Equities

Special Report

Highlights So What? China’s January credit data suggest that stimulus is here. Why? January credit growth was a blowout number. Trade uncertainty is likely to be prolonged with an extension of talks. Equity bourses in South Korea and Russia are the most likely to benefit from Chinese stimulus. Industrial metals such as copper will also benefit – with a delay. Feature New credit data for China in January improves the chances that Beijing’s stimulus measures will overshoot this year, causing China’s economy to bottom in 2019 and jumpstart global growth. In our annual outlook for this year we argued that while China was stimulating the economy, the magnitude of stimulus would be the decisive factor for the global macro environment in 2019. We argued that the type of stimulus would remain primarily fiscal – tax cuts for households and small and medium-sized enterprises – and hence that it would be modest as fiscal easing would merely offset relatively weak credit growth. This view stemmed from our assessment of the Xi Jinping administration, highlighted in April 2017, as an “elitist” (not populist) administration. Its policy priorities are to discipline the Chinese economy, and in particular to contain systemic financial risk, which President Xi has cited as a national security threat. This view is not wrong, but the latest data clearly show that Xi has decided to pause these painful efforts at limiting leverage and rebalancing China’s economy. Witness January’s decisive uptick in both total social financing (total private credit) and local government bond issuance (Chart 1). Chart 1Higher Risk Of An Overshoot A massive spike in new credit is the single most important criterion in our “Checklist For A Stimulus Overshoot.” Thus, from a policy perspective, we are now at higher risk of an overshoot (Table 1). Not only credit as a whole but also informal lending saw a surge in January, implying that the government is relenting in its crackdown on the shadow banks. The approval of local government bond issuance for early in the year – and the People’s Bank of China’s announcement of a “Central Bank Bills Swap” program – reinforce this policy shift.1 Table 1Checklist For A Chinese Stimulus Overshoot In 2019   A stimulus overshoot is positive for Chinese demand in the short run but negative for potential GDP in the long run. A “traditional” credit surge of this nature cannot be surgically targeted at SMEs or households. It will go to state-owned enterprises, privileged corporations, property developers, and the like, which have always had the advantage in China’s financial system. SOEs have taken a much larger share of new loans than private companies in recent years,2 and the only silver lining of this trend was the possibility that tighter credit controls would discipline the SOEs. That silver lining is now fading, barring some new and surprising development on the reform front. China needs to create 26 trillion renminbi in new credit over the course of the year to avoid a corporate earnings contraction. These January numbers put China on track to do just that (Chart 2), assuming that President Xi and U.S. President Donald Trump agree to a short-term, framework trade deal this year. Chart 2On Track To Avoid An Earnings Contraction Of course, a few caveats are in order. First, January’s credit number is only one data point and credit growth is always abnormally strong in the first month of the year. Early in the year, banks seek to expand their assets rapidly in a bid to get as much market share as possible before administrative credit quotas kick in. Because of Chinese New Year, it is best to combine January and February data to get a sense of the rate of credit expansion in the first part of the year. To do that, investors will have to wait for mid-March when the February data is out. This year’s January numbers are very strong relative to previous Januaries (Chart 3) and the context is more accommodative than the 2017 January credit surge, when authorities were beginning to tighten rather than ease macroprudential policy. Still a rapid rate of credit expansion will have to be sustained in the coming months in order to meet the 26 trillion RMB requirement highlighted above. Second, there is some risk that China’s households and private businesses will not respond as positively today as in the past. The intensification of Communist Party control over the society and economy, President Xi’s cancellation of term limits, and the strategic confrontation with the United States have created a bearish sentiment in the private sector. Our Emerging Markets Strategy would point out that if the propensity to consume, and money velocity,3 do not accelerate, then a surge in new credit may fail to ignite a reacceleration in China (Chart 4). Chart 4Chinese Are Holding On To Their Money Still, what we now know is that Xi Jinping and his top economic adviser, Vice Premier Liu He, are not initiating the “assault phase of reform” that their predecessors initiated in the late 1990s in order to cleanse China’s economy of bad loans and zombie companies. Instead, they are likely reestablishing the “Socialist Put” in order to reverse the current deceleration, demonstrate China’s continued economic might and face down the United States’ threat of tariffs. Bottom Line: China’s stimulus measures are increasingly likely to overshoot, with positive implications for both Chinese and global growth. China is still facing a corporate earnings recession, but the odds of averting it are increasing.    Trade Deadline More Likely To Be Extended What of the trade war? First, we would warn clients that China’s annual credit origination is a much bigger factor for the global economy than China’s exports to the United States (Chart 5). The trade war can escalate from here and yet, if China’s stimulus works as it has in the past, the results will be manageable for China’s economy save for Chinese companies expressly exposed to the U.S. economy through exports. In reality, both the U.S. and China are now effectively stimulating their economies and in this sense global trade as a whole will benefit regardless of bilateral tariffs. Chart 5Watch China Credit, Not So Much The Trade War But it is possible that just as global equity markets ignored China’s economic slowdown and only sold off when the tariffs were levied (Chart 6), they may not continue to rally much on China’s credit data. Given the already considerable rally in global risk assets since October, markets may not be satisfied merely with one or two months of solid credit data out of China without a clear resolution to the trade conflict. After all, if a collapse in U.S.-China trade talks portends a new Cold War, then institutional investors may be justified in taking a wait-and-see approach despite China’s credit cycle upswing. Chart 6Will Equities Ignore China Data (Again)? In the past, we have highlighted that the U.S. and China are not economically prohibited from engaging in a trade war – the export exposure is too small – and China’s new stimulus reinforces this point. However, President Trump is concerned about causing a sell-off in the tech sector and hence the broad equity market which could translate into a bear market and raise the probability of a recession occurring prior to November 2020. Meanwhile, in China, given Beijing’s reported trade concessions, there is apparently a desire to pacify the relationship and discourage U.S. unilateral tariffs and sanctions that could become seriously destabilizing for the Chinese economy and society. The need to have a happy 2021 centenary celebration for the Communist Party may factor into policymakers’ thinking. The latest news flow is mildly positive for the odds of getting a framework deal sometime this year. President Trump visited the Chinese negotiators in Washington, D.C. while President Xi reciprocated with the American negotiators in Beijing. Trump has signaled that an extension of the March 1 deadline is possible, and a two-month extension is being bandied about in the press. China’s National People’s Congress is likely to pass a new Foreign Investment Law that ostensibly guarantees many of the American demands on forced tech transfer, intellectual property theft, and discriminatory treatment of U.S. companies (Table 2). Even the second Trump summit with Kim Jong Un, this time in Vietnam, should be seen as a mild positive for U.S.-China negotiations. Table 2New Foreign Investment Law Would Be A Positive For U.S.-China Negotiations However, Presidents Trump and Xi have yet to schedule a new summit, which is probably necessary for a final deal. And there are murmurs from the press suggesting that China’s new law and other concessions are not going to satisfy the U.S. negotiators on the critical point of “structural changes” and a verification process. This leaves us inclined to change our trade war probabilities to increase the odds of an extension (Table 3). The improvement in U.S. financial conditions and China’s stimulus, if anything, make it more likely that negotiations will be extended, as both sides feel their economic and financial constraints less acutely. Table 3Updated Trade War Probabilities Bottom Line: Global and Chinese risk assets should rally on China’s credit uptick, but the lack of resolution of the trade war could continue to inhibit animal spirits – and the odds of a March 1 resolution are declining. Who Are The Equity Winners Of China’s Stimulus? China’s strong January credit number is supportive of global equity markets. That much is obvious. But which equity markets will benefit the most? In what follows we examine the relationship between Chinese credit and MSCI equity returns of various countries. We find that Malaysian, Australian, South Korean, and Indonesian equities are the most highly correlated with Chinese credit growth and are thus most likely to benefit from the recent upturn (Chart 7). On the other hand, France and Italy stand out as countries whose bourses are more insulated. Out of the markets that are positively correlated, South Korea and Russia stand out as relatively cheap (Chart 8). Thus we expect these equities to do especially well. By contrast, while Indonesia and the Philippines are highly leveraged to China, these markets are currently relatively expensive. BCA’s Emerging Markets Strategy is currently overweight Korean and Russian equities within the EM space, neutral Turkey (although recently upgraded from underweight), and underweight Indonesia and the Philippines. In addition to credit stimulus, we expect Chinese household consumption to also gain support going forward. This will likely be driven by policy stimulus targeting the consumer specifically and is best exemplified by the recently announced tax cuts (Chart 9), which we expect to trickle down to greater consumer demand and growth in retail sales. Our base case calls for 8%-10% growth in household consumption over the coming 12 months, up from the current 3.5%. However, consumer sentiment in China is weak. BCA’s Emerging Markets Strategy’s proxy for household marginal propensity to spend ticked up recently, after falling since early last year (see Chart 4 above). A resumption in the decline would highlight that households are increasingly unwilling to spend, which would translate into weaker retail sales despite policy efforts to boost consumption. Such a scenario – in which credit growth accelerates without a substantial uptick in consumer spending – is plausible, given that it occurred between mid-2015 and mid-2016 (Chart 10). In any case, whether Chinese stimulus comes in the form of the traditional credit channel, or instead in the form of fiscal stimulus to household consumption, the same equity markets will generally benefit the most (Chart 11). Chart 10...But Flattish Retail Sales Are Also A Possibility Indeed, global equity markets react the same way regardless of the type of stimulus implemented. For instance, MSCI returns for the Philippines, Sweden, Malaysia, Indonesia, and Turkey are more closely correlated to both Chinese credit growth and retail sales growth compared to Italy, Japan, and France.  The same conclusion is reached when we look at the correlations between Chinese credit growth or consumption growth and individual MSCI sectors such as industrials and consumer discretionary (Chart 12). The relatively stronger correlation between Chinese credit growth and equity returns – as opposed to Chinese retail sales and equity returns – can be put down to the nature of Chinese imports. While industrial goods account for the bulk of China’s purchases of foreign goods, consumer goods excluding autos make up only 15% of China’s imports (Table 4). However, as Chart 12 illustrates, the relationship between China’s retail sales growth and global equities is much tighter in the case of the consumer discretionary sector, whether the latter is compared to global industrials sectors or the overall MSCI index. Table 4Import Composition Of Chinese Imports Equity market exposure to China is not always in line with the extent of each country’s trade exposure to China (Chart 13). There are some clear exceptions – most notably Mexico, which has the highest correlation coefficient with Chinese credit and consumption variables since 2010. However, this is likely due to idiosyncratic factors.4 Correlation does not imply causation, and we cannot conclude with certainty that Mexican equities will outperform amid China’s new round of stimulus. Nevertheless, given that Mexico is a very deeply liquid market that benefits amid EM bull markets, this may not be entirely coincidental. The correlations between global equity markets and Chinese credit peak two months after the stimulus measures are first implemented (Chart 14). This is more or less in line with adjusted total social financing’s correlation versus industrial metals. However BCA’s Commodity & Energy Strategy has shown that copper’s correlations versus other measures of Chinese money and credit peak after roughly three quarters (Chart 15).5 This is evident in both the 2012 and 2015-16 stimulus episodes in which the bottom in copper prices lagged the bottom in China’s credit growth. Thus we may witness a rebound in equity markets on the back of China’s credit splurge before we see an improvement in annual returns on copper prices.  Chart 15Copper Rallies Lag China Credit Stimulus Bottom Line: South Korean and Russian equities are best positioned to benefit from the positive surprise in China’s credit data. France and Italy are the worst positioned. Copper prices will rebound with a delay.  Investment Implications BCA’s Geopolitical Strategy recommends that investors stay long Chinese equities ex-tech relative to the emerging market benchmark. This is a tactical call initiated in August 2018 that is now becoming a cyclical call on the basis of the credit upswing. We also remain long the “China Play Index,” a basket of China-sensitive assets, and long China’s “Big Five” banks relative to other banks. A rebound in China’s credit data and stronger global growth will support copper demand. Prices are still 15% below the mid-2018 peak and are poised to benefit in this environment, especially given that global inventories are already falling. BCA’s Geopolitical Strategy recommends that investors go long copper. Meanwhile, BCA’s China Investment Strategy recommends (for now) staying only tactically overweight Chinese equities relative to the global benchmark, pending higher conviction that the pace of credit growth will be strong enough to overwhelm the negative ramifications of a continued deceleration in actual activity over the coming few months on sentiment and 12-month forward earnings expectations. Over the long run, Geopolitical Strategy would look to underweight Chinese equities, as we are not optimistic about China’s productivity and potential GDP. This is because of the negative structural consequences of continuing the Socialist Put (i.e., bad loans, zombie companies, trade protectionism).  We would expect CNY/USD to remain relatively buoyant in the context of both trade negotiations with the U.S. and fiscal-and-credit stimulus. The trade talks can hardly succeed if CNY/USD is falling. Depending on whether and how soon China’s stimulus results in a durable economic bottom, global growth could stabilize and the USD could see a substantial countertrend selloff.   Matt Gertken, Vice President Geopolitical Strategy mattg@bcaresearch.com Roukaya Ibrahim, Editor/Strategist roukayai@bcaresearch.com   Footnotes 1          Please see Emerging Markets Strategy Special Report titled “China: Prepping A Bazooka?” dated February 14, 2019 available at ems.bcaresearch.com 2      Please see Nicholas Lardy, “The State Strikes Back: The End Of Economic Reform In China?” Peterson Institute For International Economics, January 29, 2019, available at piie.com. 3          Please see Emerging Markets Strategy Weekly Report titled “Dissecting China’s Stimulus,” dated January 17, 2019 available at ems.bcaresearch.com 4       The 2012 election of President Enrique Peña Nieto caused Mexican equities to outperform their EM counterparts. Similarly in 2015-16, U.S. outperformance relative to EM also supported Mexico relative to EM because Mexico’s economy is highly leveraged to its northern neighbor. In both periods Mexico’s outperformance was not caused by – but instead coincided with – Chinese credit stimulus. These idiosyncratic events biased the correlation between Mexico’s equity markets and Chinese credit growth to the upside. 5      Please see Commodity & Energy Strategy Weekly Report titled “Trade Wars, China Credit Policy Will Roil Global Copper Markets,” dated June 21, 2018, available at ces.bcaresearch.com.                  

Underweight In mid-2017, we went underweight the S&P pharma index and booked healthy gains roughly a year later when we lifted exposure to neutral. Since then, Big Pharma has enjoyed a reprieve on the back of congressional inaction and the fact that the Trump Administration’s drug pricing wrath was less severe than initially feared. However, the time has come to trim the S&P pharma index to underweight. The top panel shows that pharmaceutical companies have been nearly uninterruptedly raising prices for the past four decades. Higher selling prices have been synonymous with higher profits and thus higher share prices. However, profit margins crested in the midst of the late-1990’s M&A boom and have never reclaimed their previous zenith (middle panel). Neither have relative share prices. Worryingly, pharma prices have hit a wall during the past four years and can barely keep up with overall inflation, despite still being opaque (bottom panel). As both Democrats and Republicans are united to bring down health care costs in general and drug prices in particular, pharma profits will likely suffer a secular downdraft. The implication is that, as pharma revenues erode they will deal a blow to profits. Consequently, the outlook for relative share prices is dim. Bottom Line: We trimmed the S&P pharma index to underweight yesterday; please see our Weekly Report for more details. The ticker symbols for the stocks in this index are: BLBG: S5PHAR – JNJ, PFE, MRK, LLY, BMY, ZTS, AGN, MYL, NKTR, PRGO.

Overweight Biotech stocks have been the center of attention recently as the BMY/CELG deal put the whole sector in play, and yesterday we boosted exposure to overweight in the S&P biotech index. We doubt the merger mania is over and we continue to believe that more mega deals are in store, either intra or inter-industry, with Big Pharma hungry and in a hurry to replenish their drug pipeline. In our Weekly Report, we highlight a number of positive catalysts that can propel the S&P biotech index higher but surprisingly, the sell-side community does not share our enthusiasm. Relative profit growth is forecast to be nil in the next year. In the coming five years, biotech stocks are expected to trail the overall market’s profit growth by 4%/annum (second panel, Chart 8). This is extremely pessimistic and a first in the 24-year history of the I/B/E/S data set, and it is contrarily positive. Relative revenue growth forecasts are also grim for the upcoming 12 months and both revenue and profit forecasts present low hurdles to overcome (third panel). Meanwhile, from a valuation perspective, the S&P biotech index trades at a 25% discount to the SPX forward P/E and well below the historical mean (bottom panel). Bottom Line: We lifted the S&P biotech index to overweight yesterday; please see our Weekly Report for more details. The ticker symbols for the stocks in this index are: BLBG: S5BIOT – ABBV, AMGN, GILD, BIIB, CELG, VRTX, REGN, ALXN, INCY.

Special Report

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. 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. 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:   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). 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).  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.  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. 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: 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. 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.

Highlights Portfolio Strategy The path of least resistance is higher for the broad equity market on the back of a reflationary impulse and a less dogmatic Fed. Now that the SPX forward EPS bar has been lowered to the ground, upward surprises loom, especially if the third catalyst we have been highlighting in recent research materializes: a positive resolution to the U.S./China trade spat. The recent M&A fever, a less dogmatic Fed that has suppressed the 10-year Treasury yield and a pick up in the U.S. credit impulse can serve as catalysts to unlock excellent value in the S&P biotech index. Upgrade to overweight. A profit margin squeeze on the back of soft pharma pricing power, weak operating conditions and a race to buy out biotech stocks to build up drug pipelines warn that the derating phase has just began for the S&P pharma index. Downgrade to underweight. Recent Changes Boost the S&P biotech index to overweight today. Trim the S&P pharma index to underweight today. Table 1 Featured The S&P 500 has been flirting with its 200 day moving average and once it categorically clears this hurdle there are high odds that previous resistance will turn into support. The next important level is 2,800, as we highlighted in recent research, a level where the SPX failed numerous times last year.1 Encouragingly, the character of the market has changed from December’s extreme daily weakness to this year’s significant daily resilience. As we first posited on January 18, while everyone is looking for a retest to re-enter the equity market, we already had the retest in December and are now in a slingshot recovery eerily similar to the 2016 and 1998 episodes.2 Importantly, what has changed since the post-December Fed meeting carnage is that the bond market has completely priced out Fed hikes for 2019 and the 10-year Treasury yield is 15bps lower. Chart 1 highlights this reflationary backdrop for U.S. stocks. Our proprietary Reflation Gauge (RG, comprising oil prices, interest rates and the U.S. dollar) is probing levels last hit in 2012. Historically, our RG and equity momentum have been joined at the hip and the current message is to expect a rebound in the latter. Chart 1Heed The Reflation Message The latest ISM manufacturing survey also corroborates the signal from our RG. The jump in the ISM new orders-to-inventories ratio underscores that the rebound in stocks has further to run (bottom panel, Chart 1). Granted, a lot rests on EPS and in order for stocks to propel to fresh all-time highs later this year, as we expect, profits will have to deliver. On that front, despite recent steep downward EPS revisions across the board, we believe the level of quarterly EPS will hit fresh all-time highs in the back half of the year, carrying stocks into uncharted territory (Chart 2). As a reminder, BCA’s view remains that the U.S. will avoid recession in 2019. Chart 2Joined At The Hip One key profit driver that has put pressure on recent earnings releases and will continue to weigh on internationally-exposed P&Ls is the greenback. With a delayed effect, the first two quarters of this year should bear the brunt of last year’s steep U.S. dollar climb, but that effect will reverse in the back half of 2019. Not only is the greenback inversely correlated with the SPX, but also with the global manufacturing PMI (trade-weighted U.S. dollar shown inverted and advanced, Chart 3). Chart 3Dollar The Reflator... Thus, the greenback is a key macro variable that we are closely monitoring. On that front, global U.S. dollar based liquidity is one of the most important determinants/drivers of global growth. The longer U.S. dollar liquidity gets drained, the more downward pressure it will put on SPX momentum and SPX EPS (Chart 4). Once U.S. dollar based liquidity starts to get replenished at the margin, it can serve as a catalyst for a global growth recovery. A Fed tightening cycle pause and recent acknowledgment that the balance sheet asset roll off is important and the Fed stands ready to tweak it, are a net positive for at least a trough in global U.S. dollar liquidity. Chart 4...But Watch Global Dollar Liquidity Adding it up, the path of least resistance is higher for the broad equity market on the back of a reflationary impulse and a less dogmatic Fed. Now that the SPX forward EPS bar has been lowered to the ground, upward surprises loom, especially if the third catalyst we have been highlighting in recent research materializes: a positive resolution to the U.S./China trade spat.3 This week we make a couple of subsurface changes to a defensive sector; these changes do not alter our recommended benchmark allocation to the overall sector. Biotech’s Gain Is... Biotech stocks have been the center of attention recently as the BMY/CELG deal put the whole sector in play, and today we are boosting exposure to overweight in the S&P biotech index. We doubt the merger mania is over and we continue to believe that more mega deals are in store, either intra or inter-industry, with Big Pharma hungry and in a hurry to replenish their drug pipeline. While this is not the sole reason for an above benchmark allocation, 50-60% M&A deal premia are a boon for investors (Chart 5). Chart 5M&A Frenzy From a long-term macro perspective biotech stocks have been the primary beneficiaries of the 35-year bond bull market. In other words, the multi-decade grind lower in the U.S. Treasury yield has been synonymous with biotech outperformance (10-year U.S. Treasury yield shown inverted, Chart 6). Chart 6Biotech Equities And Rates Move In Opposite Direction The Fed’s recent monetary policy U-turn is a welcome development and these high growth stocks will benefit from the 55bps fall in the 10-year Treasury yield since the early-November peak. In addition, another macro tailwind is working in the S&P biotech index’s favor. The resurgent U.S. credit impulse is unambiguously bullish for this health care index that excels when margin debt availability is rising and liquidity is plentiful (bottom panel, Chart 7). Chart 7Revving Credit Impulse Says Buy Biotech Stocks Surprisingly, the sell-side community does not share our enthusiasm on any of these positive catalysts. Relative profit growth is forecast to be nil in the next year. In the coming five years, biotech stocks are expected to trail the overall market’s profit growth by 4%/annum (middle panel, Chart 8). This is extremely pessimistic and a first in the 24-year history of the I/B/E/S data set, and it is contrarily positive. Relative revenue growth forecasts are also grim for the upcoming 12 months and both revenue and profit forecasts present low hurdles to overcome (fourth panel, Chart 8). Chart 8Analysts Have Thrown In The Towel With regard to technicals and valuations, investors are doubtful that biotech stocks can stage a playable turnaround. Cyclical momentum remains moribund, printing below the zero line. Meanwhile, the S&P biotech index trades at a 25% discount to the SPX forward P/E and well below the historical mean (second & bottom panels, Chart 8). Chart 9 shows that biotech stocks are also cheap on a relative dividend yield basis. The S&P biotech index has been so oversold that it now sports a dividend yield higher than the S&P 500. Nevertheless, there is one key risk we are closely monitoring. Biotech initial public offerings are at all-time highs, with private equity and venture capital funds rushing for the exit doors. This is worrisome as it offsets the supply reduction owing to the M&A fever and has historically coincided with biotech relative share price peaks (Chart 10). Chart 9Compelling Relative Value Chart 10Watch This Risk Netting it all out, the recent M&A fever, a less dogmatic Fed that has suppressed the 10-year Treasury yield and a pick up in the U.S. credit impulse can serve as catalysts to unlock excellent value in the S&P biotech index. Bottom Line: Boost the S&P biotech index to overweight today. The ticker symbols for the stocks in this index are: BLBG: S5BIOT – ABBV, AMGN, GILD, BIIB, CELG, VRTX, REGN, ALXN, INCY. …Pharma’s Pain In mid-2017 we went underweight the S&P pharma index and booked healthy gains roughly a year later when we lifted exposure to neutral. Since then, Big Pharma has enjoyed a reprieve on the back of congressional inaction and the fact that the Trump Administration’s drug pricing wrath was less severe than initially feared. However, the time has come to trim the S&P pharma index to underweight. Chart 11 shows that pharmaceutical companies have been nearly uninterruptedly raising prices for the past four decades. Higher selling prices have been synonymous with higher profits and thus higher share prices. Chart 11Margin Trouble But, something happened in the new millennium. Relative performance peaked as pharma embarked on a mega M&A boom in the late-1990s with the Pfizer/Warner Lambert deal breaking all-time industry M&A records. Why? Because profit margins crested and have never reclaimed their previous zenith (top and middle panels, Chart 11). Neither have relative share prices. Worryingly, pharma prices have hit a wall during the past four years and can barely keep up with overall inflation, despite still being opaque (bottom panel, Chart 11). As both Democrats and Republicans are united to bring down health care costs in general and drug prices in particular, pharma profits will likely suffer a secular downdraft. The implication is that, as pharma revenues erode they will deal a blow to profits. Consequently, the outlook for relative share prices is dim. Importantly, pharma executives have not been frugal enough to offset the soft pricing power backdrop. Headcount has been expanding consistently since 2012 and a wide gap has opened up relative to industry selling price inflation, akin to the one in the mid-2000s that suppressed relative share prices (Chart 12). Chart 12Pricing Power Pressure Similar to the M&A boom of the late-1990s, there has been a global pharma M&A race with multiple deal announcements in the past few months, underscoring that the industry is not standing still. As Big Pharma CEOs compete to outdo their peers and buy drug pipelines mostly in the biotech space (Chart 5), they will continue to degrade the industry balance sheet (third panel, Chart 12). Our strategy is to overweight the hunted (biotech) and avoid the hunters (Big Pharma). On the operating front, a supply check reveals that pharma wholesale and manufacturing inventories are growing, whereas shipments are on the verge of contraction. Pharma industrial production has petered out and industry productivity gains are waning (Chart 13). This deteriorating operating backdrop will weigh on relative profits. Chart 13Deteriorating Operating Metrics... With regard to the macro front, a vibrant U.S. economy – with the ISM manufacturing survey ticking higher and the labor market firing on all cylinders – suggests that defensive pharma relative profits will resume their downtrend (bottom panel, Chart 13). Tack on the U.S. dollar’s reversal since the November peak and defensive pharma equities will remain under pressure (second panel, Chart 14). Chart 14...But EPS Bar Is On The Floor Nevertheless, there are three risks to our negative S&P pharma view. First, the M&A fever dies down and there are no additional purchases of biotech outfits. Second, Congress and the President drag their feet and fail to agree on new hawkish pharma pricing legislation. Finally, sell-side analysts have thrown in the towel and maybe most of the bad news is reflected in bombed out relative profit and sales growth estimates (third & fourth panels, Chart 14). In sum, a profit margin squeeze on the back of soft pharma pricing power, weak operating conditions and a race to buy out biotech stocks to build up drug pipelines warn that the derating phase (bottom panel, Chart 14) has just began for the S&P pharma index. Downgrade to underweight. Bottom Line: Trim the S&P pharma index to underweight. The ticker symbols for the stocks in this index are: BLBG: S5PHAR – JNJ, PFE, MRK, LLY, BMY, ZTS, AGN, MYL, NKTR, PRGO. Health Care Remains In The Neutral Column Despite these two subsurface health care sector moves, our overall exposure to the S&P health care sector remains intact at neutral. Please look forward to reading our upcoming research where we will be updating the S&P managed health care, S&P health care facilities and S&P health care equipment subsectors.   Anastasios Avgeriou, Vice President U.S. Equity Strategy anastasios@bcaresearch.com Footnotes 1      Please see BCA U.S. Equity Strategy Weekly Report, “Trader’s Paradise” dated January 28, 2019, available at uses.bcaresearch.com. 2      Please see BCA U.S. Equity Strategy Insight Report, “Don’t Bet On A Retest” dated January 18, 2019, available at uses.bcaresearch.com. 3      Please see BCA U.S. Equity Strategy Weekly Report, “Dissecting 2019 Earnings” dated January 22, 2019, available at uses.bcaresearch.com. Current Recommendations Current Trades Size And Style Views Favor value over growth Favor large over small caps

Highlights Stay tactically overweight to equities for the time being. Close the overweight to industrial commodities versus equities. The financials, basic resources, and industrials equity sectors can continue to outperform for a few months longer. EM can also continue to outperform DM for a few months longer. Overweight Germany’s DAX versus German bunds. The second half of the year is going to be much tougher than the first half. Feature Chart of the WeekPessimism Was Overdone: The Classical Cyclicals And EM Are Rebounding Locked In An Intimate Embrace Last week, we highlighted a frustrating truth: for the past 16 months the broad equity market has been on a journey to nowhere. Yet the journey has been far from boring. There have been exciting detours of 10-15 percent in both directions, albeit these moves have been short-lived, lasting no more than three months at a time. The same truth applies to the broad bond market: for the past sixteen months the global long bond yield – defined here as the average of the yields on the 30-year German bund yield and 30-year T-bond – has also ended up going nowhere. On this journey too, there have been exciting detours of up to 50 basis points in both directions, but these moves have also lasted no more than three months before retracing. It follows that for the past 16 months, the strategic allocation to equities, bonds and cash has had zero impact on investment performance, but the tactical allocation to the asset classes has had a huge impact. Yet here’s the thing: the sharp tactical moves in the bond market and in the stock market have been intimately embraced. When the global long bond yield has approached the top of its range, it has catalysed a sharp sell-off in equities; and when the bond yield has approached the bottom of its range, it has catalysed a sharp rally in equities (Chart I-2). In fact, over the past 16 months, asset allocation has boiled down to a very simple trading rule based on the global long bond yield: above 2.2 percent, sell equities; below 1.95 percent, buy equities. Today, the yield stands at 1.85 percent, suggesting a tactically overweight stance to equities. Chart I-2The Sharp Tactical Moves In The Bond Market And Stock Market Are Intimately Connected The Persistent Trends Are In Sectors Some investors cannot shift their portfolios quickly enough to exploit the tactical opportunities in the markets. They need trends that persist for at least six months to a year. The good news is that these more persistent trends do exist, but to find them you have to look at equity sectors, and specifically the classically cyclical sectors (Chart of the Week). The financials and basic resources sectors were in strong relative downtrends through most of 2018; but for the last four months these classically cyclical sectors have flipped into very clear uptrends (Chart I-3 and Chart I-4). The same is true for industrials, albeit the end of the downtrend has happened more recently (Chart I-5). Chart I-3Financials Are Rebounding Chart I-4Basic Resources Are Rebounding Chart I-5Industrials Are Rebounding For the avoidance of doubt, technology is not a classically cyclical sector because the sales of technology products – particularly to consumers – are relatively insensitive to short-term fluctuations in the economy. In fact, the relative performance of technology is an almost perfect mirror-image of financials (Chart I-6). Chart I-6The Technology Sector Is Not A Classical Cyclical Neither is the chemicals sector a classical cyclical. Given that raw material prices are an input cost for chemical manufacturers, the chemicals sector can underperform when raw material prices are rising in a cyclical up-oscillation (Chart I-7). It follows that the three true classically cyclical sectors are: financials, basic resources and industrials. Chart I-7The Chemicals Sector Is Not A Classical Cyclical What if your investment process does not allow you to invest in sectors and benefit from their well-defined and longer trends? The good news is that you can play these same trends through regional and country stock market indexes. We refer readers to previous reports for the details, but the crucial message is that regional and country relative performances stem from nothing more than the stock markets’ defining sector skews combined with sector relative performances.1 This revelation of what truly drives regional and country relative performance is bittersweet. It is sweet because it simplifies an investment process that can be very complicated. But it is also bitter because it highlights that the investment industry is still replete with unnecessary layers of complexity. Still, just to drive home the point, we would like the charts to do the talking. The relative performance of financials, the relative performance of Italy’s MIB, and the relative performance of Emerging Markets (EM) versus Developed Markets (DM) are all effectively one and the same story (Chart I-8 and Chart I-9). Chart I-8One And The Same Story: Financials And Italy... Chart I-9...And Financials And EM Versus DM What Are The Markets Telling Us, And Do We Agree? Another very common question we get is: what is our forecast for economic growth and profits growth? For example, two questions on everyone’s lips right now are: can Germany avoid a technical recession, and what is our forecast for Germany’s growth from here? These are indeed important questions, but for investors they are not the most important questions. Financial markets are a discounting mechanism. So for investors, the most important question should always be: what is discounted in the current market price, and is that too optimistic or too pessimistic? Over-optimism and over-pessimism on the economy are especially important for the classically cyclical sectors because their profits have a very high operational gearing to their sales: a small change in the sales outcome has a huge impact on the profit outcome and, therefore, the price.  If the price is discounting a booming economy and what actually transpires is that the economy grows modestly, then a seemingly benign outcome of respectable growth will paradoxically cause the price to slump. Conversely, if the price is discounting a very pessimistic outcome and what actually transpires is anything better than the ultra-pessimism, then even a bad outcome will paradoxically cause the price to soar. In this regard, the recent underperformance of Germany’s DAX versus German bunds is at an extreme not far from that during the euro sovereign debt crisis in 2011-12 (Chart I-10). So the important question for investors is: will the actual economic outcome transpire to be as extreme as that? Our answer is that the extreme underperformance of the DAX versus bunds is discounting an overly pessimistic outcome, and on that basis the correct stance is to be overweight the DAX versus bunds.   Chart I-10Overly Pessimistic: The DAX Versus Bunds Turning to the classical cyclicals, these sectors have rebounded because their embedded assumptions for growth reached peak pessimism in October. Since then, the pessimism has abated at the margin because of improving short-term impulses from Chinese stimulus, lower global bond yields, and sharply lower energy prices. Given that positive (and negative) impulse phases reliably tend to last for six to eight months, our expectation is that this tailwind for the classical cyclical sectors – financials, basic resources, and industrials – can continue for a few months longer. Which means that the outperformance of EM versus DM can also continue for a few months longer. In terms of asset allocation, long industrial commodities versus equities worked very powerfully at the end of last year, but the relative merits of the two asset classes are now more evenly balanced. Hence, we are now closing this position in profit. Finally, our major concern is for later in the year when the aforementioned improving short-term impulses will inevitably fade, and even potentially reverse. Bear in mind that the impulses arise from the short-term changes in credit flows, bond yields, and the oil price. It follows that to recreate these positive impulses for later in the year, bond yields and/or the oil price have to keep falling. This is not our base case, so enjoy the positive impulses while they last! As the year progresses the investment environment is going to get much tougher. Fractal Trading System* The sharp underperformance of the Nikkei 225 versus the Hang Seng is at the limit of tight liquidity that has signaled all of the recent trend reversals in this relative position. Accordingly, this week’s recommended trade is to go long the Nikkei 225 versus the Hang Seng. Set a profit target of 4.5 percent with a symmetrical stop-loss. We now have seven open positions. For any investment, excessive trend following and groupthink can reach a natural point of instability, at which point the established trend is highly likely to break down with or without an external catalyst. An early warning sign is the investment’s fractal dimension approaching its natural lower bound. Encouragingly, this trigger has consistently identified countertrend moves of various magnitudes across all asset classes. Chart I-11 The post-June 9, 2016 fractal trading model rules are: When the fractal dimension approaches the lower limit after an investment has been in an established trend it is a potential trigger for a liquidity-triggered trend reversal. Therefore, open a countertrend position. The profit target is a one-third reversal of the preceding 13-week move. Apply a symmetrical stop-loss. Close the position at the profit target or stop-loss. Otherwise close the position after 13 weeks. Use the position size multiple to control risk. The position size will be smaller for more risky positions. * For more details please see the European Investment Strategy Special Report “Fractals, Liquidity & A Trading Model,” dated December 11, 2014, available at eis.bcaresearch.com Dhaval Joshi, Senior Vice President Chief European Investment Strategist dhaval@bcaresearch.com Footnote 1 Please see the European Investment Strategy Weekly Report “Oil, Banks, And Bonds: The Oddities Of 2018”, dated November 29, 2018 available at eis.bcaresearch.com Fractal Trading System Recommendations Asset Allocation Equity Regional and Country Allocation Equity Sector Allocation Bond and Interest Rate Allocation Currency and Other Allocation Closed Fractal Trades Trades Closed Trades Asset Performance Currency & Bond Equity Sector Country Equity Indicators Bond Yields Chart II-1Indicators To Watch - Bond Yields Chart II-2Indicators To Watch - Bond Yields Chart II-3Indicators To Watch - Bond Yields Chart II-4Indicators To Watch - Bond Yields Interest Rate Chart II-5Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch - Interest Rate Expectations Chart II-8Indicators To Watch - Interest Rate Expectations