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Beating the Odds: Eddie Brown's Investing and Life by Eddie Brown

By Eddie Brown

Beating the Odds is the unbelievable, inspiring autobiography of monetary guru Eddie C. Brown, one of many nation's most sensible inventory pickers and cash managers. It info how Brown skillfully stored Brown Capital administration afloat in the course of the dot-com bust, Sept. 11 and the good Recession. Born to a 13-year-old unwed mom within the rural South, this African-American funding whiz created a Baltimore-based monetary company that gathered greater than $6 Billion lower than management.
Brown delves into the profound heartbreak and disorientation upon the demise of his loved grandmother – who was once his surrogate mom -- and recounts how Brown's moonshine-running Uncle Jake hence turned the dominant grownup determine in Brown's existence. His unflinchingly sincere, easy-to-read memoir info how highbrow interest, abiding self-belief, labor and divine windfall helped Brown earn an electric engineering measure, develop into a military officer, and later a civilian IBM engineer. Readers will research of the strife that ensued while Brown surrender IBM to earn an MBA, resulting in funding jobs that ready him to begin his personal funds administration corporation in 1983.

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Extra info for Beating the Odds: Eddie Brown's Investing and Life Strategies

Example text

5 Conjugate Classes of Distributions The above three cases are contained in the general framework that we are going to discuss in this section. Let the observation vector X have independent components, which — up to some weighting — have the same conditional distribution F& given  = &. We look at the family of possible marginal distributions F = {F& : & 5 }. In insurance practice, the specification of the family F is in itself a problem. Sometimes, there are indications about which families might be appropriate.

0 =  + n j=1 x0 The Bayes estimator for  is therefore given by e = E [|X] =  +n ³ ´. 40) can be written in another, more easily interpretable form. e. the estimator that one might use if we only had data and no a priori information. This is given by n bMLE = & ³ ´. 40) can now be written as where bMLE + (1  ) ·  , e =·&   43 4 3 µ ¶ µ ¶ 1 n n X X X X j DC j D ln ln . 20. 7 Summary Here, we give a short summary of the results of this chapter. Bayes model: • F = {F& : & 5 }: family of distributions indexed with the parameter & 5 .

6 Another Type of Example: the ParetoGamma Case The model and the results of this section are taken from [Ryt90]. Motivation A frequent assumption in the practice of reinsurance is that the claim amounts of those claims which exceed a given limit x0 are Pareto distributed. Typically, based on information from numerous portfolios, the reinsurer has an “a priori” idea about the level of the “Pareto parameter” &. He also collects information from the primary insurer about all claims exceeding a particular limit c0 , and he can also use this information to estimate &.

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