cm_mod2_out.m

Neural Network in Finance (神经网络在金融界:赢得预言性的优势)全部原码。内容包括预测与估计

function [error, yhat] = cm_mod2(beta);
global squasher data1 minx miny maxx maxy malags neuronxarg_ygap cthres_ygap neuronxarg_inf cthres_inf neuronxarg_yvol cthres_yvol ;
y = data1(:,1);
x = data1(:,2:end-1);
[nx, cx] = size(x);
if squasher == 1,
    yy = (y - miny)/(maxy-miny);
    for i = 1:cx, xx(:,i) = (x(:,i)-minx(i))/(maxx(i)-minx(i));
    end
else yy = y; xx = x;
end
if squasher == 1,
    cxarg_ygap = (cthres_ygap-minx(neuronxarg_ygap)) / (maxx(neuronxarg_ygap)-minx(neuronxarg_ygap));
else cxarg_ygap = cthres_ygap;
end

ny = length(yy);
yhat1 = yy; 
% xx1 = xx * abs(beta(1:cx))' + ones(ny,1) * abs(beta(cx+1));
xx1 = xx * (beta(1:cx))' + ones(ny,1) * abs(beta(cx+1));

ehat(1:malags,1) = zeros(malags,1); 
neuron1(1:malags,1) = zeros(malags,1); 
for i = malags+1:ny, 
% neuronx = abs(beta(cx+2))* (xx(i-1,neuronxarg_ygap)-cxarg_ygap);
neuronx = abs(beta(cx+2))* (xx(i-1,neuronxarg_ygap)-cxarg_ygap);

neuron1(i,:) =  2 ./ (1 + exp(-2 * neuronx)) -1;
EXX = ehat(i-malags:i-1,:);
% yhat1(i,:) = xx1(i,:)  + neuron1(i,:) * abs(beta(neuronxarg_ygap)) * xx(i,neuronxarg_ygap) + beta(cx + 3:cx+malags+2) * EXX;; 
yhat1(i,:) = xx1(i,:)  + neuron1(i,:) * (beta(neuronxarg_ygap)) * xx(i,neuronxarg_ygap) + beta(cx + 3:cx+malags+2) * EXX;; 

ehat(i,:) = yy(i,:) - yhat1(i,:);
end;
error = 0
if squasher == 1,
    yhat = yhat1 * (maxy-miny) + miny;
else yhat = yhat1;
end;