# Investment_2022_fall_Assignment4&6&7

Long time no see! It’s turn for update 3 in 1. Notice that code of all assignments is on a github repo.

# Assignment 4

In a firm’s life, it may experience many kind of events, such as IPO, SEO, merge, addition into SP500 index, etc. We want to know how much these event could make effect on stock’s return. That is called Event Study. Detailed story see A2.

In this task, we need to wrap a function in Python to deal with normal event study. There is actually little modification, compared with A2.

# Assignment 5

We would like to reproduce Jegadeesh and Titman (1993)’s work on momentum effect.

Momentum means a stock market phenomenon that "Winner always win, loser always lose". We could trust the return on win stocks would increase in the future, who have earned a good profit in past time.

In this task, we need to generate two groups containing winner and loser stocks. Then we "buy" winner and "sell" loser, calculate "buy-sell" returns. In fact, we do not assume we had capital and long or short position. We just sum the cumulative return.

In detail, we define winner as the stocks having higher CARs in past [3,6,9,12] months, while loser have lower CARs in past [3,6,9,12] months. Then we construct a sheet to find how to combine the two periods to earn the highest return.

p.s.
Remember that we use log returns.
$$R_{ln,t} = ln(\frac{Pt}{P{t-1}})$$
This simplified return cumulation and turn it into arithmetic sum of log returns.
$$R{t+1}=(1+R{t})(1+R{t+1})$$
$$R {ln,{t+1}} = ln(\frac{Pt}{P{t-1}})+ln(\frac{P{t+1}}{P{t}})$$
It could avoid many problems…

# Assignment 7

We need to construct Fama French factors, SMB and HML, and compare with the ones in Fama’s data library. See the reference I note in code.

The tricky part is, we need to construct 6 portfolios:SH, SM, SL, BH, BM, BL and calculate the difference by:
$$SMB = \frac{R{SL} + R{SM} + R{SH} – R{BL} – R{BM} – R{BH}}{3}$$
$$HML= \frac{R{SH} + R{BH} – R{SL} – R{BL}}{2}$$
but not:
$$SMB = R{S}-R{B}$$
$$HML= R{H} – R{L}$$
If you want to know why, ask Fama…

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