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机器学习是量化金融中一个越来越重要和有争议的话题。 关于机器学习技术是否可以成为实用的投资工具，一直存在激烈的争论。 尽管机器学习算法可以发现微妙的、上下文的和非线性的关系，但在尝试从嘈杂的历史数据中提取信号时，过度拟合会带来重大挑战。 在本文中，我们描述了一些围绕机器学习的基本概念，并提供了一个简单的例子，说明投资者如何使用机器学习技术来预测股票收益的横截面，同时限制过度拟合的风险。

The theory and practice of finance draws heavily on probability theory. All MBA programs prepare finance majors for their career in the profession by requiring one generalist course in probability theory and statistics attended by all business majors. While several probability distributions are covered in the course, the primary focus is on the normal or Gaussian distribution. Students find it easy to understand and apply the normal distribution: Give them the expected value and standard deviation and probability statements about outcomes can be easily made. Moreover, even if a random variable of interest is not normally distribution, students are told that a theorem in statistics called the Central Limit Theorem proves that under certain conditions the sum of independent random variables will be asymptotically normally distributed. Loosely speaking, this means that as the number of random variables are summed, the sum will approach a normal distribution. Armed with this rudimentary knowledge of probability theory, finance students march into their elective courses in finance that introduce them to the quantitative measures of risk (the standard deviation) and the quantitative inputs needed to implement modern portfolio theory (the expected value or mean and the standard deviation). In listing assumptions for most theories of finance, the first assumption on the list is often: “Assume asset returns are normally distributed.” The problem, however, is that empirical evidence does not support the assumption that many important variables in finance follow a normal distribution. The application of the Central Limit Theorem to such instances is often inappropriate because the conditions necessary for its application are not satisfied. And this brings us to the purpose of this book. Our purpose is fourfold. First, we explain alternative probability distributions to the normal distributions for describing asset returns as well as defaults. We focus on the stable Paretian (or alpha stable) distribution because of the strong support for that distribution that dates back four decades to the seminal work of Benoit Mandelbrot. Second, we explain how to estimate distributions. Third, we present empirical evidence rejecting the hypothesis that returns for stocks and bonds are normally distributed T and instead show that they exhibit fat tails and skewness. Finally, we explain the implications of fat tails and skewness to portfolio selection, risk management, and option pricing. We must admit that our intent at the outset was to provide a “nontechnical” treatment of the topic. However, we could not do so. Rather, we believe that we have provided a less technical treatment than is provided in the many excellent books and scholarly articles that deal with probability and statistics applied to finance and risk management. The book is not simple reading. It must be studied to appreciate the pitfalls that result from the application of the normal distribution to real-world financial problems.

break function myBreak() { for(var i = 0; i < 5; i++) { if(i == 3) { break; } console.log(i); } } myBreak(); 输出： 0 1 2 break: 直接跳出 当前 的循环，从当前循环外面开始执行,忽略循环体中任何其他语句和循环条件测试。它只能跳出一层循环，如果你的循环是嵌套循环，那么你需要按照你嵌套的层次，逐步使用break来跳出。 continue function myContinue() { for(var i = 0; i < 5; i++) { if(i == 3) { co

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生成回型矩阵并且可以按照回型拆分矩阵。可以自定义生成回型矩阵的维数、

UITextView与UITextField区别，以及一个小demo的介绍使用和代码实例来学习如何通过return键隐藏，以及自定义的UITextView隐藏键盘按钮 继承关系 UITextField继承自UIView，UITextView继承自UIScrollView； 文本行数 UITextView支持多行输入，可以滑屏垂直滚动，UITextField仅支持单行输入； Placeholder UITextField支持设置Placeholder属性(未输入时，有灰色字体)，即在用户输入文本前显示提示性的内容，而UITextView则没有这个功能。

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Java中return语句实例.pdf 学习资料 复习资料 教学资源

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