Techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.

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24
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5answers
4k views

Machine Learning vs Regression and/or Why still use the latter?

I come from a different field (Machine learning/AI/data science), but aim to ask a philosophical question with the utmost respect: Why do quantitative financial analysts (analysts/traders/etc.) prefer ...
16
votes
3answers
2k views

Please give a step-by-step explanation on how to build a factor model

Factor models such as Fama-French or the other ones that are partially summarized here work on the cross-section of asset returns. How are the factors built, how are sensitivities/coefficients ...
14
votes
1answer
5k views

Which approach to estimating fundamental factor models is better, cross-sectional (unobservable) factors or time-series (observable) factors?

There are many approaches to estimating fundamental factor equity models. I would like to focus on two traditional methods: The time-series regression approach of Fama and French. Factors are ...
14
votes
3answers
254 views

Regression model when samples are small and not correlated

I received this question during an onsite interview for a quant job and I'm still scratching my head on how to solve this problem. Any help would be appreciated. Mr Quant thinks that there is a ...
12
votes
5answers
6k views

Using linear regression on (lagged) returns of one stock to predict returns of another

Suppose I want to build a linear regression to see if returns of one stock can predict returns of another. For example, let's say I want to see if the VIX return on day X is predictive of the S&P ...
12
votes
4answers
6k views

R: Fast and efficient way of running a multivariate regression across a (really) large panel (First pass of Fama MacBeth)

I am attempting to run a rolling multivariate regression (14 explanatory variables) across a panel of 5000 stocks: For each of the 5000 stocks, I run 284 regressions (by rolling over my sample ...
9
votes
1answer
2k views

How do I reproduce the cross-sectional regression in “Intraday Patterns in the Cross-section of Stock Returns”?

Recently I was trying to reproduce the results of "Intraday Patterns in the Cross-section of Stock Returns" (published in the Journal of Finance 2010). The authors used cross-sectional regression to ...
9
votes
1answer
5k views

Time Series Regression with Overlapping Data

I am seeing a regression model which is regressing Year-on-Year stock index returns on lagged (12 months) Year-on-Year returns of the same stock index, credit spread (difference between monthly mean ...
9
votes
1answer
254 views

Regression in liquidity risk model of Jarrow/Protter

In the paper "Liquidity Risk and Risk Measure Computation" authors describe a linear supply curve model for liquidity risks in presence of market impact, i.e. impact-affected asset price $S(t,x)$ is ...
8
votes
4answers
2k views

How to perform risk factor calculation?

I am studying Arbitrage Pricing Theory (APT) and I have a question about calculating factor exposures. Assume: \begin{equation} r = \beta_1r_1 + \beta_2r_2 + ... + \beta_kr_k + r_e \end{equation} ...
8
votes
1answer
2k views

What are the steps to perform properly a risk factor analysis on a portfolio?

I have been asked to perform a factor analysis on a given portfolio, assume it's a Swiss portfolio in CHF. First step, I chose which factors I would like to see in my analysis. The first factors I ...
8
votes
3answers
365 views

Does Kalman filter always improve over linear regression?

If I have a simple linear regression that has statistical signification but I would like to improve the overall prediction results. Will a Kalman filter be always an improvement or as least achieve ...
8
votes
3answers
332 views

How to improve the consistency of explained variance statistics in a linear equity model?

I have an intraday equity returns linear model that, overall, shows good values in terms of $R^2$, p-value and other explained variance statistics. Around 70% of the stocks show consistent R-squared ...
8
votes
1answer
1k views

Expected return from a multiple linear regression?

How can I compute the predicted return from a linear regression that includes a number of different terms. For instance, suppose my equation is: $r_{future} = \alpha + \beta_1 r_{history} + \beta_2 ...
8
votes
0answers
673 views

Testing Valuation, Size and Momentum (proprietary factors) from 1988-2013: No evidence of driving cross-sectional returns

I am currently testing whether three proprietary factors - Valuation, Size and Momentum - explain cross-sectional returns. A sample of 3000 securities was tested using Fama-MacBeth two-pass ...
7
votes
2answers
466 views

Efficiency vs. Robustness - To use a constant or not in single factor time-series regression?

Arbitrage pricing theory states that expected returns for a security are linear combination of exposures to risk factors and the returns on these risk factors. Betas, or the exposures of the security ...
7
votes
3answers
122 views

Return Attribution: Possible remedies for multicollinearity

Let's say I have the following regression setup, which I am using for portfolio return attribution: $R = 1*\beta(1) + A*\beta(2) + B*\beta(3) + C*\beta(4) + \epsilon $ where A is dummy matrix of ...
7
votes
3answers
1k views

Using rolling returns in a multivariate linear regression?

I am trying to use fundamental factors such as PE, BV, & CFO in a multivariate linear regression with the response variable being the rolling 1 month returns. But this approach seems flawed as the ...
7
votes
1answer
722 views

How to run an asset replication regression?

I am doing extensive research on portfolio replication and was hoping to get some help with some problems I am encountering. I am running a regression between 2 assets that I believe replicate ...
6
votes
2answers
566 views

Why are regressors squared and not ^1.5 or ^2.2 or ^2.5?

When a researcher in economics or finance wants to apply a linear regression model but suspects a non-linear relationship between one of the regressors and the dependent variable, it is typical to ...
6
votes
3answers
630 views

Testing the validity of a factor model for stock returns

Consider the following m regression equation system: $$r^i = X^i \beta^i + \epsilon^i \;\;\; \text{for} \;i=1,2,3,..,n$$ where $r^i$ is a $(T\times 1)$ vector of the T observations of the dependent ...
6
votes
2answers
496 views

How to interpret the French-Fama SMB factor?

I regressed ten portfolios on the Fama French factors and get significant loadings on the SMB factor. However, if I look at the actual average market cap of these portfolios, the portfolios with the ...
6
votes
0answers
93 views

Is Least Median Squares (LMS) regression commonly used in Finance?

Least Median Squares is often argued to give more stable results than does OLS. Whereas in OLS one minimises the mean of squared residuals, in LMS, one instead minimises the median of squared ...
5
votes
2answers
195 views

How does the number of free dimensions of a model affect its required size of sample?

Adding more variables to a model usually increases its accuracy. However, without adequate analysis it could also lead to curve fitting. Another question (How much data is needed to validate a ...
5
votes
1answer
214 views

From $AR(p)$ to SDE

Let the Vasicek model to be $$\Delta r_{t}=k(\theta - r_{t-1})\Delta t+\sigma\Delta z_{t}$$ Due to the fact that $$\Delta r_{t}=r_{t}-r_{t-1}$$ if you let $\Delta t=1$, it is easy to see by ...
5
votes
1answer
165 views

Filtering out AR(1) effects before using stochastic volatility model

I wonder if I first filter out AR(1) (autoregressive model with lag 1) effects from univariate time series and then fit stochastic volatility model does above procedure introduce any bias at first or ...
5
votes
1answer
3k views

How to use Newey West covariance corrector?

I have implemented the following model: daily_vol(t+1) = A*daily_vol(t) + B*weekly_vol(t) + C*monthly_vol(t) + error where vol means volatility, and A, B, C are ...
5
votes
4answers
681 views

Regressor: Nominal return, continuous return or first difference?

Suppose the application is linear models in financial econometrics. If we want to analyze stocks, the standard approach is to take the continuous/log return: $\ln{ \frac{P_t}{P_{t-1}} }$. Suppose, ...
4
votes
2answers
122 views

Fama-Macbeth second step confusion

I am confused on how to run the second step of the Fama Macbeth (1973) two step procedure. I have monthly stock returns and monthly Fama-French factors, for around 10,000 stocks. This creates an ...
4
votes
2answers
89 views

Degrees of freedom in calculating significance of GARCH coefficients

I am trying to determine the significance of coefficients of a GARCH model by calculate the p-values using the following Matlab formula: pvalues = 2*(1-tcdf(abs(t),n-v)), where $t$ is the ...
4
votes
1answer
439 views

Linear regression and assets direction prediction

I have the following asset returns Y and the predictions for the same periods Y': Y = { 10, 200, -1000, -1, -7 } Y' = { 1, 2, -3, -4, -5 } The OLR R-squared for ...
4
votes
0answers
98 views

Dividend Index Futures

My question is dealing with the proportionality between Dividend Index Futures prices and Index prices. Indeed, we in the past we used to do a simple regression between these variables and use the ...
4
votes
0answers
540 views

How to determine ratios for mean-reverting basket

Suppose I have a basket of 3 securities A, B, and C. I believe that the basket is cointegrated and I want to create a mean-reverting trade. I fit the model: ...
4
votes
1answer
1k views

What is a persistent variable?

What is a persistent variable in the context of regression analysis? For example, dividend to price ratio (D/P) is considered to be persistent variable when used to model future returns (Stambaugh ...
4
votes
1answer
458 views

Moving window forecasting in Python

I am looking to create some code that will out-of-sample forecast the HAR-RV model. The model itself is formulated as the following, and the betas are estimated through HAC-OLS or Newey-West. ...
3
votes
3answers
69 views

Interpretation of t-test in event study with dummy regression

I am not sure about my interpretation of the t-ratios in dummy regression models for event studies. I have the results for two different groups of models examining the impact of news on stock returns ...
3
votes
1answer
182 views

Estimating Beta from unevenly spaced price history

I have a certain non-stock asset that has 1 transaction every 1 to 8 months. I also have a price index of that class of asset compiled by another party on monthly basis. If I regress $price = \alpha' ...
3
votes
2answers
616 views

What's the meaning of the intercept in asset pricing model?

I would like to understand the role of alpha (intercept) in the regression-based asset pricing model or $n$-factor models; one of the most famous of those one is the Fama-French 3-factor model. ...
3
votes
1answer
186 views

Minimum Variance Hedge Ratio in Binomial Framework

In order to find the minimum variance hedge ratio when holding a portfolio of vanilla call options and hedging with stock, you can do an OLS regression. In a binomial model framework, given ...
3
votes
0answers
185 views

GMM time-series regression factor model with factors that are not returns

Factor models with factors that are not returns are usually estimated and tested by cross-sectional regressions. However, there is a way to use time-series regression to estimate and test the model. ...
3
votes
0answers
347 views

How to balance two Forex crosses correctly to do a linear regression?

I have two cross and an account in EUR: EUR/USD GBP/USD I would like to do a balanced linear regression using R. With "balanced" I mean that I would like to normalize it by calculating the ...
2
votes
3answers
466 views

Why do long-term equity return forecast models use dependent observations?

I've been reading up on different models used to forecast the equity risk premium, and I've seen a couple of papers that had questionable methods. For example, this paper by Javier Estrada goes into ...
2
votes
3answers
262 views

Ran multivariate linear regression, checked normal probability plot, residuals are not normal. What can I do?

One of the required assumptions for multiple linear regression is that residuals are normally distributed, correct? After running my regression, my normal probability plot is showing the typical ...
2
votes
4answers
142 views

How to interpret regression coefficients with dummy explanatory variables?

I am a bit confused about the interpretation of the regression coefficients in a regression model: $R_{t}=\beta_0+\beta_1R_{mt}+\beta_2D_{t}+\epsilon_t$ where $R_{t}$ is the log return of some ...
2
votes
3answers
573 views

Time Series or Regression

I'd like to research the impact of certain events and characteristics on the liquidity of the stocks over time. I've got a sample of 200 stocks and I use several measures of liquidity (Amihud, Bid-Ask ...
2
votes
2answers
253 views

Using Financial Ratios to get credit rating or PD

Hello I'm looking for papers, aside from ones that use CDS spreads, about credit rating development or estimating default probability based on financial ratios that also include methodology and maybe ...
2
votes
1answer
54 views

Linear Regession 3 methods different results

Morning, So I use a package called Ninja Trader that has a linear regression method, I have also written my own method and compared the results to excels linear regression method. All three are ...
2
votes
2answers
177 views

Transforming Variables in Regression

I have a very simple problem that hopefully someone could help me with or at least point me in the right direction. I am testing to see which factors affect index returns the most and would like to ...
2
votes
3answers
698 views

What data transformations to use in regression of credit spreads on equity prices?

Clearly there is a strong relationship between credit spreads and equity prices (both theoretically and empirically). But how would one go about formulating a regression which seeks to explain this ...
2
votes
1answer
60 views

How to deal with missing returns when creating value (equal) weighted returns

recently I am doing cross sectional regressions, and getting confused about missing returns. Suppose we have 100 stocks, then we want to construct a value weighted return (or equal weighted return). ...