est entrain de changer sous l’impulsion des nouvelles règles prudentielles, par exemple avec Bâle III. C’est une très bonne chose que toutes les banques doivent augmenter leurs fonds propres afin de pouvoir faire face aux futures crises, que ce soit des problèmes de liquidité sur une période de temps plus ou moins longue, ou une crise plus profonde.

Vu la globalisation de l’économie, l’interconnexion de plus en plus importante entre les différents acteurs financiers (les banques, les assurances mais aussi l’ensemble de tous les autres services financiers) et surtout l’omniprésence de la finance au sein de l’économie réelle, il est également primordial d’imposer des fonds propres supplémentaires pour les banques dites systémiques, afin qu’elles puissent répondre à un choc sans mettre en péril l’ensemble du système financier. Les banques systémiques sont des institutions financières susceptibles de mettre à mal l’ensemble du système financier, ce qui est potentiellement porteur d’une crise financière. Bâle III veut s’attaquer aux banques systémiques, ce qui est nécessaire mais deux éléments peuvent lui être reprochés.

Premièrement, le Comité de Bâle se focalise en priorité sur les banques au lieu de prendre également en compte d’autres institutions financières.  Effectivement, il n’y a pas que les banques qui peuvent entrainer un dysfonctionnement du système financier, il y a également d’autres institutions financières. Les assurances en sont un très bon exemple. Elles ont un business model moins risqué avec peu d’activités de crédit. Elles se financement principalement via les détenteurs de police d’assurances (c’est-à-dire sur du long-terme) et présentent donc une structure du bilan plus stable. Néanmoins, le grand assureur américain AIG a participé activement à la crise financière via l’exposition de sa filiale AIG Financial Products, filiale basée à Londres et qui investissait massivement dans des produits complexes. La structure d’AIG groupe étant fortement fragile, elle a dû faire face à une crise de liquidités le 16 septembre 2008. Sans l’aide de l’état Américain et de la Fed (Banque centrale américaine) qui ont injecté de grosses sommes (par exemple, la Fed a versé, officiellement, 180 milliards de dollars), AIG n’existerait plus à l’heure actuelle. Elle a été sauvée afin de ne pas entrainer le système dans une crise encore plus grande qu’elle ne l’était déjà. D’autres exemples (avec le fond spéculatif Long Term Capital Management en 1998) existent et montrent qu’une institution financière, qu’elle soit bancaire ou autre, peut être le déclencheur d’une crise financière qui potentiellement peut s’étendre à l’ensemble de l’économie réelle.

Deuxièmement, il existe de nombreuses méthodologies afin d’évaluer le risque systémique, mais elles sont loin de faire l’unanimité puisqu’aucun consensus n’existe. Elles présentent chacune leurs avantages et leurs inconvénients qu’il est très difficile de déterminer si une institution est systémique ou non. Effectivement, selon le choix de la mesure de risque systémique, les institutions porteuses de ce risque peuvent être totalement différentes, ce qui n’aide aucunement à la prise de décision. Dès lors, subsiste toujours le risque qu’une banque, déclarée comme non-systémique, soit le déclencheur d’une potentielle crise future.

Que faire dans un tel environnement ? Comment faire pour que les problèmes liés au secteur financier ne se répercutent plus à l’ensemble de l’économie réelle ? Des solutions existent comme par exemple l’exigence de fonds propres plus importants pour l’ensemble des acteurs du monde financier, et plus particulièrement pour les plus importants. Une autre solution, utopique je l’admets, serait d’imposer une séparation des activités financières au sein des établissements financiers. De cette manière, un établissement financier ne pourrait fournir qu’un nombre limité de services, ce qui améliorerait sa transparence et surtout sa gestion des risques. Prenons l’exemple des banques commerciales. Une réduction de taille des plus grandes banques ainsi qu’un recentrage de leurs activités permettrait de mieux contrôler les risques auxquels elles font face. Selon moi, les banques à taille humaine se limitant à quelques services sont plus facilement contrôlables vu que leur business model est limité et que leurs comptes annuels sont nettement plus déchiffrables que ceux des grandes banques.

Bien entendu, recourir à des petites banques demande un changement de mentalité de la part des clients, ce qui n’est pas près d’arriver vu que ceux-ci exigent de plus en plus un seul et unique intermédiaire pour l’ensemble de leurs opérations financières. Dès lors, par notre comportement, nous aussi contribuons à ce que les grandes banques deviennent encore plus grandes, au détriment d’un système financier plus stable et plus transparent.

Publié dans Contagion and Systemic Risk | Laisser un commentaire

Attendre, avant d’y voir plus clair

Depuis quelques jours, les marchés financiers sont de plus en plus volatiles et terminent régulièrement leur séance dans le rouge, surtout pour les marchés américains. Ils sont suspendus, d’une part, au « government shutdown »  aux États-Unis. En effet, le Congrès n’est pas parvenu à se mettre d’accord sur le budget 2014 malgré une date limite initialement fixée au 30 septembre. Les Républicains (majoritaires à la Chambre des représentants) et les Démocrates (majoritaires au Sénat) se renvoient la balle continuellement puisqu’ils sont incapables de trouver un compromis sur le budget 2014, chacun voulant protéger ses propres intérêts. Dès lors, sans accord sur le budget, de nombreux fonctionnaires de l’administration américaine ont été mis au chômage économique puisqu’aucun cadre légal n’a pu être établi. Néanmoins, les services essentiels (Police, Armée, Pompiers, Sécurité Sociale,…) sont quand même maintenus, afin d’éviter une paralysie totale du pays. Ce « shutdown » n’est pas la principale inquiétude des marchés financiers même si il est fort probable que cela entrainera une diminution de la croissance du PIB américain (en effet, les fonctionnaires qui ne sont pas payés vont réduire de manière significative leur consommation). Selon IHS (une société américaine de recherche sur les marchés financiers), le « shutdown » a coûté 1.6 milliard de dollars la semaine passée.

D’autre part, l’absence d’accord sur le relèvement du plafond de la dette aux États-Unis représente la plus grande partie de la volatilité observée récemment (Pour ceux qui n’ont aucune idée de ce qu’est le plafond de la dette, je vous invite à lire l’article suivant : http://www.lecho.be/dossier/journal/Le_plafond_de_la_dette_US_pour_les_nuls.9416641-2971.art). En résumé, ce plafond de la dette représente le montant maximum des dettes que le gouvernement peut avoir. Ceci est fait dans l’unique but de contrôler les dépenses du gouvernement et cette limite est fixée par le Congrès. Si les Républicains et Démocrates ne se mettent pas d’accord sur un relèvement du plafond de la dette avant le 17 octobre (date d’estimation à laquelle le gouvernement américain n’aura probablement plus assez de fonds pour payer les intérêts sur sa dette), les États-Unis seront techniquement dans une situation de défaut puisqu’ils ne seront plus capables d’emprunter sur les marchés financiers pour rembourser leurs dettes. Une telle situation n’est pas du tout souhaitable, puisque si elle se produit, cela entrainerait une perte de confiance importante des acteurs de marché dans la fiabilité des États-Unis. Une perte de confiance se reflèterait directement sur les taux d’intérêt qui exploseraient, entrainant un krach aussi bien sur le marché des actions que des obligations, mais également une récession très grave.

Bien entendu, nous ne sommes pas encore à ce point et même si l’ensemble des acteurs de marché sondés par Bloomberg prédit une crise sans précédent en cas de non-relèvement du plafond de la dette, cette situation est improbable selon ces mêmes personnes. Effectivement, lorsque j’entends ou lis des propos d’acteurs de marché, la quasi majorité évoque une probabilité nulle pour ce type de situation. Ils font confiance au Congrès et sont certains qu’un accord sera trouvé entre Républicains et Démocrates et que le relèvement de la dette sera opéré.

Malgré cette confiance aveugle qu’ont les acteurs de marché envers les politiciens américains, certains faits montrent qu’il est nécessaire de ne pas négliger cet événement (dont la probabilité d’occurrence est extrêmement faible mais pas nulle, ce qui rend les conséquences encore plus catastrophiques si celui-ci se passe). En effet, le VIX (l’indice représentant la volatilité future, également appelé « l’indice de la peur ») a gagné plus de 50% entre le 20 septembre (13.12) et aujourd’hui (20.34), ce qui montre bien la tension à laquelle doivent faire face les marchés. Cet indice de volatilité évolue actuellement à son niveau le plus haut de l’année, ce qui reflète très bien la crainte actuelle des intervenants de marché, malgré les propos optimistes qu’ils peuvent tenir devant les caméras.

Que cela soit aux États-Unis, en Europe ou en Asie, la majorité des indices nationaux sont également orientés à la baisse depuis plusieurs jours. A titre illustratif, le S&P 500 reste sur 11 séances de baisse lors des 14 dernières séances. Bien entendu, ces baisses ne sont pas d’une très grande ampleur et semblent être contrôlées, mais la persistance de celles-ci doit inciter à la prudence.

En effet, même si de nombreuses opportunités d’achat se dessinent grâce à une baisse continue du prix des actions, il est nécessaire de rester très prudent et surtout patient afin de voir si le Congrès américain est capable de trouver un accord sur le relèvement du plafond de la dette. Afin de ne pas être exposé à cet événement peu probable mais aux conséquences désastreuses, attendre semble être la meilleure stratégie à adopter, avant d’y voir plus clair…

Publié dans Financial Markets | Laisser un commentaire

Le vin, une alternative d’investissement

A l’heure actuelle, bon nombre d’investisseurs se demandent dans quoi investir. En effet, les taux d’intérêt que proposent les banques sont très faibles et ne permettent pas de se protéger entièrement contre l’inflation. Ensuite, beaucoup de marchés financiers se retrouvent à des sommets et les investisseurs commencent à se rendre compte que ces hausses n’étaient pas justifiées par rapport aux mauvais fondamentaux de l’économie. Les marchés ont subi des pertes importantes la semaine passée, ce qui n’était plus arrivé depuis plus de 10 mois et beaucoup de personnes se demandent si les marchés ne vont pas se retourner.

Tout d’abord, de manière générale, avant d’investir de l’argent, il faut bien connaître le marché dans lequel on souhaite investir, faire des recherches et se faire sa propre opinion. Ensuite, chaque investisseur a tout intérêt à avoir un portefeuille un minimum diversifié, c’est-à-dire qu’il ne doit pas mettre tout son patrimoine dans une seule catégorie d’actifs mais qu’il doit le diversifier en fonction de son profil de risque. Enfin, il n’existe aucun investissement sans risque et si l’investisseur décide d’investir dans des produits avec un haut potentiel de rendement, ces produits présentent également un potentiel élevé de baisse.

Cet article poursuit l’unique objectif de mettre en avant une alternative aux investissements classiques, le vin.  Regardons de plus près l’évolution de la valeur du vin par rapport aux actions.
Sans titre2 Sans titre3

Les deux premiers graphiques montrent bien que sur une période de long terme, les indices Liv-ex fine Wine 100 et Liv-ex fine Wine Investables sont nettement moins sujets aux périodes de baisse. La volatilité mensuelle de ces deux indices tourne autour de 3% alors que celle de l’EuroStoxx50 et du CAC40 est de l’ordre de 5.5%. D’après ces deux graphiques, nous pouvons conclure que la volatilité mensuelle du vin est plus faible que celle des actions, ce qui laisse sous entendre un risque plus faible pour le marché du vin.

La corrélation entre le Liv-ex fine Wine 100 et l’EuroStoxx50 (et respectivement le CAC40) sur la période juillet 2001- décembre 2012 est égale à 0.221 (respectivement 0.237) alors que la corrélation entre les deux indices d’actions est égale à 0.98, ce qui montre bien que les indices d’actions sont fortement corrélés entre eux. En période de crise, les actifs financiers tendent même à être plus corrélés, ce qui rend la recherche d’alternative d’investissements primordiale.

Le troisième graphique montre bien que si un investisseur avait investi le 31 juillet 2001 dans le Liv-ex fine Wine 100, il aurait eu un rendement annualisé de 8.21% alors que le même investissement dans l’EuroStoxx50 aurait été un mauvais investissement (rendement annualisé de -3.33%. Même chose pour le CAC40 où le rendement annualisé aurait été égal à -2.54%).

Pourquoi la valeur du vin a augmenté si rapidement ces dernières années? Premièrement, la demande a fortement augmenté ( grâce aux pays émergents comme la Chine, la Russie et l’Inde) et l’offre s’est stabilisée. Deuxièmement, l’accès à cet investissement alternatif est de plus en plus accessible, ce qui détourne les investisseurs des produits traditionnels (actions, obligations). Enfin, lors de crash sur les marchés financiers, les investisseurs n’hésitent plus à se tourner vers des investissements alternatifs comme le vin puisqu’ils sont peu corrélés avec les actions.

Enfin, avant d’investir dans le vin, il est essentiel de prendre en compte plusieurs éléments. Tout d’abord, il existe différents facteurs qui déterminent la valeur du vin : sa qualité, ses conditions de stockage, sa longévité, sa rareté et ses origines. Ses facteurs doivent être correctement étudiés et compris avant quelconque investissement dans le vin. Deuxièmement, il est très important d’investir dans des vins ayant une bonne réputation car si votre fournisseur tombe en faillite, vous pouvez perdre jusqu’à l’entièreté de votre investissement. Troisièmement, il ne faut pas oublier que chaque investissement présente des risques. Enfin, les coûts liés au stockage des bouteilles de vin et à l’achat du vin peuvent varier fortement en fonction des intermédiaires et doivent être pris en compte.


 

Publié dans Investments | 3 commentaires