利用机器学习算法预测经济衰退

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英文标题：
《Predicting Economic Recessions Using Machine Learning Algorithms》
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作者：
Rickard Nyman, Paul Ormerod
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最新提交年份：
2017
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英文摘要：
  Even at the beginning of 2008, the economic recession of 2008/09 was not being predicted. The failure to predict recessions is a persistent theme in economic forecasting. The Survey of Professional Forecasters (SPF) provides data on predictions made for the growth of total output, GDP, in the United States for one, two, three and four quarters ahead since the end of the 1960s. Over a three quarters ahead horizon, the mean prediction made for GDP growth has never been negative over this period. The correlation between the mean SPF three quarters ahead forecast and the data is very low, and over the most recent 25 years is not significantly different from zero.   Here, we show that the machine learning technique of random forests has the potential to give early warning of recessions. We use a small set of explanatory variables from financial markets which would have been available to a forecaster at the time of making the forecast. We train the algorithm over the 1970Q2-1990Q1 period, and make predictions one, three and six quarters ahead. We then re-train over 1970Q2-1990Q2 and make a further set of predictions, and so on. We did not attempt any optimisation of predictions, using only the default input parameters to the algorithm we downloaded in the package R.   We compare the predictions made from 1990 to the present with the actual data. One quarter ahead, the algorithm is not able to improve on the SPF predictions. Three and six quarters ahead, the correlations between actual and predicted are low, but they are very significantly different from zero. Although the timing is slightly wrong, a serious downturn in the first half of 2009 could have been predicted six quarters ahead in late 2007. The algorithm never predicts a recession when one did not occur.   We obtain even stronger results with random forest machine learning techniques in the case of the United Kingdom.
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中文摘要：
即使在2008年初，也没有预测到2008/09年的经济衰退。未能预测衰退是经济预测中的一个永恒主题。专业预测者调查（SPF）提供了自20世纪60年代末以来，美国总产出（GDP）增长预测的数据，预测了一、二、三和四个季度的增长。在未来三个季度，对GDP增长的平均预测在此期间从未出现过负增长。未来三个季度的平均SPF预测与数据之间的相关性非常低，在最近25年中，与零没有显著差异。在这里，我们展示了随机森林的机器学习技术有可能对衰退进行早期预警。我们使用金融市场的一小部分解释变量，预测者在做出预测时可以使用这些变量。我们在1970年第2季度至1990年第1季度对算法进行了训练，并提前1、3和6个季度进行了预测。然后，我们对1970年第2季度至1990年第2季度进行了重新训练，并进行了进一步的预测，以此类推。我们没有尝试对预测进行任何优化，只使用我们在R包中下载的算法的默认输入参数。我们将1990年到现在的预测与实际数据进行比较。提前一个季度，该算法无法改善SPF预测。在接下来的三个季度和六个季度，实际和预测之间的相关性很低，但它们与零之间的相关性非常显著。虽然时机稍有错误，但预计2009年上半年将出现严重的经济下滑，预计将在2007年末提前六个季度出现。该算法从未在衰退未发生时预测衰退。在英国的案例中，我们使用随机森林机器学习技术获得了更强的结果。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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关键词：机器学习算法 学习算法 经济衰退 机器学习 Quantitative