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SDS (Seductive Dividend Stocks)
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Sorry I hide my true identity but I'm a physicist/engineer, native contrarian and idea generator. I am an eclectic dividend investor with motto "In God We Trust, All Others Pay Cash" applied to companies I invest in. I like to read /and read a lot - did you look on my SA photo 8-)? /... More
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  • Against Whom Do You Trade?

    Oct. 3, 2013

    I took Dividend Champions list (from David Fish's table dripinvesting.org/tools/U.S.DividendCham... published 9/30/2013) and added numbers for from MSN.com (also from Yahoo.com if MSN doesn't provide info) about Insider and Institutional Ownerships (see table at the end of this post).

    In average institutions hold about 71.5% and insiders about 3.5% of outstanding shares. Except very few exceptions the institutions hold more than half of shares (see picture below).

    Presumably the insiders and institutions are "smart hands" with better understanding of a company than a small retail investor. It is also know that nowadays the institutions hold shares for less than 1 year (data before high-frequency trading became popular). All these mean that most probably your trading partner has "smart hands". Do you still trade often? I'm not. I think the only 2 advantages I have to compare with my trading partners are ability to hold stocks for long period (several years or decades) /see also seekingalpha.com/instablog/725729-sds-se.../ and possibility to buy nano-caps without price distortion (and with the same effect to my income as large caps have in case of equal weighting) /see also http://seekingalpha.com/instablog/725729-sds-seductive-dividend-stocks/787591-the-last-resort-a-few-words-about-liquidity-and-small-investors/.

    SymbolInsidersInstitutionsMktCap ($Mil)"Smart Hands"
    ABM11.2485.65146396.89
    ADM1.7376.872435178.6
    ADP1.6980.553492382.24
    AFL0.8265.622883966.44
    APD0.3385.82231686.13
    ATO2.5271.79385974.31
    AWR1.5376.03106477.56
    BCR1.3790.91919392.28
    BDX0.4387.21949287.63
    BEN32.756.083202888.78
    BF-B4.9931.51456036.49
    BKH2.0775.13220277.2
    BMS2.887.04401489.84
    BRC2.0783.69157785.76
    BWL.A34.4116.387050.79
    CB0.585.52325286
    CBSH3.7257.6395061.32
    CINF5.0562.03771167.08
    CL2.4375.645533378.07
    CLC1.5396.76278298.29
    CLX0.4871.851074072.33
    CSL2.1191.18445793.29
    CSVI4.1229.9344834.05
    CTAS1.7578.46625380.21
    CTBI5.2547.4263252.67
    CTWS4.740.6434845.34
    CVX0.0572.0723344972.12
    CWT2.1563.897065.95
    DBD2.6475.78187078.42
    DCI4.2893.43563997.71
    DOV1.0493.171537194.21
    ED0.3546.981615147.33
    EFSI12.991.818014.8
    EGN0.4284.31551384.73
    EMR0.6874.294638374.97
    EV9.5783.78456693.35
    FDO4.4195.15828199.56
    FRT1.196.69660997.79
    FUL2.3192.08225694.39
    GPC0.980.281254281.18
    GRC0.668.3884268.98
    GWW12.0273.221823285.24
    HCP0.996.731861797.63
    HP3.0495.4733898.44
    HRL0.9183.091114584
    ITW0.2183.33429183.51
    JNJ0.0870.1424357370.22
    KMB0.1972.433624672.62
    KO1.1866.4316841467.61
    LANC2.2252.65213654.87
    LEG2.5773.47439676.04
    LOW0.2780.555080080.82
    MCD0.11679634567.11
    MCY0.2438.54265438.78
    MDT0.5183.495376784
    MGEE0.1632.32126132.48
    MHFI1.1690.871799192.03
    MKC1.2980.3854781.59
    MMM0.1976.968217877.15
    MO0.4161.436876961.84
    MSA8.4661.58190370.04
    MSEX2.140.6233942.72
    NC5.6561.2745366.92
    NDSN8.0289.63472297.65
    NFG3.3563.53574566.88
    NUE0.8281.841563382.66
    NWN0.8655.65113256.51
    ORI1.2379.27395480.5
    PEP0.2874.3412306674.62
    PG0.266.9520758567.15
    PH1.2988.21623289.49
    PNR0.3286.821312487.14
    PNY2.250.3249152.5
    PPG0.4884.192395684.67
    RAVN9.3777.91119287.28
    RLI7.2587.9186395.15
    RPM2.0169.65467471.66
    SCL7.5957.73130265.32
    SHW1.9292.031852293.95
    SIAL0.5891.611023692.19
    SJW14.7659.3456274.1
    SON1.8270.7399772.52
    SRCE4.9262.9365667.85
    STR1.4694.34394795.8
    SWK1.6988.11404489.79
    SYY0.7884.111887384.89
    T0.0956.418198556.49
    TDS0.5482.44320382.98
    TGT0.2992.654061592.94
    TMP6.0548.466754.45
    TNC2.6483.8113286.44
    TR25.5325.67183951.2
    TROW6.6677.581857284.24
    UBSI2.4950.14145952.63
    UGI1.2881.1447082.38
    UHT1.4753.0953154.56
    UVV3.9595.73118899.68
    VAL2.4981553383.49
    VFC0.7988.62175189.39
    VVC0.3866.16274566.54
    WAG0.6967.575098668.26
    WEYS12.0631.3130543.37
    WGL1.1363.22220964.35
    WMT0.9384.8624244185.79
    XOM0.4252.0338141552.45
    AVERAGE3.45504871.522476229203.1238174.97752381

    I really do not understand DGis who report about their portfolio each quarter and adjust it even more frequently.I think they must ask themselves the question I use in the title of this blogpost.

    Oct 03 11:42 PM | Link | Comment!
  • S&P Yield 1926-2012

    24 Aug. 2013

    I just plotted data from good WW site www.measuringworth.com/datasets/sap/result.php

    to see what happens:

    Distribution S&P Annual Yield

    Quantiles

       

    100.0%

    maximum

    8.7100

    99.5%

     

    8.7100

    97.5%

     

    7.2820

    90.0%

     

    6.0500

    75.0%

    quartile

    5.2300

    50.0%

    median

    3.9400

    25.0%

    quartile

    2.9800

    10.0%

     

    1.9480

    2.5%

     

    1.2120

    0.5%

     

    1.1400

    0.0%

    minimum

    1.1400

    Moments

      

    Mean

    4.0421839

    Std Dev

    1.5707756

    Std Err Mean

    0.1684048

    Upper 95% Mean

    4.3769616

    Lower 95% Mean

    3.7074062

    N

    87

    So in last 87 years average Yield was ~ 4% and together with magic number ~ 6.65% of DGR (seekingalpha.com/instablog/725729-sds-se...) it gives us ~ 10.6% of return which is very close to number often quoted as total return.

    On another hand Internet bubble drived S&P total return to high number (from ~ 8% before bubble to ~ 11% after the bubble collaps in ~ 2001). Nevertheless after ~ 1997 and till now market cap still seems on high note to compare with US economy (see picture below from www.vectorgrader.com/indicators/market-c...)

    (click to enlarge)

    I don't know if we will return to previously normal market-cap/GDP ~ 0.6 or ratio ~ 0.8 will be new norm.

    21 Nov 2013

    Just for fun I compared S&P500 prices in 1950-1975 (multiplied to 20) and from 1990 till today using Yahoo data for adjusted closed prices for ^GSPC. The graph below

    (click to enlarge)

    show IMO some similarities. Of course today (21 Nov 2013) we do not have 25 years data starting from 1990, so future will show if history repeat itself. Disclaimer: I don't like technical analysis.

    Aug 24 1:49 PM | Link | 2 Comments
  • Book Review “Professional Investor Rules: Top Investors Reveal The Secrets Of Their Success” By Jonathan Davis

    Home bias is well recognized in behavior finance phenomenon when an investor prefers stocks of companies located in the same country as the investor. To some extend home bias effect exist in finance literature - for example amateur US investors more familiar with ideas of US money managers than with ideas of non-US professional investors. The book "Professional Investor Rules: Top investors reveal the secrets of their success" by Jonathan Davis closed this gap exposing readers to ideas of money managers from different countries /mostly UK probably again because home bias of the publisher 8-) /. A reader will find several gems in the book that are ready for immediately use. If you like books like "Market Wizards" and similar you find "Professional Investor Rules" as the enjoyable and useful reading.

    6 July 2013

    Jul 06 7:13 PM | Link | Comment!
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