Can someone PLEASEEEE help me with this project!!!!

Research multi-factor linear equity model.

You are given historical time series of daily returns of 10 stocks (also given is SP500 series of returns).[Attched image is the sample ]

Build a monthly multi-factor linear model of the universe of these 10 stocks. For the monthly model convert the given time series into

monthly returns using a simple compounding formula.

When building the model:

• Try to determine what are the best possible factors that will
  • describe the universe and
  • can be extracted from the given data set
• You may rely on any additional data you can get hold of (look at BBG, google finance, etc.)

Build the model using the first 4 years of the given time series. Use last year to verify model predictions

The model is built in the following stages:

1. Compute the market beta of all 10 stocks using the time series of SP500 index as market factor
2. Compute the residual returns of all 10 stocks (stock return minus beta times the market factor)
3. Pick 4 possible factors for the model
4. Build the exposure matrix. For simplicity assume that all exposures are constant.
5. Using cross-sectional regression and forward stepwise feature selection algorithm decide which factors should be used in the model (all 4, just 3, just 2?)
6. In the final model, test the idiosyncratic component (stock returns minus factor returns times exposures) for correlation with the stock returns, with each factor, and with themselves. Explain your results.
7. From cross-sectional regressions build time series for all factors (these will extend to 4 years)
8. Compute factor covariance matrix and idiosyncratic components covariance matrix using simple covariance matrix formula
9. Compute factor covariance matrix and idiosyncratic components covariance matrix using EWMA model
10. Analyze the matrices - explain what you see and reason if observed covariances and correlations between factors make sense. How much do the matrices change when you change the model?
11. Construct an equally weighted portfolio of all stocks in the universe.
12. Compute the forecast of volatility of that portfolio based on the factor covariance matrix, factor time series, idiosyncratic covariance matrix .
13. Using both covariance matrix models compute the volatility of that portfolio based on the actual stock returns 'in sample' (for the first 4 years of the data points). Compute the volatility of the portfolio 'out of sample' (based on the last year of the points you did not use).
14. Compare all three volatilities. Compare the contributions to total portfolio risk (volatility) from factors to idiosyncratic contributions.
15. Compute contributions from individual factors to total risk. Analyze the results

Date SPY TSLA MTLS MTB MITK IMMR IBM CFG AMD AFG AAPL O ######## -NM no 000 9 10 11 12 13 1 ######## -0.01438 -0.01135 -0.01486 -0.01685 -0.01232 -0.03409 -0.01567 -0.01795 -0.03367 -0.00972 0.023805 2 ######## -0.00038 0.001836 -0.01783 -0.00491 0.009975 -0.01176 -0.0113 -0.00894 0.052265 -0.0078 0.035387 3 ######## 0.009993 0.020416 0.058659 0.000692 0.020988 0.025132 0.010451 0.003279 -0.00331 0.013548 0.033999 4 ######## -0.00387 0.024848 -0.05013 -0.00726 -0.01451 -0.01419 -0.01169 -0.01552 0.004983 -0.00441 -0.00562 5 ######## 0.000187 0.004576 -0.00833 0.001916 -0.00982 0.04712 0.006695 0.003735 0.018182 0.003759 -0.01166 6 ######## 4.71E-05 -0.00824 0.002801 0.002608 -0.00991 -0.00875 -0.00271 -0.00083 0.001623 -0.00495 -8.81E-05 7 ######## 0.011245 0.002834 0.001397 0.003208 0.01627 0.007566 0.006993 0.005792 0.019449 0.014787 -0.00018 8 ######## 0.006301 0.005896 0.005579 0.007347 0.008621 0.006258 0.003729 -0.00288 0.012719 0.006226 0.009423 9 ######## -0.00548 0.004941 0.009709 0.004891 -0.00977 0.004975 -0.00724 0.007013 0.028257 -0.02054 -0.01666 10 ######## -0.0081 0.007423 0.07967 -0.02527 -0.0111 -0.01238 -0.00645 -0.01844 -0.03511 -0.00941 0.001508 11 ######## 0.006208 -0.01522 0.007634 0.012001 0.028678 0.028822 0.01812 0.014607 0.03481 0.004206 0.00186 12 ######## 0.004963 0.002235 -0.04924 0.001558 0.030303 -0.01827 0.009695 0.004114 0.007645 0.014728 0.007605 13 ######## -0.00905 -0.027 0.017264 -0.01011 -0.02706 -0.02109 -0.00114 -0.01475 0.01214 -0.00759 -0.01553 14 ######## 0.007546 0.016592 0.005222 0.01362 0.002418 0.034221 0.00468 0.027443 0.035982 0.006306 0.007755 15 ######## -0.0024 0.047395 0.036364 -0.00775 0.024125 -0.01471 -0.00781 -0.00445 0.005789 -0.0084 -0.00469 16 ######## -0.0051 -0.01072 0.046366 0.011111 -0.01531 -0.01119 -0.0073 0.015854 0.002878 0.009547 0.004266 17 ######## 0.004425 -0.01395 0.014371 0.013565 -0.00718 0.002516 0.003962 0.023209 -0.02726 0.013053 0.000443 18 ######## 0.000696 -0.03579 -0.04486 0.002372 -0.04337 -0.03388 -0.00127 0.004693 0.026549 -0.0025 0.00743 19 ######## -0.00343 -0.02184 -0.01483 -0.00431 -0.01889 0.015584 -0.00771 -0.0074 -0.03017 0.001977 0.001493 14 15 16 17 18 19 20 21 Date SPY TSLA MTLS MTB MITK IMMR IBM CFG AMD AFG AAPL O ######## -NM no 000 9 10 11 12 13 1 ######## -0.01438 -0.01135 -0.01486 -0.01685 -0.01232 -0.03409 -0.01567 -0.01795 -0.03367 -0.00972 0.023805 2 ######## -0.00038 0.001836 -0.01783 -0.00491 0.009975 -0.01176 -0.0113 -0.00894 0.052265 -0.0078 0.035387 3 ######## 0.009993 0.020416 0.058659 0.000692 0.020988 0.025132 0.010451 0.003279 -0.00331 0.013548 0.033999 4 ######## -0.00387 0.024848 -0.05013 -0.00726 -0.01451 -0.01419 -0.01169 -0.01552 0.004983 -0.00441 -0.00562 5 ######## 0.000187 0.004576 -0.00833 0.001916 -0.00982 0.04712 0.006695 0.003735 0.018182 0.003759 -0.01166 6 ######## 4.71E-05 -0.00824 0.002801 0.002608 -0.00991 -0.00875 -0.00271 -0.00083 0.001623 -0.00495 -8.81E-05 7 ######## 0.011245 0.002834 0.001397 0.003208 0.01627 0.007566 0.006993 0.005792 0.019449 0.014787 -0.00018 8 ######## 0.006301 0.005896 0.005579 0.007347 0.008621 0.006258 0.003729 -0.00288 0.012719 0.006226 0.009423 9 ######## -0.00548 0.004941 0.009709 0.004891 -0.00977 0.004975 -0.00724 0.007013 0.028257 -0.02054 -0.01666 10 ######## -0.0081 0.007423 0.07967 -0.02527 -0.0111 -0.01238 -0.00645 -0.01844 -0.03511 -0.00941 0.001508 11 ######## 0.006208 -0.01522 0.007634 0.012001 0.028678 0.028822 0.01812 0.014607 0.03481 0.004206 0.00186 12 ######## 0.004963 0.002235 -0.04924 0.001558 0.030303 -0.01827 0.009695 0.004114 0.007645 0.014728 0.007605 13 ######## -0.00905 -0.027 0.017264 -0.01011 -0.02706 -0.02109 -0.00114 -0.01475 0.01214 -0.00759 -0.01553 14 ######## 0.007546 0.016592 0.005222 0.01362 0.002418 0.034221 0.00468 0.027443 0.035982 0.006306 0.007755 15 ######## -0.0024 0.047395 0.036364 -0.00775 0.024125 -0.01471 -0.00781 -0.00445 0.005789 -0.0084 -0.00469 16 ######## -0.0051 -0.01072 0.046366 0.011111 -0.01531 -0.01119 -0.0073 0.015854 0.002878 0.009547 0.004266 17 ######## 0.004425 -0.01395 0.014371 0.013565 -0.00718 0.002516 0.003962 0.023209 -0.02726 0.013053 0.000443 18 ######## 0.000696 -0.03579 -0.04486 0.002372 -0.04337 -0.03388 -0.00127 0.004693 0.026549 -0.0025 0.00743 19 ######## -0.00343 -0.02184 -0.01483 -0.00431 -0.01889 0.015584 -0.00771 -0.0074 -0.03017 0.001977 0.001493 14 15 16 17 18 19 20 21