Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)

I've only been through the first 4 chapters so far. I found the way the material was presented to be very good and the authors did a very good job presenting and explaining the mater. Having understood the material which I would credit to the author's great clarity and presentation, I decided to run the Matlab code the author provides. This is were everything started going wrong. The functions are full of error and would not run. I had to make changes to the m-file for the proram to run. This was also very hard since the code is very poorly documented (input variables are not even explained). Even after fixing the erros, the programs did not give the results the author claims. In the example on page 78, the author claims that the genetic algorithm gives a result very close to 4 which is not true (some results were less than 2). I then tried to work the example on page 81. Again I got errors trying to run the program. In the file ffnet9.m, the author has an if statement if the number of arguments is 8 instead of the 12 expected by the function while in the example, the number of arguments is 9 and therefore you get an error trying to run the function ffnet9. second, it seems the author had modified a previous function which took 8 arguments since the function is actually called ffnet8 in the file while the file is called ffnet9.m (very bad programming). After fixing the problem, the linear model gave an R-squared in the 0.55 range and the second degree polynomials gave a result in the range of 0.91 however, the neural network R-squared was in the range of 0.73 and not 0.99 as claimed by the author! the line search in the function fminunc is exiting due to the line search. By the way, don't run the program on page 81 1000 times as done in the for loop as this will take forever and I'm not sure way the author did it. This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction.

McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong.

* Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance
* Includes numerous examples and applications
* Numerical illustrations use MATLAB code and the book is accompanied by a website This book shows how neural networks may be put to work for more accurate forecasting, classification, and dimensionality reduction for better decision making in financial markets-particularly in the volatile emerging markets of Asia and Latin America, but also in domestic industrialized-country asset markets and business environments.