A finished FIN-655 Topic 5 factor exposure dq post example, cutting a manager's 2.5-point CAPM alpha to 0.3 with size and value factors and crediting the tilt to allocation. Searches like "fin 655 topic 5 assignment example", "fin655 topic 5 sample" and "fin-655 topic 5 example" land here.
What a finished FIN-655 Topic 5 factor exposure dq post looks like
The post states its answer first: the result is mostly exposure. Its illustrative five-year figures give the fund 12.1 percent a year against 3.0 for cash, an excess of 9.1. With a market beta of 1.10 and a market premium of 6.0, the capital asset pricing model predicts 6.6 of that excess, leaving the reported alpha of 2.5. The post then adds the Fama and French size and value factors, which returned 2.0 and 3.0 over the period. Loadings of 1.00 on the market, 0.50 on size and 0.60 on value predict 8.8, and alpha shrinks to 0.3, with a t-statistic near 0.2. The post names the joint hypothesis problem Eugene Fama described: any alpha is measured against a model, so a claim of skill is also a claim about which model is right.
How a FIN-655 Topic 5 example is structured
Four paragraphs make up the post, with a reply beneath. Its first paragraph commits to a position and the one number behind it, alpha falling from 2.5 to 0.3 once size and value enter. Paragraph two tables both regressions, the loadings, the factor returns and the predicted excess return from each, so a classmate can recompute every figure. The third turns to significance: residual volatility of 3.0 percent over five years puts the standard error of alpha near 1.3, so even the original 2.5 falls short of conventional significance. The fourth takes up efficiency, presenting the risk explanation Fama and French favor and the mispricing account Lakonishok, Shleifer and Vishny argued, and notes that neither makes the tilt the manager's skill. The fund and its figures are labeled composite and illustrative. Last comes the reply, to a classmate arguing that choosing the tilt was itself skill.
The answer in one number
Alpha measured against the market alone is 2.5 points a year; measured against market, size and value together it is 0.3, and the post leads with that change.
Two regressions set side by side
Adding size and value lowers the market loading from 1.10 to 1.00 and attributes 2.8 points to the two tilts, leaving 0.3 of the original 2.5.
Significance checked before skill is claimed
A standard error near 1.3 points leaves even the original 2.5 below the usual threshold of 2, and the adjusted 0.3 is indistinguishable from nothing.
Risk premium or mispricing, the same verdict
Whether value and size pay for risk or for investor error, a tilt held through five years is available cheaply and belongs to allocation.
A reply on whether tilting is skill
The reply grants that choosing the tilt was a decision, then asks whether the loadings moved ahead of factor returns, which is what timing skill would show.
Where marks go in FIN-655 Topic 5
The characteristic failure here takes the most: a post that accepts the 2.5 points as skill because the manager beat the market after adjusting for beta, when the size and value exposures account for nearly all of it. Posts that add factors but never report the loadings leave classmates unable to see where the return went. Treating a factor-adjusted alpha as final, without the joint hypothesis caveat, overstates what any single model can settle. Declaring markets efficient, or inefficient, as a premise rather than an argument skips the evidence the prompt asks for. Omitting significance lets a five-year average speak with more confidence than its standard error allows. Replies that praise the tilt as clever leave the thread where it was, because the question is whether the committee could have bought it for a fraction of the fee.
Get a FIN-655 Topic 5 example written to your instructions
Send the FIN-655 Topic 5 prompt and your classroom rubric, plus any return data or readings your section assigns. We write a custom example to that prompt, with the single-factor and multifactor regressions tabled, significance tested, the efficiency debate presented fairly and a reply that tests a classmate's claim of skill, in 24 to 48 hours. The first one is free.
FIN-655 Topic 5 questions, answered
What are the Fama and French factors?
Return series built from portfolios that go long one group of stocks and short another: small companies minus large for size, and high book-to-market minus low for value. Eugene Fama and Kenneth French found that these factors, added to the market, explain much more of the variation in returns across stock portfolios than the market alone. Later versions add profitability and investment factors, and momentum is often included as well.
What is the joint hypothesis problem?
Market efficiency, Eugene Fama pointed out, can never be tested by itself. To say a return was abnormal, one must first say what normal return a model predicts, so every test of efficiency is also a test of the model. An alpha that disappears when factors are added shows the return came from the tilts, but whether those tilts earn a reward for risk or exploit a mispricing depends on which model is believed.
Does the post evaluate a real fund's manager?
No. The fund, its returns, the loadings and the factor returns are illustrative, picked because the two regressions then tell different stories that a classmate can check by hand. Real factor regressions depend on the data source, the period, the factor definitions and the standard errors, all of which change the answer. The post shows the kind of argument FIN-655 discussions reward, and it offers no view on any actual manager or product.