www.quantandfinancial.com
Open in
urlscan Pro
2a00:1450:4001:801::2013
Public Scan
Submitted URL: http://quantandfinancial.com/
Effective URL: https://www.quantandfinancial.com/
Submission Tags: falconsandbox
Submission: On June 02 via api from US — Scanned from DE
Effective URL: https://www.quantandfinancial.com/
Submission Tags: falconsandbox
Submission: On June 02 via api from US — Scanned from DE
Form analysis
1 forms found in the DOMhttps://www.quantandfinancial.com/search
<form action="https://www.quantandfinancial.com/search" class="gsc-search-box" target="_top">
<table cellpadding="0" cellspacing="0" class="gsc-search-box">
<tbody>
<tr>
<td class="gsc-input">
<input autocomplete="off" class="gsc-input" name="q" size="10" title="search" type="text" value="">
</td>
<td class="gsc-search-button">
<input class="gsc-search-button" title="search" type="submit" value="Search">
</td>
</tr>
</tbody>
</table>
</form>
Text Content
WEDNESDAY, JUNE 24 FACTOR INVESTING AND FAMA-FRENCH MODEL This notebook illustrates factor investing and five-factor Fama-French model. RISK FACTOR Certain characteristic of economy (Inflation/GDP) or stock market itself (S&P 500) FACTOR MODEL Factor model uses movements in risk factors to explains portfolio returns QUESTIONS WHICH FACTOR INVESTING ANSWERS * Why different asset have systematically lower or higher average returns? * How to manage the asset portfolio with the underlying risks in mind? * How to benefit of our ability to bear specific types of risks to generate returns? FAMA-FRENCH MODEL Assumes linear relationship between empirical factors and stock returns: * Market Factor (MER) * Size Factor (SMB) * Value Factor (HML) * Profitability Factor (RMW) * Investment Factor (CMA) Factors are constructed daily from definitions, as illustrated previously * They are global for the entire stock market Factor sensitivities are calibrated using regression * They represent “reward for taking a specific risk”, which is different for every stock * Risk/Reward relationship is expected to hold over time * Objective: maximize the model’s predictive power R2 MARKET EXCESS RETURN (MER) * Market excess return (over RF rate) alone explains around 80% of asset movements * Daily returns are ~normally distributed * Relationship between returns of the overall market and returns of selected portfolio SIZE (SMB) FACTOR * Small-cap companies typically bear additional risk premium - was it always the case? * Python can help you to see that this factor has a different prevalence in different economic regimes VALUE (HML) FACTOR * Value companies trade at higher yields to compensate for lack of growth potential * Python can help you to see that this factor has different explanatory power in different market situations and on different portfolios (very interesting) PROFITABILITY AND INVESTMENT FACTORS * Profitability factor (RMW) to attribute superior returns of companies with robust operating profit margins and strong competitive position among peers * Investment factor (CMA) to segment companies based on their capital expenditures * Analysts opinion: High capex structurally associated with growth companies, which puts usefulness of this factor in question EVALUATING 5-FACTOR MODEL * Analyst opinion: High correlations between risk factors puts usefulness of 5-factor model into question. * R2 10-20% for RMW, CMA * 5 factor improvement only by 0.2% Posted by Ondrej Martinsky No comments: Email ThisBlogThis!Share to TwitterShare to FacebookShare to Pinterest Labels: Factor investing Older Posts Home Subscribe to: Posts (Atom) NAVIGATION * Home * GitHub repository SEARCH THIS BLOG BLOG ARCHIVE * ▼ 2020 (1) * June (1) * ► 2019 (1) * January (1) * ► 2017 (2) * May (1) * February (1) * ► 2016 (2) * November (2) * ► 2013 (2) * August (1) * July (1) * ► 2012 (4) * November (1) * October (1) * August (2) LABELS * AAD * AMC * American Monte Carlo * Automatic Differentiation * Binomial Tree * Black-Litterman * Bootstrapping * C++ * CCAR * Cox-Ross-Rubinstein * Early Exercise * Efficient Frontier * Factor investing * Financial Modelling * FRTB * Greeks * Heath Jarrow Morton * HJM * Longstaff Schwartz * Mathematics * Mean Variance * Monte Carlo * Mortgage * Multi Factor * NAG * Operator Overloading * Option Valuation * Plain Vanilla Bond * Portfolio Optimization * Python * Quantitative Analysis * Spot Rates * Templates * Time Value of Money * TVM * Volatility Calculation * Yield Curve ABOUT ME Ondrej Martinsky View my complete profile POPULAR POSTS * Mean-Variance Portfolio Optimization This article introduces readers to the mean-variance optimization of asset portfolios. The underlying formulas are implemented in Pyth... * Treasury Yield Curve Bootstrapping Link: IPython notebook In the previous post, we have introduced readers to basic principles of time value of money and presented Python i... * Portfolio Optimization II : Black-Litterman model In the previous post , we have been discussing conventional approach to the portfolio optimization, where assets' expected return... * Binomial Option Pricing Model Link: IPython notebook So far we have been discussing mostly pricing and valuation of asset classes with certain and predictable cash fl... * Time Value of Money Calculator Link: IPython notebook This article shows how to use the principle of offsetting annuities to solve basic TVM problems, such as yields on ... * Factor Investing and Fama-French model This notebook illustrates factor investing and five-factor Fama-French model. Risk Factor Certain characteristic of economy (Inflation/GDP... * Heath Jarrow Morton Multi Factor Model The IPython notebook which is subject of this post contains working implementation of a multi factor Heath Jarrow Morton (HJM) model. As m... * Longstaff-Schwartz and American Monte Carlo This notebook illustrates Longstaff-Schwartz (AMC) algorithm for pricing options and other derivatives with early-exercise features. This... * Automatic differentiation via C++ operators overloading Link: Implementation on GitHub Automatic differentiation is a powerful technique which allows calculation of sensitivities (derivatives) o... * PYBOR - multi-curve interest rate framework in Python I have been recently working on a project PYBOR, a multi-curve interest rate framework and risk engine based on multivariate optimization te... Copyright © 2017 Ondrej Martinsky. Simple theme. Powered by Blogger. Diese Website verwendet Cookies von Google, um Dienste anzubieten und Zugriffe zu analysieren. Deine IP-Adresse und dein User-Agent werden zusammen mit Messwerten zur Leistung und Sicherheit für Google freigegeben. So können Nutzungsstatistiken generiert, Missbrauchsfälle erkannt und behoben und die Qualität des Dienstes gewährleistet werden.Weitere InformationenOk