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Seven Steps to Better Stock Trading

Seven Steps to Better Stock Trading

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Seven Steps to Better Stock Trading

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  1. Seven Steps to Better Stock Trading Teresa Lo, Founder,

  2. Research Philosophy Perry Mehrling, Fischer Black and the Revolutionary Idea of Finance: “The best problems, like the best toys, are hard to exhaust. You can approach them from a variety of different angles, each new angle making the problem fresh again, and bringing the opportunity to discover something new. Any idea, no matter how crazy seeming, might work and can be worth exploring. Indeed, the harder the problem, the more degrees of freedom one can allow in tackling it. Fischer relished hard problems because he relished that freedom, but in practice he did not try just anything. In his view, if a problem does not yield to known methods, that doesn’t mean we need more sophisticated methods, indeed probably just the opposite. Usually problems are hard not because our technique is deficient but because our understanding is deficient.”

  3. The Mission What type of trading? I am focused on directional trading, that is, trading when price moves up or down in one direction. We’re not talking about any type of arbitrage, pairs trading, market making, portfolio timing, etc.

  4. The Mission The goal of directional trading is to • Be long when price is rising; • Be short when price is falling; and, • Be out of the market when it’s drifting. We can also position for major reversals that set up after a trend has been in place and exploit mistakes made by uninformed traders.

  5. What is Time Series Analysis? The U.K. Office for National Statistics writes: • A time series is broadly defined as a series of measurements of a variable taken at regular time intervals. • The information provided by any time series can be used as input for further analysis through time series modelling. There are two main goals of time series modelling. Firstly, it is used to identify and formalise the dynamic behaviour observed in time series data. This is known as time series estimation. • Secondly, it is used to predict the future values of time series variables. This is known as time series forecasting. Time series forecasting is based on the idea that the past behaviour of a variable may continue into the future. Consequently, current and past data may provide useful information for predicting future values. • Time series models can be broadly grouped into two categories: univariate and multivariate time series models. • Univariate time series models focus on a single variable. Their goal is to identify and estimate the relationship between the current value of a variable and its own past values.

  6. What is Time Series Analysis In short, most traders are actually performing UNIVARIATE TIME SERIES ANALYSIS of price or market statistic. They are basically attempting to forecast the future based on current and past numbers, usually without the benefit of a background in probability and statistics. Economists call this econometrics. They share the same difficulties as traders.

  7. People Are Difficult to Predict Federal Reserve Bank of Cleveland, March 2007: Mirror, Mirror, Who’s the Best Forecaster of Them All?

  8. Predictions Worse Than Random -- William Eckhardt in The New Market Wizards: Conversations with America's Top Traders

  9. Limitations of Time Series Analysis Methods available are best suited to certain types of data but it doesn’t stop people of trying on anything and everything. Econometricians perform tests on available data to see if it can fit a certain model in order to make a forecast, but this research is not typically performed by the average trader.

  10. Method Without a Model Christopher Chatfield, Time Series Forecasting: “[U]nivariate methods are particularly appropriate when there is a large number of series to forecast, when the analyst’s skill is limited or when multivariate methods require forecasts to be made of explanatory variables. . . . [I]t is important to distinguish between a forecasting method and a model. A model is a mathematical representation of reality, while a method is a rule or formula for computing a forecast. The latter may, or may not, depend on a model. Arising from this distinction, we look in turn at univariate forecasting methods based on fitting a univariate model to the given data . . . and then at intuitively reasonable, but essentially ad-hoc, computational procedures. These two types of method are quite different in character.”

  11. Why It Works Until It Doesn’t Price and market statistics are single, observable data points, the result of complex interactions between short term factors (animal spirits, shocks) and long term factors (demographics, economy). Research indicates that asset prices are non-stationary, making them theoretically impossible to forecast, no matter what the advertisement says. All academics know this.

  12. Further Reading Introduction to Time Series Analysis “This section will give a brief overview of some of the more widely used techniques in the rich and rapidly growing field of time series modeling and analysis.”

  13. The Problem in a Nutshell We basically know that the asset prices go through periods of trend and “chop”. Have to be able to tell the difference between the two in order to use the appropriate tools. We know there is no way to make accurate forecasts with known statistical and econometric methods; otherwise, economists would be able to do so consistently.

  14. Reframing The Approach 99.9% of people enter trading with the belief that there is order in chaos, and therefore, forecasting (with fundamentals, indicators, cycles, E-wave, Fibonacci numbers, etc.) becomes the focus of their research efforts. From what I have observed, history does repeat mainly because people behave somewhat predictably in that their reaction to events goes through typical phases. The catch is that circumstances are never exactly like the last time.

  15. Know Our Limitations = Profits Once we know what we don’t know, once we know the limitations of the usual tools used by the average trader, we are well ahead of the crowd that thinks there is a way to make accurate forecasts. We work with some simple definitions and what can see with our eyeballs. We also learn what uninformed traders are up to in order to exploit their mistakes.

  16. Trade Selectively with Visual Analysis • Most traders look for patterns, and in the end, they all see the same thing and try to execute the same “breakout” trades. • Most traders look at a number of moving averages, now routinely announced on financial TV. • We must let uninformed traders take action on these “setups of last resort”.

  17. Color The Price Bars

  18. Trade What You Can Identify Price bars are classified according to the definitions below and colored accordingly. Price bars that do not fit the definition are not colored. • UP (green) = higher high and higher low than the previous bar. • DOWN (red) = lower low and lower high than the previous bar. • INSIDE (yellow) = lower high and higher low than than the previous bar. • OUTSIDE (cyan) = higher high and lower low than the previous bar.

  19. Connect The Swings

  20. Trade What You Can Identify Price swings are connected according to the principle that upswings will feature mostly up bars and downswings will feature mostly down bars. The swings are connected accordingly and will help identify emerging patterns, tests and retracements as they unfold. There is no guessing, only a process of elimination.

  21. Karaoke Trading

  22. Now for Some Stylized Facts Stephen J. Taylor, Asset Price Dynamics, Volatility, and Prediction: “General properties that are expected to be present in any set of returns are called stylized facts. There are three important properties that are found in almost all sets of daily returns obtained from a few years of prices. First, the distribution of returns is not normal. Second, there is almost no correlation between returns for different days. Third, the correlations between the magnitudes of returns on nearby days are positive and statistically significant. These properties can all be explained by changes through time in volatility. . . . Incidentally, the three major stylized facts are pervasive across time as well as across markets. They are apparent in daily returns at the Florentine currency market from 1389 to 1432 (Booth and Gurun 2004), the London market for stocks from 1724 to 1740 (Harrison 1998), and the London fixed-income market from 1821 to 1860 (Mitchell, Brown, and Easton 2002).”

  23. Step 1: Sentiment Analysis Start by looking at recent news headlines. This information helps us determine which phase of the Investor Sentiment Cycle the stock is in. Check (i) Google Finance for market cap, recent news stories and discussion, (ii) Yahoo Finance for the News & Info and Analyst Coverage sections, and (iii) CNBC Video to see who said what on air or at the site.

  24. Justin Mamis Investor Sentiment Cycle

  25. Ask Questions If this stock has been rising into expected good news, will investors take profits on the announcement? If this stock has been falling into expected bad news, will buyers show up on the announcement? Has everyone who wants to buy it bought? If so, who is left to take it higher? Has everyone who wants to sell it sold? If so, who is left to drive it lower?

  26. Step 2: Check the Moving Averages There are many superstitions in the world of folk finance. We must know “the signals” because people act on them. For example, the 50/200 “cross” is a very popular scan criteria used by legions of retail traders. Their thesis goes something like this: the up or “golden” cross (50-day MA moves above 200-day MA) is supposed to be bullish while the down or “death” cross (50-day MA moves below 200-day MA) portends to weakness.

  27. The Moving Average Cross

  28. Step 3: Check Volatility A stock that is thrashing wildly increases risk for traders since a stop loss must be wide enough to accommodate the price action. Standard deviation (“historical”, or “realized” volatility), true range and my _Smarter.Range indicator provides us with a measurement. In general, a low reading means the stock price is pretty tame (even though it may be in an up or downtrend) while a high reading means the stock is all over the place and demands extraordinary risk management.

  29. Measuring Range and Volatility

  30. Step 4: Check Relative Momentum Relative momentum is an objective measurement of the price of A against the price of B over a period of time. This type of analysis is often called “relative strength” (NOT the same as Wilder’s RSI or the IBD ranking) or “ratio charts”, but since there is so much confusion, I call it relative momentum. We compare the stock against a benchmark index, a broad stock index. Is the stock large cap, mid cap or small cap? Is it a constituent of any of the major stock indexes such as the S&P 500 Index, the NASDAQ 100 Index or the Russell 2000 Index?

  31. RMI Histogram and PaintBar Studies The RMI Histogram compares price action of a stock (or sector) against an appropriate benchmark index while the RMI PaintBar colors price bars according to the status of the histogram values. The position of the green and red histogram bars relative to the grey threshold line is very important. A number of combinations and permutations can occur. Price bars are colored based on information contained in the histogram according to these rules: • Green = RELATIVE OUTPERFORMANCE = Histogram > 0 AND Histogram > Threshold Line • Red = RELATIVE UNDERPERFORMANCE = Histogram < 0 AND Histogram < Threshold Line • Yellow = POTENTIAL CHANGE OF TREND = all other combinations

  32. Example: GLD vs. S&P 500 Index

  33. Example: GLD vs. NASDAQ 100 Index

  34. Example: GOOG vs. S&P 500 Index

  35. Example: RIMM vs. S&P 500 Index

  36. Example: WYNN vs. S&P 500 Index

  37. Example: BRK.B vs. S&P 500 Index

  38. Step 5: Is There a Technical Trade Setup? Is there a valid reason for entering this trade? Is there a technical trade setup on this chart? Identify trades by the process of elimination, from smallest to largest patterns. In the era of quantitative analysis, pattern-based technical analysis remains 100% valid because chart patterns are like foot prints. We track these like a predator tracks his prey. We draw swing lines with Smarter.Swings, connect the dots and objectively identify the pattern.

  39. List of Discretionary Trade Setups • The Pause (1 bar) • The Wunderbar (1 bar) • The Fast Flag (1 swing, 2-4 bars) • The Pennant (2 swings) • The Classic Flag (2 swings) • The Holy Grail (Linda Raschke 20EMA/ADX combo) • The ABC Correction (3 swings) • The Wedge (3 swings or more on decreasing volatility) • The Reversal Patterns (Spike, Test) • The Breakout (“Last Resort”)

  40. Example: INSP Moving Averages

  41. Example: INSP Volatility and Range

  42. Example: INSP Relative to S&P 500

  43. Example: INSP Swing Chart Look at the structure of the swings connected by the swing lines. Is there a pattern of higher swing highs and higher swing lows? That is an uptrend. Is there a pattern of lower swing highs and lower swing lows? That is a downtrend. Is there no pattern? It’s probably choppy.

  44. Step 6: Position Size and Execution Are all your eggs in one basket? Be sure to calculate appropriate position size to limit risk to capital. Traders should consider using call or put spreads to further reduce risk. Bull call spreads and bear put spreads can be selected using the dots generated by Smarter.Stops.

  45. The Only Game in Town -- Jack Treynor, Treynor On Institutional Investing

  46. Example: INSP VR (grey line) is the Volatility Ratio Position Sizer (yellow line) is a more accurate indication of size relative to a risk free benchmark.

  47. Step 7: Where is the Stop Loss? Perhaps more than any other game, active management resembles Five Card Stud: In the long run, the "cards" research deals the portfolio manager may matter less than his judgement about when to raise and when to fold. --Jack Treynor, Treynor On Institutional Investing

  48. Example: INSP Smarter.Stops are engineered to reflect observed volatility and range, providing with a real edge over other so-called volatility-based indicators or bands. BUY RULE: A *close* above a blue dot is buy signal. SELL RULE: A *close* below a pink dot is a sell signal. Stops must accurately reflect volatility and range. Stops must never, ever be based on what the trader can afford to lose. They must be placed where they ought to be and we reduce our trading size as required to manage risk to capital. This is our definitive edge.

  49. How Much Diversification? Diversifiable vs. Non-diversifiable risk (Facts and Fallacies of Financial Engineering, Kim, 2010)

  50. Conclusion and Implications • Common indicators have a weak theoretical basis but we must be aware of superstitions because people act on these signals. Informed traders capitalize on mistakes. • We must be aware of market sentiment because stocks can look good from far, but are far from good. • Volatile periods identified on the daily chart cluster together. • We objectively measure volatility, price strength and weakness. • Chart patterns are valid, perhaps especially in this day and age. • Draw swing lines objectively to trace out patterns. No setup, no trade. • Position size must be calculated conservatively. Do not put all eggs in one basket. Do not load all the baskets onto one truck. • Use a stop loss. He who fights and runs away, lives to fight another day.