墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
1.8k累计阅读
1.9k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
最近研究了一篇申万宏源证券的研报--《异常财务指标因子研究》。该研报提出六个基本面财务指标，不仅在一定程度上能识别上市公司财务造假的情况，更能有效预测未来盈余。本文按照该研报的描述尝试复现研究结果，验证异常财务因子的有效性。
首先我们列出6个异常财务指标的选取原因：
以及其计算公式：
上述六个财务指标均采用季度数据构建，且均采用同比数据，能够消除季节性影响，为不同公司间可比，使用统一更新频率（5月1日、9月1日、11月1日更新新数据，不含年报数据）。
在进一步研究之前，我们先对每个财务指标进行单因子检验，在全部A股中剔除金融行业股票，将股票按照各个异常因子值分为十组，各组内等权计算十分组表现，以及其IC、IR等指标，所有指标均经过市值行业中性化调整。
1.异常存货因子
异常存货因子在样本期年化收益率的区分上较为明显，单调性也较好。IC均值为2.87%，IC_IR为0.53，多空组合年化收益率约为2.98%。
中信期货财务因题专题报告：财务因子之单因子测试之因子数据清理（异常值处理）
异常值，缩尾法（winsorization），将小于 1%分位数和大于 99%分位数的样本点剔除。因为本文的因子数据是截面数据，所以本文的因子缺失值直接剔除。
A股财务造假预测模型的建立--一个模型化的全新识别系统
A股财务造假预测模型的建立--一个模型化的全新识别系统
GD32单片机开发环境搭建（Keil5安装）
1.安装MDK5 由于GD32 Keil 版本例程的工程是基于Keil MDK-ARM 5.25 uVision5 创建的，所以我在此也安装MDK5.25版本。 下载链接：https://pan.baidu.com/s/1E4tcDQvsVl0xezU3pEyJTA 提取码：s1fr 其他版本下载链接：https://cloud.tencent.com/developer/article/1605195 选择安装路径(不要装到中文路径下，避免不必要的麻烦)： 用户信息随便...
切比雪夫插值
切比雪夫插值是一种基于切比雪夫节点的多项式插值方法，其优势是减少插值误差(特别是龙格现象：表现为高维插值时在边缘处插值误差骤增)。本文对其基本操作进行说明。
HFSS仿真中如何提取最大E/H场强与频率关系曲线
本文详细讲解了在HFSS仿真中提取最大E/H场强与频率关系曲线的完整流程。通过使用场计算器（Field Calculator）和创建场报告，工程师可以准确获得器件在整个频带内的电场或磁场强度峰值，并进一步推导出关键的功率容量曲线，为高功率射频器件（如滤波器、耦合器）的设计提供至关重要的安全评估依据。
ROS2中间件选型指南：FastDDS与CycloneDDS零拷贝机制对比与实战
在机器人开发中，通信中间件是连接分布式节点的核心纽带，其性能直接影响系统的实时性与可靠性。DDS作为ROS2默认的通信标准，通过发布订阅模型和QoS策略，为高频大带宽场景提供了灵活的数据分发能力。并行计算与高性能传输的需求，推动了零拷贝技术的演进——从传统序列化到共享内存乃至指针传递，旨在减少数据复制开销、降低延迟。在激光雷达点云、相机图像等场景下，中间件的选型尤为关键。FastDDS凭借与ROS2的深度集成和丰富文档成为入门首选，而CycloneDDS在低延迟和资源占用上表现更优，借助iceoryx实现了
高效爬取懂车帝车型数据：从静态解析到动态反爬实战
本文详细解析了如何高效爬取懂车帝车型数据，从静态HTML解析到动态接口反爬的完整实战路径。文章重点介绍了使用浏览器开发者工具定位数据、应对CSS混淆与参数加密等反爬策略，并提供了整合静态与动态抓取逻辑的Python代码示例，帮助开发者稳健地获取结构化车型信息。
RK3568 Android14 调试 RTL8211F 千兆以太网 (RGMII)
调试 RTL8211F 的核心在于：确认地址（看电阻贴片情况）和 搞定延时（配置 rgmii-id 或微调 delay 值）。一旦这两点对上了，RK3568 的千兆网络就能稳定运行。希望这篇结合原理图的实战记录能帮到你！
【Python】-- 趣味代码 - 发牌游戏
Python -- 趣味代码 - 发牌游戏
从零开始学习CANoe（十六）—— Graphics
Graphics是一种以时间轴为横坐标，符号值为纵坐标的显示分析方法，相对于Trace来说，更直观的观察出信号的跳变
LinePhone3.2.7(android端最新版本) sip客户端
SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
SAP B1中文培訓教材_SDK
公司内部培训资料，对开发工具 ，DIAPI， UIAPI，Add-ON有详细解说
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
354
扫一扫
