Change in Minneapolis Homicide Rate

There has been some discussion about increasing crime in Minneapolis and whether there is a causal relationship between policy and crime rates. Changes in Minneapolis police policy have mostly amounted to rhetoric. The funding cut made to the Police Department last year was less than 1% of the department's budget. Despite this, the rhetoric appears to have had an impact; many officers have left the job apparently due to low morale. The chart below shows the year-over-year difference in the Minneapolis homicide rate for months from January 2020 to May 2021. Crime rates are usually much higher in summer months, but these numbers are year-over-year (for example, the January 2020 data point is the increase from January 2019), so they can be thought of as "seasonally adjusted". The rates are per 100,000 population.

 I should also clarify that these are increases in the rate, not percentage increases. The total homicide rate peaked in August 2020 at 4.18. The national murder/nonnegligent manslaughter rate according to the FBI was 5.0 for both 2018 and 2019 (the 2020 numbers are not yet available), but that's an annual rate. The annual rate for Minneapolis was 10.7 in 2019. Based on the numbers I obtained from the City of Minneapolis website, the 2020 rate should be 21.7. This is alarming seeing as it is much higher than any other year in recent history, but to keep things in perspective, the Jackson, Mississippi rate was 46.5 in 2019. Minnesota has the sixth lowest homicide rate in the country, but the geography of Minneapolis makes its crime statistics appear worse than they actually are. A large percentage of the metropolitan population resides in suburbs, thereby skewing the official population of Minneapolis. On the topic of population, the data here is based on the official 2019 and 2020 population estimates, 435,885 and 382,618 respectively. Yes, this suggests that the city's population decreased 12%. This may partly be due to differences in the way the estimates are made. The 2019 estimate is a projection from the 2010 census, while the 2020 number is based on the 2020 census. Still, I think it is reasonable to assume that the pandemic did result in some population decrease in Minneapolis, particularly because of fewer college students residing in the city. For 2021 I used the 2020 population number. 

Multiples and Return for 32 Mega Cap Stocks

 In August last year I published Multiples and Total Return for 33 Mega Cap Stocks. The results of my analysis then, which tracked the returns of stocks from November 2019 to August 2020, showed that only PEG ratio correlated with returns in the expected direction. For the charts below, I used the same multiple and entry point data, but extended to a much longer exit point. I assumed the stocks would be sold at the April 23, 2021 closing price. The results were not fundamentally different. Again, PEG ratio was the only multiple with a negative slope for its linear regression. I'm still hesitant to jump to the conclusion that multiples are not useful. Three of the four best performing stocks were in the information technology sector. All four of the financials sector and all four of the consumer staples sector stocks were in the bottom 18 performing stocks. This may suggest that, for the time frame analyzed, sector rotation drove performance more so than multiples. In a future analysis, I may compare performance and multiple correlation within a sector. 

Some notes on the data:

The new analysis compares only 32 stocks rather than the original 33 because China Mobile Ltd was delisted.

I should clarify that where I've used the term 'total return' I meant the simple stock appreciation plus dividends; I did not make any calculations for reinvestment of dividends. For the time period and stocks selected I don't think dividend reinvestment would make a meaningful difference to the outcome. 

The data here for Apple (AAPL) is split adjusted.

Timing the Market

 "Don't try to time the market" is some of the most frequently given advice to beginner investors. I decided to do some testing of the theory against a frequently discussed strategy: "buy the dip". I backtested investing in Apple from February 24, 2020 to February 8, 2021. I assumed that one investor (the anti timer) bought one share at the opening price every Monday, while a second investor (the dip buyer) bought one share at the opening price on the first day following a down day (a day with a closing price lower than the previous close). The logic here is that an investor might identify a stock they are bullish on over the weekend. The anti timer would simply buy as soon as possible, while the dip buyer would wait for the price to decline. If the Friday before that weekend was a down day, then I assume both the anti timer and dip buyer would buy at the Monday opening price. I ignored weeks with a market holiday, and weeks with an ex-dividend date. 

The anti timer outperformed the dip buyer in this analysis. The dip buyer paid a lower price in only six of the 39 weeks. The anti timer paid a total of $3,896.64, while the dip buyer paid $3,908.43 for the 39 shares. The close on February 12, 2021 (the final week of this analysis) was 135.37, making the 39 shares worth $5,279.43. Ignoring dividends, that is a 35.5% gain for the anti timer, and a 35.1% gain for the dip buyer. Not a huge difference. The logic against buying the dip is that equities generally increase over time, and the dips are unpredictable, so it is best to simply buy as early as possible. While this analysis confirms that, I wouldn't rule out the possibility that a more sophisticated dip buying strategy may be effective. This might be a topic for a future post. 

The six instances of the dip buying strategy paying a lower price are in bold.

Date	Monday Open	First Dip Open
Feb 24, 20	74.32	71.63
Mar 2, 20	70.57	70.57
Mar 9, 20	65.94	65.94
Mar 16, 20	60.49	61.85
Mar 23, 20	57.02	57.02
Mar 30, 20	62.69	62.69
Apr 13, 20	67.08	71.85
Apr 20, 20	69.49	69.49
Apr 27, 20	70.45	71.18
May 11, 20	77.03	78.04
May 18, 20	78.25	79.17
Jun 1, 20	79.44	79.44
Jun 8, 20	82.56	86.18
Jun 15, 20	83.31	87.85
Jun 22, 20	87.84	87.84
Jul 6, 20	92.5	94.18
Jul 13, 20	97.27	94.84
Jul 20, 20	96.42	96.42
Jul 27, 20	93.71	93.71
Aug 10, 20	112.6	112.6
Aug 17, 20	116.06	116.06
Aug 24, 20	128.7	126.18
Aug 31, 20	127.58	127.58
Sep 14, 20	114.72	114.72
Sep 21, 20	104.54	104.54
Sep 28, 20	115.01	113.79
Oct 5, 20	113.91	113.91
Oct 12, 20	120.06	121
Oct 19, 20	119.96	119.96
Oct 26, 20	114.01	114.01
Nov 9, 20	120.5	120.5
Nov 16, 20	118.92	118.61
Nov 30, 20	116.97	122.6
Dec 7, 20	122.31	122.31
Dec 14, 20	122.6	122.6
Jan 4, 21	133.52	133.52
Jan 11, 21	129.19	128.5
Jan 25, 21	143.07	139.52
Feb 8, 21	136.03	136.03
Total	3896.64	3908.43

Monopoly Strategy

 Most people think it is all about Boardwalk and Park Place, but designing an algorithm for winning Monopoly is fairly complicated. Listed below is every property along with its location on the board, price, rent, price/rent ratio, and intrinsic value, which is the price the property would cost to make the price/rent ratio equal 12.14 (the average for all properties). For the utilities, I assume the dice roll is seven (which should be the average when rolling two dice). The big caveat here is that I didn't consider the various ways of increasing the value of a property (getting both utilities rather than one, multiple railroads, monopolies, houses, and hotels). Some might argue that additional value should be assigned to Mediterranean, Baltic, Park, and Boardwalk because they only require two purchases to get a monopoly. 

Property	Location	Price	Rent	Price/Rent	Intrinsic Value
Mediterranean Avenue	1	60	2	30	24
Baltic Avenue	3	60	4	15	49
Reading Railroad	5	200	25	8	303
Oriental Avenue	6	100	6	17	73
Vermont Avenue	8	100	6	17	73
Connecticut Avenue	9	120	8	15	97
St. Charles Place	11	140	10	14	121
Electric Company	12	150	28	5	340
States Avenue	13	140	10	14	121
Virginia Avenue	14	160	12	13	146
Pennsylvania Railroad	15	200	25	8	303
St. James Place	16	180	14	13	170
Tennessee Avenue	18	180	14	13	170
New York Avenue	19	200	16	13	194
Kentucky Avenue	21	220	18	12	218
Indiana Avenue	23	220	18	12	218
Illinois Avenue	24	240	20	12	243
B. & O. Railroad	25	200	25	8	303
Atlantic Avenue	26	260	22	12	267
Ventnor Avenue	27	260	22	12	267
Water Works	28	150	28	5	340
Marvin Gardens	29	280	24	12	291
Pacific Avenue	31	300	26	12	316
North Carolina Avenue	32	300	26	12	316
Pennsylvania Avenue	34	320	28	11	340
Short Line	35	200	25	8	303
Park Place	37	350	35	10	425
Boardwalk	39	400	50	8	607
Average				12.14

People might be surprised that Boardwalk did not come in as the best deal, but is tied with Park Place and the railroads behind the best buys on the board: the utilities. I charted the properties by location and price/rent below.

The instinct that the deals improve as one moves toward the end of the board is generally true, but one ironic thing to point out: Mediterranean Avenue (by far the worst deal) is on square 1, so when playing with two dice it will actually on average take longer to land on than any other property on the board.

Buying Teslas with Bitcoin

 A brief comment must be made about recent predictions for bitcoin to become some kind of international currency standard. Personally, I've never managed to get excited about a nonproductive asset with no usefulness outside of its perceived value, but a lot of people have gotten excited about it, and made a lot of money with it. Tesla announced last month that it purchased $1.5 billion worth of bitcoin, and plans to start accepting it as payment. Below is a chart comparing bitcoin futures to the Euro/Dollar over the last year. 

What I'm trying to understand is the fact that predictions for bitcoin adoption seem to be tied to its increasing value. For normal currencies, deflation is usually seen as a cause for major concern. One could imagine that if everyone is buying their Teslas with bitcoin, Tesla will have to continually decrease their prices (as the value of bitcoin rises). This will cause buyers to hold off on purchases. Why pay 1 bitcoin for a Tesla this week if it will probably only cost me .9 bitcoin next week? Meanwhile, Tesla is further stressed from the other side of the equation as the price it is now trying to sell cars for is less than what it paid workers and suppliers to build them. By the way, I originally planned to chart a few other major currencies for comparison, but they appeared to have lines pretty much identical to the Euro.