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Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

Simple Test of RSI as an Abnormal Returns Indicator

A reader asked: “Jason Kelly from the Kelly Newsletter posted this remark in January 2008: ‘A good way to judge trading opportunities on indexes is by watching their MACD and RSI scores. Both together, along with the price chart, give good indications as to whether the odds favor rising or falling from here.’ Is this true?” Here is a simple test of the 14-day Relative Strength Index (RSI), as calculated by the template at StockCharts.com, on a tradable proxy for the S&P 500 index. Note that this indicator measures the strength of price for an asset relative to its own recent past, not relative to other assets. We use the conventional interpretation that values of RSI below 30 (above 70) indicate oversold (overbought) conditions ripe for reversion. Using daily dividend-adjusted closing prices for the S&P Depository Receipts Trust (SPY) from 1/29/93 (the earliest available) through 2/29/08, we find that: Keep Reading

Between the Hedges Net Portfolio Position

A reader suggested that we evaluate the performance of Between the Hedges, a “portfolio manager’s commentary on investing and trading in the U.S. financial markets.” One prominent and systematic feature of the commentary in that blog is the daily net portfolio position, expressed as percentage long. This position changes frequently, and the portfolio manager presumably manages it to exploit expected short-term trends in the broad stock market. If the expectation has value, the net portfolio position should relate positively to near-term broad market behavior. Using the Between the Hedges daily net portfolio position for 2/2/04-2/26/08 (1,024 trading days) and contemporaneous daily data for the S&P 500 index, we find that: Keep Reading

Tony Caldaro’s “Objective Elliott Wave” Outlooks

Several readers have requested that we evaluate the market timing value of Tony Caldaro’s “Objective Elliott Wave (OEW)” analysis. Mr. Caldaro describes OEW as incorporating missing tenets, such that: “Applying these newfound tenets to the market, …the waves were crystal clear. The turning points were precise, to the day, because they were quantitatively derived. There was no question when a wave ended and another began.” Based on the record of “MEDIUM TERM” outlook synopses in Tony Caldaro’s daily commentary blog and contemporaneous daily data for the S&P 500 index over the period January 2006 through January 2008 (523 trading days), we conclude that: Keep Reading

Review of IntelligentValue’s Retracement-Value Portfolio

A reader poses the following question: “Have you looked at IntelligentValue? They claim pretty impressive results, apparently certified by FinancialContent. Their Retracement-Value portfolio, particularly, shows impressive results [“508% in its 1st 18.5 months!”]. Are you able to evaluate this newsletter service?” Based on the information provided on IntelligentValue’s web site, especially the closed trade analysis for the [now removed] Retracement-Value portfolio (171 round-trip trades, apparently from portfolio inception on 5/16/06 through 1/9/08, with a starting portfolio value of $10,000), we conclude that: Keep Reading

Review of Mark Leibovit’s VRTrader.com “Track Record”

Several readers have requested that we evaluate the expertise of Mark Leibovit, Chief Market Strategist for VRTrader.com, According to VRTrader.com: “His technical expertise is in volume analysis, providing short-term, high performance stock trades and market timing…” Based on the information provided at VRTrader.com, especially the “Track Record” encompassing 3,388 round-trip trades during 2001-2007, we conclude that: Keep Reading

Befriend the Trend Trading’s Trend Trades

A reader asked: “Please evaluate the performances of the The Trend Trade Letter and The Cheap Stocks Letter offered by Befriend the Trend Trading.” We focus here on the The Trend Trade Letter, which has a much larger sample of historical trades than its sibling. Both the company and newsletter names imply a momentum-centric trading approach. The newsletter focuses on fairly continuous short-term technical trading (long and short) of liquid, volatile stocks with no more than ten positions at a time. Based on the information provided on the Befriend the Trend Trading web site, especially the monthly closed trade lists for The Trend Trade Letter (over 1,300 round-trip trades, from inception on 10/7/02 through 12/11/07), we conclude that: Keep Reading

Does a Long-Term Moving Average Indicator Predict Big Days?

A reader offered the following observation and question: “For many market observers, the 200-day moving average is the point of being in or out of the market. Does being above or below the 200-day moving average make a material difference with respect to missing the the best/worst 10, 20 or 100 days?” To check, we return to the data set for our “Trend Implications of Big Up and Down Days”, which identifies the 40 biggest up days (daily return > 3.50%) and the 40 biggest down days (daily return < -3.09%) for the S&P 500 index during January 1950 through November 2007. Calculating the 200-day moving average (MA) at the close for each day just before these 80 biggest up/down days, we find that: Keep Reading

The “Double 9-to-1 Up Day” Signal

Mark Hulbert’s 9/5/07 column addresses the 9-to-1 up day event, a bullish technical signal publicized by Martin Zweig in a 1986 book. It occurs when at least 90% of daily NYSE volume belongs to advancing issues. When the signal occurs in multiples over short periods, as it has recently, prospects for equities are “quite bullish” according to Mark Hulbert. A reader comments and inquires:

“A statistician [David Aronson, author of Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals] confirms the significance of Zweig’s original observation. I don’t know whether he considered all possible confounding factors, such as low volume days, effect of externalities on the market, and others I can’t think of. This analysis sounds like so much epidemiological research, finding associations but never proving causality. For example, in the decade of the 1980s, alternate papers found that coffee consumption (greater than three cups per day) is and is not associated with increased risk of cancer of the pancreas. How much credence do you place in Hulbert’s article?”

Using S&P 500 index data for 1942-2006 (67 years), David Aronson finds an average return of about 5.2% in the 60 trading days after double 9-to-1 up days, significantly greater than the average return of about 1.1% during intervals of 60 trading days when there has not been such a signal. To follow up, we pose some questions to David Aronson and then consider strategies an investor might employ to exploit double 9-to-1 up day signals, as follows: Keep Reading

Does the Bullish Percent Index Predict Market Direction?

Is the Bullish Percent Index a useful indicator of overall stock market or sector direction by reliably identifying overbought/oversold conditions from which stock prices are likely to revert? In a study published in the 2005 Journal of Technical Analysis, Andrew Hyer relates the simple average Bullish Percent across 40 stock market sectors (BPAVG) to future broad stock market returns. Using weekly levels of BPAVG as calculated by Dorsey, Wright & Associates and overall stock market returns over the next 100 calendar days based on the Value Line Geometric Index for a total sample period of 1/6/98-1/24/05 (about 368 weeks or 26 intervals of 100 calendar days), he concludes that: Keep Reading

Does Technical Trading Work with Commodity Futures?

Do relatively low transaction costs and ease of short selling enable profitable technical trading in commodity futures markets? In their recent paper entitled “Can Commodity Futures be Profitably Traded with Quantitative Market Timing Strategies?”, Ben Marshall, Rochester Cahan and Jared Cahan investigate the effectiveness of 7,846 quantitative trading rules from five rule families (Filter, Moving Average, Support and Resistance, Channel Breakouts, and On-Balance Volume) for 15 kinds of commodity futures contracts. They test these rules for cocoa, coffee, cotton, crude oil, feeder cattle, gold, heating oil, live cattle, oats, platinum, silver, soy beans, soy oil, sugar and wheat futures. Their testing includes two bootstrapping methodologies, adjustment for data snooping bias and evaluations over different time periods. Using daily price and volume data for 1984-2005, they conclude that: Keep Reading

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