Buyers are more and more exploring various indicators to realize an edge in monetary markets. Conventional indicators, similar to earnings experiences or macroeconomic knowledge, usually include delays or might already be priced in. Because of this, unconventional metrics have attracted consideration. For instance, current focus has been on development stock statistics, the place massive stockpiles have been interpreted as a sign of weakening demand for development exercise. This, in flip, might replicate broader client and company hesitancy to spend, implicitly suggesting a possible decline in asset costs. On this article, we look at a special sort of different indicator: the Producer Value Index (PPI) for the Corrugated and Stable Fiber Field Manufacturing trade, together with corrugated containers and pallets. Our motivation is to guage this index’s effectiveness as a predictive sign for the S&P 500 ETF, sector-specific ETFs, and particular person shares similar to Amazon (AMZN), one of many largest customers of supplies tracked by this index. We current a number of funding methods that incorporate this indicator and assess whether or not it will possibly improve risk-adjusted returns.
Motivation
Relying solely on conventional prediction inputs similar to inventory costs, quarterly earnings experiences or ahead steering comes with a structural limitation. Monetary markets normally incorporate expectations lengthy earlier than the knowledge turns into public. Because of this, a lot of the anticipated efficiency is already mirrored within the value by the point earnings are launched. Getting into positions shortly earlier than these bulletins primarily turns right into a wager on whether or not the outcomes will outperform what’s already priced in. This introduces a stage of event-driven danger that’s troublesome to handle and gives little basis for constructing systematic methods with steady danger profiles.
This creates a transparent incentive to discover indicators that seize actual financial exercise with out being instantly absorbed by market expectations. With the PPI for Corrugated and Stable Fiber Field Manufacturing, we construct on an intuitive concept. Rising manufacturing or consumption of packaging supplies indicators greater demand for these inputs. Firms buy extra corrugated containers and pallets when they should ship a bigger quantity of products. Elevated packaging demand due to this fact serves as a proxy for rising order flows, stronger client exercise and better throughput throughout provide chains. These situations usually accompany durations of financial enlargement, which are likely to help company revenues and earnings. If this relationship holds, an upward development in packaging-related PPI knowledge might precede rising costs in broad market ETFs, sector-specific ETFs or particular person shares similar to Amazon.
Information overview
The evaluation depends solely on month-to-month knowledge. This frequency is dictated by the traits of our major indicator, the PPI for Corrugated and Stable Fiber Field Manufacturing, which is launched solely as soon as per thirty days and sometimes not on a set schedule. Because of this, all technique selections are carried out with a one-month delay and evaluated at month-to-month intervals.
Our predictor sequence is accessible from March 1980, offering a protracted historic window for testing. For comparability with market devices, we embrace a number of broadly used exchange-traded funds and particular person equities, every with its personal begin date based mostly on knowledge availability. Amazon (AMZN) enters the evaluation from February 2000. Sector ETFs are integrated starting in December 1998, particularly Client Discretionary (XLY), Utilities (XLU), Well being Care (XLV) and Client Staples (XLP). These funds characterize distinct segments of the financial system and supply a structured option to observe how totally different sectors reply to modifications in underlying financial exercise. The broad market proxy SPY, monitoring the S&P 500, is included from December 2004. For a low-risk asset for instances of uncertainty, we use BIL, a short-term Treasury ETF, accessible from June 2007, which serves as a cash-like various inside our technique assessments.
A number of extra ETFs had been reviewed throughout the exploratory part, together with Industrials (XLI), Retail (XRT) and the On-line Retail ETF (ONLN). Their outcomes didn’t differ meaningfully from the sectors already offered, and in some instances the accessible historical past was too brief to supply strong conclusions. For readability and relevance, they’re due to this fact not included within the closing set of devices mentioned on this article.
AR fashions and their limitations
One pure concept was to discover autoregressive fashions, since it’s not the extent of the indicator that seems most related, however its change. This naturally results in working with first variations. If month-to-month actions seize significant shifts in financial exercise, one may assemble a rule by which deviations of a sure measurement generate purchase or promote indicators. Conceptually, this resembles making a band across the differenced sequence and reacting at any time when the index strikes outdoors that band.
Though interesting, this strategy carries an necessary implicit assumption. It really works provided that the variability of the sequence stays roughly fixed over time. In statistical phrases, the tactic assumes homoskedasticity. In our case, nonetheless, the variability of the PPI variations doesn’t stay steady. Over the lengthy pattern, the fluctuations grow to be bigger and the amplitude of actions will increase. When the variance grows, the differenced sequence begins to supply a major quantity of noise, making the indicators unstable and decreasing the reliability of any band-based technique. As an alternative of capturing significant modifications in financial exercise, the mannequin more and more reacts to the altering scale of the information itself.
Idea of heteroskedasticity
A time sequence is heteroskedastic in our setup when variance of its first variations just isn’t fixed throughout time. Durations of calm are adopted by durations of bigger swings, and the general dispersion grows or shrinks (un)predictably. For monetary or financial indicators, this can be a widespread phenomenon. The primary drawback arises when a mannequin assumes fixed variance however the knowledge don’t fulfill that assumption. Methods that depend on mounted thresholds or mounted bands can grow to be distorted as a result of what seems to be like a major deviation in a single interval might merely be routine volatility in one other. In apply, heteroskedasticity results in unreliable indicators, extreme turnover and a decrease signal-to-noise ratio, all of which cut back the effectiveness of strategies based mostly on easy autoregressive dynamics or static thresholds.
Incorporating this time-varying volatility right into a predictive mannequin considerably complicates its design. The presence of heteroskedasticity signifies that fixed-threshold guidelines or static bands not translate persistently throughout your entire pattern, and the mannequin should account for the altering scale of fluctuations. Even makes an attempt to explicitly introduce time-dependent variance changes don’t essentially enhance predictive energy, as a result of the signal-to-noise ratio should still stay low and the underlying financial relationships is probably not captured just by scaling the sequence. Because of this, methods based mostly on naive autoregressive frameworks or static thresholds can lose reliability when utilized to knowledge with evolving variability.
MA fashions
Following the damaging expertise with autoregressive fashions, consideration turned to shifting common (MA) approaches. In contrast to AR fashions, MA fashions are much less delicate to heteroskedasticity as a result of they clean the sequence domestically, making the long-term construction of the variance perform largely irrelevant. Two pure instructions emerge when working with MA fashions. The primary is to trace N-month maxima, whereas the second is to match values towards the N-month shifting common.
Methods based mostly on each approaches had been examined, however N-month maxima carried out comparatively poorly. This seems to be as a result of maxima primarily seize traits slightly than short-term deviations, that are the main target of our predictive efforts. By the point an area most is reached, the chance for early sign detection is already partially misplaced. In distinction, evaluating the present worth to the N-month shifting common permits us to establish important deviations from the development, successfully capturing uncommon actions relative to the anticipated stage. From a broader perspective, this is sensible, as smoothing the sequence gives a clearer view of the underlying dynamics. Primarily based on these issues, we undertake the moving-average-based strategy as the muse for the methods offered on this article.
Swap fashions and financial uncertainty
In a number of of our earlier articles (for instance, about BTC ETFs or VIX-based property), we’ve got employed what are generally known as change methods. These methods are based mostly on the precept of dynamically adjusting portfolio allocations in line with the indicators generated by a number of indicators. Moderately than sustaining a set allocation throughout all property or sectors, the technique “switches” between totally different portfolios relying on the noticed state of the indicator.
Swap technique motivation
The change technique will also be understood when it comes to sector sensitivity to financial situations. For instance, a Client Staples ETF sometimes represents firms producing important items, similar to meals and family merchandise, which stay in demand even throughout financial slowdowns. In distinction, a Client Discretionary ETF covers sectors like retail, leisure, or luxurious items, the place demand is extra delicate to client spending and financial cycles. In durations of weakening demand or heightened danger, traders usually tend to cut back spending on discretionary objects, inflicting these property to underperform relative to staples. The change technique leverages this perception by allocating capital between defensive ETFs, similar to Client Staples, and extra unstable, growth-oriented ETFs, like Client Discretionary, in line with the sign from our indicator. When the indicator suggests robust demand or financial enlargement, the technique favors discretionary sectors; when the indicator indicators warning, it rotates towards staples to guard the portfolio. This strategy permits for a scientific response to shifts in financial situations whereas balancing danger and return.
SPY fashions as a naive benchark
Armed with these insights, we will now flip to empirical evaluation. To start out, we assemble a set of comparatively easy benchmark assessments to guage the efficiency of our strategy. As an preliminary train, we apply the technique to the SPY ETF, which tracks the S&P 500 and serves as a broad-market reference. By testing on SPY first, we set up a baseline understanding of how the indicator-driven change technique behaves in apply, earlier than extending the evaluation to sector-specific ETFs or particular person shares. This preliminary benchmark helps spotlight each the potential advantages and the constraints of our technique in an easy, managed setting.
SPY technique
1. Open place in SPY, when field predictor < 3M/6M/9M MA of field predictor.
2. Shut place in SPY, when field predictor reaches its shifting common.
3. If no place is opened, keep in money.
Desk 1: Efficiency metrics of SPY-based change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
SPY portfolio
10.48%
14.84%
-50.76%
0.71
0.21
3M SPY portfolio
4.41%
10.68%
-27.62%
0.41
0.16
6M SPY portfolio
6.14%
9.94%
-19.44%
0.62
0.32
9M SPY portfolio
5.16%
8.25%
-19.44%
0.63
0.27
The outcomes clearly present that making use of this technique on to SPY just isn’t a promising strategy (when it comes to return). The explanation lies within the construction of the S&P 500 itself. As a broad market index, it incorporates firms from nearly each sector, lots of that are solely marginally affected by fluctuations in consumer-sensitive industries. Though some portion of the index naturally displays modifications in client demand, the impact is diluted throughout a large and heterogeneous set of constituents. Because of this, the sign derived from packaging-related PPI knowledge doesn’t translate into sufficiently robust or well timed actions within the index. As well as, the technique stays inactive for prolonged durations, avoiding sure market downturns however on the similar time failing to seize sufficient of the index’s development phases. This mixture of weak linkage to the underlying indicator and inadequate participation in rising markets results in general efficiency that’s removed from compelling when utilized to SPY.
Although the general efficiency on SPY was not significantly robust, the outcomes reveal an necessary sample. The methods improved risk-adjusted metrics and due to this fact are a doable start line for our evaluation.
Swap fashions for sector ETFs
Earlier outcomes confirmed that introducing a change mannequin can enhance risk-adjusted efficiency, however the strategy used thus far had two notable limitations. First, the defensive facet of the change rule remained unchanged all through the evaluation, which restricted the mannequin’s capacity to do at the very least one thing in several market environments. Second, by making use of the technique to the broad S&P 500, we diluted the financial sign that originates from consumer-dependent exercise. This diminished the effectiveness of the indicator and restricted the technique’s potential.
A pure subsequent step is due to this fact to shift the main target from your entire market to sector-specific ETFs. These supply extra direct publicity to the financial mechanisms our indicator captures. By inserting Client Discretionary (XLY) on the offensive facet and Client Staples (XLP) on the defensive facet, we align the technique with the elemental development we goal to use: discretionary spending expands strongly in favorable situations, whereas staples present resilience throughout downturns. It is usually cheap to check longer moving-average home windows to seize extra steady dynamics and probably produce extra strong outcomes. This mixture permits us to raised match the indicator with the sectors most delicate to the underlying financial exercise and thereby improve the efficiency of the change mannequin.
XLY – XLP change technique
1. Open place in XLY, when field predictor < 3M/6M/9M/12M/24M/36M MA of field predictor.
2. Shut place in XLY, when field predictor reaches its shifting common. Open place in XLP.
3. Shut place in XLP, when field predictor falls beneath its 3M/6M/9M/12M/24M/36M MA.
Desk 2: Efficiency metrics of XLY – XLP change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
XLY portfolio
9.70%
19.24%
-54.93%
0.5
0.18
XLP portfolio
6.73%
12.23%
-32.82%
0.55
0.21
3M XLY – XLP change portfolio
9.92%
15.98%
-33.95%
0.62
0.29
6M XLY – XLP change portfolio
11.12%
16.23%
-33.02%
0.69
0.34
9M XLY – XLP change portfolio
10.43%
15.84%
-28.05%
0.66
0.37
12M XLY – XLP change portfolio
9.54%
15.05%
-30.19%
0.63
0.32
24M XLY – XLP change portfolio
9.64%
14.57%
-28.05%
0.66
0.34
36M XLY – XLP change portfolio
7.33%
13.81%
-36.06%
0.53
0.20
The sector-level outcomes reveal that the change mannequin turns into a extremely efficient choice device when utilized to XLY and XLP. The enhancements are seen throughout all key metrics: general returns rise, the Sharpe ratio will increase, and the Calmar ratio strengthens as effectively. This means that aligning the mannequin with consumer-driven financial cycles gives a a lot cleaner sign than working on the broad-market stage.
There may be, nonetheless, one necessary caveat. Such a powerful enchancment raises the likelihood that the technique is partially overfitted to the precise sector pair or the chosen parameter home windows. To handle this concern, it’s helpful to broaden the scope of the evaluation and discover ensemble-type approaches—fashions that mix a number of change methods directly. By aggregating a number of indicators, we might cut back sensitivity to any single sector, time window, or parameter alternative, and probably get hold of extra steady efficiency out-of-sample.
As a subsequent step, we assemble portfolios that evenly mix a number of moving-average home windows to cut back sensitivity to any single parameter alternative. Particularly, we create one set of portfolios that averages indicators throughout 3- to 12-month home windows, and one other set that spans 3- to 36-month home windows.
Desk 3: Efficiency metrics of mixing XLY – XLP change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
3+6+9+12M XLY – XLP change portfolio
10.42%
14.76%
-26.57%
0.71
0.39
3+6+9+12+24+36M XLY – XLP change portfolio
9.90%
13.68%
-27.16%
0.72
0.36
Combining these a number of moving-average methods reduces each danger and drawdowns, making the strategy significantly efficient. By diversifying throughout totally different time home windows, the portfolio turns into much less delicate to remoted fluctuations and short-term noise, whereas nonetheless capturing significant market traits. This not solely smooths the fairness curve but in addition enhances the steadiness of risk-adjusted returns, reinforcing the sensible worth of the multi-window change technique.
Improve of defensive a part of portfolio
Having Client Staples (XLP) because the defensive part is definitely helpful, however it might not present ample diversification by itself. Each Utilities (XLU) and Well being Care (XLV) characterize sectors which can be traditionally resilient throughout financial downturns. Utilities have a tendency to supply regular money flows and are much less delicate to client spending cycles, whereas Well being Care advantages from constant demand for medical companies and merchandise, unbiased of broader financial situations. Together with these sectors alongside XLP broadens the defensive publicity and reduces the danger of counting on a single sector for defense.
To implement this, we change the one XLP allocation with an evenly weighted defensive portfolio consisting of XLU, XLV, and XLP (marked as XLUVP in graphs and tables).
Desk 4: Efficiency metrics of XLY – XLUVP change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
XLY portfolio
9.70%
19.24%
-54.93%
0.5
0.18
XLUVP portfolio
7.96%
11.38%
-33.45%
0.70
0.24
3M XLY – XLUVP change portfolio
10.83%
15.62%
-34.29%
0.69
0.32
6M XLY – XLUVP change portfolio
11.67%
15.77%
-36.79%
0.74
0.32
9M XLY – XLUVP change portfolio
11.70%
15.16%
-33.45%
0.77
0.35
12M XLY – XLUVP change portfolio
11.21%
14.27%
-35.14%
0.79
0.32
24M XLY – XLUVP change portfolio
10.34%
13.81%
-33.45%
0.75
0.31
36M XLY – XLUVP change portfolio
8.50%
13.06%
-33.45%
0.65
0.25
The introduction of a defensive combine composed of XLP, XLU, and XLV has noticeably improved the efficiency metrics. We attribute this enchancment to 2 components. First, the mix of those three sectors inherently displays stronger risk-adjusted traits in comparison with any single part. Second, combining a number of defensive property typically reduces general portfolio danger, smoothing returns and reducing drawdowns.
Constructing on this precept, it’s pure to increase the identical multi-window aggregation strategy that we beforehand utilized to XLP alone. By averaging indicators throughout totally different moving-average home windows for the mixed defensive portfolio, we goal to additional cut back volatility and improve stability. This enables the change technique to learn concurrently from each sector diversification and time-window diversification, probably producing much more strong risk-adjusted outcomes.
Desk 5: Efficiency metrics of mixing XLY – XLUVP change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
3+6+9+12M XLY – XLUVP change portfolio
11.51%
14.23%
-31.83%
0.81
0.36
3+6+9+12+24+36M XLY – XLUVP change portfolio
10.93%
13.11%
-31.43%
0.83
035
As soon as once more, the outcomes verify that the defensive mixture of XLP, XLU, and XLV outperforms a portfolio utilizing solely XLP on the defensive facet. The mixture gives stronger risk-adjusted metrics, reduces drawdowns, and delivers a extra steady return profile, demonstrating the advantages of each sector diversification and multi-window sign aggregation inside the change technique.
Does this strategy work for particular person shares as effectively?
A brand new query naturally arises from our sector-level outcomes. The change technique works exceptionally effectively with sector ETFs, which is encouraging, however its utility might be additional enhanced if it had been relevant to chose particular person shares. To discover this, we flip our consideration to Amazon (AMZN), one of many largest customers of packaging supplies tracked by our indicator. By making use of the identical multi-window, switch-based strategy, we goal to analyze whether or not the technique can seize comparable risk-adjusted enhancements and generate significant indicators on the single-stock stage.
Desk 6: Efficiency metrics of AMZN change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
AMZN portfolio
20.72%
38.79%
-86.04%
0.53
0.24
3M AMZN change portfolio
21.67%
27.81%
-60.75%
0.78
0.36
6M AMZN change portfolio
18.18%
26.52%
-64.72%
0.69
0.28
9M AMZN change portfolio
20.14%
26.57%
-52.66%
0.76
0.38
12M AMZN change portfolio
17.61%
22.89%
-51.40%
0.77
0.34
24M AMZN change portfolio
17.94%
18.00%
-22.72%
1.00
0.79
36M AMZN change portfolio
9.01%
12.95%
-19.40%
0.70
0.46
We observe that as we lengthen the moving-average window within the MA mannequin, the uncooked returns have a tendency to say no. Whereas risk-adjusted metrics enhance barely, the discount in absolute efficiency is a priority. Furthermore, longer home windows usually lead to extended durations of inactivity, throughout which the technique merely holds no place. To handle this, we take into account integrating BIL, a short-term Treasury ETF, into the portfolio. By allocating idle money to BIL, the technique can generate at the very least modest returns throughout inactive durations, bettering general effectivity with out materially growing danger. This adjustment permits the mannequin to stay conservative when indicators are weak whereas nonetheless placing idle capital to productive use.
Altering the statement interval led to a discount in uncooked returns, however it additionally sometimes improved sure risk-adjusted metrics.
Desk 7: Efficiency metrics of AMZN – BIL change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
AMZN portfolio
25.64%
39.15%
-51.92%
0.65
0.49
3M AMZN – BIL change portfolio
19.68%
21.24%
-34.10%
0.93
0.58
6M AMZN – BIL change portfolio
17.80%
20.66%
-22.52%
0.86
0.79
9M AMZN – BIL change portfolio
15.73%
19.30%
-31.57%
0.81
0.50
12M AMZN – BIL change portfolio
14.46%
17.54%
-29.63%
0.82
0.49
24M AMZN – BIL change portfolio
11.60%
14.25%
-19.58%
0.81
0.59
36M AMZN – BIL change portfolio
6.57%
10.06%
-12.01%
0.65
0.55
We will additionally take into account whether or not combining a number of window lengths provides worth. To discover this, we revisit the mixed-horizon strategy used earlier and take a look at mixtures of three–12 month and three–36 month MA-based methods.
Desk 8: Efficiency metrics of mixing AMZN – BIL change methods.
PORTFOLIO
CAR p.a.
VOL p.a.
MAX DD
SHARPE
CALMAR
3+6+9+12M AMZN – BIL change portfolio
17.23%
18.07%
-25.33%
0.95
0.68
3+6+9+12+24+36M AMZN – BIL change portfolio
14.69%
14.82%
-19.27%
0.99
0.76
As soon as once more, the outcomes present that combining a number of indicators produces a extra steady and balanced consequence. The nominal return decreases, however the general habits of the technique turns into extra disciplined, much less unstable and extra resilient in troublesome market durations. Mixing totally different window lengths persistently improves risk-adjusted efficiency, which might be extra beneficial than purely maximizing uncooked returns.
Abstract and conclusion
Over the course of our evaluation, we experimented with a variety of approaches constructed across the concept of utilizing the corrugated-box PPI as a substitute market sign. We evaluated AR and MA fashions, totally different window lengths, single-indicator methods and blended multi-window techniques. The clearest conclusion is that this system works most naturally with sector ETFs, the place the indicator gives a significant set off for rotating between offensive property similar to XLY and defensive property similar to XLP, XLU or XLV. Sector-level dynamics react extra visibly to modifications in underlying financial exercise, which makes the switching mechanism each intuitive and efficient.
When making use of the identical logic to particular person equities, the outcomes weren’t as clear. The indicator doesn’t translate as instantly into firm-level habits, which signifies that whereas nominal returns usually decline, we will nonetheless obtain enhancements in risk-adjusted metrics like Sharpe and Calmar. This comes at a noticeable price in uncooked efficiency, however it demonstrates that the core concept retains some worth even in a much less favorable setting.
Lastly, it’s value emphasizing that this PPI sequence is just one instance inside a much wider universe of different indicators. Many unconventional macro or micro-level metrics might carry predictive construction that conventional price-based indicators fail to seize. Exploring these sources systematically can reveal new views on market habits and probably uncover strong choice frameworks for lively allocation.
Creator:
David Belobrad, Junior Quant Analyst, Quantpedia
? Then utilise our Quantpedia’s Black Friday Deal and get extra quant insights within the 12 months 2026! You’ve gotten final 24 hours! Solely annually, Quantpedia’s Black Friday Deal gives you 33% extra of our companies for a similar value.
Purchase Quantpedia Prime now and get:3 + 1 months subscription for $29912 + 4 months subscription for $39936 + 12 months subscription for $699
Purchase Quantpedia Premium now and get:3 + 1 months subscription for $44912 + 4 months subscription for $59936 + 12 months subscription for $1199
Or purchase Quantpedia Professional and get:3 + 1 months subscription for $59912 + 4 months subscription for $79936 + 12 months subscription for $1599
Are you searching for extra methods to examine? Join our e-newsletter or go to our Weblog or Screener.
Do you wish to study extra about Quantpedia Premium service? Test how Quantpedia works, our mission and Premium pricing supply.
Do you wish to study extra about Quantpedia Professional service? Test its description, watch movies, evaluation reporting capabilities and go to our pricing supply.
Are you searching for historic knowledge or backtesting platforms? Test our listing of Algo Buying and selling Reductions.
Would you want free entry to our companies? Then, open an account with Lightspeed and luxuriate in one 12 months of Quantpedia Premium for gratis.
Or observe us on:
Fb Group, Fb Web page, Twitter, Linkedin, Medium or Youtube
Share onLinkedInTwitterFacebookConfer with a buddy
