Machine Learning for Financial Data December 2020 FEATURE ENGINEERING (CONCEPTS – PART 2) Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Feature Engineering Contents ◦ Feature Improvement ◦ Feature Construction Feature Improvement Data Probability Distribution Variable Transformations ▪ Linear and logistic regression assume that the variables are normally distributed ▪ If they are […]
REGRESSION – CONCEPTS (PART 1) Machine Learning for Financial Data January 2021 Contents ◦ Supervised Learning ◦ Linear Regression ◦ Polynomial Regression ◦ Regularized Regression ◦ Time Series Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Regression Supervised Learning Supervised Learning Supervised learning is the machine learning task of learning a function that
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Machine Learning for Financial Data February 2021 CLASSIFICATION (CONCEPTS – PART 2) Contents ◦ Naive Bayes Classifier ◦ Support Vector Machine (SVM) ◦ Random Forest ◦ Logistic Regression Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Classification Naive Bayes Classifier Naïve Bayes classifier relies on the probability function of pedal & sepal measures
Machine Learning for Financial Data December 2020 FEATURE ENGINEERING (CONCEPTS – PART 1) Copyright (c) by Daniel K.C. Chan. All Rights Reserved. 2 Feature Engineering Contents ◦ Financial Data Sources ◦ What is Feature Engineering ◦ Feature Understanding ◦ Feature Improvement Financial Data Source Yahoo Finance Yahoo Finance is one of the reliable sources of
MGMTMFE 407: Empirical Methods in Finance Homework 1 Prof. . Lochstoer TA: January 11, 2022 Copyright By PowCoder代写 加微信 powcoder Please use R to solve these problems. You can just hand in one set of solutions that has all the names of the contributing students on it in each group. Use the electronic drop box
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Problem 1: ARMA basics Empirical Methods in Finance Homework 3: Solution Prof. . Lochstoer TA: January 23, 2022 Copyright By PowCoder代写 加微信 powcoder Consider the ARMA(1,1): yt = 0.95×yt−1 −0.9×εt−1 +εt, (1) where εt is i.i.d. Normal with mean zero and variance σ2 = 0.052. In the below you need to show your work in
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Chapter 1 Introduction 1.1 Statistical Computing Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and nu- merical approaches to solving statistical problems. Statistical computing tra- ditionally has more emphasis on numerical methods and algorithms, such as optimization and random number generation, while computational statistics may
Chapter 4 Generating Random Processes 4.1 Stochastic Processes A stochastic process is a collection {X(t) : t ∈ T} of random variables indexed by the set T, which usually represents time. The index set T could be discrete or continuous. The set of possible values X(t) can take is the state space, which also can
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Assignment-2-UG-2022-Notebook-1 October 3, 2022 0.1 FNCE30012: Foundations of Fintech – Assignment 2 The aim of this assignment is to build a ‘toy robo-adviser’ that computes, based on a set of external inputs, the optimal portfolio for a person. We will then compare our results to a real world robo- advisor, offered by Six Park*. The
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Software Construction & Design 1 The University of Sydney Page 1 Software Design and Construction 2 SOFT3202 / COMP9202 Introduction Software Testing School of Information Technologies Dr. Basem Suleiman The University of Sydney Page 2 Copyright Warning COMMONWEALTH OF AUSTRALIA Copyright Regulations 1969 WARNING This material has been reproduced and communicated to you by or
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