Tag Archives: stock market

“Reminiscences of a Stock Operator” by Edwin Lefevre

Thoughts on stock trading from “Larry Livingston”, a pseudonym for Jesse Livermore, successful trader around the turn of the century.

  • “Give up trying to catch the last eighth, or the first.  These two are the most expensive eights in the world.”  In other words, don’t buy too early (without a clear upward signal) and don’t sell too late.
  • It’s more important to catch the big, market-wide bull/bear runs than the little fluctuations of an individual stock.  I think of the “buy the dip” approach — this only works if you are in a bull market.
  • Ease into positions.  If you think you want 500 shares, start at 100 shares and see how the market goes.  If it goes up 1%, then by the next 100 and wait again.  Repeat until you have your whole position.  Always have a -1% trailing stop in mind.
  • Price doesn’t really matter – what you want to know is the “line of least resistance.”
    • Maybe in our day and age, he would be a proponent of order book analysis?
  • Traders must reverse the natural inclination of hope and fear.
    • Natural:
      • Hope when your stock is down that it will come back up, so you hold too long
      • Fear when your stock is up that it will go down, so you sell out too soon
    • Must change to:
      • Hope when your stock is up – hope it keeps rising; don’t sell winners too soon
      • Fear when your stock is down – fear it could drop even lower; don’t hang on to losers
  • Tips are worthless — realize you are being manipulated.
  • Towards the end of the book, lots of stories about market manipulation that didn’t seem very applicable to us little fish, except to recognize such things happening amongst the whales.
    • If you get to be a whale, remember that liquidity matters.
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“Trading in the Zone” by Mark Douglas

This is a book on trading psychology.  Too many traders are governed by emotion (both good, like euphoria and a feeling of invincibility; and bad, like fear and greed) which prevent them from being consistent winners.  The key is to look at trading objectively, from a probability standpoint.  You must accept that the probability of a win is never 100%.

Fear manifests itself when we, either consciously or subconsciously, avoid information which would “prove us wrong.”  Eg. we avoid positive news about a market you already exited (because you would have to admit you exited too soon) or we avoid negative news about a current trade (especially one that’s already a loser that we hope will “bounce back” soon).

Consistent winning can be problem too, if we get a “can’t lose” attitude and become reckless with larger and larger trades.

There is always going to be uncertainty.  The key is to find a strategy that gives an edge, and then don’t worry if it sometimes is a loser – account for that.  Before every trade, predefine: risk (probabilities of up/down), loss-cutting point, profit-taking point.  Don’t emotionally consider recent wins or losses.  Over and over again trade when you see your edge (only risking some predetermined, small percentage of your equity) and don’t worry when you sometimes lose; just make sure your edge wins on average.  Sounds like he is advising traders to be like an automated algorithm!

But … (the big but) how do you find an edge???  He doesn’t really go into that at all; it seems his intended audience are technical analysts who already have an edge but fail to use it consistently.  For those without, well… find one with quantopian?

I like his approach to “scaling out” profits.  He reports noticing that 1 in 10 trades go down and hit his initial stop immediately.  Another 2-3 in 10 go up a few ticks but then go down to the stop.  What to do = scale out of trade gradually.  When up a few ticks, sell 1/3 of position.  At some other predefined rise (something higher than a few ticks), sell another 1/3 and reset your stop on the remaining 1/3 to your entry position.  Now you have already captured some profit and have a “risk-free” position to see how it turns out.

 

“How to Make Money in Stocks” by William J. O’Neil

make_money_in_stocks

William O’Neil is the famous founder of Investor’s Business Daily (ironically now printed only weekly) and seems to deserve his reputation as a pro stock picker.  He shares his secrets in this book (along with plenty of plugging for IBD products…).  He outright refutes the concept of a “random walk” (throwing darts directly at Malkiel?), claiming it is definitely possible to beat the market.

CAN SLIM is O’Neil’s stock picking system (it’s not a weight loss system as the name may imply):

  • C – Current earnings per share >20% higher than same quarter one year ago. Also check that sales are increasing over the last three quarters.
  • A – Annual earnings per share growth of 25 – 50% over past 3 years. Also look for high ROE.
  • N – positive News. Don’t be afraid to buy a stock making new highs. Don’t worry about P/E ratio at all. Focus on newer companies (<10 years since IPO).
  • S – prefer companies with smaller number of outstanding Shares – easier to budge price.
  • L – (Leaders vs. Laggards) DON’T buy stock which has retreated to the point it looks like a bargain – too much risk it will keep falling. Buy when going up and hope it keeps going higher. Look for breakout after 7-8 weeks of stable base. Look for high, increasing relative strength. Average up if you must (when your pick is up a few %) but never average down.
  • I – look for increasing Institutional sponsorship, especially from high performing mutual funds.
  • M – sign of Market top: “distribution day” (distribution as in “selling”) – major indices flat or down on increased volume from previous day, occurring on 4-5 days in 4-5 week period.  When a bear market is detected, get into cash fast.  Then wait for the signs of a bull market before jumping back in: look for an up day, followed within 4-7 days by a “follow-through” day of very large gains (~2%) on heavier volume than the preceding day.

After all that…O’Neil admits that only 10 – 20% of his picks have ever turned out to be real winners. So, the savvy investor must be aggressive about limiting losses. Always cut and run if stock goes down 7-8%.  Think of it like insurance.  A variant that yields same results but is maybe easier to swallow: sell half at -5%, other half at -10%.  Given that expected gains on remaining winners are 20-25% you’ll still come out ahead if you can pick winners only 1/3 of the time.  If you must invest during a bear market (not recommended), lower your acceptable loss to 3% and profit taking to 15%.

Other sell signs (some confusing and contradictory – maybe why the aforementioned 20% rule exists):

  • largest daily gain or loss occurring after many days of solid gains
  • heavy volume with no price change or loss
  • rapid price run-up for 7-8 days out of 10
  • 4-5 down days for every 2-3 up days (whereas it had been the reverse)
  • new high on lower volume
  • close at day’s low for several days
  • 8% decline from peak
  • major publicity with good news
  • overabundance of optimism
  • deceleration in quarterly earnings increases for two quarters in a row

Don’t buy stocks <$15. O’Neil recommends holding no more than 5 stocks, since timing is important and monitoring multiple holdings may cause you to miss something important. Don’ be afraid to use margin once you are comfortable with the system and are seeing success.

Biggest mistakes: stubbornly holding on to losses for too long, buying on the way down, and not sticking to rules!

Research winning companies/industries to find opportunities in supplier companies – eg. Monogram, maker of chemical toilets for Boeing during airline boom.

There’s a lot of technical analysis charting discussed in the book, primarily focused on the perfect buy point.  I didn’t spend too much time squinting at the charts – seems like there are plenty of points on the charts which meet the cup-and-handle or stable base criteria but did NOT turn out to be a perfect buy point.  Anyway, cup-and-handle with large volume increase on the handle seems to be the recommended buy point.  Basically looking for a point where price and volume steadily drops for a time, then slowly picks back up until a large volume, large increase day.  (Only buy solid CAN SLIM companies – not just anything which meets the technical pattern!)

I plan to test out a few things from this book on Quantopian, particularly the market direction signals.  Even if you could just time the market and jump into and out of index funds at the appropriate time you would end up miles ahead.

“One Up on Wall Street” by Peter Lynch

one_up_wall_street

Peter Lynch is famous for heading up the Fidelity Magellan fund during the eighties, when he averaged +29% annual returns.  Just lucky?  Maybe!  But in this book he shares his secrets of stock picking anyway.  He is a fundamentalist, long stock only investor – serious about buying pieces of good companies at deal prices, betting that the market will eventually realize what gems the companies really are.  The specific examples in the book are quite dated, but the principles hopefully are sound.

  • First, a knock against index funds.  Lynch says that the index funds’ gains are usually propped up by a small number of stocks in the index. I wonder if this is still true?  He says to look at the advance/decline numbers (number of stocks rising vs those falling) and you can see this.
  • “To me, an investment is simply a gamble in which you’ve managed to tilt the odds in your favor.”  Lynch puts his success ratio at around 60%, but some of these were “tenbaggers” (increased 10x in stock price) or better.  The trick is to realize companies which are in a good position before the market at large realizes it too.  Because when it does, that is when stocks shoot up.  He even goes as far as to recommend dull-sounding companies in dull industries, which have little or no institutional ownership or analyst coverage.
  • Be aware of companies growing – new products or entering new markets.  Put these companies on radar screen, then check fundamentals next.
  • Search for companies or industries with large earnings growth as % of market
  • Slow growers, medium growth, fast growth, cyclical (vulnerable in recession), turnaround, asset plays (holds assets land etc which aren’t yet reflected in share price).  Important for knowing how much profit to take.  Slow – moves @ GDP growth, medium – 25%/yr, fast – sky’s the limit.
  •  Simplicity is a virtue: “When somebody says, ‘Any idiot could run this joint,’ that’s a plus as far as I’m concerned, because sooner or later any idiot probably is going to be running it.”
  • Insider buying is a strong indicator that things are looking up.  Many reasons for selling but only one for buying – believe stock is going up
  • Prefer companies that buyback stock rather than make dubious acquisitions.  (“diworseification”)
  • Don’t buy anything without earnings.
  • Don’t buy company too dependent on a single customer
  • Use p/e to classify companies – higher p/e than average indicates sentiment of faster earnings growth
  • Increase Earnings – reduce costs, raise prices, expand to new markets, sell more in old markets, revitalize or sell losing operation
  • Balance sheet: (cash + marketable securities – long term debt) / total shares outstanding =  available cash per share.  If available cash per share is close to the share price, then the stock is probably a great deal.  (Don’t count other “assets” since their stated book value is probably much higher than they could ever be sold off for). Also check balance sheet for: decreasing debt, decreasing # of shares, increasing cash.  P/e should be roughly equal to earnings growth rate.  If lower p/e than % earnings growth, good.  Also compare long term debt to total stockholder equity.  Want equity >> debt to ensure low bankruptcy risk.
  • Three phases: startup, expansion, saturation.  Want to get stocks out of risky startup phase but still in expansion.
  • Make yourself write short paragraph on each buy decision – what is the compelling story that is making you buy this company?
  • Don’t sell when stock goes up or down some set percentage; sell if you think the company’s “story” has changed.  Simple sell test – “Would I buy this stock again right now?” (per all the rules) If not, sell.
  • Some typical “story-changers” that indicate it is time to sell: no insider buying during past year, slowing earnings growth rate, p/e much higher (50%) than industry average
  • “It can’t possibly go lower!”. Oh yes it can.  Beware stocks in free fall.
  • Don’t mess with options or futures.  Ought to be outlawed.  Very expensive since they expire; you don’t own the companies.

“A Random Walk Down Wall Street” by Burton G. Malkiel

random_walk

Here’s the secret to making money in the stock market: buy low-cost index funds which cover the whole market.  There, not so hard, right?  Burton Malkiel has been espousing this for 40 years, when the first edition of this book was published and index funds didn’t even exist yet.  The data, as he presents in the book, shows him out well.  Sure, some years some active fund managers score big, but they are no more likely than any other manager to outperform the market the following year(s).  The only consistent winner is a properly-weighted portfolio of index funds.

That said, Malkiel gives lots of bits of advice on manual methods of picking stocks.  After all, if you see a $100 bill on the street, don’t be like the finance professor who said “it must not be real; otherwise someone would have already picked it up.”  Really you should respond “I must pick it up quickly, otherwise someone else will come and pick it up very soon.”  There still are market inefficiencies to be exploited … it is just very hard to do so consistently.  Keep your core in index funds, but keep a small pool of funds ready to pick up the $100 bills when you find them…

Stock Picking

As far as stock picking goes, Malkiel sees all technical indicators as junk.  Well, besides maybe some short term value to a relative strength strategy.  But…if any indicator really did work well, the discoverer is surely not telling anyone about it.  Too many people doing the same thing would change the market dynamics such that the indicator no longer works.

Fundamental analysis doesn’t bear much fruit either.  And we must avoid bubbles, even though they are hard to spot.  Here’s a tip: be very wary of buying into something touted as “the future” if it isn’t making solid profits now.

Malkiel does give a “secret formula” for picking stocks late in the book:  Long-run equity return = Initial dividend yield + growth rate (of earnings and dividends combined).  The trick is knowing what the expected growth rate will be.  The typical stand-in here is the P/E ratio — if it is high, then this (might) signify the expectation of growth.  But then again, value investors like to buy low P/E stocks…

There is a pretty strong correlation of overall market P/E ratio to forthcoming returns.  The lower the P/E ratio of the market, the higher the returns.  Based on historical data, a market P/E ratio of < 10.6 has yielded on average 16.4% returns over the next decade while a P/E ratio of > 25.1 yields on average 3.7% returns over the next decade.  The returns vs. P/E curve is generally linear between these two points.  (Malkiel’s chart on pg. 347)  As of this writing, current market P/E is 24.89…we are unfortunately in for a decade or so of single digit returns.  (Note: MMM has this point covered as well.)

Smart Beta

There are a couple of strategies that Malkiel mentions may have some potential to beating the market.  Maybe.  (He’s is oh so careful about admitting that anything could ever beat the market long-term!)

1) Value wins.  Tilt towards low price-earnings ratios.  VVIAX

2) Tilt towards smaller cap companies – they have more room to grow.  (IWM – Russell 2000; or IWN, DFSVX – combo of 1 & 2)

3) Momentum and reversion to the mean (AMOMX)

4) Low volatility bought on margin (SPLV)

And a final bit of advice: buy closed-end funds which are selling at discount vs NAV.  The WSJ maintains a listing where they already have the discount computed.

Diversification

There’s a chart on almost the final page of the book which kind of blew me away.  The 2000’s decade is called the “lost decade” because overall market returns ended up pretty much where they started.  But … a diversified portfolio of 5 different funds (using the “age 55” mix), rather than just total market, was up ~100%.

diversification

(Note: Malkiel gives the exact same weights for International and Emerging Markets, so on this plot they are right on top of each other.)

In an individual stock portfolio, diversification is also very important in lowering risk.  The beneficial effect seems to diminish after about 50 stocks or so.  Should also make sure to get about 20% exposure to international stocks.

“Flash Boys” by Michael Lewis

flash_boys

An interesting account of the development of high frequency trading over the last few years (starting ~2010-ish) and an attempt to counter its “predation.”  The book begins with a story of Spread Networks building a fiber optic line a straight as possible between Chicago and New York, so that information about market arbitrage between the futures market in Chicago and the stock market in New York could be sent and acted on as quickly as possible.  Then there was/is the push by high frequency trading firms to expand the number of exchanges (so as to maximize arbitrage opportunities) and also attempts to co-locate their machines as near as possible to the exchanges’ “matching engines” which actually make trades.  Per the book, this allows the HFT firms to front-run everyone else’s orders (eg. they can see someone is trying to buy a certain stock, so they buy up everything within a certain price on all the other exchanges, and then jack up the price before the other party’s orders arrive at those exchanges to buy) and preform other nefarious and predatory schemes.

There’s been lots of discussion about this book.  Here’s an article that claims HFT doesn’t really concern little investors like you an me, but mainly “institutional investors.”

Computational Investing part I on Coursera

I recently finished up my second course on Coursera – Computational Investing I by Tucker Balch.  Learned a little bit more about how the stock market works, and (maybe somewhat) how the quants do their thing.  A little bit of what they do could probably be called order book exploitation — looking for arbitrage opportunities between buy and sell orders at the microsecond level.  This type of computational investing is pretty much out of reach for anyone outside of a big firm with direct trading floor access.

What’s left for the little guy is offline, day-to-day analysis of stock information.  Dr. Balch has created a Python package, QSTK, which pulls downloaded stock price information into a data analysis friendly format within Python.  I really liked how the assignments all fit together — I now have a basic, yet functional, “Market Sim” in Python.  It is not really a predictive simulation for future market movement; rather it is an evaluation tool for how a certain metric-based investment strategy would have performed if executed during a past window of time.  For instance, the final assignment involved setting up an “event study” looking for instances where a Bollinger band metric behaved in a fashion showing that the stock was falling while the overall market was rising.  Whenever the event was observed for a given stock, the tool logs a buy order for the stock followed by a sell order 20 days later.  Then, a separate piece of code reads in the buy/sell orders and computes what the overall rate of return (and other metrics like Sharpe ratio) would be given actual stock price historical data, then compares the strategy to the overall performance of the market during the same time period.  Thus the Market Sim developed in this course would allow the user to try out different investment strategies, evaluating them against real historical data.

It’s a simple yet pretty cool project that I’m glad to have completed.  Especially glad for the opportunity to get back into Python, which I still love (two timing now with Matlab, I guess…) and which I’ve got a pretty decent workflow going with PyScripter (nice tool).

But I don’t think I’ll be going into business anytime soon as a day trader.  My first inclination was to somehow meld the Market Sim developed in this course with a neural network or other machine learning strategy — then you wouldn’t need to explicitly define any “events.”  But a quick Google search shows that many have tried this and failed.  Maybe it is due to my second reservation, which is that past performance is not a reliable indication of future performance.  (But it’s the best we have to go with, so maybe that’s ok…)  Third reservation is that many strategies would involve too many individual trades to be profitable for the little guy, both when trade commissions and when the time required to execute trades manually are taken into account.  (Are there any systems where you can specify auto-trades upon a certain event?  That would be cool, but scary too … if the perfect storm comes and your algorithm gets really fouled up, it could clear out your bank account real fast.  Automated gambling, anyone?)

Not saying it is impossible for the little guy to be profitable using these methods, but it would be difficult.  The course itself talks about the fundamental law, basically saying that if you aren’t able to execute a small number sure-fire big money trades, then your only recourse is to execute a very large number of more modest trades, each with a slim yet positive probabilistic chance of profitability.  But lots and lots of trades are impractical for the little guy for reasons discussed above.  Also, I tend to believe in the efficient market hypothesis, simply because there are so many people working on this problem, with so much more knowledge and resources than I have to devote to it.  I think I’ll stick with index investing.

On a side note, shortly before taking this course I read an article on the Aladdin system used by Blackrock.  (I can’t find the article now … it wasn’t this Economist piece, nor was it this one from FT.)  A lot of money is controlled by algorithms now, for better or for worse.  I couldn’t help thinking of the Asimov story from I, Robot, where artificial intelligences are directly controlling the economy . . . are we there yet?