Technical Tools Partnered With Other Methodologies. May 15, 2014 Adam Grimes, CIO, Waverly Advisors

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1 Technical Tools Partnered With Other Methodologies May 15, 2014 Adam Grimes, CIO, Waverly Advisors

2 Outline: First principles: the market problem Understanding the market in terms of probabilities Approaches to solving the market problem What can Technical Tools do? Ideas for combining methodologies.

3 The Market Problem Understanding the nature of the market shows us that any solution has to deal with probabilities. Financial markets have a high degree of randomness. Some studies do not find any non-randomness, which means that they find all market action is random. (Bad news, if true, but applies to certain markets and timeframes.) Volatility is unpredictable, and brings challenges to traders and investors. Somewhat more predictable than price, under some conditions. Human psychology makes us vulnerable to making mistakes in the market. What is a trader or investor to do?

4 Option 1: Avoid Markets Entirely Recognizing the risk and uncertainty in markets, one choice might be to avoid all involvement. But, is this possible? Consider the extreme situation of burying cash in a hole in the ground: Buying power varies against other currencies. Even in the home country, buying power will change over time (interest rates and inflation).

5 Option 2: Index This may be a rational choice for many investors, and is the preferred alternative for the random walk crowd. Recognizing that markets are highly random, humans tend to make sub-optimal decisions, and there are costs associated with trading, this argument says it makes sense to trade as little as possible. However, many markets do appreciate over time, perhaps exceeding the impact of inflation on the currency. (Baseline drift.) Buy a diversified portfolio and make few, if any, trading decisions. (Buy and hold.)

6 Option 3: Trade Actively A wide range of alternatives here, from high frequency trading to making adjustments on a yearly (or longer) horizon. The goal, in all cases, is to beat the baseline return from buy and hold. Why? We assume risks and incur costs with trading, and must be compensated for that risk. So, this is the measure: can we beat buy and hold? (Not can we make money trading? )

7 The Market Problem Because markets are highly random, incorporating new information which arrives more or less randomly, we never know anything with certainty. Any approach to trading or investing deals with probabilities. Nothing is certain in markets. This is why disciplines such as risk management and position sizing are important. This is the key understanding: we are looking to find an approach that exceeds what would have been available to the buy and hold investor, thinking over a large sample of events (to allow probabilities to converge).

8 Proposed Solutions Attempting to solve the market problem, solutions fall into three broad camps: Fundamental analysis: using information from outside the market to try to understand the most probable future price path of the market. E.g., financial statement analysis, economic models, supply/demand models, economic indicators. Technical analysis: using information contained in the history of the asset. Behavioral finance: understanding human psychology and decision making in the market environment. Could argue this is a sub-set of technical analysis.

9 What is a Model? A model is a representation (simplification) of reality. Models rely on inputs and assumptions, and make predictions for the future, based on those inputs. Models are more or less useful depending on how well the assumptions match reality. Examples: An average is a very simple model. A complex multi-factor model is a more complex model. Every approach to trading or investing (except, perhaps, random selection) is model-based, though this is often forgotten.

10 Effective Use of Models Assumes the future will look somewhat like the past, or can be extrapolated (within limits) from the past. Respect those limits. E.g., some only predict over a range of inputs Allows for some tweaking of the model to match observed conditions. Effective models can have one or many inputs, but they avoid correlated inputs. In other words, inputs that say the same thing degrade model performance.

11 Uncorrelated Inputs Confirmation bias: People tend to favor, and seek out, information that confirms their beliefs. This is common in investing. For instance, if you read six things (blogs, newsletters, articles) about a subject, you are first of all more likely to weigh the ones that agree with your opinion more heavily. Solution: actively seek information that contradicts your bias. Also, if the writers are not using different methodologies, you do not actually have six different inputs. Solution: make sure some of the information you incorporate into your process comes from a different perspective. These solutions are painful They go against fundamental elements of human cognition, and it is difficult for many people to do this consistently. But they may be a key to successful investing.

12 When Might Technicals Offer an Edge? When price movements matter E.g., some fundamental models create a target value and then look at the difference between that and the market price. What if the price is declining steadily (downtrending)? Reflexivity: Price movements can influence fundamentals. In the short term Most fundamental methodologies assume price will converge on value at some point in the future, but short-term distortions are possible. When markets are emotional Behavioral factors may matter sometimes more than others.

13 Examples of Technical Tendencies Mean reversion Large moves reversed, on some timeframes. Momentum Large moves lead to further moves in the same direction. Specific patterns: Buying dips in trends (or shorting spikes in uptrends) Picking off (or avoiding) overextended spots Identifying relative strength leaders and laggards Action around support and resistance levels Identifying markets set to trade with stability vs. those that tend to have more spikes and distortions. (based on Adam Grimes s research, some of which is published in The Art and Science of Technical Analysis, Wiley 2012.)

14 What Can Technicals Do? If they work, the same as any other methodology, nothing more and nothing less: Identify conditions that allow a trader or investor to reap greater than baseline returns, on a risk-adjusted basis, over a large sample size. Offer a significant uncorrelated input to some traders and investors. If your methodology does not incorporate price analysis, then adding this analysis can bring significant new information to your process. Beware also of correlation and groupthink in technicals.

15 Price (Donchian) Channels

16 Channel Stats Trading 260 Day (~52 week) Channels (at least 5 days between entries)

17 RSI-14 (70/30 OB/OS)

18 RSI Stats Standard RSI-14

19 Fading +/- 3σ Closes Close in top 75% of range, and Open in Bottom 25% (for sells) by Adam Grimes, LLC. All rights reserved.

20 Keltner Channel Pullback Entry

21 Keltner Pullback Entry Stats

22 There Are Many Other Edges But be sure you understand the statistics. Statistics may be different in different markets and timeframes. Many of the standard tools of TA do not show a quantitative edge in tests. Moving Averages Fibonacci Ratios (See my previous IB webinars for further detail.) Understand what your tool is doing. Broadly, is it a mean reversion or momentum (continuation) tool?

23 Three Ideas for Combining Technicals as primary idea generator Technicals as a high-level filter Technicals to identify execution points

24 Technicals as Primary Idea Generator Several ways this could be possible. Technical top down Identify attractive geographies sectors stocks Typical tools might be relative strength and trend analysis Generate a list of candidates based on screening criteria. Could then filter with a fundamental methodology But consider the limitations of timeframe. (Even a bad stock could go up for 3 days!) Maybe trade most attractive names on fundamentals This concept can be extended to commodities or currencies, as well.

25 Waverly Advisors In Play Screen

26 Waverly Advisors Big Movers

27 Ranked RS in Our Research

28 Broad RS In Our Research

29 Technicals as Filter Several possibilities, but two broad concepts: Avoid markets that are strongly trending against the intended trade direction. Applies to longer-term positions. Shorter term traders may look to trade against the trend by finding overextended points. And then technicals are the primary idea generator. Avoid entering overextended markets. Even long-term positions may be able to pick up a little alpha by tweaking entries. Sometimes, what you don t do is as important as what you actually do.

30 Waverly Advisors KPos Measure KPos = Keltner Position = the close as a % of the band width. 50% = the moving average 100% = the upper band 75% = halfway between the average and the upper band. Measure can be > 100% or < 0%

31 Keltner Channel Statistics

32 Keltner Excursion Stats

33 KPos as a Filter Since there is a statistical edge for mean reversion when a market is outside the channels, one strategy would be to avoid buying or selling markets outside the bands. This would lead to a strategy that is similar to trading pullbacks. (Realize these tests work with a wide range of parameters. The concepts are much more important than the details.)

34 Filtering Tools in WA Screens

35 Identifying Execution Points A wide range of possibilities, from simple to complex. Identify fundamental candidates, execute when simple technical criteria confirm. Trade a full fledged technical system, but execute only on a pre-screened fundamental universe. Published research may be able to help with this in many ways.

36 Consistency Is Important If all trading/investing is a statistical numbers game, sample size matters. Law of large numbers. Convergence is only meaningful over a large sample size. If you are constantly changing approaches, the probabilities do not have a chance to work out. Other concerns with this may be execution errors, etc. A two-sided model of behavior is useful for many traders or investors: 1) Have a set of rules that are developed and changed slowly. 2) Monitor adherence to the rules.

37 Summary Understand the market problem, and that the solutions to the problem are probabilistic in nature. Investment/trading methodologies can, at best, provide a tilt to the probabilities nothing is known for certain. Consider the potential advantages of uncorrelated inputs in models. Understand the probabilities behind every tool you use, and how they may interact. Consider using published research and outside information in your process if it would enhance what you do or provide an additional edge.

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39 Waverly Advisors Process Our methodology is blended quantitative / discretionary. Quantitative work was both high-level and systemspecific. For breadth, our research went all the way back to commodity prices in the Middle Ages. It works. An interesting twist: our research was done to verify an existing, successful methodology, not to create one. The discretionary component is the input of a trader (me!) who has spent nearly every day of the past 20 years watching markets and prices, actively managing risk for much of that time.

40 Waverly Advisors Research Specific systems, broad tendencies, and actionable ideas in major liquid markets. Futures Currencies Stocks (indexes and individual names) Both trend-following and counter-trend components. Applicable to traders working on all timeframes. day traders swing traders investors

41 Waverly Advisors, LLC: Research Products Tactical Playbook Available on Interactive Brokers Written for the active trader on the daily/weekly timeframes Exact trade recommendations Hybrid systematic-discretionary methodology In-depth technical drill down into a set of markets. Bigger-picture overview of all liquid asset classes. Tactical Portfolio Outlook Available on Interactive Brokers Written for the longer-term manager Addresses both the allocator and the longer-term active trader. Emphasis on executing with ETFs in a long-only and long-short environment Focus on Equities, Equity Sectors, and other asset classes Macro perspective on risk factors and major economic events. Options Market Outlook Contact Waverly Directly Proprietary, quantitative analysis of options market Incorporates both volatility and directional analysis Macro risk factors and cross-asset perspective Actionable trade ideas

42 Contact: Adam Grimes CIO, Quantitative Analysis, Risk Management Waverly Advisors 228 Cedar Street Corning, NY (607) Andrew Barber CEO, Macroeconomic Analysis Chris Noye Managing Director, Head of Sales