Finanza e investimenti. Fondamenti matematici

by David G. Luenberger, Sergio Scarlatti (Editor)

La "scienza degli investimenti", di cui Luenberger offre in quest'opera una sintesi di notevole chiarezza, è l'applicazione di strumenti scientifici, soprattutto matematici, allo studio degli investimenti, con l'obiettivo di individuarne i principi generali e metterli in pratica per giungere a decisioni migliori.
Nel testo vengono illustrati sia i temi classici che quelli più moderni della finanza. Si comincia con il concetto di flusso di cassa per porre le basi dello studio dei titoli obbligazionari, fino all'analisi della struttura a temine dei tassi d'interesse.
Le tematiche di scelta di portafoglio, equilibrio di mercato e arbitraggio sono quindi approfondite e poste in grande evidenza. La valutazione e gestione dei titoli derivati è presentata dapprima nel semplice modello binomiale, e poi delineata in forma più generale fino a includere il modello di Black e Scholes. Infine anche le opportunità d'investimento "reali" vengono riconsiderate alla luce dei metodi esposti.
Il testo non richiede conoscenze matematiche avanzate. Può dunque essere utilizzato sia nei corsi di laurea triennali che nei master e nelle lauree specialistiche. inoltre rappresenta un ottimo riferimento per chi professionalmente opera in ambito finanziario.

Genres: Finance, Economics, Reference, Mathematics, Textbooks, Business, Nonfiction

528 pages, Paperback

First published February 15, 2013

Reviews

[Jim Brown]

to get the solutions manual email: footofpride@adelphia.net

[T · Rating 5 out of 5]

I've read Ch 5-9, 11, and 15. That was enough to take me from knowing nothing about portfolio analysis, to writing Python scripts that will solve for -- and simulate -- portfolios that are on the log-optimal frontier. This book is generally very clear and explains the concepts with enough rigor that you will believe them, but without being so rigorous that you are distracted from the actual application and implementation of the concepts. The reader would benefit from having a background in probability, linear algebra, and optimization. Even if you're not comfortable with optimization, Luenberger gives the sufficient conditions for optimality whenever possible; these are often linear. Thus you can skip solving a quadratic program and simply plug the linear conditions into your preferred linear equation solver and boom: optimized portfolios.

Of course I will be reading more books to gain deeper knowledge and better computational strategies, but this book has set me on an excellent course to make multi-period investment decisions that are smarter and more quantitative than just spending money on hunches or letting my money sit in index funds. Highly recommended if you have a strong background in math and want to learn about finance and investing.

[Ayushman Bahuguna · Rating 4 out of 5]

nice

[Huston · Rating 4 out of 5]

Read first half of book for upperclassman-level finance course (Ch. 1-9)

Highly theoretical treatment of mean-variance portfolio theory; more beneficial for graduate (PhD) student in Finance seeking a mathematically rigorous treatment of investment theory than for an undergraduate seeking practicable applications.

End of chapter problems test mathematical ability without much real-life application.

Would recommend prior knowledge of theoretical linear algebra (kernel, rank-nullity theorem, matrix inverse, etc.) and Multivariable Calculus/Optimization (Lagrangian multipliers).

Overall, good at what it does, but don't expect to learn how to go out and build a successful portfolio with only this book. Extremely good for learning mathematics needed for stock analysis however.

[Haim Cohen · Rating 4 out of 5]

Great book to understand options pricing models. Clear explanations accompanied by examples.