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I prefer thinking in terms of well measured vs. poorly measured rather than significant vs. insignificant: arbitrary p-value cutoffs and ignoring sensible priors can both be problematic. On the question, "can poorly measured betas from time-series regressions give rise to well measured factor premiums from cross-sectional regression?" The abstract answer is ...

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One of the best ways I came across to estimate the expected return of a stock (even with limited time-series data), is Martin and Wagner (2019): What is the expected return on a stock?. From their paper: Second, our formula provides conditional forecasts at the level of the individual stock. Rather than asking, say, what the unconditional average ...

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Expected returns are very difficult to estimate reliably without incurring estimation error as found out by Merton (1980) "On estimating the expected return on the market". This is why estimating volatility/the covariance matrix has become the default approach in the mean-variance model because volatility is easier to predict than returns. Even the global ...

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Point of clarification : are you asking about required returns in pounds sterling for projects conducted in the UK? If so, you also need to adjust for any difference in risk free interest rates between Euro and UK. You can do this be comparing 5yr UK gilt yields with 5year German govt bond yields , for example. I believe the UK yields are higher, so you ...

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It depends. Is the project being carried in the original place or moving to the UK? If the project keeps being in the original place (i.e. euro zone) and you want the IRR in GBP, then you need somehow to factor in the currency fluctuation for the cash-flows. That can be done either through adjusting the cash-flows with an expected exchange rate or by ...

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I get the maximization problem $$\max\limits_{w} \mathbb{E}\left[W_1\right] - \frac{\gamma}{2} Var(W_1)$$ $$st. W_1 = W_0(1 + r_f + w^Tr)$$ So we have \begin{align*} L(w) &= \mathbb{E}\left[W_1\right] - \frac{\gamma}{2} Var(W_1)\\ & = \mathbb{E}\left[W_0(1 + r_f + w^Tr)\right] - \frac{\gamma}{2} Var(W_0(1 + r_f + w^Tr))\\ & = W_0 + ...

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