Questions tagged [regression]

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

Filter by
Sorted by
Tagged with
1
vote
0answers
161 views

logistic regression multivariable fractional ploynomials stata vs. R

I a going through Hosmer, Lemenshow and Sturdivant's (HLS) Applied Logistic Regression (2013) and trying to interpret the difference between what STATA is doing and what R is doing. Concerning the fit ...
1
vote
0answers
36 views

Are the returns in this regression signed returns?

In this paper about combining multiple alphas are the returns signed returns? if not wouldn't they be mean zero? Also, it mentions "realized alpha returns" - does that just mean "realized" past alpha ...
4
votes
0answers
216 views

Trading signal strength: [-1 to 1] or [predicted return]?

In the context of a backtesting engine, is it better to have strategy generate trade signals in the range from -1 to 1 or as exact predicted returns (e.g. -12% or 26%). The difference lies in how to ...
1
vote
1answer
531 views

Can a momentum strategy be cast as a multilinear regression model?

Disclaimer: the question is similar to Can momentum strategies be quantitative in nature? and (to an extent) What is the expected return I should use for the momentum strategy in MV optimization ...
0
votes
1answer
83 views

Heteroskedasticity and significance of parameters

I am doing a regression analysis and my variable of interest turns out to be significant at the 5% level, but the model contains heteroskedasticity which can not be mitigated (using Box-Cox, Feasible ...
0
votes
3answers
2k views

Fama-French three-factor model vs four-factor (Carhart) and five-factor model

I'm performing a study where I compare the Fama-French three factor model to the CAPM on the Swedish industrials industry. I do this to compare which of the models is the best performer, but also if ...
1
vote
0answers
17 views

Affect of choosing different combinations of variables for multivariate regression [closed]

If I have variables x1,x2,x3,and x4 that have correlation coefficients −0.9,−0.5,0.5, and 0.9 to another variable y, what is the effect of choosing different combinations of them in a multivariate ...
2
votes
1answer
255 views

Regressing using Fama-French portfolios with small amount of stocks

I'm doing some research for my thesis and I was wondering if it is possible to only use monthly stock price data for 22 stocks and construct Fama-French portfolios out of them and then regress? What ...
1
vote
0answers
398 views

Cross-sectional Regression: Using calculated coefficient of first regression for a second regression as dependent variable

Hello stackexchange community! I am new to R and econometrics and and stuck in a step of the fama-macbeth (1973) regression, in which risk premia of stocks are estimated with a two-step regression ...
3
votes
2answers
2k 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 t-stat, $...
2
votes
1answer
112 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 ...
6
votes
3answers
1k views

Modelling and forecasting mixed frequency financial data

I was wondering if someone could provide some guidance to me. I would like to Combine various financial data of mixed frequencies (some daily, weekly, some quarterly) to a composite index. I have ...
7
votes
3answers
295 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 ...
1
vote
0answers
76 views

How to combine regression models?

Say I have three data sets of size $n$ each: $y_1$ = heights of people from the US only $y_2$ = heights of men from the whole world $y_3$ = heights of women from the whole world And I build a ...
0
votes
1answer
361 views

Deriving the single factor model

Consider the following regressions, with the common factor $x$: $y_1 = \beta_1 \cdot x + \gamma_1 \cdot \epsilon_1 $ $y_2 = \beta_2 \cdot x + \gamma_2 \cdot \epsilon_2 $ With $\epsilon_1$, $\...
18
votes
2answers
19k 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 ...
10
votes
4answers
1k views

Hedge Fund risk management on a daily basis

Since Hedge Funds/Fund of Funds report on a monthly basis usually within 10 days after the month end, monitoring and managing (hedging) potential risks is quite a difficult task. Having done some ...
1
vote
1answer
421 views

For a Fama-Macbeth regression , How does one predict the returns based on the model?

Fama-Macbeth does a two-step regression i.e a time-series and cross-sectional regression and we estimate betas and lambdas, so how does one predict based on these parameters, which one to choose?
2
votes
1answer
489 views

How to retrieve and format futures data for use in regression/time series models?

I need to form a predictive time series model for monthly Brent crude oil spot price. I am looking to form 1-12 month ahead forecast horizons. There is a bounty of previous literature which uses ...
1
vote
1answer
55 views

What is wrong in my non-linear estimation sample code?

I am trying to reproduce the code and plot you see here on pages 8,9 and 10 which was coded in MATLAB, but I'd like to convert it to R code. I believe I converted the MATLAB code below to R syntax ...
3
votes
3answers
854 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 ...
1
vote
0answers
102 views

Transforming Variables in time series regression

I have multiple quarterly time series data and trying to build a linear regression model using this dataset. Should the transformations on the LHS and RHS be the same i.e QoQ percent changes? Could ...
1
vote
1answer
827 views

Differences between dummy regression event study and regression on residuals from market model

I have two different event study approaches and I wonder if the results are exactly the same. Model 1 applies a dummy regression market model: (1) $R_{t}=\beta_{0} + \beta_{1}R_{mt}+\beta_{2}D_{t}+\...
2
votes
4answers
656 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 stock,...
1
vote
1answer
62 views

Testing day of the week effect

I am currently reading a bit about testing day of the weeks effects. I saw two different model specifications and wonder how to interpret the results. The first model type includes only 4 dummies for ...
-2
votes
1answer
61 views

regression analysis [closed]

"A model estimated with a large no. of observations may allow one to reject null hypothesis of zero coefficients for many explanatory variables.Thus we might choose to select a somewhat lower ...
1
vote
1answer
70 views

Adjust regression for thin trading

What procedures can I apply to control in a regression on company returns for thinly traded stocks? Is the inclusion of the SMB-factor a potential approach? Or just a dummy variable indicating if a ...
9
votes
3answers
1k 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 ...
1
vote
1answer
202 views

Rebucketing Risk using PCA/other methods

was working on a project and could use some help. New to the community and looking fwd to being an active part of it. My question is, let's say we have a vector of securities V, and it trades with ...
0
votes
0answers
41 views

Is there a considered floor for variation the 1st principal component must explain?

I am wondering if there is a considered floor to the percentage variation the 1st principal component must explain in general for PCA - ie. any lower and it is not worth doing PCA at all? Is the floor ...
2
votes
1answer
350 views

Orthogonal Regression/PCA

I am doing orthogonal regression. My X matrix consists of returns on a broad market index, value index, growth index, a few sectors,.....(my Y is the returns on an equity fund) I am regressing the Y ...
1
vote
0answers
31 views

Calculating rate of renewal for Certificate of Deposit

I am trying to calculate the rate of renewal for a large stock of Certificates of Deposit. These contracts are given on a fixed amount of time and some of them get renewed every time they reach ...
1
vote
1answer
85 views

Regression model syntax

I'm following the methodology outlined in Developing High-Frequency Equities Trading Models. On page 27, the author outlines an OLS regression model to obtain beta coefficients. The model is defined ...
8
votes
2answers
3k views

Using cross-sectional factor model (BARRA type) returns in a time series factor model (Fama-French type)?

This may be seen as a follow up question for the previous discussion on time-series vs cross-sectional factor models: Which approach to estimating fundamental factor models is better, cross-sectional (...
17
votes
2answers
404 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 ...
2
votes
1answer
278 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). ...
4
votes
1answer
2k 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. ...
1
vote
1answer
979 views

how can I calculate the factor loading (beta)?

I am writing my Thesis about hedge funds performance measurement and I want to use the seven factor model proposed by Fung & Hsieh (2004). Now, I am struggling to find out how to calculate the ...
1
vote
0answers
133 views

Confused on interpretation of betas/alphas in regression in finance

I ran a regression on two stocks. I don't have the data in front of me, but it is a more conceptual question. Let's say SP500 returned a total 23% return over this time period and MSFT returned 9%...
2
votes
2answers
304 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 ...
7
votes
3answers
5k 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 ...
40
votes
3answers
28k views

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 ...
0
votes
1answer
4k views

Linear Model setup for Second-pass Regression

I'm confused on modeling the second pass regression given the beta's from the first pass. First-pass regression : $r_{it} - r_{ft} = a_{i}+b_{i}(r_{Mt}-r_{ft})+e_{it}$ For estimating this model (9 ...
3
votes
5answers
378 views

Technical Indicators reference

I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
2
votes
1answer
2k views

Actually benefiting from logistic regression to estimate probability of default

Does anyone know any events where using logistic regression to estimate probability of default has led to a bank, financial institution, government or anything really to benefit in practice? I see a ...
9
votes
1answer
535 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 ...
1
vote
0answers
73 views

Should I use a correlation coefficient formula or a multiple regression formula?

I have an assignment dealing with the stock market and I'm a little lost. My instructions are to come up a method to create a score for a stock then compare the score against what the stock actually ...
2
votes
2answers
762 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
3answers
369 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 '...
7
votes
1answer
410 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 ...

1
3 4
5
6 7