Market Timing Archives - QUANTITATIVE RESEARCH AND TRADING

March 13, 2024March 13, 2024

The Misunderstood Art of Market Timing:

How to Beat Buy-and-Hold with Less Risk

What is Market Timing? – Common Misconceptions

Market timing has a very bad press and for good reason: the inherent randomness of markets makes reliable forecasting virtually impossible. So why even bother to write about it? The answer is, because market timing has been mischaracterized and misunderstood. It isn’t about forecasting. If fact, with notable exceptions, most of trading isn’t about forecasting. It’s about conditional expectations.

Conditional expectations refer to the expected value of a random variable (such as future stock returns) given certain known information or conditions.

In the context of trading and market timing, it means that rather than attempting to forecast absolute price levels, we base our expectations for future returns on current observable market conditions.

For example, let’s say historical data shows that when the market has declined a certain percentage from its recent highs (condition), forward returns over the next several days tend to be positive on average (expectation). A trading strategy could use this information to buy the dip when that condition is met, not because it is predicting that the market will rally, but because history suggests a favorable risk/reward ratio for that trade under those specific circumstances.

The key insight is that by focusing on conditional expectations, we don’t need to make absolute predictions about where the market is heading. We simply assess whether the present conditions have historically been associated with positive expected returns, and use that probabilistic edge to inform our trading decisions.

This is a more nuanced and realistic approach than binary forecasting, as it acknowledges the inherent uncertainty of markets while still allowing us to make intelligent, data-driven decisions. By aligning our trades with conditional expectations, we can put the odds in our favor without needing a crystal ball.

So, when a market timing algorithm suggests buying the market, it isn’t making a forecast about what the market is going to do next. Rather, what it is saying is, if the market behaves like this then, on past experience, the following trade is likely to be profitable. That is a very different thing from forecasting the market.

A good example of a simple market-timing algorithm is “buying the dips”. It’s so simple that you don’t need a computer algorithm to do it. But a computer algorithm helps by determining what comprises a dip and the level at which profits should be taken.

An Effective Market Timing Strategy

One of my favorites market timing strategies is the following algorithm, which I originally developed to trade the SPY ETF. The equity curve from inception of the ETF in 1993 looks like this:

The algorithm combines a few simple technical indicators to determine what constitutes a dip and the level at which profits should be taken. The entry and exit orders are also very straightforward, buying and selling at the market open, which can be achieved by participating in the opening auction. This is very convenient: a signal is generated after the close on day 1 and is then executed as a MOA (market opening auction) order in the opening auction on day 2. The opening auction is by no means the most liquid period of the trading session, but in an ETF like SPY the volumes are such that the market impact is likely to be negligible for the great majority of investors. This is not something you would attempt to do in an illiquid small-cap stock, however, where entries and exits are more reliably handled using a VWAP algorithm; but for any liquid ETF or large-cap stock the opening auction will typically be fine.

Adapting the Strategy to Other Assets and Markets

Another aspect that gives me confidence in the algorithm is that it generalizes well to other assets and even other markets. Here, for example, is the equity curve for the exact same algorithm implemented in the XLG ETF in the period from 2010:

And here is the equity curve for the same strategy (with the same parameters) in AAPL, over the same period:

Remarkably, the strategy also works in E-mini futures too, which is highly unusual: typically the market dynamics of the futures market are so different from the spot market that strategies don’t transfer well. But in this case, it simply works:

Understanding Why the Strategy Works

The reason the strategy is effective is due to the upward drift in equities and related derivatives. If you tried to apply a similar strategy to energy or currency markets, it would fail. The strategy’s “secret sauce” is the combination of indicators it uses to determine the short-term low in the ETF that constitutes a good buying opportunity, and then figure out the right level at which to sell.

Does the algorithm always work? If by that you mean “is every trade profitable?” the answer is no. Around 61% of trades are profitable, so there are many instances where trades are closed at a loss. But the net impact of using the market-timing algorithm is very positive, when compared to the buy-and-hold benchmark, as we shall see shortly.

Because the underlying thesis is so simple (i.e. equity markets have positive drift), we can say something about the long-term prospects for the strategy. Equity markets haven’t changed their fundamental tendency to appreciate over the 31-year period from inception of the SPY ETF in 1993, which is why the strategy has performed well throughout that time. Could one envisage market conditions in which the strategy will perform poorly? Yes – any prolonged period of flat to downward trending prices in equities will result in poor performance. But we haven’t seen those conditions since the early 1970’s and, arguably, they are unlikely to return, since the fundamental change brought about by abandonment of the gold standard in 1973.

The abandonment of the gold standard and the subsequent shift to fiat currencies has given central banks, particularly the U.S. Federal Reserve, unprecedented power to expand the money supply and support asset prices during times of crisis. This ‘Fed Put’ has been a major factor underpinning the multi-decade bull market in stocks.

In addition, the increasing dominance of the U.S. as the world’s primary economic and military superpower since the end of the Cold War has made U.S. financial assets a uniquely attractive destination for global capital, creating sustained demand for U.S. equities.

Technological innovation, particularly with respect to the internet and advances in computing, has also unleashed a wave of productivity and wealth creation that has disproportionately benefited the corporate sector and equity holders. This trend shows no signs of abating and may even be accelerating with the advent of artificial intelligence.

While risks certainly remain and occasional cyclical bear markets are inevitable, the combination of accommodative monetary policy, the U.S.’s global hegemony, and technological progress create a powerful set of economic forces that are likely to continue propelling equity prices higher over the long-term, albeit with significant volatility along the way.Strategy Performance in Bear Markets

Note that the conditions I am referring to are something unlike anything we have seen in the last 50 years, not just a (serious) market pullback. If we look at the returns in the period from 2000-2002, for example, we see that the strategy held up very well, out-performing the benchmark by 54% over the three-year period of the market crash. Likewise, in 2008 credit crisis, the strategy was able to eke out a small gain, beating the benchmark by over 38%. In fact, the strategy is positive in all but one of the 31 years from inception.

Comparing Performance to Buy-and-Hold

Let’s take a look at the compound returns from the strategy vs. the buy-and-hold benchmark:

At first sight, it appears that the benchmark significantly out-performs the strategy, albeit suffering from much larger drawdowns. But that doesn’t give an accurate picture of relative performance. To see why, let’s look at the overall performance characteristics:

Now we see that, while the strategy CAGR is 3.50% below the buy-and-hold return, its annual volatility is less than half that of the benchmark, giving the strategy a superior Sharpe Ratio.

Leveraging the Strategy to Enhance Risk-Adjusted Returns

To make a valid comparison between the strategy and its benchmark we therefore need to equalize the annual volatility of both, and we can achieve this by leveraging the strategy by a factor of approximately 2.32. When we do that, we obtain the following results:

Now that the strategy and benchmark volatilities have been approximately equalized through leverage, we see that the strategy substantially outperforms buy-and-hold by around 355 basis points per year and with far smaller drawdowns.

In general, we see that the strategy outperformed the benchmark in fewer than 50% of annual periods since 1993. However, the size of the outperformance in years when it beat the benchmark was frequently very substantial:

Conclusion

Market timing can work. To understand why, we need to stop thinking in terms of forecasting and think instead about conditional returns. When we do that, we arrive at the insight that market timing works because it relies on the positive drift in equity markets, which has been one of the central features of that market over the last 50 years and is likely to remain so in the foreseeable future. We have confidence in that prediction, because we understand the economic factors that have continued to drive the upward drift in equities over the last half-century.

After that, it is simply a question of the mechanics – how to time the entries and exits. This article describes just one approach amongst a great number of possibilities.

One of the many benefits of market timing is that it has a tendency to side-step the worst market conditions and can produce positive returns even in the most hostile environments: periods such as 2000-2002 and 2008, for example, as we have seen.

Finally, don’t forget that, as we are sitting out of the market approximately 40% of the time our overall risk is much lower – less than half that of the benchmark. So, we can afford to leverage our positions without taking on more overall risk than when we buy and hold. This clearly demonstrates the ability of the strategy to produce higher rates of risk-adjusted return.

October 6, 2020

Market Timing in the S&P 500 Index Using Volatility Forecasts

There has been a good deal of interest in the market timing ideas discussed in my earlier blog post Using Volatility to Predict Market Direction, which discusses the research of Diebold and Christoffersen into the sign predictability induced by volatility dynamics. The ideas are thoroughly explored in a QuantNotes article from 2006, which you can download here.

There is a follow-up article from 2006 in which Christoffersen, Diebold, Mariano and Tay develop the ideas further to consider the impact of higher moments of the asset return distribution on sign predictability and the potential for market timing in international markets (download here).

Trading Strategy
To illustrate some of the possibilities of this approach, we constructed a simple market timing strategy in which a position was taken in the S&P 500 index or in 90-Day T-Bills, depending on an ex-ante forecast of positive returns from the logit regression model (and using an expanding window to estimate the drift coefficient). We assume that the position is held for 30 days and rebalanced at the end of each period. In this test we make no allowance for market impact, or transaction costs.

Results
Annual returns for the strategy and for the benchmark S&P 500 Index are shown in the figure below. The strategy performs exceptionally well in 1987, 1989 and 1995, when the ratio between expected returns and volatility remains close to optimum levels and the direction of the S&P 500 Index is highly predictable, Of equal interest is that the strategy largely avoids the market downturn of 2000-2002 altogether, a period in which sign probabilities were exceptionally low.

In terms of overall performance, the model enters the market in 113 out of a total of 241 months (47%) and is profitable in 78 of them (69%). The average gain is 7.5% vs. an average loss of –4.11% (ratio 1.83). The compound annual return is 22.63%, with an annual volatility of 17.68%, alpha of 14.9% and Sharpe ratio of 1.10.

The under-performance of the strategy in 2003 is explained by the fact that direction-of-change probabilities were rising from a very low base in Q4 2002 and do not reach trigger levels until the end of the year. Even though the strategy out-performed the Index by a substantial margin of 6% , the performance in 2005 is of concern as market volatility was very low and probabilities overall were on a par with those seen in 1995. Further tests are required to determine whether the failure of the strategy to produce an exceptional performance on par with 1995 was the result of normal statistical variation or due to changes in the underlying structure of the process requiring model recalibration.

Future Research & Development
The obvious next step is to develop the approach described above to formulate trading strategies based on sign forecasting in a universe of several assets, possibly trading binary options. The approach also has potential for asset allocation, portfolio theory and risk management applications.

March 17, 2020March 17, 2020

Tactical Mutual Fund Strategies

A recent blog post of mine was posted on Seeking Alpha (see summary below if you missed it).

The essence of the idea is simply that one can design long-only, tactical market timing strategies that perform robustly during market downturns, or which may even be positively correlated with volatility. I used the example of a LOMT (“Long-Only Market-Timing”) strategy that switches between the SPY ETF and 91-Day T-Bills, depending on the current outlook for the market as characterized by machine learning algorithms. As I indicated in the article, the LOMT handily outperforms the buy-and-hold strategy over the period from 1994 -2017 by several hundred basis points:

Of particular note is the robustness of the LOMT strategy performance during the market crashes in 2000/01 and 2008, as well as the correction in 2015:

The Pros and Cons of Market Timing (aka “Tactical”) Strategies

One of the popular choices the investor concerned about downsize risk is to use put options (or put spreads) to hedge some of the market exposure. The problem, of course, is that the cost of the hedge acts as a drag on performance, which may be reduced by several hundred basis points annually, depending on market volatility. Trying to decide when to use option insurance and when to maintain full market exposure is just another variation on the market timing problem.

The point of tactical strategies is that, unlike an option hedge, they will continue to produce positive returns – albeit at a lower rate than the market portfolio – during periods when markets are benign, while at the same time offering much superior returns during market declines, or crashes. If the investor is concerned about the lower rate of return he is likely to achieve during normal years, the answer is to make use of leverage.

Market timing strategies like Hull Tactical or the LOMT have higher risk-adjusted rates of return (Sharpe Ratios) than the market portfolio. So the investor can make use of margin money to scale up his investment to about the same level of risk as the market index. In doing so he will expect to earn a much higher rate of return than the market.

This is easy to do with products like LOMT or Hull Tactical, because they make use of marginable securities such as ETFs. As I point out in the sections following, one of the shortcomings of applying the market timing approach to mutual funds, however, is that they are not marginable (not initially, at least), so the possibilities for using leverage are severely restricted.

Market Timing with Mutual Funds

An interesting suggestion from one Seeking Alpha reader was to apply the LOMT approach to the Vanguard 500 Index Investor fund (VFINX), which has a rather longer history than the SPY ETF. Unfortunately, I only have ready access to data from 1994, but nonetheless applied the LOMT model over that time period. This is an interesting challenge, since none of the VFINX data was used in the actual construction of the LOMT model. The fact that the VFINX series is highly correlated with SPY is not the issue – it is typically the case that strategies developed for one asset will fail when applied to a second, correlated asset. So, while it is perhaps hard to argue that the entire VFIX is out-of-sample, the performance of the strategy when applied to that series will serve to confirm (or otherwise) the robustness and general applicability of the algorithm.

The results turn out as follows:

The performance of the LOMT strategy implemented for VFINX handily outperforms the buy-and-hold portfolios in the SPY ETF and VFINX mutual fund, both in terms of return (CAGR) and well as risk, since strategy volatility is less than half that of buy-and-hold. Consequently the risk adjusted return (Sharpe Ratio) is around 3x higher.

That said, the VFINX variation of LOMT is distinctly inferior to the original version implemented in the SPY ETF, for which the trading algorithm was originally designed. Of particular significance in this context is that the SPY version of the LOMT strategy produces substantial gains during the market crash of 2008, whereas the VFINX version of the market timing strategy results in a small loss for that year. More generally, the SPY-LOMT strategy has a higher Sortino Ratio than the mutual fund timing strategy, a further indication of its superior ability to manage downside risk.

Given that the objective is to design long-only strategies that perform well in market downturns, one need not pursue this particular example much further , since it is already clear that the LOMT strategy using SPY is superior in terms of risk and return characteristics to the mutual fund alternative.

Practical Limitations

There are other, practical issues with apply an algorithmic trading strategy a mutual fund product like VFINX. To begin with, the mutual fund prices series contains no open/high/low prices, or volume data, which are often used by trading algorithms. Then there are the execution issues: funds can only be purchased or sold at market prices, whereas many algorithmic trading systems use other order types to enter and exit positions (stop and limit orders being common alternatives). You can’t sell short and there are restrictions on the frequency of trading of mutual funds and penalties for early redemption. And sales loads are often substantial (3% to 5% is not uncommon), so investors have to find a broker that lists the selected funds as no-load for the strategy to make economic sense. Finally, mutual funds are often treated by the broker as ineligible for margin for an initial period (30 days, typically), which prevents the investor from leveraging his investment in the way that he do can quite easily using ETFs.

For these reasons one typically does not expect a trading strategy formulated using a stock or ETF product to transfer easily to another asset class. The fact that the SPY-LOMT strategy appears to work successfully on the VFINX mutual fund product (on paper, at least) is highly unusual and speaks to the robustness of the methodology. But one would be ill-advised to seek to implement the strategy in that way. In almost all cases a better result will be produced by developing a strategy designed for the specific asset (class) one has in mind.

A Tactical Trading Strategy for the VFINX Mutual Fund

A better outcome can possibly be achieved by developing a market timing strategy designed specifically for the VFINX mutual fund. This strategy uses only market orders to enter and exit positions and attempts to address the issue of frequent trading by applying a trading cost to simulate the fees that typically apply in such situations. The results, net of imputed fees, for the period from 1994-2017 are summarized as follows:

Overall, the CAGR of the tactical strategy is around 88 basis points higher, per annum. The risk-adjusted rate of return (Sharpe Ratio) is not as high as for the LOMT-SPY strategy, since the annual volatility is almost double. But, as I have already pointed out, there are unanswered questions about the practicality of implementing the latter for the VFINX, given that it seeks to enter trades using limit orders, which do not exist in the mutual fund world.

The performance of the tactical-VFINX strategy relative to the VFINX fund falls into three distinct periods: under-performance in the period from 1994-2002, about equal performance in the period 2003-2008, and superior relative performance in the period from 2008-2017.

Only the data from 1/19934 to 3/2008 were used in the construction of the model. Data in the period from 3/2008 to 11/2012 were used for testing, while the results for 12/2012 to 8/2017 are entirely out-of-sample. In other words, the great majority of the period of superior performance for the tactical strategy was out-of-sample. The chief reason for the improved performance of the tactical-VFINX strategy is the lower drawdown suffered during the financial crisis of 2008, compared to the benchmark VFINX fund. Using market-timing algorithms, the tactical strategy was able identify the downturn as it occurred and exit the market. This is quite impressive since, as perviously indicated, none of the data from that 2008 financial crisis was used in the construction of the model.

In his Seeking Alpha article “Alpha-Winning Stars of the Bull Market“, Brad Zigler identifies the handful of funds that have outperformed the VFINX benchmark since 2009, generating positive alpha:

What is notable is that the annual alpha of the tactical-VINFX strategy, at 1.69%, is higher than any of those identified by Zigler as being “exceptional”. Furthermore, the annual R-squared of the tactical strategy is higher than four of the seven funds on Zigler’s All-Star list. Based on Zigler’s performance metrics, the tactical VFINX strategy would be one of the top performing active funds.

But there is another element missing from the assessment. In the analysis so far we have assumed that in periods when the tactical strategy disinvests from the VFINX fund the proceeds are simply held in cash, at zero interest. In practice, of course, we would invest any proceeds in risk-free assets such as Treasury Bills. This would further boost the performance of the strategy, by several tens of basis points per annum, without any increase in volatility. In other words, the annual CAGR and annual Alpha, are likely to be greater than indicated here.

Robustness Testing

One of the concerns with any backtest – even one with a lengthy out-of-sample period, as here – is that one is evaluating only a single sample path from the price process. Different evolutions could have produced radically different outcomes in the past, or in future. To assess the robustness of the strategy we apply Monte Carlo simulation techniques to generate a large number of different sample paths for the price process and evaluate the performance of the strategy in each scenario.

Three different types of random variation are factored into this assessment:

We allow the observed prices to fluctuate by +/- 30% with a probability of about 1/3 (so, roughly, every three days the fund price will be adjusted up or down by that up to that percentage).
Strategy parameters are permitted to fluctuate by the same amount and with the same probability. This ensures that we haven’t over-optimized the strategy with the selected parameters.
Finally, we randomize the start date of the strategy by up to a year. This reduces the risk of basing the assessment on the outcome from encountering a lucky (or unlucky) period, during which the market may be in a strong trend, for example.

In the chart below we illustrate the outcome from around 1,000 such randomized sample paths, from which it can be seen that the strategy performance is robust and consistent.

Limitations to the Testing Procedure

We have identified one way in which this assessment understates the performance of the tactical-VFINX strategy: by failing to take into account the uplift in returns from investing in interest-bearing Treasury securities, rather than cash, at times when the strategy is out of the market. So it is only reasonable to point out other limitations to the test procedure that may paint a too-optimistic picture.

The key consideration here is the frequency of trading. On average, the tactical-VFINX strategy trades around twice a month, which is more than normally permitted for mutual funds. Certainly, we have factored in additional trading costs to account for early redemptions charges. But the question is whether or not the strategy would be permitted to trade at such frequency, even with the payment of additional fees. If not, then the strategy would have to be re-tooled to work on long average holding periods, no doubt adversely affecting its performance.

Conclusion

The purpose of this analysis was to assess whether, in principle, it is possible to construct a market timing strategy that is capable of outperforming a VFINX fund benchmark. The answer appears to be in the affirmative. However, several practical issues remain to be addressed before such a strategy could be put into production successfully. In general, mutual funds are not ideal vehicles for expressing trading strategies, including tactical market timing strategies. There are latent inefficiencies in mutual fund markets – the restrictions on trading and penalties for early redemption, to name but two – that create difficulties for active approaches to investing in such products – ETFs are much superior in this regard. Nonetheless, this study suggest that, in principle, tactical approaches to mutual fund investing may deliver worthwhile benefits to investors, despite the practical challenges.

June 11, 2019

Some Further Notes on Market Timing

Almost at the very moment I published a post featuring some interesting research by Glabadanidis (“Market Timing With Moving Averages” (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425 – see Yes, You Can Time the Market. How it Works, And Why), several readers wrote to point out a recently published paper by Valeriy Zakamulin, (dubbed the “Moving Average Research King” by Alpha Architect, the source for our fetching cover shot) debunking Glabadanidis’s findings in no uncertain terms:

We demonstrate that “too good to be true” reported performance of the moving average strategy is due to simulating the trading with look-ahead bias. We perform the simulations without look-ahead bias and report the true performance of the moving average strategy. We find that at best the performance of the moving average strategy is only marginally better than that of the corresponding buy-and-hold strategy.

So far, no response from Glabadanidis – from which one is tempted to conclude that Zakamulin is correct.

I can’t recall the last time a paper published in a leading academic journal turned out to be so fundamentally flawed. That’s why papers are supposed to be peer reviewed. But, I guess, it can happen. Still, it’s rather alarming to think that a respected journal could accept a piece of research as shoddy as Zakamulin claims it to be.

What Glabadanidis had done, according to Zakamulin, was to use the current month closing price to compute the moving average that was used to decide whether to exit the market (or remain invested) at the start of the same month. An elementary error that introduces look-ahead bias that profoundly impacts the results.

Following this revelation I hastily checked my calculations for the SPY marketing timing strategy illustrated in my blog post and, to my relief, confirmed that I had avoided the look-ahead trap that Glabadanidis has fallen into. As the reader can see from the following extract from the Excel spreadsheet I used for the calculations, the decision to assume the returns for the SPY ETF or T-Bills for the current month rests on the value of the 24 month MA computed using prices up to the end of the prior month. In other words, my own findings are sound, even if Glabadanidis’s are not, as the reader can easily check for himself.

Nonetheless, despite my relief at having avoided Glabadanidis’s blunder, the apparent refutation of his findings comes as a disappointment. And my own research on the SPY market timing strategy, while sound as far as it goes, cannot by itself rehabilitate the concept of market timing using moving averages. The reason is given in the earlier post. There is a hidden penalty involved in using the market timing strategy to synthetically replicate an Asian put option, namely the costs incurred in exiting and rebuilding the portfolio as the market declines below the moving average, or later overtakes it. In a single instance, such as the case of SPY, it might easily transpire simply by random chance that the cost of replication are far lower than the fair value of the put. But the whole point of Glabadanidis’s research was that the same was true, not only for a single ETF or stock, but for many thousands of them. Absent that critical finding, the SPY case is no more than an interesting anomaly.

Finally, one reader pointed out that the effect of combining a put option with a stock (or ETF) long position was to create synthetically a call option in the stock (ETF). He is quite correct. The key point, however, is that when the stock trades down below its moving average, the value of the long synthetic call position and the market timing portfolio are equivalent.

April 16, 2019

Yes, You Can Time the Market. How it Works, And Why

One of the most commonly cited maxims is that market timing is impossible. In fact, empirical evidence makes a compelling case that market timing is feasible and can yield substantial economic benefits. What’s more, we even understand why it works. For the typical portfolio investor, applying simple techniques to adjust their market exposure can prevent substantial losses during market downturns.

The Background From Empirical and Theoretical Research

For the last fifty years, since the work of Paul Samuelson, the prevailing view amongst economists has been that markets are (mostly) efficient and follow a random walk. Empirical evidence to the contrary was mostly regarded as anomalous and/or unimportant economically. Over time, however, evidence has accumulated that market effects may persist that are exploitable. The famous 1992 paper published by Fama and French, for example, identified important economic effects in stock returns due to size and value factors, while Cahart (1997) demonstrated the important incremental effect of momentum. The combined four-factor Cahart model explains around 50% of the variation in stock returns, but leaves a large proportion that cannot be accounted for.

Other empirical studies have provided evidence that stock returns are predictable at various frequencies. Important examples include work by Brock, Lakonishok and LeBaron (1992), Pesaran and Timmermann (1995) and Lo, Mamaysky and Wang (2000), who provide further evidence using a range of technical indicators with wide popularity among traders showing that this adds value even at the individual stock level over and above the performance of a stock index. The research in these and other papers tends to be exceptional in term of both quality and comprehensiveness, as one might expect from academics risking their reputations in taking on established theory. The appendix of test results to the Pesaran and Timmermann study, for example, is so lengthy that is available only in CD-ROM format.

A more recent example is the work of Paskalis Glabadanidis, in a 2012 paper entitled Market Timing with Moving Averages. Glabadanidis examines a simple moving average strategy that, he finds, produces economically and statistically significant alphas of 10% to 15% per year, after transaction costs, and which are largely insensitive to the four Cahart factors.

Glabadanidis reports evidence regarding the profitability of the MA strategy in seven international stock markets. The performance of the MA strategies also holds for more than 18,000 individual stocks. He finds that:

“The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns.”

An Illustration of a Simple Marketing Timing Strategy in SPY

It is impossible to do justice to Glabadanidis’s research in a brief article and the interested reader is recommended to review the paper in full. However, we can illustrate the essence of the idea using the SPY ETF as an example.

A 24-period moving average of the monthly price series over the period from 1993 to 2016 is plotted in red in the chart below.

The moving average indicator is used to time the market using the following simple rule:

if P_t >= MA_t invest in SPY in month t+1

if P_t < MA_t invest in T-bills in month t+1

In other words, we invest or remain invested in SPY when the monthly closing price of the ETF lies at or above the 24-month moving average, otherwise we switch our investment to T-Bills.

The process of switching our investment will naturally incur transaction costs and these are included in the net monthly returns.

The outcome of the strategy in terms of compound growth is compared to the original long-only SPY investment in the following chart.

The marketing timing strategy outperforms the long-only ETF, with a CAGR of 16.16% vs. 14.75% (net of transaction costs), largely due to its avoidance of the major market sell-offs in 2000-2003 and 2008-2009.

But the improvement isn’t limited to a 141bp improvement in annual compound returns. The chart below compares the distributions of monthly returns in the SPY ETF and market timing strategy.

It is clear that, in addition to a higher average monthly return, the market timing strategy has lower dispersion in the distribution in returns. This leads to a significantly higher information ratio for the strategy compared to the long-only ETF. Nor is that all: the market timing strategy has both higher skewness and kurtosis, both desirable features.

These results are entirely consistent with Glabadanidis’s research. He finds that the performance of the market timing strategy is robust to different lags of the moving average and in subperiods, while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The strategy works just as well with randomly generated returns and bootstrapped returns as it does for the more than 18,000 stocks in the study.

A follow-up study by the author applying the same methodology to a universe of 20 REIT indices and 274 individual REITs reaches largely similar conclusions.

Why Marketing Timing Works

For many investors, empirical evidence – compelling though it may be – is not enough to make market timing a credible strategy, absent some kind of “fundamental” explanation of why it works. Unusually, in the case of the simple moving average strategy, such explanation is possible.

It was Cox, Ross and Rubinstein who in 1979 developed the binomial model as a numerical method for pricing options. The methodology relies on the concept of option replication, in which one constructs a portfolio comprising holdings of the underlying stock and bonds to produce the same cash flows as the option at every point in time (the proportion of stock to hold is given by the option delta). Since the replicating portfolio produces the same cash flows as the option, it must have the same value and since once knows the price of the stock and bond at each point in time one can therefore price the option. For those interested in the detail, Wikipedia gives a detailed explanation of the technique.

We can apply the concept of option replication to construct something very close the MA market timing strategy, as follows. Consider what happens when the ETF falls below the moving average level. In that case we convert the ETF portfolio to cash and use the proceeds to acquire T-Bills. An equivalent outcome would be achieved by continuing to hold our long ETF position and acquiring a put option to hedge it. The combination of a long ETF position, and a 1-month put option with delta of -1, would provide the same riskless payoff as the market timing strategy, i.e. the return on 30-day T-Bills. An option in which the strike price is based on the average price of the underlying is known as an Arithmetic Asian option. Hence when we apply the MA timing strategy we are effectively constructing a dynamic portfolio that replicates the payoff of an Arithmetic Asian protective put option struck as (just above) the moving average level.

Market Timing Alpha and The Cost of Hedging

None of this explanation is particularly contentious – the theory behind option replication through dynamic hedging is well understood – and it provides a largely complete understanding of the way the MA market timing strategy works, one that should satisfy those who are otherwise unpersuaded by arguments purely from empirical research.

There is one aspect of the foregoing description that remains a puzzle, however. An option is a valuable financial instrument and the owner of a protective put of the kind described can expect to pay a price amounting to tens or perhaps hundreds of basis points. Of course, in the market timing strategy we are not purchasing a put option per se, but creating one synthetically through dynamic replication. The cost of creating this synthetic equivalent comprises the transaction costs incurred as we liquidate and re-assemble our portfolio from month to month, in the form of bid/ask spread and commissions. According to efficient market theory, one should be indifferent as to whether one purchases the option at a fair market price or constructs it synthetically through replication – the cost should be equivalent in either case. And yet in empirical tests the cost of the synthetic protective put falls far short of what one would expect to pay for an equivalent option instrument. This is, in fact, the source of the alpha in the market timing strategy.

According to efficient market theory one might expect to pay something of the order of 140 basis points a year in transaction costs – the difference between the CAGR of the market timing strategy and the SPY ETF – in order to construct the protective put. Yet, we find that no such costs are incurred.

Now, it might be argued that there is a hidden cost not revealed in our simple study of a market timing strategy applied to a single underlying ETF, which is the potential costs that could be incurred if the ETF should repeatedly cross and re-cross the level of the moving average, month after month. In those circumstances the transaction costs would be much higher than indicated here. The fact that, in a single example, such costs do not arise does not detract in any way from the potential for such a scenario to play out. Therefore, the argument goes, the actual costs from the strategy are likely to prove much higher over time, or when implemented for a large number of stocks.

All well and good, but this is precisely the scenario that Glabadanidis’s research addresses, by examining the outcomes, not only for tens of thousands of stocks, but also using a large number of scenarios generated from random and/or bootstrapped returns. If the explanation offered did indeed account for the hidden costs of hedging, it would have been evident in the research findings.

Instead, Glabadanidis concludes:

“This switching strategy does not involve any heavy trading when implemented with break-even transaction costs, suggesting that it will be actionable even for small investors.”

Implications For Current Market Conditions

As at the time of writing, in mid-February 2016, the price of the SPY ETF remains just above the 24-month moving average level. Consequently the market timing strategy implies one should continue to hold the market portfolio for the time being, although that could change very shortly, given recent market action.

Conclusion

The empirical evidence that market timing strategies produce significant alphas is difficult to challenge. Furthermore, we have reached an understanding of why they work, from an application of widely accepted option replication theory. It appears that using a simple moving average to time market entries and exits is approximately equivalent to hedging a portfolio with a protective Arithmetic Asian put option.

What remains to be answered is why the cost of constructing put protection synthetically is so low. At the current time, research indicates that market timing strategies consequently are able to generate alphas of 10% to 15% per annum.

References

Brock, W., Lakonishok, J., LeBaron, B., 1992, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance 47, pp. 1731-1764.
Carhart, M. M., 1997, “On Persistence in Mutual Fund Performance,” Journal of Finance 52, pp. 57–82.
Fama, E. F., French, K. R., 1992, “The Cross-Section of Expected Stock Returns,” Journal of Finance 47(2), 427–465
Glabadanidis, P., 2012, “Market Timing with Moving Averages”, 25th Australasian Finance and Banking Conference.
Glabadanidis, P., 2012, “The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes”, University of Adelaide Business School.
Lo, A., Mamaysky, H., Wang, J., 2000, “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” Journal of Finance 55, 1705–1765.
Pesaran, M.H., Timmermann, A.G., 1995, “Predictability of Stock Returns: Robustness and Economic Significance”, Journal of Finance, Vol. 50 No. 4

September 13, 2018

Enhancing Mutual Fund Returns With Market Timing

Summary

In this article, I will apply market timing techniques to several popular mutual funds.

The market timing approach produces annual rates of return that are 3% to 7% higher, with lower risk, than an equivalent buy and hold mutual fund investment.

Investors could in some cases have earned more than double the return achieved by holding a mutual fund investment over a 10-year period.

Hedging strategies that use market timing signals are able to sidestep market corrections, volatile conditions and the ensuing equity drawdowns.

Hedged portfolios typically employ around 12% less capital than the equivalent buy and hold strategy.

Background to the Market Timing Approach

In an earlier article, I discussed how to use marketing timing techniques to hedge an equity portfolio correlated to the broad market. I showed how, by using signals produced by a trading system modeled on the CBOE VIX index, we can smooth out volatility in an equity portfolio consisting of holdings in the SPDR S&P 500 ETF (NYSEARCA:SPY). An investor will typically reduce their equity holdings by a modest amount, say 20%, or step out of the market altogether during periods when the VIX index is forecast to rise, returning to the market when the VIX is likely to fall. An investment strategy based on this approach would have avoided most of the 2000-03 correction, as well as much of the market turmoil of 2008-09.

A more levered version of the hedging strategy, which I termed the MT aggressive portfolio, uses the VIX index signals to go to cash during high volatility periods, and then double the original equity portfolio holdings (using standard Reg-T leverage) during benign market conditions, as signaled by the model. The MT aggressive approach would have yielded net returns almost three times greater than that of a buy and hold portfolio in the SPY ETF, over the period from 1999-2014. Even though this version of the strategy makes use of leverage, the average holding in the portfolio would have been slightly lower than in the buy and hold portfolio because, in a majority of days, the strategy would have been 100% in cash. The result is illustrated in the chart in Fig. 1, which is reproduced below.

Fig. 1: Value of $1,000 – Long-Only Vs. MT Aggressive Portfolio

Source: Yahoo Finance.

Note that this approach does not entail shorting any stock. And for investors who prefer to buy and hold, I would make the point that the MT aggressive approach would have enabled you to buy almost three times as much stock in dollar terms by mid-2014 than would be the case if you had simply owned the SPY portfolio over the entire period.

Market Timing and Mutual Funds

With that background, we turn our attention to how we can use market timing techniques to improve returns from equity mutual funds. The funds selected for analysis are the Vanguard 500 Index Admiral (MUTF:VFIAX), Fidelity Spartan 500 Index Advtg (MUTF:FUSVX) and BlackRock S&P 500 Stock K (MUTF:WFSPX). This group of popular mutual funds is a representative sample of available funds that offer broad equity market exposure, with a high degree of correlation to the S&P 500 index. In what follows, we will focus attention on the MT aggressive approach, although other more conservative hedging strategies are equally valid.

We consider performance over the 10-year period from 2005, as at least one of the funds opened late in 2004. In each case, the MT aggressive portfolio is created by exiting the current mutual fund position and going 100% to cash, whenever the VIX model issues a buy signal in the VIX index. Conversely, we double our original mutual fund investment when the model issues a sell signal in the VIX index. In calculating returns, we make an allowance for trading costs of $3 cents per share for all transactions.

Returns for each of the mutual funds, as well as for the SPY ETF and the corresponding MT aggressive hedge strategies, are illustrated in the charts in Fig. 2. The broad pattern is similar in each case – we see significant outperformance of the MT aggressive portfolios relative to their ETF or mutual fund benchmarks. Furthermore, in most cases the hedge strategy tends to exhibit lower volatility, with less prolonged drawdowns during critical periods such as 2000/03 and 2008/09.

Fig. 2 – Value of $1,000: Mutual Fund Vs. MT Aggressive Portfolio January 2005 – June 2014

Source: Yahoo Finance.

Looking at the performance numbers in more detail, we can see from the tables shown in Fig. 3 that the MT aggressive strategies outperformed their mutual fund buy and hold benchmarks by a substantial margin. In the case of VFIAX and WFSPX, the hedge strategies produce a total net return more than double that of the corresponding mutual fund. With one exception, FUSVX, annual volatility of the MT aggressive portfolio was similar to, or lower than, that of the corresponding mutual fund, confirming our reading of the charts in Fig. 2. As a consequence, the MT aggressive strategies have higher Sharpe Ratios than any of the mutual funds. The improvement in risk adjusted returns is significant – more than double in the case of two of the funds, and about 40% higher in the case of the third.

Finally, we note that the MT aggressive strategies have an average holding that is around 12% lower than the equivalent long-only fund. That’s because of the periods in which investment proceeds are held in cash.

Fig. 3: Mutual Fund and MT Aggressive Portfolio Performance January 2005 – June 2014

Source: Yahoo Finance.

Conclusion

The aim of market timing is to smooth out the returns by hedging, and preferably avoiding altogether periods of market turmoil. In other words, the objective is to achieve the same, or better, rates of return, with lower volatility and drawdowns. We have demonstrated that this can be done, not only when the underlying investment is in an ETF such as SPY, but also where we hold an investment in one of several popular equity mutual funds. Over a 10-year period the hedge strategies produced consistently higher returns, with lower volatility and drawdown, while putting less capital at risk than their counterpart buy and hold mutual fund investments.

September 12, 2018

How to Bulletproof Your Portfolio

Summary

How to stay in the market and navigate the rocky terrain ahead, without risking hard won gains.

A hedging program to get you out of trouble at the right time and step back in when skies are clear.

Even a modest ability to time the market can produce enormous dividends over the long haul.

Investors can benefit by using quantitative market timing techniques to strategically adjust their market exposure.

Market timing can be a useful tool to avoid major corrections, increasing investment returns, while reducing volatility and drawdowns.

The Role of Market Timing

Investors have enjoyed record returns since the market lows in March 2009, but sentiment is growing that we may be in the final stages of this extended bull run. The road ahead could be considerably rockier. How do you stay the course, without risking all those hard won gains?

The smart move might be to take some money off the table at this point. But there could be adverse tax effects from cashing out and, besides, you can’t afford to sit on the sidelines and miss another 3,000 points on the Dow. Hedging tools like index options, or inverse volatility plays such as the VelocityShares Daily Inverse VIX Short-Term ETN (NASDAQ:XIV), are too expensive. What you need is a hedging program that will get you out of trouble at the right time – and step back in when the skies are clear. We’re talking about a concept known as market timing.

Market timing is the ability to switch between risky investments such as stocks and less-risky investments like bonds by anticipating the overall trend in the market. It’s extremely difficult to do. But as Nobel prize-winning economist Robert C. Merton pointed out in the 1980s, even a modest ability to time the market can produce enormous dividends over the long haul. This is where quantitative techniques can help – regardless of the nature of your underlying investment strategy.

Let’s assume that your investment portfolio is correlated with a broad US equity index – we’ll use the SPDR S&P 500 Trust ETF (NYSEARCA:SPY) as a proxy, for illustrative purposes. While the market has more than doubled over the last 15 years, this represents a modest average annual return of only 7.21%, accompanied by high levels of volatility of 20.48% annually, not to mention sizeable drawdowns in 2000 and 2008/09.

Fig. 1 SPY – Value of $1,000 Jan 1999 – Jul 2014

Source: Yahoo! Finance, 2014

The aim of market timing is to smooth out the returns by hedging, and preferably avoiding altogether, periods of market turmoil. In other words, the aim is to achieve the same, or better, rates of return, with lower volatility and drawdown.

Market Timing with the VIX Index

The mechanism we are going to use for timing our investment is the CBOE VIX index, a measure of anticipated market volatility in the S&P 500 index. It is well known that the VIX and S&P 500 indices are negatively correlated – when one rises, the other tends to fall. By acting ahead of rising levels of the VIX index, we might avoid difficult market conditions when market volatility is high and returns are likely to be low. Our aim would be to reduce market exposure during such periods and increase exposure when the VIX is in decline.

Forecasting the VIX index is a complex topic in its own right. The approach I am going to take here is simpler: instead of developing a forecasting model, I am going to use an algorithm to “trade” the VIX index. When the trading model “buys” the VIX index, we will assume it is anticipating increased market volatility and lighten our exposure accordingly. When the model “sells” the VIX, we will increase market exposure.

Don’t be misled by the apparent simplicity of this approach: a trading algorithm is often much more complex in its structure than even a very sophisticated forecasting model. For example, it can incorporate many different kinds of non-linear behavior and dynamically adjust its investment horizon. The results from such a trading algorithm, produced by our quantitative modeling system, are set out in the figure below.

Fig. 2a -VIX Trading Algorithm – Equity Curve

Source: TradeStation Technologies Inc.

Fig. 2b -VIX Trading Algorithm – Performance Analysis

Source: TradeStation Technologies Inc.

Not only is the strategy very profitable, it has several desirable features, including a high percentage of winning trades. If this were an actual trading system, we might want to trade it in production. But, of course, it is only a theoretical model – the VIX index itself is not tradable – and, besides, the intention here is not to trade the algorithm, but to use it for market timing purposes.

Our approach is straightforward: when the algorithm generates a “buy” signal in the VIX, we will reduce our exposure to the market. When the system initiates a “sell”, we will increase our market exposure. Trades generated by the VIX algorithm are held for around five days on average, so we can anticipate rebalancing our portfolio approximately weekly. In what follows, we will assume that we adjust our position by trading the SPY ETF at the closing price the day following a signal from the VIX model. We will apply trading commissions of $1c per share and a further $1c per share in slippage.

Hedging Strategies

Let’s begin our evaluation by looking at the outcome if we adjust the SPY holding in our market portfolio by 20% whenever the VIX model generates a signal. When the model buys the VIX, we will reduce our original SPY holding by 20%, and when it sells the VIX, we will increase our SPY holding by 20%, using the original holding in the long only portfolio as a baseline. We refer to this in the chart below as the MT 20% hedge portfolio.

Fig. 3 Value of $1000 – Long only vs MT 20% hedge portfolio

Source: Yahoo! Finance, 2014

The hedge portfolio dominates the long only portfolio over the entire period from 1999, producing a total net return of 156% compared to 112% for the SPY ETF. Not only is the rate of return higher, at 10.00% vs. 7.21% annually, volatility in investment returns is also significantly reduced (17.15% vs 20.48%). Although it, too, suffers substantial drawdowns in 2000 and 2008/09, the effects on the hedge portfolio are less severe. It appears that our market timing approach adds value.

The selection of 20% as a hedge ratio is somewhat arbitrary – an argument can be made for smaller, or larger, hedge adjustments. Let’s consider a different scenario, one in which we exit our long-only position entirely, whenever the VIX algorithm issues a buy order. We will re-buy our entire original SPY holding whenever the model issues a sell order in the VIX. We refer to this strategy variant as the MT cash out portfolio. Let’s look at how the results compare.

Fig. 4 Value of $1,000 – Long only vs MT cash out portfolio

Source: Yahoo! Finance, 2014

The MT cash out portfolio appears to do everything we hoped for, avoiding the downturn of 2000 almost entirely and the worst of the market turmoil in 2008/09. Total net return over the period rises to 165%, with higher average annual returns of 10.62%. Annual volatility of 9.95% is less than half that of the long only portfolio.

Finally, let’s consider a more extreme approach, which I have termed the “MT aggressive portfolio”. Here, whenever the VIX model issues a buy order we sell our entire SPY holding, as with the MT cash out strategy. Now, however, whenever the model issues a sell order on the VIX, we invest heavily in the market, buying double our original holding in SPY (i.e. we are using standard, reg-T leverage of 2:1, available to most investors). In fact, our average holding over the period turns out to be slightly lower than for the original long only portfolio because we are 100% in cash for slightly more than half the time. But the outcome represents a substantial improvement.

Fig. 5 Value of $1,000 – Long only vs. MT aggressive portfolio

Source: Yahoo! Finance, 2014

Total net returns for the MT aggressive portfolio at 330% are about three times that of the original long only portfolio. Annual volatility at 14.90% is greater than for the MT cash out portfolio due to the use of leverage. But this is still significantly lower than the 20.48% annual volatility of the long only portfolio, while the annual rate of return of 21.16% is the highest of the group, by far. And here, too, the hedge strategy succeeds in protecting our investment portfolio from the worst of the effects of downturns in 2000 and 2008.

Conclusion

Whatever the basis for their underlying investment strategy, investors can benefit by using quantitative market timing techniques to strategically adjust their market exposure. Market timing can be a useful tool to avoid major downturns, increasing investment returns while reducing volatility. This could be especially relevant in the weeks and months ahead, as we may be facing a period of greater uncertainty and, potentially at least, the risk of a significant market correction.

Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.