Non-Fiction
Author(s): Neil Irwin
This book focuses first in the history of central banking and then in the aftermath of the financial crisis of 2008 and the european crisis with many details about how were the decisions taken and why.
Tuesday, September 6, 2016
The Alchemists
Non-Fiction
Author(s): Neil Irwin
This book focuses first in the history of central banking and then in the aftermath of the financial crisis of 2008 and the european crisis with many details about how were the decisions taken and why.
The Myth of the Rational Market
Non-Fiction
Author(s): Justin Fox
This book covers the rise and fall of the efficient market hypothesis. The story starts with Irving Fisher and tells the many schools of thought regarding economics during the 20th century.
Practical Methods of Financial Engineering and Risk Management
Academic / Finance
Author(s): Rupak Chatterjee
Technical book about yield curves, stochastic processes, monte carlo methods, credit derivatives, new financial regulation and power law adjustments.
Mastering Python for Finance
Academic / Programming
Author(s): James Ma Weiming
Similar to Python for Finance with some more mathematical topics. The recipes include option pricing using classic BS and different binomial trees methods, yield curves, volatility indexes, automated trading and big data.
Too Big to Fail
Non-Fiction
Author(s): Andrew Ross Sorkin
Great book with a lot of details that covers the period since the government exponsored acquisition of Bearn Stearns by JP Morgan until the direct liquidity injections under the Troubled Asset Relief Program (TARP).
Monday, September 5, 2016
Derivatives Analytics with Python
Academic / Programming
Author(s): Yves Hilpisch
The next level of Python for finance. Written by the same author, the book deals with more advanced topics like the formal framework for derivatives pricing.
Python for Finance
Academic / Programming
Author(s): Yves Hilpisch
The first applied programming book I read (after doing some online courses). The books makes a great introduction of Numpy, Scipy, dataframes, and lays down many good examples of uses of python for finance like Black-Scholes, basic strategies for backtesting, and the creation of financial graphs.
Subscribe to:
Posts (Atom)