Cobas AM: Nueva Gestora de Francisco García Paramés

32432 respuestas
  1. Jpar1971
    en respuesta a anselmo mancebo

    Re: Cobas AM: Nueva Gestora de Francisco García Paramés

    Ver mensaje de anselmo mancebo

    Sí, la verdad que vendedores de crecepelo lo hay en todos los gremios, incluso en el value investing, por lo tanto el gremio de los analistas técnicos o seguidores de tendencias tampoco iba a ser una excepción.

    Pero escuchando a AGDL en una conferencia que dio en el CEU en el que afirmaba que los seguidores de tendencias no habían conseguido rentabilidades de forma sistemática y consistente a largo plazo, pero que en cambio los Quants sí, me surgió la duda pues es una afirmación contradictoria ya que uno de los factores de inversión que usan estos últimos (además del factor value, del tamaño, calidad, etc) es precisamente el factor momentum.

    Entonces investigando si la estrategia de invertir según el factor de momentum había al menos funcionado en algún backtest (en los mercados lo máximo a lo que podemos aspirar es averiguar si algo habría funcionado en el pasado, lo cual no va a asegurarnos que siga funcionando en el futuro pero esto es común para todos los estilos de inversión) me he encontrado con este estudio mencionado en un libro que estoy leyendo sobre factor investing:

    "Ian D’Souza, Voraphat Srichanachaichok, George Jiaguo Wang, and Chelsea Yaqiong Yao, the authors of the 2016 study “The Enduring Effect of Time-Series Momentum on Stock Returns over Nearly 100-Years,” provide evidence supportive of the view that time-series momentum (also referred to as trend-following) is one of the few factors that meet all of our criteria for inclusion in a portfolio. Their study covered the 88-year period from 1927 to 2014. The following is a summary of their findings: A value-weighted strategy of going long stocks with positive returns in the prior 12 months (skipping the most recent month) and going short stocks with negative returns during the same period of time produced an average monthly return of 0.55 percent, and was highly significant (t-stat = 5.28). It has also been present following both up and down markets, producing an average monthly return of 0.57 percent (t-stat = 2.09) following down markets and 0.54 percent (t-stat = 5.30) following up markets. What is more, it was persistent across all four sub-periods the authors studied, with average monthly returns of 0.69 percent (t-stat = 2.41) in the period from 1927 through 1948, 0.47 percent (t-stat = 3.60) in the period from 1949 through 1970, 0.62 percent (t-stat = 3.84) in the period from 1971 through 1992, and 0.42 percent (t-stat = 1.91) in the period from 1993 through 2014. Thus, it meets the persistence criteria. Time-series momentum produced positive risk-adjusted returns in all 13 international stock markets the authors examined for the period from 1975 through 2014. And it was statistically significant at the 95 percent confidence level in 10 of the 13 countries. The highest return for a value-weighted strategy was in Denmark, where it had a monthly return of 1.15 percent (with a t-statistic of 5.06). Thus, it meets the pervasive criteria. Time-series stock momentum was profitable regardless of formation and holding periods for 16 different combinations. Thus, it meets the robust criteria. Time-series stock momentum fully subsumes cross-sectional stock momentum, while cross-sectional stock momentum cannot capture time-series stock momentum. In addition, the other common factors of market beta, size, and value have little power to explain time-series momentum. Thus, it is also not subsumed by other factors. Unlike with cross-sectional momentum, time-series momentum does not experience losses in January (a seasonal effect) or crashes (which occur with cross-sectional momentum during market reversals). The time-series premium can be at least partially explained by two prominent theories that describe investor underreaction (both the gradual information diffusion model and what is called the frog-in-the-pan model). For example, if time-series momentum came from gradual information flow, there should be greater time-series momentum in small-cap stocks (for which information diffuses more slowly). In fact, they found that the small size group produces the highest momentum profits (0.78 percent per month with an associated t-statistic of 5.52), while the large size group generates the lowest momentum profits (0.47 percent per month with an associated t-statistic of 4.33). As we discussed in Chapter 4, the frog-in-the-pan hypothesis suggests that investors are less aware of information that arrives continuously and in small amounts than they are of information that arrives in large amounts at discrete points in time. The analogy is that frogs will jump out of a pan of water following a sudden increase in temperature, but will underreact to increasing water temperature in the pan if it is brought to a boil slowly, and so are cooked. According to the frog-in-the-pan hypothesis, if investors underreact to small amounts of information that arrive continuously, it induces strong persistent return continuation. The authors found a monotonic increase in momentum profits for stocks with discrete information compared to stocks with continuous information. Thus, we have evidence that time-series momentum meets the explanation criteria. D’Souza, Srichanachaichok, Wang, and Yao also examined a strategy that combined the two (time-series and cross-sectional) momentum strategies. Their dual-momentum strategy buys the strongest winner portfolio and sells short the weakest loser portfolio, basically making it a market-neutral strategy. They found that the average annualized return of the dual-momentum strategy was 22.4 percent. The strategy, however, was associated with high volatility (37.5 percent per year). The data were statistically significant and also held up to tests that employed different combinations of formation and holding periods."

     "Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today (English Edition)" de Andrew L. Berkin, Larry E. Swedroe

     

  2. youz17

    Re: Cobas AM: Nueva Gestora de Francisco García Paramés

    Pego aqui enlace a un breve artículo de morningstar, que me ha recordado a todo el debate actual sobre FGP.

    ¿Cuánto tiempo hay que ser paciente con los buenos gestores?

    Un fondo que en un periodo de 15 años ha batido a su índice de referencia puede sufrir un periodo de malos resultados de entre 9 y 11 años de media.

    http://www.morningstar.es/es/news/166195/%c2%bfcu%c3%a1nto-tiempo-hay-que-ser-paciente-con-los-buenos-gestores.aspx

     

  3. Pedrob
    en respuesta a piratelife

    Re: Cobas AM: Nueva Gestora de Francisco García Paramés

    Ver mensaje de piratelife

    Cuando calculas el debt-to-equity ratio, liabilities = debt. En un sentido más estricto, debt = short-terms lonas payable + long-term loans payable + bonds payable.

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