Fama-French Global Factor Universe
What are the Fama French 5 factors?
A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns performs better than the three-factor model of Fama and French (FF, 1993).
What does the Fama French model show?
The Fama-French model aims to describe stock returns through three factors: (1) market risk, (2) the outperformance of small-cap companies. relative to large-cap companies, and (3) the outperformance of high book-to-market value companies versus low book-to-market value companies.
What is a major criticism of Fama and French model?
One of the major criticisms of the Fama French model was that the value premium was sample specific and was likely to be a “mere artifact of data mining” as indicated by Black (1993). Black (1993) argued that the existence of value premium is a mere chance unlikely to recur in future returns.
What is the Fama French HML factor?
High Minus Low (HML), also referred to as the value premium, is one of three factors used in the Fama-French three-factor model. The Fama-French three-factor model is a system for evaluating stock returns that the economists Eugene Fama and Kenneth French developed.
What is the value factor Fama-French?
The Fama and French model has three factors: the size of firms, book-to-market values, and excess return on the market. In other words, the three factors used are SMB (small minus big), HML (high minus low), and the portfolio’s return less the risk-free rate of return.
What is CMA Fama-French?
For their part, Fama and French updated their model with two more factors to further capture asset returns: robust minus weak (RMW), which compares the returns of firms with high, or robust, operating profitability, and those with weak, or low, operating profitability; and conservative minus aggressive (CMA), which …
What is the Fama-French 4 Factor Model?
Momentum is calculated by investing in firms that have increased in price while selling firms that previously decreased in price (winners minus losers). Today, the four factors of market, style, size, and momentum, constitute the Fama-French 4 Factor Model.
Is the Fama-French three factor model better than the CAPM?
Empirical results point out that Fama and French Three Factor Model is better than CAPM according to the goal of explaining the expected returns of the portfolios. However, the paper shows that the results vary depending on how the portfolios are formed.
How are Fama-French factors calculated?
The Fama-French Three Factor model calculates an investment’s likely rate of return based on three elements: overall market risk, the degree to which small companies outperform large companies and the degree to which high-value companies outperform low-value companies.
Does the Fama-French model work?
Discussion. The Fama–French three-factor model explains over 90% of the diversified portfolios returns, compared with the average 70% given by the CAPM (within sample). They find positive returns from small size as well as value factors, high book-to-market ratio and related ratios.
What are the risk factors of the Fama-French four factor model?
Today, the four factors of market, style, size, and momentum, constitute the Fama-French 4 Factor Model.
Why is Fama French better than CAPM?
It means that Fama French model is better predicting variation in excess return over Rf than CAPM for all the five companies of the Cement industry over the period of ten years. Low p values indicate that the coefficients are statistically significant.
How do you make a Fama French portfolio?
The Fama-French Portfolios are constructed from the intersections of two portfolios formed on size, as measured by market equity (ME), and three portfolios using the ratio of book equity to market equity (BE/ME) as a proxy for value.
How do you do Fama in MacBeth regression?
Fama–MacBeth regression
- First regress each of n asset returns against m proposed risk factors to determine each asset’s beta exposures.
- Then regress all asset returns for each of T time periods against the previously estimated betas to determine the risk premium for each factor.
What is cross sectional regression analysis?
In statistics and econometrics, a cross-sectional regression is a type of regression in which the explained and explanatory variables are all associated with the same single period or point in time.
What is the difference between cross-sectional and time series analysis?
The difference between time series and cross sectional data is that time series data focuses on the same variable over a period of time while cross sectional data focuses on several variables at the same point of time. Different data types use different analyzing methods.
What is the difference between time series and cross-sectional data?
The difference between cross-sectional data and time-series data is that time-series data considers the same variables over a certain period of time, whereas cross-sectional data uses different data for a given point in time.
What is the difference between panel data and cross-sectional data?
Cross sectional data means that we have data from many units, at one point in time. Time series data means that we have data from one unit, over many points in time. Panel data (or time series cross section) means that we have data from many units, over many points in time.
Why is panel data better than others?
Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data. Panel data can detect and measure statistical effects that pure time series or cross-sectional data can’t.
What is the opposite of panel data?
Cross-sectional data – Observations from subjects at a given point in time. Panel data – Observations from same subjects at multiple times.
Is panel data a primary data?
Panel data are among the most extensively used of secondary data sets, precisely because they allow us to track change.
What is the difference between panel and scanner data?
Scanner data is a type of quantitative data obtained from scanner readings of UPC codes at check-out counters. Panel data is a type of quantitative data collected from a group of consumers (the panel) over time.
What is the difference between panel data and time series?
The key difference between time series and panel data is that time series focuses on a single individual at multiple time intervals while panel data (or longitudinal data) focuses on multiple individuals at multiple time intervals.