Technical Trading

Do naive investors overvalue (undervalue) stocks with relatively high (low) recent returns, thereby causing exploitable overpricing (underpricing)? In the April 2019 version of his paper entitled “The Impact of Recency Effects on Stock Market Prices”, Hannes Mohrschladt devises and tests a measure of this recency effect based on correlation between daily returns during a month and the number of days until the end of the month. For stocks with low (high) values of this correlation:

• Recent returns are relatively high (low) and older returns are relatively low (high).
• Naive investors overvalue (undervalue) such stocks, which therefore become overpriced (underpriced).
• There is an opportunity to exploit this effect by buying (selling) stocks with high (low) values of this variable.

His principal test is to each month sort stocks into tenths, or deciles, by prior-month recency correlation and reform an equal-weighted or value-weighted hedge portfolio that is long (short) the decile with high (low) recency correlations. He also considers a 1-year lookback interval using monthly returns rather than a 1-month interval using daily returns for calculation of recency correlations. Using daily and monthly returns and other data for a broad sample of U.S. common stocks during January 1926 through December 2016, he finds that: Keep Reading

Is buy-the-dip (BTD) a reliably attractive stock market timing approach? In their April 2021 paper entitled “Buy the Dip”, Thomas Shohfi and Majeed Simaan devise and test various BTD strategies as applied to SPDR S&P 500 ETF Trust (SPY), as follows:

1. BTD with Lump Sum – 54 variations in which the investor progressively allocates a fixed percentage of an initial lump sum to SPY whenever the last real monthly return on SPY is below a specified threshold. Variations derive from different fixed allocation percentages and different return thresholds.
2. BTD with Monthly Inflows – five variations in which the investor receives cash flows at the beginning of each month and moves one monthly increment from cash to SPY whenever its prior-day return is below a specified threshold. Variations derive from different return thresholds.
3. BTD with MaxDD – nine variations in which the investor receives cash flows at the beginning of each month and initiates the strategy with 12 months of savings, allocating all cash savings to SPY whenever SPY drops below a maximum drawdown (MaxDD) threshold over a past rolling window. Variations derive from different MaxDD thresholds and different rolling window lengths.

They consider four strategy implementation dates, two associated beginnings of bull markets (January 1994 and January 2010) and two associated with beginnings of bear markets (January 2000 and January 2008). They use real (inflation-adjusted) returns on SPY and also deflate value of cash holdings accordingly. They focus on two strategy performance criteria: (1) terminal wealth at the end of 2020; and, (2) Sortino ratio. Respective benchmarks are passive strategies that allocate all cash to SPY as soon as the cash is available. They assume zero trading frictions and zero return on cash. Using daily dividend-adjusted SPY returns and monthly U.S. Consumer Price Index levels for inflation adjustments during January 1994 through December 2020, they find that: Keep Reading

Can machine learning software discover predictive stock price patterns? In their December 2020 paper entitled “(Re-)Imag(in)ing Price Trends”, Jingwen Jiang, Bryan Kelly and Dacheng Xiu apply convolutional neural network machine learning software to analyze stock price series images (depicting daily open, high, low, close and moving average prices and trading volume) in search of the patterns most predictive of future returns. Their model standardizes price series scales, recursively smooths and accentuates certain shape elements of images of the last 5, 20 and 60 days trading to isolate patterns that predict returns over the next 5, 20 and 60 days. They translate predictions into hedge portfolio performance by each month going long (short) the tenth, or decile, of stocks with the strongest (weakest) return forecasts. They benchmark performance against hedge portfolios for conventional momentum (return from 12 months ago to one month ago), 1-month short-term reversal and 1-week short-term reversal. Using daily price and volume series for all listed U.S. stocks during January 1993 through December 2019, they find that: Keep Reading

A subscriber asked about a strategy that switches between an equal-weighted portfolio of Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM) when the S&P 500 Index is above its 200-day simple moving average (SMA200) and an equal-weighted portfolio of SPDR Gold Shares (GLD) and iShares 20+ Year Treasury Bond ETF (TLT) when below. Also, more generally, is an equal-weighted portfolio of GLD and TLT (GLD:TLT) superior to TLT only for risk-off conditions? To investigate, we (1) backtest the switching strategy and (2) compare performances of GLD:TLT versus TLT when the S&P 500 Index is below its SMA200. We consider both gross and net performance, with the latter accounting for 0.1% portfolio switching frictions 0.001% daily portfolio rebalancing frictions (rebalancing one hundredth of portfolio value). As benchmarks, we consider buying and holding SPDR S&P 500 ETF Trust (SPY) and a strategy that holds SPY (TLT) when the S&P 500 Index is above (below) its SMA200. Using daily S&P 500 Index levels starting February 5, 2004 and daily dividend-adjusted levels of QQQ, IWM, GLD, TLT and SPY starting November 18, 2004 (limited by inception of GLD), all through November 25, 2020, we find that:

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A subscriber requested, as in “Combine Market Trend and Economic Trend Signals?”, testing of a strategy that combines: (1) U.S. Economic Policy Uncertainty (EPU) Index, as described and tested separately in “Economic Policy Uncertainty and the Stock Market”; and, (2) U.S. stock market trend. We consider two such combinations. The first combines:

• 10-month simple moving average (SMA10) for the broad U.S. stock market as proxied by the S&P 500 Index. The trend is bullish (bearish) when the index is above (below) its SMA10 at the end of last month.
• Sign of the change in EPU Index last month. A positive (negative) sign is bearish (bullish).

The second combines:

• SMA10 for the S&P 500 Index as above.
• 12-month simple moving average (SMA12) for the EPU Index. The trend is bullish (bearish) when the EPU Index is below (above) its SMA12 at the end of last month.

We consider alternative timing strategies that hold SPDR S&P 500 (SPY) when: the S&P 500 Index SMA10 is bullish; the EPU Index indicator is bullish; either indicator for a combination is bullish; or, both indicators for a combination are bullish. When not in SPY, we use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash, with 0.1% switching frictions. We assume all indicators for a given month can be accurately estimated for signal execution at the market close the same month. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly values for the EPU Index, the S&P 500 Index, SPY and T-bill yield during January 1993 (inception of SPY) through September 2020, we find that:

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A subscriber requested testing of a dual simple moving average (SMA) crossover strategy that holds SPDR S&P 500 (SPY) when its 30-day SMA (SMA30d, using 30 trading days) is above its 9-month SMA (SMA9m) and otherwise holds cash with yield that of 3-month U.S. Treasury bills (T-bills). To investigate, we calculate SPY SMA30d and SMA9m at the end of each month over the history of SPY and hold SPY or cash the next month as specified. As benchmarks, we consider buying and holding SPY and a strategy that is each month in SPY (cash) when SPY is above (below) its SMA9m at the end of the prior month. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. We also perform some sensitivity testing on the choices of 30-day and 9-month SMAs. Using daily dividend-adjusted prices for SPY and monthly T-bill yields during January 1993 through September 2020, we find that: Keep Reading

Is a 10-month simple moving average (SMA10) the best SMA for long-term crossing signals? If not, is there some other optimal SMA lookback interval? To check, we compare performance statistics for SMA crossing signals generated by lookback intervals ranging from 2 (SMA2) to 48 months (SMA48), as applied to the S&P 500 Index. Using monthly S&P 500 Index closes, monthly S&P 500 Composite Index dividend data from Robert Shiller and monthly average yields for 3-month Treasury bills (T-bills) during January 1928 (except January 1934 for T-bills) through June 2020, we find that: Keep Reading

Is there a way to mitigate adverse impact of price trajectory turning points (trend changes) on performance of intrinsic (absolute or time series) momentum strategies? In their May 2020 paper entitled “Breaking Bad Trends”, Ashish Garg, Christian Goulding, Campbell Harvey and Michele Mazzoleni measure impact of turning points on time series momentum strategy performance across asset classes. They define a turning point as a month for which slow (12-month or longer lookback) and fast (3-month or shorter lookback) momentum signals disagree on whether to buy or sell. They test a dynamic strategy to mitigate trend change impact based on turning points defined by disagreement between 12-month (slow) and 2-month (fast) momentum signals. Specifically, their dynamic strategy each month:

1. For each asset, measures slow and fast momentum as averages of monthly excess returns over respective lookback intervals.
2. Specifies the trend condition for each asset as: (1) Bull (slow and fast signals both non-negative); (2) Correction (slow non-negative and fast negative); Bear (slow and fast both negative); and, Rebound (slow negative and fast non-negative). For Bull and Bear (Correction and Rebound) conditions, next-month return is the same (opposite in sign) for slow and fast signals.
3. After trend changes (Corrections and Rebounds separately), empirically determines with at least 48 months of historical data optimal weights for combinations of positions based on slow and fast signals.

They compare performance of this dynamic strategy with several conventional (static) time series momentum strategies, with each competing strategy retrospectively normalized to 10% test-period volatility. They test strategies on 55 futures, forwards and swaps series spanning four asset classes, with returns based on holding the nearest contract and rolling to the next at expiration. Using monthly returns for futures, forwards and swaps for 12 equity indexes, 10 bond indexes, 24 commodities and 9 currency pairs as available during January 1971 through December 2019, they find that:

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In response to “Exploiting Chicago Fed NFCI Predictive Power”, which tests practical use of the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) for U.S. stock market timing, a subscriber suggested combining this strategy with stock market trend as in “Combine Market Trend and Economic Trend Signals?”. To investigate, we use the 40-week simple moving average (SMA40) for the S&P 500 Index to measure stock market trend. We then test two strategies that are each week in SPDR S&P 500 (SPY) or cash (U.S. Treasury bills, T-bills), as follows:

1. Combined (< Mean): hold SPY (cash) when either: (a) prior-week S&P 500 Index is above (below) its SMA40; or, (b) prior-week change in NFCI is below (above) its mean since since the beginning of 1973.
2. Combined (< Mean+SD): hold SPY (cash) when either: (a) prior-week S&P 500 Index is above (below) its SMA40; or, (b) prior-week change in NFCI is below (above) its mean plus one standard deviation of weekly changes in NFCI since the beginning of 1973.

The return week is Wednesday open to Wednesday open (Thursday open when the market is not open on Wednesday) per the NFCI release schedule. SMA40 calculations are Tuesday close to Tuesday close to ensure timely availability of signals before any Wednesday open trades. We assume SPY-cash switching frictions are a constant 0.1% over the sample period. Using weekly NFCI data since January 1973, weekly S&P 500 Index levels since April 1992, weekly dividend-adjusted opens of SPY and weekly T-bill yield since February 1993 (limited by SPY), all as specified through April 2020, we find that:

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A subscriber requested testing Martin Zweig’s Combined Super Model, which each month specifies an equity allocation based on a system that assigns up to eight points from his Monetary Model and 0 or 2 points from his Four Percent Model. We consider two versions of the Combined Super Model:

1. Zweig-Cash – Allocate to Fidelity Fund (FFIDX) as equities, with the balance in cash earning the 3-month U.S. Treasury bill (T-bill) yield.
2. Zweig-FGOVX – Allocate to FFIDX as equities, with the balance in Fidelity Government Income Fund (FGOVX)

The benchmark is buying and holding FFIDX. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio, with average monthly T-bill yield during a year as the risk-free rate for that year. We ignore impediments to mutual fund trading and any issues regarding timeliness of allocation changes for end-of-month rebalancing. Using monthly Combined Super Model allocations and monthly fund returns/T-bill yield during December 1986 through March 2020, we find that: Keep Reading