Pearson Correlation Coefficient对不同货币有何不同？

关于皮尔森相关系数我遇到的这个问题让我感到非常沮丧 . 我有一个程序，在指定的时间段内为每天的两个代码输出股票值，我还绘制了这些值的图表 . 您可以选择USD，EUR和SEK作为输出 . 但是，根据我选择的货币，我的皮尔森相关系数似乎有所不同？我无法弄清楚这是否正确或某处是否有错误？计算的实际功能似乎没问题，所以我不明白......

系数应该不同，还是应该根据货币相同？股票的欧元和瑞典克朗货币值是通过将股票的美元 Value 与当天的欧元或瑞典克朗的收盘价相乘来计算的 .

该方法计算系数：

public double CorrelationCalc(ArrayList<Integer> list1, ArrayList<Integer> list2) {
    double sumX= 0.0, sumY = 0.0, sumXX = 0.0, sumYY = 0.0, sumXY = 0.0;

    int length = list1.size();

    for (int i=0; i<length; i++) {
        int x = list1.get(i);
        int y = list2.get(i);

        sumX+=x;
        sumY+=y;
        sumXX+=x*x;
        sumYY+=y*y;
        sumXY+=x*y;
    }

        double cov = sumXY / length - sumX*sumY / length / length;

        double sigmaX = Math.sqrt(sumXX / length - sumX*sumX / length / length);
        double sigmaY = Math.sqrt(sumYY / length - sumY*sumY / length / length);

        return cov/sigmaX/sigmaY;


}

股票收盘 Value 是从雅虎财务通过.csv文件获取的，这些循环获取值并将它们添加到arraylist .

//This one is for when USD is selected. (Close value for each date located 
//at index 4 on each line in the csv file)
while ((line = buf.readLine())!=null && (line2 = buf2.readLine())!= null) {
    if(n != 0) { //First row contains text only
        close1 = (Math.round(100 * Double.parseDouble(line.split(",")[4]))/100.0);
        close2 = Math.round(100 * Double.parseDouble(line2.split(",")[4]))/100.0;

        ticker1List.add((int) Math.round(close1));
        ticker2List.add((int) Math.round(close2));
    }
    n++;
}

和 . buf3是一个读取货币汇率csv的字符串缓冲区

//This one is for non USD (SEK or EUR). multiplies the values with the currency rate
while ((line = buf.readLine())!=null && (line2 = buf2.readLine())!= null && (line3 = buf3.readLine())!= null) {
    if(n != 0) {//First row contains text only
            while (!line.split(",")[0].equals(line3.split(",")[0])) { //Makes sure that dates match
                line3 = buf3.readLine();
            }
        close1 = (Math.round(100 * Double.parseDouble(line.split(",")[4])*Double.parseDouble(line3.split(",")[4]))/100.0);
        close2 = Math.round(100 * Double.parseDouble(line2.split(",")[4])*Double.parseDouble(line3.split(",")[4]))/100.0;


        ticker1List.add((int) Math.round(close1));
        ticker2List.add((int) Math.round(close2));
    }
    n++;
}

ticker1List和ticker2List是后来发送到上述方法进行计算的列表 .

输出样本：

USD

ticker1List = {542,535,539,547,559,563,575,579,578,581,573,574,560,556,561,553,562,558,566,564,565,565,578,567 ，564,558,561,555,549,541,544,545,549,548,549,549,540,541,544,533,545,543,549,557,574,566,564,561,549 ，551,553,543,535,542,549,546,539,539,549,546,547,549,553,557,555,547,554,554,545,549,554,555,552,550 ，543,542,553,550,547,543,545,547,556,558,560,563,559,558,553,541,540,543,547,546,550,542,545,556,572 ，584,584,602,700,693,695,695,675,655,658,660,661} ticker2List = {44,44,44,44,44,44,44,43,43,43,42， 43,42,42,41,41,42,42,43,42,43,43,43,41,41,41,41,41,41,40,42,42,41,41,42,42， 42,42,42,42,43,43,43,43,48,48,49,49,49,49,48,48,46,47,48,47,47,48,49,48,48， 48,48,47,47,47,48,47,47,47,47,47,46,46,46,46,47,46,46,45,46,46,47,46,46,46， 46,46,45,44,44,44,44,44,44,44,45,45,46,46,46,47,47,47， 47,46,46,46,45,45,46}

皮尔森：0.1439484634863799

SEK

货币汇率= {8.55523,8.509,8.5914,8.564,8.5947,8.5379,8.6162,8.6554,8.54502,8.5075,8.5015,8.4947,8.4503,8.4634,8.46479,8.51185,8.4701,8.51984,8.38558,8.2967,8.23152,8.4052,8.24647， 8.23443,8.253,8.13911,8.11373,8.12167,8.09872,8.17692,8.1593,8.20512,8.2148,8.26875,8.28135,8.29895,8.3914,8.2918,8.31362,8.4409,8.58413,8.548,8.4556,8.51005,8.4676,8.4153,8.33381,8.3411， 8.3424,8.22841,8.19809,8.24101,8.23992,8.3265,8.30831,8.25121,8.2754,8.2294,8.3507,8.36978,8.40981,8.3328,8.34131,8.5058,8.60651,8.6354,8.6344,8.72934,8.6574,8.69648,8.63365,8.58971,8.714， 8.7647,8.81522,8.81353,8.75727,8.6755,8.6743,8.5591,8.52942,8.65399,8.6243,8.5889,8.586,8.58851,8.493,8.51087,8.50157,8.61801,8.6706,8.6013,8.68419,8.6546,8.7287,8.5913,8.62343,8.55167， 8.4879,8.4632,8.33779,8.32539,8.2671,8.3381,8.36064,8.3922,8.28541,8.40471,8.4139,8.3832,8.42055}

给

ticker1List = {4562,4485,4532,4600,4634,4722,4808,4826,4781,4841,4777,4859,4752,4752,4839,4751,4902,4826,4917,4488,4898,4872,4910,4826 ，4787,4789,4818,4764,4739,4684,4640,4664,4761,4754,4804,4835,4758,4742,4736,4576,4701,4721,4754,4866,4953,4889,4857,4775,4577 ，4593,4649,4545,4468,4461,4543,4503,4476,4492,4525,4504,4482,4520,4609,4644,4621,4605,4693,4716,4611,4695,4755,4687,4587,4557 ，4561,4499,4576,4548,4497,4455,4446,4470,4504,4528,4541,4585,4610,4594,4561,4549,4445,4508,4590,4649,4659,4611,4610,4707,4831 ，4962,4965,5120,5978,5997,5991,5935,5799,5607,5655,5613,5659}

ticker2List = {369,370,371,370,365,368,367,361,356,359,353,363,357,359,354,356,363,361,369,364,372,369,365,353 ，350,352,352,349,351,349,354,355,359,360,365,368,367,370,367,358,370,371,372,378,413,415,423,417,406 ，405,406,398,386,384,395,391,393,397,301,398,394,392,397,396,391,392,403,404,396,404,403,395,385,383 ，384,379,386,384,378,373,374,376,378,374,375,374,377,376,373,373,363,369,372,378,375,377,377,378,385 ，388,389,397,398,406,407,389,396,393,390,386,396}

Pearson：0.20617640237659246