Mastering the Market

With the continued rise in interest rates and the positive results from the Federal Reserve’s recent stress test, it’s easy to see why many are bullish on the financial sector. Add to the fact that many bank stocks continue to trade at a discount relative to other sectors. Many are wondering when the turn-around will come. If this trend  continues, one could buy any of the major bank stocks and probably see it rise alongside all the others. I am however, interested in finding the one that will benefit the most out of this cohort.

The ‘stress test’ is conducted by the Federal Reserve, which is carried out yearly since the Great Recession to ensure banks can stay solvent. Banks with assets of $50 billion or more (now changed to $100 billion) must be able to withstand a 60% drop in the stock market, 30% drop in the housing market and a 10% unemployment level. 

Having read an article on zacks.com about the biggest winners of the current stress test, I came across M&T Bank (MTB). They expected MTB’s earnings to grow 32% in Q2, which it ended up exceeding. This was the largest earnings beat out of the five banks. This caught my eye and I decided to do more research on the stock.

Right off the bat, one will notice it’s stock price is down 11.3% from it’s march 5 high of $196.81. If we can find some convincing catalysts, I think this current price would make a very opportune entry point.

In the Q2 2018 earnings conference call, the vice president and CFO Darren King reiterated the fact that MTB would benefit from a rise in interest rates: “we continue to estimate that a hypothetical future 25-basis point increase in short-term interest rates should result in a 5 to 8-basis point benefit to the net interest margin.”

The result of the stellar quarter was attributed to a rising net interest margin, going up 12 basis points. Another big positive was the fact that the bank was able to reduce it’s credit losses: “The provision for credit losses was $35 million in the second quarter of 2018, compared with $52 million in the year-earlier quarter and $43 million in 2018’s first quarter”.

I thought it would be interesting to compare MTB with another financial services company to see how it holds up among it’s peer. I choose Bank of America (BAC) because it was another winner of the stress test. Although BAC is more than 10 times larger than MTB in terms of market cap. Typically, smaller companies can grow faster, therefore I want to explore this hypothesis and see if MTB can outperform BAC over the next year. In terms of Q3 2018 earnings, MTB’s are expected to rise 4 cents per share ($3.26 (Q2/18) to $3.30 (Q3/18)). BAC’s earnings are only expected to rise 1 cent per share ($0.63 (Q2/18) to $0.64 (Q3/18)).  So clearly the market expects MTB to grow faster than BAC. This is also seen in their P/E ratio of 19.75 which is more expensive than BAC’s P/E of 17.84.

After the successful June 2017 stress test, BAC repurchased $12 billion of it stock and raised it’s quarterly dividend 60%. Since then, BAC stock has risen almost 30% year to date.

Similarly, after the successful June 2017 stress test, MTB repurchased $900 million of it’s own stock and maintained it’s $0.75 dividend. Year to date, it’s stock has risen about 11%.

After this year’s stress test; BAC will repurchase $20.6 billion (a 72% increase since 2017) of it’s own stock and raise it’s quarterly dividend by 25%.

Similarly, after this year’s stress test; MTB  announced that they will repurchase $1.8 billion (a 100% increase since 2017) of their stock and raise their quarterly dividend 25%.

As we can see, MTB’s stock repurchase and dividend increase is more aggressive this year than it was in 2017. Even more aggressive than that of BAC’s.

There are concerns with their rising expenses which have gone up 3% this quarter as a result of increased advertising spending.  Their efficiency rating is however getting lower; it was 52.7% in Q2/2017 and has gone down to 52.4% this quarter. This is a good sign, because it shows their profitability is increasing as their expenses rise.

On a macro scale, it is important to consider the tightening yield curve while studying  a bank stock. The gap between short and long term interest rates has been closing which is perceived as a sign of a future economic slowdown. For the banks this is especially bad news considering a part their income is tied to the long term interest rates in the form of loans and mortgages. Although this is an important point to consider, it is debated among experts whether a tightening yield curve is negative for banks in the long term or merely in the short term. In the years 2000 to 2004, we experienced a similar tightening yield curve and during that period the banking sector outperformed the market. If the economy does end up slowing or even going into a recession, of course the banks will be severely affected. We can see however, that a tightening yield curve does not always lead to a recession. I would therefore say that we should remain cautious and monitor the situation but it is not a reason in and of itself to stay out of the financial sector.

Overall I am bullish on the stock. Their aggressive stock buybacks and increased dividend shows confidence that this  year will see strong growth. Rising interest rates could also be another catalyst that would supplement the stock’s growth. I think the combination of these factors will be very beneficial to MTB over the next year.

Sources:

Bank of America Investor Relations

Barrons

M&T Bank Investor Relations

Yahoo finance

zacks.com

Last summer I read a book called “Beating the Dow”. Author Michael B. O’Higgins, the inventor of the Dogs of the Dow trading strategy, promises a “high-return, low risk” strategy that outperforms the Dow Jones Industrial Average.

The Dogs of the Dow is actually pretty simple and gives competitive returns.  The basic theory is that you choose from the list of the 30 Dow companies: first by identifying the top 10 dividend yielding stocks and then amongst those 10: choose the 5 cheapest (based on the stock price) stocks. You now have a list of five stocks that are the ‘dogs’ of the Dow Jones Industrial Average.

Here is the list of dogs for the close of September 1, 2017. I put an X mark next to the top ten highest yielding stocks. Then, a ‘/’ mark next to the top 5 lowest priced stocks out of the 10 highest yielders. I highlighted the dogs which have both an ‘X’ and a ‘/’ marking.

Here is the excel sheet I used to compile the list;

DOGS_DOW_17.xlsx

I plan on publishing the dogs of the dow for the past 10 years. I want to compare the yearly performance since 2006. The only thing that’s taking a long time is that I have to manually find the dividend payments on Yahoo finance individually. It should be ready soon, stay tuned!

Over the summer of 2016 I read Peter Pham’s; ‘The Big Trade: Simple Strategies for Maximum Market Returns’. In it, he teaches he us  how to use excel to do quantitative analysis. He does not believe in technical analysis as he likens it to “studying chicken entrails” and which looks for arbitrary patterns. Instead, a quantitative analyst is supposed to identify patterns and assign them statistical probability based on historical data.

I made my own spreadsheet to study the opening range of 16 years worth of the daily S&P 500 quotes. Here are the results and bellow is the spreadsheet;

The average range of a particular day’s closing price and the previous day’s close is $10.56. The ‘Avg. high move’ is the day’s high subtracted by it’s open and then the average is taken. The same is done for the ‘Avg low move’ but the opening price is subtracted by the day’s low instead. For the ‘Break prev. high’ I have an IF statement on the excel sheet that “if” the day’s high is bigger than the previous days high, then a 1 will appear in the column, if not a 0 will appear. I then tallied the 1’s to see how many times this occurred in 16 years and then divided it by the total number of days. I found that 51.2% of the time the day’s high was higher than its previous high. This makes sense considering the S&P 500 has rose considerably in the past 16 years. The same thing was done for ‘break prev low’ except the low’s were used. Not surprisingly, the average low of a particular day only “surpassed” its previous low 45% of the time.

The ‘High + low break’ is another column of if statements. In this particular column a 1 is assigned if the day’s high surpassed the previous high AND the day’s close also surpassed its previous day’s low. This implies the day was particularly volatile. I found that days that meet such criteria only happened 8.9% in 16 years.

Finally, the ‘open is high’ and ‘open is low’ is simply the number of times that the open was the high or low. The open was the high 14.7% of the time, while the open was the low 17.2% of the time. The latter statistic is interesting because it implies the open ended up being the low more often than it was the high, which has bullish implications. This means  the close ended up being higher than the open, at least 17.2% of the times which suggests the market was rising (as it did in the past 16 years).

This chart is the end goal of the opening  range analysis. Peter Pham explained that you assign a probability to how much the price will move based on its historical data. The top part looks at the chance of a ‘up move’. I used the COUNTIF function to see how many times the ‘Avg high move’ was at least $2 of greater. This means; how many times did the price move $2 or more from its opening price. If it moves $2, it implies the market is bullish and perhaps it will rise more than $2. The ‘Avg high move’ was >= $2 exactly 3049 times in 16 years. Then I did the same COUNTIF function to see how many time the ‘Avg high move’ was $5 of greater. The ‘Avg high move’ was >= $5 exactly 2312 times in 16 years. I then divided the two numbers; 2312/3049 = 0.758281. This means, when the price rose at least $2, there is a 75% chance that it move up another $3 to $5 or more. The same thing was down for the “Down moves” except it looked at the day’s low instead of the high.

Here is the excel spreadsheet I used. On the left you see the collusion of 1’s and zeros that I used to define certain situations. If you scroll all the way down you’ll see the tallied numbers and statistics.

s&p_daily_16yrs