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#101 12/02/2015 15h53 → Ivy Porfolio de Mebane Faber : allocation d'actifs tactique quantitative (ivy porfolio, mebane faber)

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Dans le livre sur l’Ivy Portfolio de Mr Faber, il y a une section dediee au test d’un portefeuille "Top 1/2/3" des classes d’actifs selectionnees par la strategie v.s. un portefeuille "Toutes les classes d’actifs" selectionnees par la strategie.

Cela fait également partie de son papier "Relative Strength Strategies for Investing".

Dernière modification par Nann (12/02/2015 15h53)

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#102 13/02/2015 08h30 → Ivy Porfolio de Mebane Faber : allocation d'actifs tactique quantitative (ivy porfolio, mebane faber)

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Bonjour,

Oui, effectivement Nann !

Derniere intervention sur le concept d’Ivy Portfolio Eurozone que j’ai introduit, puis je passerai ensuite a son suivi mensuel dans une file dediee.

Qu’en est-t-il des resultats des backtests de cette strategie : performance, perte maximale, "volatilite"… ?

Il faut commencer par rappeler que d’apres plusieurs publications sur le momentum applique a des classes d’actifs (Mr Faber, Mr Antonacci…) sur de longues periodes, il faut s’attendre a quelquechose comme 10-15% de CAGR, avec une volatilite et une perte maximale mensuelles relativement contenues.

Toutefois, la plupart des etudes ont ete realisees sur des index/ETFs exprimes en dollars, sans focus particulier sur la zone Euro; il se pourrait donc que la strategie ne marche pas comme attendue (effets de change, 3 classes d’actifs Eurozone)…

Heureusement, il se trouve que les resultats de cette strategie sont bien "dans les clous", ce qui renforce la confiance dans le maintient de ses performances futures !

Maintenant, que se passerait-il si la strategie venait a ne plus fonctionner ? C’est ici que le fait de n’avoir integre que des classes d’actifs larges prend tout son sens. En effet, au pire, la strategie exposerait alors le portefeuille aux differentes classes d’actifs mondiales sans aucun avantage (reduction de la volatilite, protection du capital…) et le portfeuille beneficierait simplement de l’evolution - jusqu’ici - positive a long terme des marches financiers. Rien de folichon, mais rien de tres genant non plus…

Je vais donc entamer le suivi de cette strategie dans une file dediee, afin d’etablir un track record en temps reel; je comparerai d’ailleurs egalement les performances theoriques de la strategie a ses performances pratiques (prise en compte des ordres a J+1…).

J’espere que ces quelques messages vont ont donne quelques idees (abonner les decisions binaires, utiliser plusieurs types de backtests, utiliser les backtests pour tester d’autres choses que les parametres d’une strategie…) !

Amicalement,

R.


Développeur d'outils pour investisseurs : Le Quant 40

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#103 20/04/2015 04h56 → Ivy Porfolio de Mebane Faber : allocation d'actifs tactique quantitative (ivy porfolio, mebane faber)

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Bonjour,
J’ai lu une étude qui s’intéresse à l’utilisation de la moyenne mobile sur 10 mois des actions comme signal de vente/d’achat (comme expliqué dans le 1er message de ce sujet).

J’ai extrait quelques passages mais je vous conseille si cela vous intéresse d’aller consulter directement l’étude qui est forcément plus exhaustive :
The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules - 2013

Valeriy Zakamulin a écrit :

« In this paper we argue that the reported performance of market timing strategies usually contains a substantial data-mining bias. In addition, in a study of a technical trading rule one usually ignores important market frictions such as, for example, trading costs.
The data-mining fallacy that has been repeated over and over again in many studies consists in the following. Using a full historical data sample one tests many k-month moving average trading rules and picks up a rule that performs best. One then reports the performance of the best trading rule in a back test and either explicitly or implicitly assumes that the expected future performance of this rule will be the same as the past performance. Yet, as a matter of fact, one should expect a much poorer performance in the future than the reported performance in the past. »

« As in Faber (2007) and many other papers, when we examine the performance of the best trading rule in a back test, we do not take into account the transaction costs in order to show the best possible performance. The results are reported in Table 2. Observe that the SMA(10) rule, advocated by Faber and many others, is the best trading rule when the passive benchmark is the Standard and Poor’s Composite index »

« The main goal of this subsection is to demonstrate that the out-of-sample performance of a trading rule is sensitive to the choice of a split point between the initial in-sample and outof- sample subsets. What is more crucial is that depending on the choice of a split point the out-of-sample performance of a trading rule might by either superior or inferior as compared to that of the passive counterpart. »

« Before turning to the discussion of performance dependence on the split point, we would like to draw the reader’s attention to the fact that in every historical period the best trading rule in a back test outperforms the passive benchmark with a solid margin. This demonstrates that, when many trading rules are back tested, one can virtually always find a rule that performs better than the passive benchmark. 

Consider in details the performance of the market timing strategies versus the performance of the passive strategy when the split point between the initial in-sample and out-of-sample subsets is chosen to be December 1929. In this case the initial in-sample period is January 1926 to December 1929, whereas the initial out-of-sample period is January 1930 to December 2012.
With this choice, the Sharpe ratio of either of the best trading rules in a back test amounts to 0.16 which is about 50% higher than the Sharpe ratio of the buy-and-hold strategy. In the out-of-sample tests, the Sharpe ratio of both the trading rules amounts to 0.12 which is about 10% higher than the Sharpe ratio of the buy-and-hold strategy. Even though the reallife performance11 of the trading rules turns out to be much poorer than the performance of the best trading rules in a back test, still, judging by the Sharpe ratio criterion, we have to conclude that either of the market timing strategies delivers a better performance than that of the buy-and-hold strategy.

Note, however, that if the split point is chosen to be December 1931 (which seems to be a relatively negligible relocation of the original split point) then we arrive at the opposite conclusion: the real-life performance of the market timing is either worse than or equal to the performance of the buy-and-hold strategy. »

« Our analysis reveals that the superior performance of market timing strategies is confined to relatively short historical episodes. In particular, over our total sample 1926 to 2012 there have been only four relatively short historical periods that contributed most to the superior performance of the market timing strategies. Specifically, they are the periods of severe bear markets of 1930-31, 1973-74, 2001-02, and 2007-08. As a result, the performance of market timing strategies, relative to the performance of the passive benchmark, depends largely on whether the split point is located before or after a major stock market downturn. »

« The proponents of the market timing with moving averages and momentum rules often advocate that such a strategy allows investors both to reduce risk and enhance returns. We have to acknowledge that there is indeed a small chance of this happening. For instance, if the S&P Composite index is used as the passive benchmark, there have been 4 relatively short periods of severe market downturns when the real-life market timing strategy provided higher returns with lower risk than the buy-and-hold strategy. It happened that 2 out of 4 such periods occurred in the decade of 2000s.
As a result, this decade was an incredibly successful decade for the market timing strategies. Yet this is not a typical performance of the real-life market timing strategy. Our findings reveal that over a long run the market timing strategy is indeed less risky, but the reduction of risk always comes at the expense of reduction of returns. »

« Recall that every market timing strategy prescribes moving to cash when a Sell signal is generated. The investor, who employs the market timing strategy, hopes that during a Sell period the Treasury bills provide better return than the stocks. We define by “failure” an event in which the Treasury bills fail to deliver a higher return than the stocks during a Sell period. So how often a real-life stock market timing strategy generates false signals?
Our analysis reveals that the average failure rate amounts to about 80%. In words, this means that when the market timing strategy generates a Sell signal, in approximately 80% of cases at the end of a Sell period the market timing strategy will provide a lower return than the buyand- hold strategy.12 Again, this result demonstrates that, when the passive benchmark is a stock price index, the superior performance of the market timing strategy is confined to some relatively short particular episodes.

That means that in order the market timing strategy delivers a superior performance as compared to that of the passive counterpart, investors sometimes have to wait a very long time and experience painful emotions because their active portfolios consistently lag the benchmark. For example, the real-life performance of market timing was inferior during a 25-year period from 1975 to 1999 regardless of the choice of a passive stock market index. »

Dernière modification par NicolasV (20/04/2015 13h03)

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#104 20/04/2015 08h28 → Ivy Porfolio de Mebane Faber : allocation d'actifs tactique quantitative (ivy porfolio, mebane faber)

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Bonjour,

Un document tres interessant NicolasV.

Je rejoins de tout coeur l’auteur dans le sens ou ne considerer qu’un seul indicateur n’est pas forcement "optimal", et que tout analyste quantitatif doit/devrait avoir une vision beaucoup moins binaire des marches que on/off.

Pour le reste, certains points me derangent (calcul des signaux sur les prix non ajustes des dividendes, utilisation d’un bootstrap pour simuler des donnees historiques, critique pas assez argumentee d’un backtest walkforward…), mais a chacun de faire ses devoirs, autant en analyse technique que fondamentale !

Amicalement,

R.


Développeur d'outils pour investisseurs : Le Quant 40

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#105 24/04/2015 23h59 → Ivy Porfolio de Mebane Faber : allocation d'actifs tactique quantitative (ivy porfolio, mebane faber)

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Ravi d’apprendre que l’étude vous a plu. Pour les dividendes l’auteur dit que ça ne change pas grand chose :

Valeriy Zakamulin a écrit :

It is worth emphasizing that in the computation of trading signals we use the prices not adjusted for dividends. […] we have studied many handbooks on technical analysis of financial markets, beginning from the book by Gartley (1935), and in every handbook a technical indicator is supposed to be computed using the prices which are easily observable in the market, in contrast to, for example, dividend-adjusted prices. Therefore in the paper we stick to the standard computation of trading signals. We also used the total return and excess return for the computations of trading signal. Yet, our conclusions about the real-life performance of market timing strategies remain intact regardless of the type of return used to generate trading signals.

Concernant le modèle, je pense qu’une méthode qui se trompe 80% du temps et qui a sous-performée le marché pendant 25 ans sera difficilement tenable par l’investisseur moyen (que je suis). Bill Berstein disait à ce propos :
"[…] switching between 100% and 0% in stocks depending on your model will make you crazy, stupid, and sleep-deprived. "

Je lisais dernièrement le papier (publicitaire) d’un ETF ’smart beta’, il était intéressant de voir que ses concepteurs avaient préféré ne pas trop s’éloigner du marché, même si cela se faisait aux dépens de la performance (théorique).

Enfin la lecture de cette étude m’a permis de réaliser qu’évaluer les performances d’un modèle (pourtant relativement simple) n’est pas une mince affaire.. Du coup je me demande s’il n’est pas illusoire de croire qu’on puisse créer un modèle performant en quelques heures à partir de backtests sur 10 ans.

Dernière modification par NicolasV (25/04/2015 00h06)

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#106 25/04/2015 16h00 → Ivy Porfolio de Mebane Faber : allocation d'actifs tactique quantitative (ivy porfolio, mebane faber)

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Oui Nicolas, et je suis d’accord, qu’il vaut mieux ne pas trop longtemps sous-performer le marché !
D’ailleurs, le tracker que vous citez a "par chance" bien performé cette année.

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