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ScriptsData/distress_data.m

@@ -1,34 +1,34 @@
 % [INPUT]
-% ts = A float t-by-n matrix containing the time series.
-% offsets = A vector of length n containing the numeric date of the distress begin of each firm; offsets are equal to NaN in absence of distress.
+% data = A float t-by-n matrix containing the time series to be distressed.
+% offsets = A vector of length n containing the distress begin offsets of each firm; offsets must be equal to NaN in absence of distress.
 %
 % [OUTPUT]
 % ts = A float t-by-n matrix containing the original time series in which distressed observations are replaced with NaNs.
 
-function ts = distress_data(varargin)
+function data = distress_data(varargin)
 
     persistent ip;
 
     if (isempty(ip))
         ip = inputParser();
-        ip.addRequired('ts',@(x)validateattributes(x,{'double'},{'real' '2d'}));
+        ip.addRequired('data',@(x)validateattributes(x,{'double'},{'real' '2d'}));
         ip.addRequired('offsets',@(x)validateattributes(x,{'double'},{'real' 'vector' 'nonempty'}));
     end
 
     ip.parse(varargin{:});
 
     ipr = ip.Results;
-    [ts,offsets] = validate_input(ipr.ts,ipr.offsets);
+    [data,offsets] = validate_input(ipr.data,ipr.offsets);
 
     nargoutchk(1,1);
 
-    ts = distress_data_internal(ts,offsets);
+    data = distress_data_internal(data,offsets);
 
 end
 
-function ts = distress_data_internal(ts,offsets)
+function data = distress_data_internal(data,offsets)
 
-    if (isempty(ts))
+    if (isempty(data))
         return;
     end
 
@@ -39,18 +39,20 @@
             continue;
         end
 
-        ts(offset:end,i) = NaN;
+        data(offset:end,i) = NaN;
     end
 
 end
 
-function [ts,offsets] = validate_input(ts,offsets)
+function [data,offsets] = validate_input(data,offsets)
 
-    n = size(ts,2);
-    k = numel(offsets);
-
-    if ((n ~= 0) && (n ~= numel(offsets)))
-        error(['The number of columns in the time series (' num2str(n) ') must be equal to the number of offsets (' num2str(k) ').']);
+    if (~isempty(data))
+        n = size(data,2);
+        k = numel(offsets);
+
+        if (n ~= k)
+            error(['The number of columns in the time series (' num2str(n) ') must be equal to the number of offsets (' num2str(k) ').']);
+        end
     end
 
 end

ScriptsData/extract_firms_data.m

@@ -68,4 +68,4 @@
         end
     end
 
-end
+end

ScriptsData/extract_rolling_windows.m

@@ -1,7 +1,7 @@
 % [INPUT]
-% data = A float t-by-n matrix containing the time series.
+% data = A float t-by-n matrix containing the time series to be converted into rolling windows.
 % bw = An integer [2,252] representing the dimension of each rolling window.
-% truncate = A boolean that indicates whether to exclude all the rolling windows with a dimension less than the bandwidth (optional, default=true).
+% truncate = A boolean that indicates whether to exclude all the rolling windows with a dimension less than the bandwidth (optional, default=false).
 %
 % [OUTPUT]
 % windows = A column cell array of bw-by-n float matrices representing the rolling windows.
@@ -65,4 +65,4 @@
         end
     end
 
-end
+end

ScriptsData/forward_roll_data.m

@@ -0,0 +1,68 @@
+% [INPUT]
+% data = A float t-by-n matrix containing the daily time series to be rolled forward.
+% dates = A vector of length t containing the numeric reference dates of observations.
+% fr = An integer [0,6] representing the number of months of forward-rolling to be applied to the time series.
+%
+% [OUTPUT]
+% ts = A float t-by-n matrix containing the original time series in which distressed observations are replaced with NaNs.
+
+function data = forward_roll_data(varargin)
+
+    persistent ip;
+
+    if (isempty(ip))
+        ip = inputParser();
+        ip.addRequired('data',@(x)validateattributes(x,{'double'},{'real' '2d'}));
+        ip.addRequired('dates',@(x)validateattributes(x,{'double'},{'real' 'vector'}));
+        ip.addRequired('fr',@(x)validateattributes(x,{'double'},{'real' 'finite' 'integer' '>=' 0 '<=' 6 'scalar'}));
+    end
+
+    ip.parse(varargin{:});
+
+    ipr = ip.Results;
+    [data,dates] = validate_input(ipr.data,ipr.dates);
+    fr = ipr.fr;
+
+    nargoutchk(1,1);
+
+    data = forward_roll_data_internal(data,dates,fr);
+
+end
+
+function data = forward_roll_data_internal(data,dates,fr)
+
+    if (isempty(data) || (fr == 0))
+        return;
+    end
+
+    [~,a] = unique(cellstr(datestr(dates,'mm/yyyy')),'stable');
+    data_monthly = data(a,:);
+
+    indices_seq = [a(1:fr:numel(a)) - 1; numel(dates)];
+    data_seq = data_monthly(1:fr:numel(a),:);
+
+    data_fr = NaN(size(data));
+
+    for i = 2:numel(indices_seq)
+        indices = (indices_seq(i-1) + 1):indices_seq(i);
+        data_fr(indices,:) = repmat(data_seq(i-1,:),numel(indices),1);
+    end
+
+    data = data_fr;
+
+end
+
+function [data,dates] = validate_input(data,dates)
+
+    if (~isempty(data))
+        t = size(data,1);
+        td = numel(dates);
+
+        if (t ~= td)
+            error(['The number of rows in the time series (' num2str(t) ') must be equal to the number of dates (' num2str(td) ').']);
+        end
+    end
+
+    dates = dates(:);
+
+end

ScriptsMeasures/run_comparison.m

@@ -682,7 +682,7 @@ function plot_scores_gc(ds,id) %#ok<DEFNU>
     set(sub_2,'Box','on','XGrid','on','YGrid','on');
     title('Data Browser');
 
-    figure_title(['Granger-causality (A=' num2str(ds.GCA) ', LM=' num2str(ds.LagMax) ', LS=' ds.LagSel ')']);
+    figure_title(['Granger-causality (A=' num2str(ds.GCA * 100) '%, LM=' num2str(ds.LagMax) ', LS=' ds.LagSel ')']);
 
     pause(0.01);
     frame = get(f,'JavaFrame');