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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20 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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2answers
103 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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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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1answer
57 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 ...
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6answers
5k 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 ...
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0answers
13 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 ...
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3answers
219 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 ...
3
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0answers
61 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 ...
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3answers
277 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 ...
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1answer
57 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 ...
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0answers
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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0answers
14 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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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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0answers
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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0answers
24 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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0answers
15 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 ...
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0answers
32 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 ...
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2answers
107 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,...
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2answers
113 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 ...
2
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1answer
57 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 ...
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3answers
134 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 ...
0
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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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0answers
33 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 ...
4
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2answers
146 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 ...
9
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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 ...
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0answers
22 views

Regression on default rates and backward extrapolation

Suppose that we have bankruptcy data representative for Small and Medium-sized enterprises in a country. We can therefore calculate default rates. Furthermore suppose that we found that GDP, ...
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0answers
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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0answers
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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1answer
66 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
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1answer
47 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 ...
2
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1answer
124 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
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1answer
46 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
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3answers
80 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 ...
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0answers
52 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 ...
6
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1answer
173 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
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1answer
48 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}+\...
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4answers
148 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,...
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1answer
38 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 ...
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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
30 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 ...
8
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3answers
394 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 ...
4
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1answer
513 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
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1answer
67 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
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0answers
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 ...
7
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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 ...
2
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1answer
66 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). ...
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0answers
17 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 ...
6
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2answers
569 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 ...
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1answer
63 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 ...
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0answers
271 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 (...