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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300 views

What data should be used for regression-based model backtesting?

I ran regressions using historical valuation data and now want to backtest the models I came up with. Are there any issues with using the same historical data set for the backtest that I need to be ...
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3answers
421 views

How to create a model or formula for evaluating trade opportunities

I want to build a formula to produce a score for a potential trade based on 4 variables, time, return, liquidity of security, and probability of failure. For a set of potential trades I first ...
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2answers
194 views

For Probability of Default in retail credit what is more popular logistic regression or GLM with Poisson distribution and why?

Trying to understand which regression model is more popular in retail credit card industry Logistic regression or GLM with Poisson distribution and why?
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2answers
546 views

Regression with Lagged variables

I am new to regression analysis. Let's say initially I have a linear regression x = alag(x1) + blag(x2) + clag(x3) -- eq 1 I want to predict the price x based on the the price of x from previous ...
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599 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 ...
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122 views

How to construct a deterministic trading model based on a loess (local regression) model?

Given data that has been fit to a loess model, can you make reliable decisions on future trades given a good past fit? Has anyone here done so and can give an example of their use case? I am yet to ...
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0answers
19 views

How to measure practically the performance of Venture Capital backed tech firms following an IPO?

I am currently writing a thesis about whether the fact that a tech firm backed by venture capital companies achieves higher returns following an IPO (Horizon of 3 years). I have about 800 tech ...
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10 views

subsamples versus dummy variable approach, Fama MacBeth (1973) procedure

I am running an asset pricing test (Fama MacBeth); regressing six month ahead excess stock returns on past six month return (momentum) and a number of control variables (B/M, Size etc). I have run my ...
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13 views

Deming Regression

I am trying to test the linearity = interdependence or the non-linear (contagion) between Asian countries during the Asian crises using the fluctuation of the exchange rate. Is it relevant to use the ...
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1answer
32 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 ...
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26 views

Practical Implications of Fama French Loadings

Suppose you have historical returns for a portfolio. You regress these against the Fama French factors to get the loadings/coefficients. How can you use this information? For example, can you use the ...
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17 views

Using the univariate regression coefficient to calculate cumulative return - does it make sense?

When testing a stand-alone signal usually one of the simple tests I do is a long-short equal-weight strategy to see how the wealth chart looks like. Going through my predecessor's code I see ...
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2answers
101 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 ...
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0answers
23 views

Why do people use weighted regression with returns?

For example, by ADV. Intuitively it makes sense that a very liquid high ADV stock should carry more weight, but when I try it with some real life data I get higher standard error than unweighted...is ...
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1answer
49 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 $\epsilon_1$, $\...
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14 views

Magnitude of Predictors on Logistic Regression

We are using logistic regression for calculating delinquency. We know what the major predictors are, but we don't know how to quantify the impact of each of the major predictors. We know how to rank ...
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47 views

Seasonality of Securities & Dummy Variable Regression Analysis

I have some pricing data for some securities that I am looking at for seasonality. 1 My Data is organized as: Date Ret DVar1 DVar2 ...... date % 1 0 date % 0 1 ...
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33 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 ...
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1answer
114 views

Regression extensions

I'm trying to find extensions for my regression and obviously would like to use PE, BV and CFO. But I've got monthly data, while all company's fundamentals are semi-annually... Can I deal with it ...
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1answer
42 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 ...
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1answer
527 views

Trading Strategies and Portfolio Constructions based on Cross Sectional Regression? [closed]

I often see trading strategies and portfolio construction that are based on cross-sectional regression. For example, I often see regressing some numbers against some factors. I was wondering how ...
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1answer
754 views

How to calculate the weight of the stocks using the linear regression?

I do a simple example with the follow three series(stocks prices): ...