BCA Indicators/Model

When we first set out to create ETS, we leveraged the vast amount of research on stock market anomalies and started with factors that had a proven track-record. We also wanted to minimize the risk of data mining, and as a result, we decided to keep things simple. We stuck with established metrics, and avoided venturing down the long, windy path of custom metrics and adjustments: the more parameters that are included, the easier it is to find spurious results. We also opted for simple, linear mappings – lower is better, or higher is better. When deviating from this approach, it is easy to lose sight of the big picture and spend time optimizing minute details that will not have a large impact on the overall performance of the model. Fast-forward to 2019. We have a stable model that has performed well out of sample, and we are now comfortable taking a second pass over our factors. We can now analyze each model input in detail to see if we can make any improvements. The first factor under the microscope is the Altman Z-Score. This metric was first developed by Edward I. Altman in 1968 to estimate the probability of bankruptcy over a two-year horizon. It uses various items from a firm’s income statements and balance sheet and combines them using a set of weights to yield the so-called Altman Z-Score.1 Since this metric relies on estimated coefficients for a specific set of data, and given that these coefficients date back to the 60s, we wanted to investigate the possibility of using a more modern model of distress in place of the original Altman-Z score.  Our research process led us to review the academic literature, replicate the most promising results internally, and finally, to implement our own variation of the Altman-Z score given our unique dataset.   A Proxy For Bankruptcy In order to assess the ability of a distress model to predict insolvency, it is necessary to have a historical dataset of failure events. If not, it is impossible to measure the success of a given model. Failure can be defined in several ways, the most obvious of which is a Chapter 7 or Chapter 11 bankruptcy filing, at least in the U.S. To ensure that we captured the maximum possible failure events for our global set of stocks, we avoided the Chapter 7/11 definition and developed a simple market-cap based proxy for bankruptcy. We define the failure event of a firm as the point in time where the market-cap drops to 1% of its historical maximum. We also enforce that this event must occur after the maximum to prevent false failures at the beginning of a firm’s trading history. We found that this definition signals failure at the appropriate time for a selection of large-cap firms known to have experienced distress; a notable example is Enron Corp. (Chart 1). In general, we deem this a reasonable proxy for bankruptcy since the price of shares will drop substantially upon news of insolvency. Ultimately, even though it may, in some cases, flag firms that are technically not bankrupt, for our purposes, we are happy to avoid buying firms that plunge below 1% of their previous valuation peak. Chart 1 Academia To The Rescue? A review of the modern finance literature suggested that the Altman Z-Score, while methodologically sound, still had room for improvement as a distress measure. We anticipated that using a more refined distress model, we could amplify the effect of the existing Altman-Z Factor in ETS. In light of this, our first attempt at replacing the Altman-Z score involved running a logistic regression (logit) model presented in Campbell et al. (2010)2 on the ETS universe of stocks and constructing a new factor based on the output from this model. Following the authors’ methodology, we constructed a set of eight variables (three accounting ratios, and five market-based variables) and computed the probability of failure on our entire historical dataset. The probabilities were then percentile-ranked to construct a score from 0-100%, where firms with a score of 0% correspond to those with the highest probability of failure. This ranking system is consistent with the other ETS factors where lower scores are bad and higher scores are good. Although the model by Campbell et al. worked well for predicting subsequent failure (as judged by our bankruptcy proxy measure), the metric fell short performance-wise when tested within our universe of stocks. In brief, our decile-spread metric3 yielded a negative number, indicating poor separation of returns between deciles. Based on the above, we sought to construct our own distress model using similar methods, but applied to the universe of valid ETS firms. There are several advantages to this approach: First, it prevents errors in the construction of the explanatory variables. For instance, when replicating models found in academic literature, we cannot be sure that we are constructing our variables in the exact same way as reported, and hence we cannot have complete confidence in the accuracy of the computed probabilities of failure. Second, it frees us from the shackles of a complicated model, and we can seek to reduce our own model down to something more computationally tractable, and avoid overfitting the data.  Finally, it allows us to base our model on global stock data, as opposed to the U.S.-centric CRSP/COMPUSTAT dataset used by many academic institutions. Bringing It “In-House” The construction of our own distress model began with an analysis of the variables defined in Campbell et al. First, we wanted to determine which variables showed the strongest deviation between success and failure groups. From the calculation of summary statistics and histograms, the variables that showed the most prominent separations between success and failure groups were Net Income to Market Total Assets (NIMTA), Total Debt to Market Total Assets (TDMTA), monthly excess return (EXRET), and the 3-month standard deviation of daily returns (SIGMA) (Chart 2). Detailed descriptions of the variables are provided in the Appendix. Chart 2 We computed NIMTA, TDMTA, EXRET, and SIGMA at monthly frequencies with the goal of constructing a logit model that would predict failure 12 months into the future. The logit model implies that at time t, the probability of firm i failing in the next 12 months is given by: Chart   where y represents the sequence of success (0) and failure (1) events for each firm at each point in time, x represents the four explanatory variables, and α, β are the parameters to fit. The parameters were fit on a rolling basis to avoid look-ahead bias when constructing a trading strategy based on the outcome of the model. Therefore, at each year-end, we used the trailing success/failure data up to that point to estimate parameters. The fitting yielded intuitive results, suggesting that higher TDMTA, SIGMA and lower NIMTA, EXRET indicate a higher probability of failure (see parameter list in Appendix Table 1). Another positive characteristic of these parameters is that they remain relatively stable throughout time, alluding to the robustness of the model. From visual inspection of the parameters, we see that EXRET has the strongest effect on failure probability, followed by SIGMA, TDMTA, and NIMTA. It is not surprising that EXRET has the strongest effect since it is based on a 12-month trailing weighted average. Therefore, we can expect this variable to capture the prolonged period of descent that firms typically undergo before reaching a bankruptcy event. Indeed, firms in our failure group experienced, on average, a failure event 5 years after reaching peak market cap. As for the remaining variables, it is natural to expect increased volatility and subpar financial statements to signal imminent failure. Model accuracy was tested by looking at its predictions on an individual and aggregate basis. Reassuringly, we found individual examples of large firms with known failure events showing the expected behavior (Chart 3). However, the more significant result is that on average, the model predicts a high probability of failure for the group of true failure firms (N=3213) relative to a random sample of true success firms (Chart 4). Chart 3 Chart 4 Armed with a successful distress model, we then translated the predictions into an ETS factor, giving it a score from 0-100%. Using a relative change condition in the parameters as a guide,4 we chose the factor inception date to be year-end 2004. Since our stock data begins in 1995, this allows for a sufficient “burn-in” time for the distress model to stabilize. The latter is important given the spike in U.S. Chapter 11 filings starting in 2001, which is also captured by our bankruptcy proxy (Chart 5).  Chart 5 With the factor constructed, we replaced the existing Altman-Z factor in the full model, keeping the same initial weight.  As expected, this replacement led to an improvement in the overall model according to our standard performance metrics, which are described in a previous report.5 Specifically, we observed a 6% improvement in the decile ordering metric (DECORD) and a small (<1%) reduction in mean absolute difference in BCA Score (MAD), leading to a 7% increase in our overall risk-reward ratio (DECORD / MAD). The new factor, simply named Distress on the platform, also performs well when examined in isolation. To test this, we ran the equivalent of an equal-weighted, daily-rebalanced backtest on ten deciles separated by the Distress score. Calculating the Sharpe ratio on the decile returns, we observe an increasing trend in the performance of the factor as a function of decile (Chart 6). Therefore, with this factor we obtain the desired separation in performance between deciles on a risk-adjusted basis. Chart 6  What About Machine Learning? Disclaimer: This section involves a small digression on machine learning One statistical challenge with our success/failure classification problem is that the number of “success” events (i.e. no indication of bankruptcy) greatly outnumbers the failure events. Indeed, with our bankruptcy proxy, approximately 1% of the 3.5 million firm-month observations in our dataset are marked as failures. Despite the small failure rate, classic logistic regression can still provide good results; however, we wished to explore the effectiveness of machine learning techniques in tackling this type of classification problem. The use of machine learning (ML) was motivated by an analogous problem outside the world of stocks, namely the detection of credit card fraud. One can imagine that out of the massive number of credit card transactions registered daily, only a small fraction is actually fraudulent. Now, suppose that for a given period we obtained transaction data that includes characteristics such as dollar amount, time of day, and whether or not the transaction was normal or fraudulent. We could then use this prior data to train an ML model to recognize fraudulent transactions when presented with new, out-of-sample data. A specific class of ML models known as “autoencoders” have been shown to be effective in handling this task (See Box 1 for more details). Given that the credit-card problem is directly analogous to our distress problem, we set up a simple autoencoder to test whether it could detect firm failure with a higher degree of accuracy than the logit model. As inputs to the model, we used the same eight variables from Campbell et al. Perhaps surprisingly, we found that the best version of our autoencoder model did not surpass the accuracy of the logit model, at least in terms of maximizing precision and recall simultaneously.6 Furthermore, the predictions of the autoencoder were less effective at separating winners from losers after being transformed into a factor score between 0-100%. This, of course, does not mean that ML techniques are hopeless. It simply means that, for this particular set of parameters and model structure, the autoencoder was not able to provide additional predictive value over the simpler, more computationally efficient logit model.     Box 1 Autoencoders The rise of machine learning has brought with it a slew of jargon that seems intimidating at first, but can actually be understood without extensive training in mathematics or computer science. The term “autoencoder” may very well fall into this category. To get right into it, an autoencoder is a type of neural network tasked with learning a compressed representation of the original input or “training” data. The flow of data through an autoencoder is shown below (Box Diagram 1). The goal of the autoencoder is to optimize the weights connecting nodes in the network so that the difference between the input and output is minimized. However, due to the action of the “reduction” or “encoding” phase of the autoencoder, the output, i.e., the “reconstructed” or “decoded” data, will always be a compressed approximation of the original input. The latter is important for classification problems because it means that post-training, any data presented to the network that does not resemble the original training data will likely be poorly reconstructed, and flagged as anomalous.  Going back to our problem of identifying distressed firms, let’s step through how we use an autoencoder to distinguish between “success” and “failure” groups. Separate the data (i.e., the observations for all accounting and market-based variables) into success and failure groups according to the bankruptcy proxy measure. Train the autoencoder to reconstruct the success group. Each variable of interest corresponds to a node in the “input” layer of the network. Feed the autoencoder the failure group data and record the error in reconstructing this data. Set an error threshold (a number) for determining when a firm should be marked a failure vs. success. Generate predictions based on this threshold. Analyze the accuracy of these predictions using metrics such as precision and recall. Image Future Work A key priority of the ETS team is to use modern techniques in computing to uncover new sources of alpha for our clients. With the recent explosion of advancements in data science and machine learning, we are excited about leveraging the knowledge from these fields to improve the ETS model. We believe that this report provides a glimpse into the style of factor analysis we expect to conduct in the near future. As for the distress model developed here, we are content with the performance of the simple logistic regression for now, but we will continue to examine whether modern classification techniques can help improve accuracy.  One method in particular that piques our interest is so-called “Gradient Boosting”, which has made big waves in the data science community for its use in winning competitions on Kaggle.com.7 Overall, we will continue to monitor the model’s ability to exploit stock-market anomalies and adjust accordingly, whether that means adjustments to existing factors, or the development of new ones. In addition, we will strive to keep ETS an integral part of your asset management workflow via new features on the web platform. As always, we hope you find the recent additions valuable, and please do not hesitate to reach out with any comments or questions.   Spencer Moran, Senior Analyst Equity Trading Strategy spencerm@bcaresearch.com   Appendix Variable Definitions Here we provide the definitions of the variables used in the distress model. The denominator in NIMTA and TDMTA represents “Market Total Assets” and is given by Market Cap. plus Total Debt.  The formulas are as follows: Image EWM is a 12-month exponential weighted moving average used to incorporate more history into the variable but provide greater weight to more recent observations. STD is an annualized, trailing three-month standard deviation. The symbols R, Rm, and r represent monthly returns, monthly market returns, and daily returns, respectively. Parameters Here we provide the parameters (aka a and b’s) obtained from the rolling fit of the distress model. Recall that parameters are estimated at the end of each year, and then these parameters are used to compute predictions for the following year to avoid look-ahead bias. Image Footnotes 1      The original Z-score formula was as follows: Z =   1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5, where: X1 : working capital / total assets X2 : retained earnings / total assets X3 : earnings before interest and taxes / total assets  X4 : market value of equity / book value of total liabilities X5 : sales / total assets 2  Campbell, John Y., Jens Dietrich Hilscher, and Jan Szilagyi. 2011. Predicting financial distress and the performance of distressed stocks. Journal of Investment Management 9(2): 14-34. 3   The decile-spread is a metric we use to evaluate the performance of a trading system. Essentially, it looks at the sum of the differences between the annual compounded growth rates of each score decile. Ideally, the deciles should be in order, and the upper deciles should outperform the lower deciles. This metric captures both these aspects, and summarizes them in a single number. 4       The factor inception date was chosen to be the date when the relative change in the magnitude of the parameter set (treated as a vector) fell below 1%. 5      Please see Equity Trading Strategy, “ETS Goes Global,” dated October 31, 2016, available at ets.bcaresearch.com 6      Precision is defined as the ratio of actual failures to all failures predicted by the model. Meanwhile, recall is the ratio of actual failures to the sum of actual failures plus false successes predicted by the model. 7      Kaggle is a popular online community (recently acquired by Google) where members compete to solve open problems in data science and machine learning.

Trepidation engulfs commodity markets like a fog weaving through half-deserted streets. Central bankers huddle in muttering retreats, growing more cautious by the day. EM growth concerns – particularly slowing trade volumes, and the drama surrounding Sino – U.S. trade negotiations – contribute to this. Europe’s slowdown as Brexit approaches, and a U.S. government that seems forever at loggerheads also sap investor confidence. Nonetheless, the level of industrial commodity demand – oil and copper in particular – continues to hold up. By our reckoning, EM growth still is positive y/y. And central bank caution – along with less-restrictive policies – provides a supportive backdrop for industrial commodities down the road. The production discipline we expect from OPEC 2.0 this year sets the stage for a continued rally in oil prices. Given our view on EM growth, we continue to favor staying long oil exposure, and remaining exposed to industrial commodities generally via the S&P GSCI position we recommended on December 7, 2017. Highlights Energy: Overweight. We are closing our open long call spreads in 2019 Brent, having lost the ~ $1/bbl premium in each. We are opening a new set of similar positions in anticipation of the next up-leg in Brent. At tonight’s close of trading, we will go long Brent $70 Calls vs. short $75 Calls in June, July and August 2019. Base Metals/Bulks: Neutral. Metal Bulletin’s benchmark iron ore price index for China traded through $90/MT earlier this week, as supply concerns continue to weigh on markets in the wake of evacuations from areas close to tailings dams used by miners.1 Precious Metals: Neutral. Bullion broker Sharps Pixley reported the PBOC’s gold reserves total almost 60mm ounces, up 380k ounces from end-2018 levels. Russia’s state media outlet RT proclaimed: “China on gold-buying spree amid global push to end US dollar dominance” on Tuesday. Ags/Softs: Underweight. Last week’s USDA WASDE report estimates world ending stocks for grains will be up slightly for the 2018-19 crop year at 772.2mm MT vs 766.6mm MT previously estimated in December. A January report was not issued due to the U.S. government shutdown. Feature In discussions with clients in the Middle East last week, few contested the assertion OPEC 2.0 is determined to keep supply below demand this year, in order to draw down global oil and refined product inventories.2 This strategy worked well for the coalition after it was stood up in November 2016. Back then, production cutbacks, an unexpected collapse of Venezuelan output, and random outages in Libya and elsewhere combined with above-average global demand to keep consumption above production. This led to a drawdown in OECD inventories of 260mm barrels between January 2017 and June 2018. OPEC 2.0 is off to a strong start on its renewed effort to rein in production and draw down inventories. OPEC (the old Cartel) cut nearly 800k b/d of production in January m/m, bringing members’ total crude output to 30.8mm b/d.3 The largest cut once again came from KSA, which reported it reduced output by just over 400k b/d m/m in January. This follows a 450k b/d reduction in December 2018 reported by the Kingdom in last month’s OPEC Monthly Oil Market Report. For March, KSA already is indicating it plans to drop production to 9.8mm b/d – 1.3mm b/d less than it was pumping in November 2018. There are some signs of discord within OPEC 2.0. Rosneft CEO Igor Sechin once again is arguing against the coalition’s production-cutting strategy, this time in a letter to Russian President Vladimir Putin.4 This is not the first time such disagreements were aired: In November 2017, leaders of Russia’s oil industry walked out of a meeting with Energy Minister Alexander Novak following a disagreement with the government on extending OPEC 2.0’s production-cutting deal launched at the beginning of the year. In the end, the deal was extended after President Putin weighed in.5 A Deeper Look At Demand Uncertainty These supply-side issues are not trivial, and pose significant risks to our price view. All the same, Russia does benefit from higher oil prices, in that inelastic global demand in the short-to-medium term produces a non-linear price increase when supply is reduced. Russia’s OPEC 2.0 quota calls for production to fall from 11.4mm b/d production basis its October 2018 reference level (11.6mm b/d at present) to 11.2mm b/d in 2019. As long as Russia’s participation in the OPEC 2.0 coalition advances its economic and geopolitical interests – i.e., higher revenues than could be expected without having a direct role in global production management, and in deepening its ties with KSA – we expect it to remain a member in good standing in OPEC 2.0. At the moment, the bigger issues center on the state of global demand for industrial commodities. Unlike the situation that prevailed during the first round of OPEC 2.0 cuts, global markets no longer are seeing a synchronized global recovery in aggregate demand. Rather, EM commodity demand growth – the engine of global growth – has been trending down at a slow and constant pace since the beginning of 2018. This is not news: It shows up in our new Global Industrial Activity (GIA) index, and we’ve been writing about it and accounting for it in our metals and oil demand projections for months (Chart of the Week). Chart of the WeekCommodity Demand May Be Bottoming Commodity Demand May Be Bottoming Commodity Demand May Be Bottoming BCA’s GIA index is heavily weighted to EM commodity demand. Based on our estimates, it appears to be close to or in a bottoming phase and ready to turn up within the next quarter. It is worthwhile pointing out that even with the slowdown over the past year or so, BCA’s GIA index still stands significantly higher than the level registered during the manufacturing downturn of 2015-16. This also adds color as to why the OPEC market-share war launched in November 2014 was so devastating to prices – demand was contracting while supplies were surging from OPEC 2.0 states and from U.S. shale-oil producers. Pessimism Is Overdone We have maintained for some time commodity markets are overly pessimistic on the global growth outlook, mainly because of their gloomy view on the Chinese economy, and anticipated knock-on effects for EM growth arising from this view. Our colleagues at BCA’s Global Fixed Income Strategy succinctly capture the current mood pervading global markets: “… this current soft patch for the global economy is occurring alongside an extreme divergence between plunging growth expectations and more stable readings on current economic conditions. The fall in expectations is visible in the most countries, according to data series that measure confidence for businesses, consumers and investors.”6 We continue to expect the slowdown in EM to persist in 1H19 based on our modeling and actual consumption data. Part – not all – of this is due to the slowdown in China, where policymakers are moving to reverse earlier financial tightening with modest fiscal and monetary stimulus in 1H19. We continue to expect the Communist Party leadership in China will want to start increasing stimulus later this year or in 1H20, so that it hits the economy full force in 2021 in time for the 100th anniversary of the founding of the CCP. Such stimulus will bolster industrial commodity demand. Still, this is difficult to call, particularly the form stimulus will take. President Xi appears committed rebalancing China’s economy – i.e., supporting consumer-led growth – and may want to keep policy powder dry, so to speak, to counter a recession in 2020 or thereafter. Stimulating the consumer economy in China could boost consumption of gasoline, and demand for white goods like household appliances at the expense of heavy industrial demand. Oil and base metals used in stainless steel would benefit in such an environment. Timing this rebound remains difficult. It appears to us that oil and, to a lesser extent, base metals have undershot their fair-value levels (based on our modeling) on the back of negative expectations and sentiment. If we are correct in this assessment, this should limit the negative surprises going forward and open upside opportunities for commodity prices (Chart 2). Chart 2Technically, Oil's Oversold Technically, Oil's Oversold Technically, Oil's Oversold Under The Hood Of BCA’s Newest Model Because demand is so difficult to capture, we continually are looking for different gauges to measure it and cross-check against each other. We developed our Global Industrial Activity index to target the actual performance of commodity-intensive activities globally. Each component is selected based on its sensitivity to the cycle in global industrial activity, hence on the cycle of global commodity demand. This is different from the BCA Global Leading Economic Indicator (LEI), which uses a GDP-weighted average of 23 countries’ LEI. By relying on GDP, the LEI weights in the indicator favor DM countries and do not account for the growing share of the service sector in these economies (Chart 3).7 Chart 3GIA Captures Commodity Demand GIA Captures Commodity Demand GIA Captures Commodity Demand Our GIA index focuses on commodity demand, which is fundamentally different from proxies of global real GDP growth or global economic activity. Nonetheless, we included the BCA global LEI with a small weight (~ 10%) in our index to capture DM economies. This inclusion does add information to our new gauge. Our GIA index correlates with Emerging Markets’ GDP, copper and oil prices with lags of one to three months. This index is designed to measure the strength of the underlying demand for commodities. It does not account for the supply side and other idiosyncratic shocks that affects each commodity. For instance, our index captures ~ 55% of the variation in the y/y movement in oil prices; adding our oil market supply and sentiment indicators on top of the demand variable raises this to more than 80% (Chart 4). Chart 4Combined Indicators Work Best Combined Indicators Work Best Combined Indicators Work Best The index is divided into four main components, which gauge the demand-side impacts of (1) trade; (2) currency movements; (3) manufacturing demand; and (4) the Chinese economy, given its importance to overall commodity demand. The GIA index’s Trade Component combines EM import volumes and an estimate of global dry bulk shipping rates to gauge demand. Readers of the Commodity & Energy Strategy are familiar with our use of EM trade volumes as a proxy for EM income.8 This week, we introduce a new proxy for shipping rates using the Baltic Dry Index (BDI) as a proxy of global economic activity. Our methodology is based on the approaches taken by James D. Hamilton and Lutz Kilian in their respective models that use the BDI to proxy global growth.9 We created two alternative measures based on each of their approaches and average them to come up with our own proxy of the cyclical factor of global shipping rates driven by demand. Both of our alternative measures use a rebased version of the real BDI, which uses the U.S. CPI to deflate the nominal value. Because it picks up the surge in shipping activity in 2H18 resulting from the front-running of tariffs in the Sino – U.S. trade war, the Trade Component of our GIA index gives the most positive readings of all the components (Chart 5, panel 1). By the end of this month, we expect the effects of this front-running to avoid tariffs will wash through the gauge, and we will have greater clarity on the state of global trade. Chart 5Performance Of GIA Components Performance Of GIA Components Performance Of GIA Components The Currency Component uses a basket of currencies that are sensitive to global growth – i.e., the currencies of countries heavily engaged in trade – and the Risky vs. Safe-haven currency ratio built by BCA’s Emerging Market Strategy.10 This allows us to capture the information regarding the state of global economic activity contained in the highly efficient and forward-looking currency markets. This component collapsed in March 2018, but seems to have bottomed recently (Chart 5, panel 2). The Manufacturing Component looks at the PMIs and various business conditions and expectations surveys for countries that have large industrial exposures to the economic health of EM.11 Currently, this component signals a continuation of the downward trend first observed at the beginning of 2018 (Chart 5, panel 3). Lastly, the Chinese Economy Component uses two indicators of the country’s industrial output: the Li Keqiang Index, and our China Construction Indicator. Despite the fact that the slowdown in China is at the center of investor pessimism re global demand, this component is still holding well (Chart 5, panel 4). It has a moderate negative trend, but is not alarming for commodity demand. Moreover, we expect some stimulus in the second half of the year, which should keep this component supportive for commodity prices. Industrial Commodity Demand Still Holding Up Our GIA index proxies demand for industrial commodities, which is closely aligned with EM GDP – as GDP grows, demand for industrial commodities grows (Chart 6, panel 1). The GIA index is more correlated with copper prices than with oil prices, but it still provides an excellent snapshot of the state of demand for these commodities (Chart 4). Chart 6GIA, Meet Dr. Copper GIA, Meet Dr. Copper GIA, Meet Dr. Copper Also, it is interesting to note there appears to be only one large specific supply shock that affected the copper market’s relationship with global demand (Chart 6, panel 2). Our new index supports the Market’s “Dr. Copper” argument, in the sense that copper prices are pretty much always aligned with global industrial activity. We also note that the recent Sino – U.S. trade tensions have pushed copper below the value that is explained by our demand proxy. Bottom Line: The resolve of OPEC 2.0 to reduce production is not in doubt. OPEC (the old Cartel) reported this week its member states cut nearly 800k b/d of production in January m/m, bringing members’ total crude output to 30.8mm b/d. On the demand side, new GIA index indicates things are not as bad as sentiment and expectations would indicate. If anything, we expect the combination of OPEC 2.0’s resolve and rising demand for industrial commodities – oil and copper in particular – to lift prices as the year progresses.   Hugo Bélanger, Senior Analyst Commodity & Energy Strategy HugoB@bcaresearch.com Robert P. Ryan, Senior Vice President Commodity & Energy Strategy rryan@bcaresearch.com Footnotes 1      Please see “Brazil evacuates towns near Vale, ArcelorMittal dams on fears of collapse,” published by reuters.com on February 8, 2019. 2      OPEC 2.0 is the name we coined for the producer coalition of OPEC states, led by the Kingdom of Saudi Arabia (KSA), and non-OPEC states, led by Russia, which recently agreed to cut production by ~ 1.2mm b/d to drain commercial oil inventories and re-balance markets globally. 3      Please see the February 2019 issue of OPEC’s Monthly Oil Market Report, which is available at opec.org. 4      Please see “Exclusive: Russia’s Sechin raises pressure on Putin to end OPEC deal,” published by uk.reuters.com February 8, 2019. 5      Please see “Russian oil unsettled by talk of longer production cuts,” published by ft.com November 15, 2017. 6      Please see “A Crisis Of Confidence?” published by BCA Research’s Global Fixed Income Strategy, published February 12, 2019.  It is available at gfis.bcaresearch.com. 7      The components of the global LEI are also different from our GIA index, and more market-oriented. For details on each series included in the LEI, please see “OECD Composite Leading Indicators: Turning Points of References Series and Component Series,” published February 2019. It is available at oecd.org. 8      Please see BCA Research’s Commodity & Energy Strategy Weekly Report “Trade, Dollars, Oil & Metals ... Assessing Downside Risk,” where we discussed the relationship between EM imports volume, EM income and commodity prices, published August 23, 2018, and is available at ces.bcaresearch.com. 9      The best approach is still debated in the literature. For more details on Hamilton and Kilian’s measurements, please see James D Hamilton, “Measuring Global Economic Activity,” Working paper, August 20, 2018 and Lutz Kilian, “Measuring Global Real Economic Activity: Do Recent Critiques Hold Up To Scrutiny?” Working paper, January 12, 2019. By selecting EM only import volumes and our proxy shipping rate based on the BDI, we narrow our Trade Component to factors that are mainly linked to industrial activity and commodity-intensive sectors. 10     Our basket of currencies includes Korea, Sweden, Chile, Thailand, Malaysia and Peru. The risky vs. safe-haven currency ratio average of CAD, AUD, NZD, BRL, CLP & ZAR total return indices relative to average of JPY & CHF total returns (including carry). 11     This includes Korea, Singapore, Sweden, Germany, Japan, China and Australia. Investment Views and Themes Recommendations Strategic Recommendations Tactical Trades     TRADE RECOMMENDATION PERFORMANCE IN 4Q18 Image Commodity Prices and Plays Reference Table   Trades Closed in 2019 Summary of Trades Closed in 2018 Image

Key Portfolio Highlights The S&P 500 has started 2019 with a bang as dovish cooing from the Fed has proven a tonic for equities. While we have not entirely retraced the path to the early-autumn highs, our strategy of staying cyclically exposed, based on our view of an absence of a recession in 2019, has proven a profitable one as investor capitulation reached extreme levels (Charts 1 & 2). Chart 1Capitulation Capitulation Capitulation Chart 2Selling Is Exhausted Selling Is Exhausted Selling Is Exhausted Importantly, risk premia have been deflating as the end-of-year spike in volatility has subsided and junk spreads have narrowed from the fear-induced heights in December (Chart 3). Chart 3Risk Premia Renormalization Risk Premia Renormalization Risk Premia Renormalization Nevertheless, in order for the reflex rebound since the late-December lows to morph into a durable rally, the macro/policy backdrop has to turn from a headwind to a tailwind. We are closely monitoring three potential positive catalysts: A definitively more dovish Fed, which would help restrain the greenback A continuation of the earnings juggernaut A positive U.S./China trade resolution With respect to the first of these, the S&P 500 convulsed following the December 19 Fed meeting and suffered a cathartic 450 point peak-to-trough fall two months ago. The Fed likely made a policy error, and Fed Chair Powell’s resolve is getting tested as has happened with every Chair since Volcker (Charts 4 & 5). Chart 4Powell's Resolve Getting Tested Powell's Resolve Getting Tested Powell's Resolve Getting Tested Chart 5Fed Policy Mistake Fed Policy Mistake Fed Policy Mistake The rising odds of a pause in the Fed tightening cycle, at least for the first half of the year, will likely serve as a welcome respite for equities. Our second catalyst has been gaining steam through the Q4 earnings season which has seen continuation of the double-digit earnings growth of the prior three quarters. Our earnings model points to a moderation of earnings growth in the year to come, in line with sell-side expectations (Chart 6). Our 2019 year-end target remains 3,000 for the SPX, based on $181 2020 EPS and a 16.5x multiple.1 This represents a 6% EPS CAGR, assuming 2018 EPS ends near $162. Chart 6EPS Growth > 0 EPS Growth > 0 EPS Growth > 0 Chart 7 In Chart 7, we show that financials, health care and industrials are responsible for 61% of the SPX’s expected profit growth in 2019 while technology’s contribution has fallen to a mere 7.2%. While the risk of disappointment encompases financials, health care and industrials, there are high odds that tech surprises to the upside as it has borne the brunt of recent negative earnings revisions (Charts 8 & 9). Chart 8Earnings Revisions... Earnings Revisions... Earnings Revisions... Chart 9...Really Weigh On Tech ...Really Weigh On Tech ...Really Weigh On Tech Lastly, the negativity surrounding the slowdown in China is likely fully reflected in the market (Chart 10), implying an opportunity for a break out should a positive resolution to the U.S./China trade spat be delivered. China’s reflation efforts suggests that the Chinese authorities remain committed to injecting liquidity into their economy (Chart 11). Chart 10China Slowdown Baked In The Cake China Slowdown Baked In The Cake China Slowdown Baked In The Cake Chart 11Reflating Away Reflating Away Reflating Away Already, the PBOC balance sheet, with over $5.5tn in assets, is expanding anew. Empirical evidence suggests that SPX momentum and the ebb and flow of the PBOC balance sheet are joined at the hip, and the current message is positive (Chart 12). All of these underlie our style preferences for cyclicals over defensives2 and international large caps over domestically-geared small caps. Chart 12Heed The PBoC Message Heed The PBoC Message Heed The PBoC Message Chris Bowes, Associate Editor chrisb@bcaresearch.com S&P Financials (Overweight) The divergence between the directions for our CMI and valuation indicator (VI) for S&P financials has reached stunning levels, with the former accelerating into pre-GFC territory and the latter falling to two standard deviations below fair value. Our technical indicator (TI) is sending a relatively neutral message, though this does not diminish the most bullish signal in our cyclical indicator’s history (Chart 13). Chart 13S&P Financials (Overweight) S&P Financials (Overweight) S&P Financials (Overweight) The ongoing strength of the U.S. economy is the driver of such a positive indicator, particularly with respect to the key S&P banks sub index. Our total loans & leases growth model and BCA’s C&I loan growth model (second & bottom panels, Chart 14) are in positive territory. The latter is significant given that C&I loans are the single biggest credit category in bank loan books. Importantly, C&I loans have gone vertical recently topping the 10.5% growth mark despite softening capex intentions and CEO confidence. Further, multi-decade highs in consumer confidence are offsetting the Fed’s tightening cycle and suggest that consumer loans, another key lending category, will also gain traction (third panel, Chart 14). In the context of the generationally high employment rate, the implied lower defaults should drive amplified profit improvement from this credit growth. We reiterate our overweight recommendation. Chart 14Loan Growth Drives Profits Loan Growth Drives Profits Loan Growth Drives Profits S&P Industrials (Overweight) The still-solid domestic footing has maintained our industrials CMI close to its cyclical highs, which are also some of the most bullish in the history of the indicator. However, stock prices have not responded accordingly and our VI has descended mildly from neutral to undervalued. Our TI sends a much more definitive message and stands at a full standard deviation into oversold territory (Chart 15). Chart 1515. S&P Industrials (Overweight) 15. S&P Industrials (Overweight) 15. S&P Industrials (Overweight) While their cyclical peers S&P financials are almost exclusively a domestic play, S&P industrials have been weighed down by trade flare ups for most of the past year (bottom panel, Chart 16). Accordingly, much of the benefit of positive domestic capex indicators and the more tangible capital goods orders maintaining a supportive trajectory has failed to show up in relative EPS growth (second & third panels, Chart 16), though the latter has recently hooked much higher. Chart 16Industrial Earnings Growth Has Recovered Industrial Earnings Growth Has Recovered Industrial Earnings Growth Has Recovered S&P Materials (Overweight) Our materials CMI has made a turn, rising off its lowest level in 20 years. This has coincided with our VI bouncing off its cyclical low, though it remains in undervalued territory. The signal is shared by our TI which has only recently recovered from a full standard deviation into the oversold zone, a level that has historically presaged S&P materials rallies (Chart 17). Chart 17S&P Materials (Overweight) S&P Materials (Overweight) S&P Materials (Overweight) When we upgraded the S&P materials sector to overweight earlier this year, we noted that China macro dominates the direction of U.S. materials stocks. On the monetary front, the Chinese monetary easing cycle continues unabated and the near 150bps year-over-year drop in the 10-year Chinese Treasury yield will soon start to bear fruit (yield change shown inverted and advanced, bottom panel, Chart 18). The renminbi also moves in lockstep with relative share prices. The apparent de-escalation in the U.S./China trade tensions has boosted the CNY/USD and is signaling that a playable reflation trade is in the offing in the S&P materials sector (top panel, Chart 18). Chart 18Chinese Data Drives Materials Performance Chinese Data Drives Materials Performance Chinese Data Drives Materials Performance S&P Energy (Overweight) Our energy CMI has moved horizontally for the past six quarters, though this followed a snap-back recovery from the extremely depressed levels of 2016 and 2017. Meanwhile both our VI and TI have descended steeply into buying territory with the former approaching two standard deviations below fair value (Chart 19). Chart 19S&P Energy (Overweight) S&P Energy (Overweight) S&P Energy (Overweight) As with the CMI, the relative share price ratio for the S&P energy index has moved laterally since our mid-summer 2017 upgrade to overweight. Interestingly, the integrated oil & gas energy subindex neither kept up with the steep oil price advance until the end of September, nor with the recent drubbing in crude oil prices (top panel, Chart 20). Put differently, oil majors never discounted sustainably higher oil prices, and are also refraining from extrapolating recent oil prices weakness far into the future. Chart 2020. The Stage Is Set For A Recovery In Crude Prices 20. The Stage Is Set For A Recovery In Crude Prices 20. The Stage Is Set For A Recovery In Crude Prices Nevertheless, the roughly 30% per annum growth in U.S. crude oil production is unsustainable and, were production to remain near all-time highs and move sideways in 2019, then the growth rate would fall back to the zero line. Such a paring back in the growth rate would likely balance the oil market and pave the way for an oil price recovery (oil production shown inverted, bottom panel, Chart 20). This echoes BCA’s Commodity & Energy Strategy service, which continues to forecast higher oil prices into 2019, a forecast which should set the stage for a sustainable rebound next year in S&P energy profits, the opposite of what analysts currently expect (Chart 7). S&P Consumer Staples (Overweight) An improving macro environment is reflected in our consumer staples CMI that has vaulted higher in recent months. However, the strong recent relative outperformance has also shown up in our VI which, though still in undervalued territory, has recovered significantly. Our TI has fully recovered and now sends a neutral message (Chart 21). Chart 21S&P Consumer Staples (Overweight) S&P Consumer Staples (Overweight) S&P Consumer Staples (Overweight) The surging S&P household products sector has been carrying the S&P consumer staples index on its back as solid pricing efforts have been dragging results and forward guidance higher. While household product sales have been enjoying a multi-year growth phase (second panel, Chart 22), it has largely been driven by volumes. However, the recent resurgence in pricing power (third panel, Chart 22) has given volume gains an added kick, pushing sales further. Meanwhile, exports have continued their two-year ascent despite the tough currency environment and the upshot is that relative EPS growth will likely remain upbeat (bottom panel, Chart 22). In light of challenged EM consumer spending growth, this signal is very encouraging. Chart 22Household Products Is Carrying Staples Household Products Is Carrying Staples Household Products Is Carrying Staples S&P Health Care (Neutral) Our health care CMI has been treading water recently. Further, a recovery in pharma stocks has taken our VI from undervalued to a neutral position, while our TI sends a distinctly bearish message as health care stocks have been overbought (Chart 23). Chart 23S&P Health Care (Neutral) S&P Health Care (Neutral) S&P Health Care (Neutral) Healthcare stocks have outperformed in the back half of 2018. Recently a merger mania that has swept through the pharma and biotech spaces has underpinned relative share prices. The last three months have seen an explosion of deals, including the largest biopharma deal ever (Bristol-Myers Squibb buying Celgene for approximately $90 billion) with other global deals falling not too far behind (Takeda buying Shire for $62 billion mid-last year). Such exuberance has clearly confirmed that merger premia are alive and well in the S&P pharma index. It is not merely rising premia that have taken pharma higher either. Pricing power has entered the early innings of a recovery (top panel, Chart 24) while the key export channel points to increasingly bright days ahead (second panel, Chart 24). However, the rise of regulatory pressure from the Trump administration may cause better pricing to prove fleeting. Chart 24Merger Mania In Pharma Merger Mania In Pharma Merger Mania In Pharma Further, pharma’s consolidation phase has come at a cost to sector leverage ratios that have dramatically expanded (bottom panel, Chart 24). Such profligacy may come to haunt the sector should the pricing power recovery falter. S&P Technology (Neutral) Our technology CMI has been moving laterally for the better part of the last three years, though the S&P technology index has ignored the macro headwinds and soared higher over that time. Our VI remains on the overvalued side of neutral, despite the recent tech selloff while our TI has been retrenching into oversold territory (Chart 25). Chart 25S&P Technology (Neutral) S&P Technology (Neutral) S&P Technology (Neutral) Until the end of last year, we maintained a barbell portfolio within the sector by recommending an overweight position in the late-cyclical and capex-driven technology hardware, storage & peripherals and software indexes while recommending an underweight position in the early-cyclical semi and semi equipment indexes. However, we recently upgraded the niche semi equipment to overweight for three reasons. First, trade policy uncertainty has dealt a blow to this tech subindex. Not only are 90% of sales foreign sourced, but a large chunk is also China-related sales. Second, emerging market financial indicators are showing some signs of life, underscoring that semi equipment demand may turn out to be marginally less grim than currently anticipated (second panel, Chart 26). Third, long term semi equipment EPS growth estimates have recently collapsed to a level far below the broad market, indicating that the sell side has thrown in the towel on this niche sector (third panel, Chart 26). Chart 26A Bottom In Semi Equipment A Bottom In Semi Equipment A Bottom In Semi Equipment Overall, and despite our more bullish view on semi equipment, we continue to recommend a neutral weighting in S&P technology. S&P Utilities (Underweight) Our utilities CMI has recovered recently, bouncing off its 25-year low, driven by the modest easing in interest rates, (Chart 27). This has also manifested in a recovery in the S&P utilities index as this fixed income proxy has reacted to the recent fall in Treasury yields (change in yields shown inverted, top panel, Chart 28) and jump in natural gas prices. Further, utilities are typically seen as a domestic defensive play and the recent trade troubles have made utilities soar in a flight to safety. Chart 27S&P Utilities (Underweight) S&P Utilities (Underweight) S&P Utilities (Underweight) We think the tailwinds lifting utilities are transitory and likely to shift to headwinds. First, one of our key themes for the back half of the year is rising interest rates; a move higher in yields will have a predictably negative impact on these high-dividend paying equities. Second, a flight to safety looks fleeting; the ISM manufacturing new orders index usually moves inversely in lock step with utilities and the most recent message is negative for the S&P utilities index (ISM manufacturing new orders index shown inverted, second panel, Chart 28). Meanwhile, S&P utilities earnings estimates have continued to trail the broad market, having taken a significant step down this year (third panel, Chart 28). Chart 28Rising Rates In Late-2019 Will Be A Headwind For Utilities Rising Rates In Late-2019 Will Be A Headwind For Utilities Rising Rates In Late-2019 Will Be A Headwind For Utilities Our VI and TI share this bearish message as the VI is deeply overvalued and the TI is in overbought territory (Chart 27). S&P Real Estate (Underweight) Our real estate CMI has recently started to turn up, though this is off the near decade-low set last year and remains deeply depressed relative to history (Chart 29). This is principally the result of the backup in interest rates since late last year and the lift they have given to the sector, which has been a relative outperformer over the past six months (top panel, Chart 30). Much like the S&P utilities sector in the previous section, and in the context of BCA’s higher interest rate view, we continue to avoid this sector. Chart 29S&P Real Estate (Underweight) S&P Real Estate (Underweight) S&P Real Estate (Underweight) Along with the modest reprieve in borrowing rates, multi family construction continues unabated (second panel, Chart 30), likely driven by all-time highs in CRE prices (third panel, Chart 30). In the absence of an outright contraction in construction, recent weakening in occupancy (bottom panel, Chart 30) will likely prove deflationary to rents, and thus profit prospects. Chart 30Falling Occupancy Will Hurt REIT Profits Falling Occupancy Will Hurt REIT Profits Falling Occupancy Will Hurt REIT Profits Our VI suggests that REITs are modestly overvalued, though the recent outperformance has driven our TI to an overbought condition (Chart 29). S&P Consumer Discretionary (Underweight) Our consumer discretionary CMI has ticked up recently, pushed higher by resiliency in consumer data. However, the S&P consumer discretionary index has clearly responded, pushing against 40-year highs relative to the S&P 500 and taking our VI to two standard deviations above fair value (Chart 31). Much of this should be attributed to Amazon (roughly 30% of the S&P consumer discretionary index) and their exceptional 12% outperformance relative to the broad market over the past year. Chart 31S&P Consumer Discretionary (Underweight) S&P Consumer Discretionary (Underweight) S&P Consumer Discretionary (Underweight) While we have an underweight recommendation on the S&P consumer discretionary index, we have varying intra-segment preferences, highlighted by the recent inception of a pair trade going long homebuilders and short home improvement retailers (HIR). Housing starts and building permits are extremely sensitive to interest rates, depend on first time home buyers and move in lockstep with the homeownership rate. Currently, interest rates are easing, the homeownership rate is coming out of its GFC funk and first time home buyers are slated to make a comeback this spring selling season. This is a boon for homebuilders at the expense of HIR (top & middle panels, Chart 32). Further, the price of lumber is a key determinant of relative profitability: lumber represents an input cost to homebuilders whereas it is an important selling item in Big Box building & supply retailers that make a set margin on it. The recent drubbing in lumber prices should ease margin pressures on homebuilders but eat into HIR profits (momentum in lumber prices shown inverted and advanced in bottom panel, Chart 32). Chart 32Long Homebuilders / Short Home Improvement Retailers Long Homebuilders / Short Home Improvement Retailers Long Homebuilders / Short Home Improvement Retailers S&P Communication Services (Underweight) As the newly-minted communication services has little more than four months of existence, we do not have adequate history to create a cyclical macro indicator. However, we have created Chart 33 with a number of valuation indicators, though we caution that they too are less reliable than the other indicators presented in the preceding pages, owing to a dearth of history. Chart 33S&P Communication Services (Underweight) S&P Communication Services (Underweight) S&P Communication Services (Underweight) Rather, we refer readers to our still-fresh initiation of coverage on the sector3 and look forward to being able to deliver something more substantive in the future. Size Indicator (Favor Large Vs. Small Caps) Our size CMI has been hovering near the boom/bust line, as it has for most of the last two years (Chart 34). Despite the neutral CMI reading, we downgraded small caps in the middle of last year,4 and moved to a large cap preference, based on the diverging (and unsustainable) debt levels of small caps vs. their large cap peers (bottom panel, Chart 35). This size bias remains a high conviction call for 2019. Chart 34Favor Large Vs. Small Caps Favor Large Vs. Small Caps Favor Large Vs. Small Caps Macro data too has turned against small caps. Recent NFIB surveys have shown that small business optimism has continued to fall through the end of the year, albeit from a very high level (top panel, Chart 35). This has coincided with the continued slide of small cap stocks relative to their large cap peers. Chart 35Small Caps Have A Big Balance Sheet Problem Small Caps Have A Big Balance Sheet Problem Small Caps Have A Big Balance Sheet Problem Further, the percentage of small businesses with planned labor compensation increases continues to set new all-time highs and deviates substantially from the national trend (second panel, Chart 35). This divergence becomes more worrying when plotted against those same firms increasing prices (third panel, Chart 35), which has trailed for some time and recently flattened. The inference is that margin pressure is intensifying and likely to continue for the foreseeable future. In the context of the absence of small cap balance sheet discipline during the past five years, ongoing large cap outperformance seems ever more likely. Footnotes 1      Please see BCA U.S. Equity Strategy Weekly Report, “ Catharsis,” dated January 14, 2019, available at uses.bcaresearch.com. 2      Please see BCA U.S. Equity Strategy Weekly Report, “ Don't Fight The PBoC,” dated February 4, 2019, available at uses.bcaresearch.com. 3      Please see BCA U.S. Equity Strategy Daily Insight, “New Lines Of Communication,” dated October 1, 2018, available at uses.bcaresearch.com. 4      Please see BCA U.S. Equity Strategy Daily Insight, “Small Caps Have A Big Balance Sheet Problem,” dated May 10, 2018, available at uses.bcaresearch.com.