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FIN42330 Python for Financial Data Science
创建日期：2024-09-09 17:00:40
所属分类：金融Finance
标签： FIN42330
Python for Financial Data Science
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FIN42330 Python for Financial Data Science
Assignment 
(Please read all instructions very carefully)
Custom Functions & Applications
Moving average timing investment strategy
Suppose you are considering investing in an exchanged traded fund that tracks the S&P
500, the SPDR S&P 500 ETF Trust (ticker: SPY), using a moving average (MA) in
dicator. The MA indicator is one of the most popular timing strategies using technical
analysis.
Denote by Rt the simple return and by Pt the corresponding prices on SPY at time
t. The MA at day t of lag l is defined as
At,l =
P
t−(l−1) + Pt−(l−2) + · · · , +Pt−1 + Pt
l
,
(1)
which is the average price of the past l days including day t. Consider 10-day MAs. On
each trading day t, if the last closing price Pt−1 is above the MA price At−1, we will invest
in the SPY for the trading dy t; otherwise, we will not invest. So the MA provides an
investment timing signal with a lag of 1 day. The idea of the MA is that an investor
should hold an asset when the asset price is on an uninterrupted up trend. However,
when the trend is broken, the investor should then sell the asset.
Mathematically, in the absence of transaction costs, the excess rate of return on the
MA timing strategy on day t is
˜
Rt = ( Rt − Rt f , if
Pt−1
> At−1,l;
0,
otherwise
,
(2)
where Rt f is the day t rate of return on the risk-free asset.
1. Download daily prices (Adj Close) of SPY spanning the period from 3rd December
2018 to 1st December 2023 from Yahoo Finance. As a proxy for the risk-free
asset, consider the daily data on the 1-month T-bill rate (DBT4WK) from the
U.S. Federal reserve economic database https://fred.stlouisfed.org/series/
DTB4WK. Assume 252 trading days per year.
2. Write Python scripts and functions, as appropriate, to implement the MA timing
strategy detailed above. Report the mean, volatility, and Sharpe ratio of the excess
returns on the trading strategy.
13. Write a Python function to evaluate the predictive performance of the MA timing
strategy (deployed in the task just above) using the market timing test of Pesaran
and Timmermann (1992). The test is defined in equation 6 of the paper.
Submit your source code (as a Python file or preferably, as a Jupyter notebook) and PDF
file of your report via the dedicated submission folder (Assignments via the Assessment
tab) on Brightspace by Tuesday, 12th December 2023 at 5pm. Please include your
solutions to all questions in one single document. Source code for all questions are required
and should be appropriately labelled to the extent that it is obvious which answers relate
to which questions.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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