Questions tagged [finance]

The science that describes the management, creation and study of money, banking, credit, investments, assets and liabilities.

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0answers
300 views

Cumulative Abnormal Returns (CARs) vs ARIMAX

What is the difference in using an ARIMA model with covariates ($X_i$) to estimate the shock of $X$ on time series $Y$ and using a Cumulative Abnormal Returns (CAR)? I have limited knowledge about ...
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0answers
19 views

How do we get the variance for five or more assets (actions)?

For a finance project I have to compute my portfolio's variance. I know that for two asset(actions) it is : But what is the variance for five or more assets (actions) ? Is it : $$\operatorname {...
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1answer
339 views

Exchange rate forecasting using ARIMA

I would like to conduct an ARIMA forecast of an exchange rate with 3030 daily observations. I followed these steps; I looked for the stationary for the original series ($P_t$) and concluded that the ...
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1answer
140 views

Regression using cyclical S&P500 closing prices

I'm trying to figure out whether how the S&P500 reacts from the change in bullish sentiments of people and the change in people's allocation in stocks. The bullish proportion and stock allocation ...
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0answers
191 views

Multivariate regression analysis with 7 variables

I have 7 variables: 1 dependent and 6 independent variable 3 of these independent variables affect 3 of the other independent variables (currency, interest rates, and revenues in respective ...
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2answers
787 views

Possible stock market project using dataset and ANOVA (analysis of variance) [closed]

It's a class project (however, I want to expand it to make it bigger than just a class project for my own sake) and the professor gives us freedom to choose whatever we want as long as it includes ...
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1answer
112 views

Independent vs Dependent Regression correlation?

I am trying to look at stock tickers. For example "MSFT" is the stock ticker for Microsoft. Using the package quantmod, an R package, I can get the latest 365 days of stock prices. Each quarter a ...
2
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1answer
72 views

Prediction of price reversal after event/shock - What is this called?

I have a specific but interesting problem at hand: I know that the price of a stock falls after a negative shock (in t-1). Now it is the day after the shock (t) and I want to know whether the price ...
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1answer
103 views

Sample Covariance Matrix Computation

The covariance matrix has the property that it is positive semi definite. Occasionally when calculating the sample covariance matrix this is not the case. What can be done in these cases? Many ...
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2answers
4k views

Finding correlations between financial time series

I have a task which is related to finding correlations between time series. I have two financial time series given, which contain daily interest rate offers of two financial contributors and I want to ...
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0answers
550 views

Predict stock market using twitter

I'm trying to predict the daily positivity or negativity of stock market value through Twitter. I researched a lot about this topic and I found this article to start. Basically, what I've done is get ...
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1answer
695 views

Should I rescale inputs for a neural networks? (Application on predicting stock market)

I'm implementing an ANN that predicts the stock market but I have a doubt about which inputs should I take into account and the best way to normalize them. I want to predict the daily highest value ...
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0answers
30 views

Granger causality: which kind of variables can you compare?

Can I use Granger causality for studying if a financial variable (stock price of a company) can be caused by a non-financial variable (like the number of hours the employers work)? Or do I have to ...
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0answers
22 views

Situation where something autocorrelated is misidentified as being informative?

I work in finance, and there's a class of models that works something like this: Market A -> Market B Very simply, you theorise that market A predicts market B, and you build a model along the lines ...
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1answer
233 views

How can I identify market regimes with a Hidden Markov Model?

I am trying to identify market regimes (2 states: bull or bear) with percent changes in equity returns. Can you help me in the mathematicl modeling of this? So far, I thought that for each day, there ...
4
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1answer
2k views

Dynamic Conditional Correlation (DCC) model yields unexpected sign of fitted correlations

I'm calculating DCC between S&P500 and US 10-year bond index in R. However the results are in the unexpected sign. For example, as published by many, DCC between S&P500 and 10-year bond index ...
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1answer
101 views

Including omitted variables in intervention analysis with ARIMA

I am wondering about omitted variables in the context of intervention analysis. In my research, I have a time series of price differences between two regional commodity markets as the dependent ...
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1answer
2k views

Using Regression to Determine whether the CAPM holds

I am taking a beginners course in econometrics and have some questions regarding regressions and the CAPM. Using data from Yahoo Finance and Kenneth French I run a regression according to the ...
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0answers
498 views

Neural networks and signal-to-noise ratio

My guess is that neural networks do not work very well in noisy environments, i.e. the lower the signal-to-noise ratio, the worse the result of a neural network, if compared to other statistical ...
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0answers
54 views

beta distribution and Yale crash indice

I am trying to use Yale stock market crash confidence indice to make a crash probability estimate. The Crash Confidence Index is the percentage of respondents who think that the probability of a crash ...
0
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1answer
43 views

Can CAPM be applied in this non-financial context?

I want to get a "score" which measures the efficiency of truck planners at a site which despatches boxes of identical size to multiple destinations. This is measured by how full each truck is, on ...
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1answer
995 views

Mean and SD of daily log returns

Assume a given stock's log returns are normally distributed, its average annual log return = 100% and annual standard deviation (or volatility) = 200%. Given a trading year of 250 days, what are $\mu$ ...
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0answers
84 views

How much of an indicator can machine learning provide in detecting good versus bad market conditions (risk)?

I generally buy and sell weekly as stock prices fluctuate with volatility. I am exploring the idea of using a machine learning algorithm to consider various economic conditions (inputs) and provide ...
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0answers
95 views

Can oil price volatilily be used as an explanatory variable?

I'd like to see if the oil price trend (as measured by WTI or Brent indexes) can help to predict the volatility of a stock. I am using a GARCH approach to model volatility of the FTSE. Now, if I ...
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0answers
32 views

Logit to calculate the likelihood of distress in a firm

Suppose I use a logit model to calculate the probability of financial distress for a firm. I use some ratios that I believe are relevant in determining the success/failure of a Company and, using ...
6
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2answers
1k views

How does numer.ai make predictions about the future?

Numer.ai is a crowd sourced hedge fund that uses the individual classifiers of its users to predict future asset prices. They themselves do not provide a lot of information on how it works. There is ...
7
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2answers
4k views

Estimating the CAPM Beta via OLS Regresson

I am studying econometrics from the third edition of 'Introduction to Econometrics' by James H. Stock and Mark W. Watson. On page 166 it digresses into the beta of the stock. It says Those betas ...
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0answers
437 views

Event study - impact of Brexit idea

I am doing about analysing the impact of Brexit on British stock market i.e. FTSE 100 and FTSE 250. I did run OLS for estimation window to find the market model for each company in FTSE 100 and FTSE ...
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1answer
3k views

Forecasting with ARIMA and GARCH: does my plan look alright?

I have a time series containing the daily close price for a stock and I would like to perform a 10 days forecast of the volatility. I'm trying to follow this tutorial: https://talksonmarkets.files....
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2answers
696 views

How can machine learning be applied to stock price prediction?

In machine learning, for a given input instances you get an output what are present at the same time. But in stock market you have to predict the next price based on previous inputs. So if you want to ...
0
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1answer
270 views

Test statistical significance of a trading strategy

I have created a trading strategy which operate every single day on the DAX 30, for the last 1700 trading sessions (some years). I have the daily returns of my strategy and also the daily returns of ...
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0answers
58 views

What is a good place to start working on using machine learning for credit risk modelling?

For my masters thesis,I need to work on credit risk modelling using machine learning. What are some of the resources to find some literature on the work that has currently been done in this area? Also ...
0
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1answer
189 views

Elastic Net: How to get more sparsity than “lambda.1se” in R package glmnet

Package glmnet provides a cross validation function called cv.glmnet that allows us to choose between two suggested models (from the many), labelled "lambda.min" and "lambda.1se". However even "lambda....
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0answers
760 views

Cointegration with Multiple Structural Breaks

I am currently studying whether stock markets in the GCC region are cointegrated. All series are I(1); however, the Johansen test provides no robust evidence of cointegration. I then used the Bai ...
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1answer
117 views

Appropriate measure of dispersion for q-gaussian distribution data

I have financial data (prices) which are believed to be q-Gaussian. I want to measure the dispersion for fixed periods in the dataset. Which tool is better, the mean absolute deviation, the ...
0
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1answer
59 views

Year dummies versus Macro-specific factors

My sample is balanced panel data and includes 6,071 firm-year observations for the period 2002-2014. I test the book leverage (total debt/total asset) by using lagged factors as follows: 9 firm-...
1
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1answer
274 views

classification using logistic regression on stock data

I have made a model which is supposed to classify the trend of a stock index as an "up day" (=1) or a "no change"/"down day"(=0), where I have coded an "up day" as when the percent change for the ...
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0answers
658 views

Training Neural Networks on variable length vectors

(Please note I have read through "How to handle changing input vector length with neural networks" but somehow this is different) As most of us know, financial trade data exhibit different volume and ...
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1answer
287 views

Outlier detection in GARCH(1,1) in R by Doornik & Ooms (2002)

I try to find additive and innovative outliers in the German Stock Index (DAX) using the method Doornik & Ooms explained in 2002: Estimate the baseline GARCH model to obtain log-likelihood ($lb$) ...
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2answers
146 views

Model to test seasonality of funds

My dissertation is about funds seasonality. The model that I am using is an OLS regression with dummies to check if January has a return greater than the remaining period: $$ R_t = B_0 + B_1 D_{mt} + ...
2
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3answers
792 views

Understanding a paper combining cross section with time series in finance

I am reading a paper to do with reversal signals in finance. (Apologies I am not allowed to share the actual paper.) In the paper it says it does monthly cross sectional regressions and collects the ...
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1answer
484 views

ARIMA-GARCH instead of ARIMA for intervention analysis

I'm looking to carry out an intervention time series analysis on the S&P500 to see how presidential elections affected the stock market. I want to use an ARMA-GARCH process to model S&P500 ...
1
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1answer
141 views

Negative probabilities - what are the two ordinary pgfs that correspond to the gf of a half-coin?

In Half of a Coin: Negative Probabilities, author considers pgf of a fair coin represented by random variable, $X = 1_H$: $$G_X(z) = E[z^X] = \sum_{x=0,1} z^xP(X=x) = (z^0)(1/2) + (z^1)(1/2) = \frac{...
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0answers
96 views

Extreme value theory: GPD larger expected value than average

We're using extreme value theory to model tail risks on our portfolio. After we choose the threshold, we fit generalized Pareto distribution to our data over the threshold. The expected value of GPD ...
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0answers
51 views

How to separate two classes when the features values predicting them are so similar ?

What should be my approach. I got 13 principal components from 21 numerical features. The 13 features have a gaussian distribution. The plot below is between the top two components. Should I clean the ...
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3answers
468 views

How to calculate the expected loss of a credit card transaction?

I have used an algorithm to calculate the probability of a credit card transaction to be fraudulent. The algorithm outputs a classification (fraud/no fraud) and the probability of each, such that $P(\...
1
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1answer
26 views

Run-like pattern in candle chart

To my untrained eye a pattern appears in this candle chart, where down-days (dark purple) tend to occur consecutively. I have a very basic understanding of statistics and R software, but it's been a ...
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1answer
2k views

Interpreting regression Output for CAPM

I have an interpretation problem. As you can see below there's a linear regression output for the CAPM. I don't know how to interpret the significance level. ExIndex has a very low p-value, but the ...
4
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1answer
409 views

Why is my kalman filter trusting so much my observations?

This question follows the one asked there. I am trying to filter an equity index (Stoxx 600) time series using kalman filter. I'm using the R package dlm and my code is inspired from the dlm ...
0
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1answer
347 views

Low performance of SVM (and neural network) in out-of-sample data with high test accuracy of 10-fold cross validation in a financial time series

I'm using SVM and (neural network) for a time series prediction data-set in MATLAB R2016a with 800 samples. Currently I'm using 10-fold cross validation and grid search to find best SVM parameters. I'...