This document summarizes topics related to portfolio management and financial modeling. It discusses robust regression in Stata, residual plots, and identifying influential factors using multivariate modeling. It introduces concepts for part 2 of the course like discrete and continuous returns, matrix algebra, asset classes, and standard measures like standard deviation and correlation. Key models are discussed like the CAPM, security market line, efficient frontier, and modern portfolio theory. Excel skills are taught like using matrix functions to calculate returns, variance, covariance and the efficient frontier using Solver.
This talk provides a critical view on employing machine learning / deep learning methods in algorithmic trading. We highlight the particular challenges that we meet in this domain along with approaches to tackle some of these challenges in practice. Even though experience has shown that algorithmic trading using advanced machine learning can be successful, the crucial issue remains that predictive patterns utilizing market inefficiencies quickly become void as soon as competing market participants use them too. The conclusion is that the crucial advantage is – and has always been – to know more and to be faster than competitors.
Our Speaker: Dr. Ulrich Bodenhofer
MSc (applied math, Johannes Kepler University, Linz, Austria, 1996)
PhD (applied math, Johannes Kepler University, Linz, Austria, 1998)
Since June 2018: Chief Artificial Intelligence Officer at QUOMATIC.AI (Linz, Austria)
Modern Portfolio Theory (MPT), a hypothesis put forth by Harry Markowitz in his paper "Portfolio Selection," is an investment theory based on the idea that risk-averse investors can construct portfolios to optimize or maximize expected return based on a given level of market risk, emphasizing that risk is an inherent part of higher reward. It is one of the most important and influential economic theories dealing with finance and investment.
This talk provides a critical view on employing machine learning / deep learning methods in algorithmic trading. We highlight the particular challenges that we meet in this domain along with approaches to tackle some of these challenges in practice. Even though experience has shown that algorithmic trading using advanced machine learning can be successful, the crucial issue remains that predictive patterns utilizing market inefficiencies quickly become void as soon as competing market participants use them too. The conclusion is that the crucial advantage is – and has always been – to know more and to be faster than competitors.
Our Speaker: Dr. Ulrich Bodenhofer
MSc (applied math, Johannes Kepler University, Linz, Austria, 1996)
PhD (applied math, Johannes Kepler University, Linz, Austria, 1998)
Since June 2018: Chief Artificial Intelligence Officer at QUOMATIC.AI (Linz, Austria)
Modern Portfolio Theory (MPT), a hypothesis put forth by Harry Markowitz in his paper "Portfolio Selection," is an investment theory based on the idea that risk-averse investors can construct portfolios to optimize or maximize expected return based on a given level of market risk, emphasizing that risk is an inherent part of higher reward. It is one of the most important and influential economic theories dealing with finance and investment.
Learn about the different types of algorithmic trading and how it actually works. Algorithmic trading is a growing trend. I Know First has an advanced self-learning algorithm that has helped many investors achieve magnificent returns. I Know First's live portfolio returned 60.66% in 2013, beating the S&P 500 by over 30%!
Nowadays, investors are highly concerned about the costs of active investment structures if these do not add significant value to their portfolio. This leads to significant inflows to ETFs, which cover a broad range of indices, asset classes and investment styles providing high liquidity at comparably lower costs.
Especially for passive investment styles, there is almost no incentive to keep mutual funds in the portfolio if equity index tracker ETFs provide higher net performance with comparable risks.
However, the main problem is that on one side equity index ETFs typically often outperform many active equity funds but cannot protect against drawdowns during market crises.
In this paper we show that advanced overlay technologies can preserve significant index performance and protect against large drawdowns as well…so…the best of both worlds…
There are plenty of concepts around identifying unfavorable financial market phases in order to early detect market crises. Just to name a few: Volatility, VaR/CVaR, Turbulence Indicators, Log Periodic Power Law Singularity, Sentiment Indices...and many, many more.
Even when these concepts are properly back-tested with historical time-series, we often have to conclude that there are several shortcomings in practice like: Lag, missing precision, missing exits and entries.
We suggest considering newer technologies, which are more mathematically advanced and nowadays available due to the abundance of computational capacity.
Improved stock prediction accuracy using ema techniquePrashant Singhal
In this research paper, we aim at finding a technique for effective stock prediction. The paper discusses some machine learning algorithms which have been used to predict the rise and fall of stock prices. To give more importance to the recent stock data, the paper applies a data pre-processing technique called exponential moving average (EMA) to the historical stock data and observes its effect towards improving the accuracy of stock price predictions.
Learn about the different types of algorithmic trading and how it actually works. Algorithmic trading is a growing trend. I Know First has an advanced self-learning algorithm that has helped many investors achieve magnificent returns. I Know First's live portfolio returned 60.66% in 2013, beating the S&P 500 by over 30%!
Nowadays, investors are highly concerned about the costs of active investment structures if these do not add significant value to their portfolio. This leads to significant inflows to ETFs, which cover a broad range of indices, asset classes and investment styles providing high liquidity at comparably lower costs.
Especially for passive investment styles, there is almost no incentive to keep mutual funds in the portfolio if equity index tracker ETFs provide higher net performance with comparable risks.
However, the main problem is that on one side equity index ETFs typically often outperform many active equity funds but cannot protect against drawdowns during market crises.
In this paper we show that advanced overlay technologies can preserve significant index performance and protect against large drawdowns as well…so…the best of both worlds…
There are plenty of concepts around identifying unfavorable financial market phases in order to early detect market crises. Just to name a few: Volatility, VaR/CVaR, Turbulence Indicators, Log Periodic Power Law Singularity, Sentiment Indices...and many, many more.
Even when these concepts are properly back-tested with historical time-series, we often have to conclude that there are several shortcomings in practice like: Lag, missing precision, missing exits and entries.
We suggest considering newer technologies, which are more mathematically advanced and nowadays available due to the abundance of computational capacity.
Improved stock prediction accuracy using ema techniquePrashant Singhal
In this research paper, we aim at finding a technique for effective stock prediction. The paper discusses some machine learning algorithms which have been used to predict the rise and fall of stock prices. To give more importance to the recent stock data, the paper applies a data pre-processing technique called exponential moving average (EMA) to the historical stock data and observes its effect towards improving the accuracy of stock price predictions.
The free state-by-state guides walk through the benefits and uses of three major types of geothermal applications: power generation, direct use and heat pumps.
Myanmar is hot market that faces many challenges and opportunities. I take the conference through the key success stories and frameworks that could guide the development of the Myanmar financial ecosystem over the coming decade
Nick Wade Using A Structural Model For Enterprise Risk, Dst Conference 2011...yamanote
On why a multi-factor or structural model of risk might be a good idea at the enterprise level, rather than the more common VaR models based simply on historical returns
Mf0010 & security analysis and portfolio managementsmumbahelp
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Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Korea Stock Exchange, Australian Stock Exchange, New York Stock Exchange, NAS...Tai Tran
This presentation consists of 2 sections
1. An overview of Korea Stock Exchange and Australian Stock Exchange, accompanied by a comparison of the two exchanges
2. A discussion of Bennett & Li "Market Structure, Fragmentation, and Market Quality" article which looks into market fragmentation on New York Stock Exchange and NASDAQ
This was done as part of a project for University of New South Wales
St.George's Acquisition by Westpac AnalysisTai Tran
HotK team's Analysis on the St.George Acquisition by Westpac. The time in the presentation was set at April 2008. The information is our view and does not necessarily reflect what would have happened after April 2008.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
4. Residual Plot reg y x1 ... xn, b predict res, r predict yhat scatter res x1 sktest res Read http://www.polsci.wvu.edu/duval/ps602/Notes/STATA/residuals.html Right after running regression
5. Part 1 of the course Stata Statistics Multivariate modeling Practical applications Identify influential factors and patterns Model and forecast stock performance and other business performance indicators (sales, NOPAT, growth, attrition...) Reflect in stock selection, portfolio optimization and investment strategy Example: "Do Wal-Mart, Target, Woolworths and Coles do well near Christmas?" You can test
6. FINS3640 - Investment Management Modeling Part 2 Week 6 - 25 Aug 2010 Introduction to using Excel and Matrix Algebra for Financial Modeling
7. Discrete return, excess return, continuous return, arithmetic return, geometric return Matrix algebra Normal distribution, cumulative distribution Asset classes: Equity, Fixed income, Alternative asset classes (real estate, venture capital, private equity) Index funds, large vs. small stocks Standard deviation, variance, covariance, correlation, variance-covariance matrix, correlation matrix Annualized values Nominal rate Utility function Fixed income: term structure (market expectation, liquidity premium), duration, convexity Equity and fixed income valuation models Active and passive portfolio management Efficient market hypothesis Behavioural finance Modern Portfolio Theory Risk and Return CAPM Single-index model (SIM) Efficient frontier with and without short-sale Capital Market Line Security Market Line Portfolio Adjustments Assumptions of each model Revision (you should have known)
8. Required Reading: Reeves J. J. 2008 Simon Benninga (SM), Financial Modeling 3e Chapters 1, 8-13, 15, 18,25-29, 31, 33-35 http://finance.wharton.upenn.edu/~benninga/ Bodie Kane Marcus (BKM), Investments 8e Chapters 1-9, 11-16, 18, 24-27 Other materials that cover the same concepts are accepted http://www.mhhe.com/bkm
9. James Farrell, Portfolio Management, 2e Recommended Reading Philippe Jorion, Value at Risk, 3e http://merage.uci.edu/~jorion/