CHAPEL HILL, N.C. (MarketWatch) — Chalk up another win for one of the best-performing stock market timing systems of all time.
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After neatly sidestepping most of the May-June correction, this system became 100% long during the last week of June — just in time for the explosive rally that soon ensued.
And, then, the system went back to 100% cash at last Friday’s close, thereby avoiding the big down day that greeted investors on the first trading day of this week.
Timing Solution software is designed to forecast stock market future moves, to help you make better trading decisions. Timing Solution models are based on classical cyclical analysis, Neural Network technology, astronomical cycles, pattern recognition and many other methods to.
What is this timing system with such nimble footwork?
It is the Seasonality Trading System, which was created by Norman Fosback in the early 1970s. Fosback was president of the Institute for Econometric Research from the early 1970s through the late 1990s, and he currently edits an investment advisory service called Fosback’s Fund Forecaster.
Fosback introduced this timing system in the mid-1970s, calling it one of the best short-term indicators he had ever encountered. The system calls for being 100% in stocks at the turns of each calendar month and prior to exchange holidays. It is in cash at all other times.
Believe it or not, that’s it.
Timing Stock Market
The Seasonality Timing System’s performance has been superb on a risk-adjusted basis. It is well ahead of any of the other market timing systems the Hulbert Financial Digest (HFD) has tracked over the last three decades.
Consider a hypothetical portfolio constructed by the HFD that has been invested in the Wilshire 5000 Index whenever Fosback’s system was on a buy signal and in 90-day Treasury bills at all other times. Since the early 1980s, this hypothetical portfolio has produced an 11.4% annualized return in contrast to the Wilshire’s 11.1%.
In other words, despite being in cash more than half the time, the portfolio has nevertheless made more money than the market itself. That’s a winning combination.
Though it has had some hiccups in recent years, the system shows no signs of losing its touch. Over the last decade, for example, it is ahead of a buy-and-hold by 0.1 percentage point per year, on an annualized basis.
Amazingly, for being such a star performer, this system couldn’t be simpler. The only thing you need to know is what day of the month it is. No need for fancy computers or sophisticated software — or to track the markets on a minute-by-minute, or day-by-day basis.
In fact, there is no need to track the market at all. It is possible to specify, months and years in advance, when the system will be in stocks and when in cash. The next time it will go into the market, for example, is July 27.
Why does this system work? Research conducted by Wharton University Professor Donald Keim has found that there are distinct calendar-based patterns to how stocks trade, most likely because of when periodic retirement-plan contributions hit the market, tax-loss selling, and short-term traders’ reluctance to be exposed to stocks over a long holiday weekend.
Several words of caution are in order, however: Because the system involves lots of trading — around 17 round trips per year, in fact — it is crucial that you follow the system using no-load mutual funds with no redemption fees. With that amount of trading, furthermore, it is not eligible for the more favorable tax treatment reserved for long-term capital gains.
And, perhaps most importantly, no system’s success is guaranteed.
Still, I know of no other market timing system that was created as many decades ago as this one that has been as successful in real time as this one. If you want to bet on past performance, this is definitely one to consider.
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Market timing is the strategy of making buying or selling decisions of financial assets (often stocks) by attempting to predict future market price movements. The prediction may be based on an outlook of market or economic conditions resulting from technical or fundamental analysis. This is an investment strategy based on the outlook for an aggregate market, rather than for a particular financial asset.
7What some financial advisors say
7.2Federal Reserve Bank of New York
7.3Federal Reserve Bank of San Francisco
7.4Federal Reserve Board
Difference in views on the viability of market timing[edit]
Whether market timing is ever a viable investment strategy is controversial. Some may consider market timing to be a form of gambling based on pure chance, because they do not believe in undervalued or overvalued markets. The efficient-market hypothesis claims that financial prices always exhibit random walk behavior and thus cannot be predicted with consistency.
Some consider market timing to be sensible in certain situations, such as an apparent bubble. However, because the economy is a complex system that contains many factors, even at times of significant market optimism or pessimism, it remains difficult, if not impossible, to predetermine the local maximum or minimum of future prices with any precision; a so-called bubble can last for many years before prices collapse. Likewise, a crash can persist for extended periods; stocks that appear to be 'cheap' at a glance, can often become much cheaper afterwards, before then either rebounding at some time in the future or heading toward bankruptcy.
Proponents of market timing counter that market timing is just another name for trading. They argue that 'attempting to predict future market price movements' is what all traders do, regardless of whether they trade individual stocks or collections of stocks, aka, mutual funds. Thus if market timing is not a viable investment strategy, the proponents say, then neither is any of the trading on the various stock exchanges. Those who disagree with this view usually advocate a buy-and-hold strategy with periodic 're-balancing'.
Others contend that predicting the next event that will affect the economy and stock prices is notoriously difficult. For examples, consider the many unforeseeable, unpredictable, uncertain events between 1985 and 2013 that are shown in Figures 1 to 6 [pages 37 to 42] of Measuring Economic Policy Uncertainty.[1] Few people in the world correctly predicted the timing and causes of the Great Recession during 2007–2009.
Market-timing software and algorithms[edit]
The Federal Reserve Bank of Kansas City has published a review of several relatively simple and statistically successful market-timing strategies.[2] It found, for example, that 'Extremely low spreads, as compared to their historical ranges, appear to predict higher frequencies of subsequent market downturns in monthly data' and that 'the strategy based on the spread between the P/E ratio and a short-term interest rate comfortably and robustly beat the market index even when transaction costs are incorporated'.
Institutional investors often use proprietary market-timing software developed internally that can be a trade secret. Some algorithms, like the one developed by Nobel Prize–winning economist Robert C. Merton, attempts to predict the future superiority of stocks versus bonds (or vice versa),[3][4] have been published in peer-reviewed journals and are publicly accessible.
Moving average[edit]
Market timing often looks at moving averages such as 50- and 200-day moving averages (which are particularly popular).[5] Some people believe that if the market has gone above the 50- or 200-day average that should be considered bullish, or below conversely bearish.[6] Technical analysts consider it significant when one moving average crosses over another. The market timers then predict that the trend will, more likely than not, continue in the future. Others say, 'nobody knows' and that world economies and stock markets are of such complexity that market-timing strategies are unlikely to be more profitable than buy-and-hold strategies.
Moving average strategies are simple to understand, and often claim to give good returns, but the results may be confused by hindsight and data mining.[7][8]
Curve fitting and over-optimization[edit]
A major stumbling block for many market timers is a phenomenon called 'curve fitting', which states that a given set of trading rules tends to be over-optimized to fit the particular dataset for which it has been back-tested. Unfortunately, if the trading rules are over-optimized they often fail to work on future data. Market timers attempt to avoid these problems by looking for clusters of parameter values that work well[9] or by using out-of-sample data, which ostensibly allows the market timer to see how the system works on unforeseen data. Critics, however, argue that once the strategy has been revised to reflect such data it is no longer 'out-of-sample'.
Independent review of market-timing services[edit]
Several independent organizations (e.g., Timer Digest and Hulbert Financial Digest) have tracked some market timers' performance for over thirty years. These organizations have found that purported market timers in many cases do no better than chance, or even worse. However, there were exceptions, with some market timers over the thirty-year period having performances that substantially and reliably outperformed the general stock market, such as Jim Simons' Renaissance Technologies, which allegedly uses mathematical models developed by Elwyn Berlekamp.[10]
A recent study suggested that the best predictor of a fund's consistent outperformance of the market was low expenses and low turnover, not pursuit of a value or contrarian strategy.[11] However, other studies have concluded that some simple strategies will outperform the overall market.[12] One market-timing strategy is referred to as Time Zone Arbitrage.
Evidence for market timing[edit]
Mutual fund flows are published by organizations like Investment Company Institute and TrimTabs.[13] They show that flows generally track the overall level of the market: investors buy stocks when prices are high, and sell stocks when prices are low. For example, in the beginning of the 2000s, the largest inflows to stock mutual funds were in early 2000 while the largest outflows were in mid-2002. These mutual fund flows were near the start of a significant bear (downtrending) market and bull (uptrending) market respectively. A similar pattern is repeated near the end of the decade.[14][15][16][17][18] Chien of the Federal Reserve Bank of St. Louis confirms the correlation showing return-chasing behavior.[19]
This mutual fund flow data seems to indicate that most investors (despite what they may say) actually follow a buy-high, sell-low strategy.[20][21] Studies confirm that the general tendency of investors is to buy after a stock or mutual fund price has increased.[22] This surge in the number of buyers may then drive the price even higher. However, eventually, the supply of buyers becomes exhausted, and the demand for the stock declines and the stock or fund price also declines. After inflows, there may be a short-term boost in return, but the significant result is that the return over a longer time is disappointing.[23]
Researchers suggest that, after periods of higher returns, individual investors will sell their value stocks and buy growth stocks. Frazzini and Lamont find that, in general, growth stocks have a lower return, but growth stocks with high inflows have a much worse return.[22]
Studies find that the average investor's return in stocks is much less than the amount that would have been obtained by simply holding an index fund consisting of all stocks contained in the S&P 500 index.[24][25][26][27][28]
For the 20-year period to the end of 2008, the inflation-adjusted market return was about 5.3%. The average investor managed to turn $1 million into $800,000, against $2.7 million for the index (after fund costs).[29] More recent results show a bigger difference, but the investor beating inflation slightly.
Studies by the financial services market research company Dalbar say that the retention rate for bond and stock funds is three years. This means that in a 20-year period the investor changed funds seven times. Balanced funds are a bit better at four years, or five times. Some trading is necessary since not only is the investor return less than the best asset class, it is typically worse than the worst asset class, which would be better.[30] Balanced funds may be better by reason of investor psychology.[31]
What some financial advisors say[edit]
Financial advisors often agree that investors have poor timing, becoming lessrisk averse when markets are high and more risk averse when markets are low, a strategy that will actually result in less wealth in the long-term compared to someone who consistently invests over a long period regardless of market trends.[32][33] This is consistent with recency bias and seems contrary to the acrophobia explanation. Similarly, Peter Lynch has stated that 'Far more money has been lost by investors preparing for corrections or trying to anticipate corrections than has been lost in the corrections themselves.'[34] Academic theory often assumes that investors are like Mr. Spock of Star Trek, capable in most circumstances of logical, emotionally-detached analysis.[35] In fact, most investors cannot process information like Mr. Spock.[36]
'The only problem is that, unlike Mr. Spock of Star Trek fame, humans are not entirely rational beings.'[37]
Proponents of the efficient-market hypothesis (EMH) claim that prices reflect all available information. EMH assumes that investors are highly intelligent and perfectly rational. However, others dispute this assumption. 'Of course, we know stocks don't work that way'.[38] In particular, proponents of behavioral finance claim that investors are irrational but their biases are consistent and predictable.
in 1987, Kenneth R. French, G. William Schwert, and Robert F. Stambaugh wrote that an unexpected increase in volatility lowers current stock prices.[39]
Total factor productivity (TFP) growth volatility is negatively associated with the value of U.S. corporations. An increase of 1% in the standard deviation of TFP growth is associated with a reduction in the value-output ratio of 12%.[40] Changes in uncertainty can explain business cycle fluctuations, stock prices, and banking crises.[41]
Bull Bear Spread[edit]
The Investors Intelligence Advisors Sentiment Survey reports the attitudes of U.S. advisors. A large difference between the percentage bullish vs. bearish indicates more risk.
The 30% difference is increased risk.
At 40% difference, consider defensive measures.[42]
On January 16, 2018, Peter Boockvar said that the Investors Intelligence had the highest bull bear spread since 1986. Boockvar said that there was an extraordinary level of overboughtness.[43]
Major turns in the Conference Board’s Present Situation Index tend to precede corresponding turns in the unemployment rate—particularly at business cycle peaks (that is, going into recessions). Major upturns in the index also tend to foreshadow cyclical peaks in the unemployment rate, which often occur well after the end of a recession. Another useful feature of the index that can be gleaned from the charts is its ability to signal sustained downturns in payroll employment. Whenever the year-over-year change in this index has turned negative by more than 15 points, the economy has entered into a recession.[44]
Federal Reserve Bank of San Francisco[edit]
Using Sentiment and Momentum to Predict Stock Returns[edit]
Expect a below-average stock return over the next month, when
For weeks, there is negative return momentum and
For the past year, there the sentiment is less than in the previous year.[45]
Federal Reserve Board[edit]
Changes in Shares Predict Stock Returns[edit]
In the years after a company repurchases shares, then the stock returns greater than companies where the number of shares remain the same. Companies that issue new shares annually, often have relatively less returns.[46]
See also[edit]
Dynamic factor In econometrics, a dynamic factor (also known as a diffusion index) were originally designed to help identify business cycle turning points.[1]
^staff, CNBC.com (13 October 2014). 'The technical indicator that made the market tank'.
^Hulbert, Mark. 'Good enough'.
^Zakamulin, Valeriy (14 July 2014). 'The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules' – via papers.ssrn.com.
^Zakamulin, Valeriy (11 December 2015). 'A Comprehensive Look at the Empirical Performance of Moving Average Trading Strategies' – via papers.ssrn.com.
^Pruitt, George, & Hill, John R. Building Winning Trading Systems with TradeStation(TM), Hoboken, N.J: John Wiley & Sons, Inc. ISBN0-471-21569-4, p. 106-108.
^Berlekamp, [email protected] - Elwyn. 'Finance and Business'. math.berkeley.edu.
^Malkiel B.G. (2004) Can predictable patterns in market returns be exploited using real money? Journal of Portfolio Management, 31 (Special Issue), p.131-141.
^Shen, P. Market timing strategies that worked — based on the E/P ratio of the S&P 500 and interest rates. Journal of Portfolio Management, 29, p.57-68.
^'Estimated Long-Term Mutual Fund Flows - Data via Quandl'. www.quandl.com. Retrieved 2015-10-01.
^Kinnel, Russel (15 February 2010). 'Bad Timing Eats Away at Investor Returns'.
^Worldwide Mutual Fund Assets and Flows, Fourth Quarter 2008[permanent dead link]
^You Should Have Timed the MarketArchived 2010-10-11 at the Wayback Machine on finance.yahoo.com
^Landy, Michael S. Rosenwald and Heather (26 December 2008). 'Investors Flee Stock Funds' – via www.washingtonpost.com.
^'CHART: Investors Buy And Sell Stocks At Exactly The Wrong Times'.
^'Chasing Returns Has a High Cost for Investors | St. Louis Fed On the Economy'.
^'If You Think Worst Is Over, Take Benjamin Graham's Advice'. Archived from the original on 2009-05-30. Retrieved 2017-01-17.
^'Since When Did It Become Buy High, Sell Low?: Chart of the Week: Market Insight: Financial Professionals: BlackRock'.
^ ab'Dumb money: Mutual fund flows and the cross-section of stock returns'(PDF). Archived from the original(PDF) on 2014-07-31. Retrieved 2013-08-14.
^http://www.econ.yale.edu/~af227/pdf/Dumb%20money%20Mutual%20fund%20flows%20and%20the%20cross-section%20of%20stock%20returns%20-%20Frazzini%20and%20Lamont.pdfArchived 2014-07-31 at the Wayback Machine Dumb money: Mutual fund flows and the cross-section of stock returns. by Andrea Frazzinia, Owen A. Lamont. University of Chicago, Graduate School of Business & Yale School of Management. Journal of Financial Economics 88 (2008) 299–322. Page 320, paragraph 2
^Anderson, Tom. 'Fund Investors Lag As S&P 500 Nears All-Time High'.
^'Fact Sheet: Morningstar Investor Return'(PDF).
^'Black Swans, Portfolio Theory and Market Timing'.
^'Mutual funds far outperform mutual fund investors'. MarketWatch.
^'Market Timing Usually Leads to Lower Returns - BeyondProxy'. Beyond Proxy. Archived from the original on 2018-02-09. Retrieved 2014-06-25.
^'CHART: Proof That You Stink At Investing'. Business Insider.
^Richards, Carl (18 March 2018). 'Forget Market Timing, and Stick to a Balanced Fund' – via NYTimes.com.
^Lieber, Ron (8 October 2008). 'Switching to Cash May Feel Safe, but Risks Remain' – via NYTimes.com.
^'Emotions And Market Timing, Emotions and Timing'. www.fibtimer.com.
^As quoted in 'The Wisdom of Great Investors: Insights from Some of History’s Greatest Investment Minds, by Davis Advisers, p. 7
^'Rethinking thinking'. The Economist.
^'How Are Investment Decisions Made?'(PDF).
^WHY INVESTORS DON’T BEAT THE MARKET
^Jim Cramer's Getting Back to Even, pp. 63-64
^https://umdrive.memphis.edu/cjiang/www/teaching/fir8-7710/paper/FrenchExpectedStockRtnsVolatility.pdf Expected Stock Returns and Volatility by Kenneth R. French, G. William Schwert and Robert F. Stambaugh
^http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2243705 Risk, Economic Growth and the Value of U.S. Corporations by Luigi Bocola and Nils Gornemann
^http://bfi.uchicago.edu/events/20121206_uncertainty/papers/Orlik.pdf Understanding Uncertainty Shocks by Anna Orlik and Laura Veldkamp
^Bush Wealth Management | MARKET COMMENTARY 11/28/2017 | Stacy Bush & Kent Patrick | November 28, 2017
^Stock market’s wild flip flop comes as warning signs build | JANUARY 16, 2018 | Patti Domm, NBR, CNBC.com
^Federal Reserve Bank of New York, Consumer Confidence: A Useful Indicator of . . . the Labor Market?Jason Bram, Robert Rich, and Joshua Abel ... Conference Board’s Present Situation Index This article incorporates text from this source, which is in the public domain.
^Using Sentiment and Momentum to Predict Stock Returns | Kevin J. Lansing and Michael Tubbs | SENTIMENT * MOMENTUM variable | FRBSF Economic Letter 2018-29 December 24, 2018
^Why Does the Change in Shares Predict Stock Returns? William R. Nelson | Federal Reserve Board January 1999
Stock Market Timing Software
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