Market Perspectives Q4 2023

• Our equity positioning remains at a modest underweight; we are cautious over the short term as we  anticipate better entry points to come. Market leadership has broadened slightly but remains narrow and  continued strength in global equity prices, combined with rising long term bond yields, have compressed  equity risk premiums further. Historically, valuations around current levels have suggested limited  potential returns in the near term unless earnings growth reaccelerates. 

• As resilience in the labour market persists and inflation continues to normalize in line with the  expectations of the Bank of Canada, we believe monetary policy pivoting to rate cuts over the next nine  to 12 months is less likely than is currently priced by investors. This may translate into a slower decline in  interest rates. On the flipside, this may also imply higher for longer income returns within the asset class.  We continue to believe that fixed income will outperform equities over the next 12 months and that bonds  can still provide diversification benefits, reduce overall portfolio volatility and preserve capital. 

• We believe that an allocation to alternative assets can benefit diversified portfolios especially when  implemented over the long-term. Alternative assets can provide inflation protection and attractive  absolute returns, while acting as long-term portfolio stabilizers via their diversification benefits and less  correlated income streams. 

• In recent months, the yield on cash and equivalents has risen alongside further rate hikes from the Bank of Canada. As key economic data continues to normalize further, the risks of additional monetary  policy tightening are now balanced. If monetary policy takes longer to ease than investors currently  expect, there would be less reinvestment risk associated with today’s yield on cash and equivalents.  Therefore, we have a neutral allocation to cash & equivalents.

Quarter In Review/h2

Over the quarter, investors and markets alike  continued to be consumed by questions of timing.  When will we see inflation finally begin to recede,  and when will economies truly enter the slowdown  that indicators seem to have been anticipating for  months now? Answers remain thin on the ground as  indicators remain divided on what the exact path will  be going forward, but we may have seen some early  signs of movement during the quarter and believe we can expect market volatility to persist for the near term.  

In Canada, second quarter gross domestic product  (“GDP”) contracted by 0.2%, easing some concerns  that growth had accelerated substantially above trend in early 2023 (which would have had negative  inflation implications). This GDP contraction reflected  a marked weakening in consumption growth and a  decline in housing activity, but it also meant that the  Bank of Canada (“BoC”) was able to hold interest  rates in their September announcement. This hold  may not be a sign of ongoing relief from hawkish  policy, however, as the BoC has stated that it remains  concerned about core inflation and persistent  inflationary pressures. Markets are still pricing in the  potential for additional interest rate hikes before the  year is out which could have a meaningful effect  throughout the Canadian economy as the debt  burden for Canadians remains high. 

The U.S. is currently in a resilient position with respect to the rest of the world, exhibiting modest  economic growth, but it is not out of the woods yet.  Core inflation still remains above the U.S. Federal  Reserve’s (“the Fed’s”) targets. Tightening credit  conditions are weighing on economic activity, hiring  and inflation, and the extent of the effects on growth  are yet to be seen. Job numbers in August were strong  but manufacturing activity continued to contract for  the tenth consecutive month. Overall government  and consumer spending has strengthened the U.S.  economy, but the Fed is still on guard for shifts in the  sand and isn’t counting out additional interest rate  hikes either.  

Global growth slowed in the second quarter of  2023, largely reflecting a significant deceleration  in China. With ongoing weakness in the property  sector combined with domestic consumer spending  challenges, confidence in the growth prospects in  China have diminished. New policies to support  the real estate sector as well as the sales of  electric vehicles have been announced, but there is  skepticism about how meaningfully these moves will  buoy consumer confidence.  

During the quarter, we were positioned to defend  against continued uncertainty in the market, with a  maximum overweight to fixed income. In August, we  downgraded to a modest overweight in fixed income  and strategically increased our allocation to cash &  equivalents in order to increase optionality and to  be ready to take advantage of opportunities as they  make themselves clear going forward. With respect to  equities, we currently favor North American equities  over the Eurozone, where the growth outlook remains  particularly weak.  

Overall, while we cannot create an exact timeline,  we do not expect the slowdown or the recovery to  be a perfectly straight line. Based on the available  indicators, we anticipate upswings and downswings  

of sentiment and challenging times ahead. This continues to emphasize the importance of active  management to manage risk, reduce volatility and  provide the potential to deliver attractive returns.  Our experienced investment teams at TD (TSX:TD) Asset  Management Inc. (“TDAM”) continue to focus on how to appropriately allocate assets within portfolios  while zeroing in on companies that can generate  consistent profits and provide the best opportunity  for outperformance. As a part of this process,  we consider new innovations, such as artificial  intelligence (“AI”), that are likely to disrupt their fields and create opportunities going forward. In the next section, we will highlight some of the ways  in which we believe we will see AI challenge existing  models of business and what we are watching for going forward. 

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h2 The Impact of AI: A Data-Driven Future/h2

AI might seem as if it has appeared overnight, but that’s not quite true. Academics have been discussing it for  almost a century now, and machine learning, which is the branch of AI most of us think of when we think of AI,  really started to gain interest and traction in the mid-2000s. So why are we only hearing about it now? Simple:  in 2023, metamorphic advances in generative AI technology allowed it to become meaningfully useful for real  world applications, rather than just interesting in a theoretical test environment. 

What we are all currently witnessing is the embryonic stages of an AI adoption transformation that we believe  will profoundly alter the operational dynamics and growth trajectories of countless sectors. AI can process  vast volumes of data, extract meaningful insights and execute tasks with precision. It has already begun  to infiltrate industries and will only continue to do so in the coming years. To really wrap your mind around  how broad the implications are, try and think about how different the world was before and after the internet  became mainstream. While we are in the middle of the AI changeover right now, we believe this is how we  will likely feel about AI in the coming years. And we should also remember that this technology is only in its  infancy. Today, all eyes are on what generative AI can do to supplement human ingenuity and productivity;  tomorrow will invariably bring even further advances and breakthroughs in new areas of this exciting field. It’s a long and promising journey that we here at TDAM are watching closely. 

Below we have highlighted some sectors where we see AI having an impact in the near term and will outline  how we believe this new technology will create value for quality companies ready to utilize the tools of the future.

AI and Agriculture/h2

Farming has traditionally been a manual and labour intensive industry with significant environmental  and social impacts. While responsible for sustaining  human life, agriculture, forestry and related land  use account for nearly a quarter of greenhouse gas  emissions. Despite prolific advances in crop and  farming science and technology, 25% of the global  population still faces food insecurity. Globally, the  industry is under substantial pressure to feed a  growing population while using less energy, less  water and with a shrinking farm labour force. 

The requirement for ever-increasing efficiency  makes agriculture an industry ripe for AI disruption. We believe there is a strong opportunity for AI-driven  improvements in yield, emissions and biodiversity. 

Precision agriculture is the concept of leveraging  large amounts of data to improve the productivity  and precision of farming practices. Companies  like Deere & Company (NYSE:DE) (“John Deere”) have made  significant investments to precision agriculture,  establishing the building blocks for a high-technology  and autonomous future in farming equipment.  Through the integration of sensors, software and  data analytics, John Deere’s machinery and software  stack helps farmers create a digitally informed  ecosystem around their farm. On the machinery side,  innovation goes beyond simply autonomous tractors.  For instance, John Deere’s See & Spray technology  will leverage computer vision and machine learning  to precisely identify weeds and spray herbicides in a targeted manner, avoiding spraying the intended  crops entirely. This reduces herbicide use, increases  crop yields and improves crop and food quality for consumers. 

This is just one example of the numerous innovations  of applied AI in the agriculture industry. 

Farmers will be able to leverage data to better  allocate resources, optimize planting and harvesting  schedules, improve crop yields and reduce resource  consumption, bringing the industry one step closer  to reducing hunger and improving carbon footprints.  

From an investment perspective, precision agriculture  and AI-enabled farming technology will be  transformative to the business model of companies  like John Deere. By 2030, Deere aims to generate 10%  of total enterprise revenues from recurring revenue  streams. The company sees a future where farmers  will pay a per-acre fee to implement and support a  technology stack behind a connected network of  tech-enabled farm equipment. Investors have seen  value created in the software space as companies  transitioned to recurring revenue models, and John  Deere seeks to follow in those footsteps. John Deere is  just one example of a long-standing and entrenched  business model on the brink of transition driven by  AI and complimentary technologies. As seen here,  we believe there is a lot to gain from being aware of  the impact AI will have on business models across all  sectors of the economy.

AI and the Consumer/h2

Not only can AI quickly understand and influence the consumer experience by being able to predict your  next favourite movie or put together a personalized playlist just for you, AI has also started to impact the  fundamentals of consumer companies. AI applications can have significant implications on both revenue and earnings across consumption stages: pre-purchase, during-purchase and post-purchase.  

Pre-purchase:/h3

• eCommerce continues to collect tremendous amounts of data, and the ability to utilize machine learning  algorithms to harness it will only further enhance customized recommendations for consumers. Even  now early adopters are using AI tools to track down the best prices on the web, but if a business can offer  the convenience of solving a complex and undefined search from seemingly limitless options (e.g. “I have  $200, what are the most popular headphones I can buy?”) to a convenient and customized online shelf  for that customer, that will have a significant positive impact on their experience (and thereby likelihood  of purchase and return business). If you are in the business of selling products online, AI will also be able  to help draft product descriptions, design a logo, write a blog post or interpret customer behaviours all  removing supply frictions. 

• On the product discovery side, AI can push the boundaries of human limitations. Using the fashion  industry as an example, creativity is key in driving brand value over time but even the most creative minds  occasionally hit a wall. AI may be able to forecast fashion trends and give fresh perspectives with nearly  infinite patterns and styles after analyzing vast amounts of data from social media and other platforms.  New ideas can be quickly visualized and adjusted, saving time and money in drafting new products.  

During purchase:/h3

• The lodging industry has truly been embracing AI for revenue management. For a long time, hotels have  set real-time pricing. Using predictive modeling to forecast demand and manage room availability can  significantly enhance revenue by selling more rooms at higher prices during peak periods, and reducing  inventory during off-peak periods. For example, Hilton Hotels & Resorts has done a good job of factoring in  historical data, market demand, local events and competitor prices and this has been a benefit to them. 

• Inventory management is one of the most challenging tasks in the consumer sector, especially when  dealing with large volumes. Fast Retailing Co. Ltd., the parent company of Uniqlo Co. Ltd., manufactures  many products in lots of 1 million units so operational costs swing significantly based on demand  variability. They have been leveraging AI in forecasting the right demand at the right time for the right  location, towards the goal of optimizing order fulfilment in the most profitable way. 

Post-purchase:/h3

The proliferation of counterfeit goods has been a persistent challenge for the luxury industry. With increasingly  sophisticated methods to replicate products, traditional detection techniques often fall short. AI has emerged  as a game changer in the fight against counterfeit products. For instance, AI-powered systems can compare  material textures against authentic items to ascertain legitimacy. It can integrate with blockchain technology  to trace and authenticate products. 

AI and Health Care/h2

In the Health Care sector, use cases for AI enhancement range from (I) diagnostics and  treatment planning in areas like cancer, (II) predictive  analytics to help intervene earlier in a disease’s  progression, (III) AI-driven virtual assistants which  can potentially improve patient access while helping  labour-constrained healthcare systems in triaging  patients and (IV) improving surgical outcomes  through more precise robotic surgery. 

That said, out of all the potential use cases, using AI to improve drug discovery is arguably the biggest  opportunity for Health Care.  

Developing a new drug is a difficult endeavor; it’s  expensive, time consuming and the failure rate is  high (approximately 90% of drugs that enter Phase 1  clinical trials ultimately fail). Drug discovery is a multi-factorial problem, with scientists trying to solve  for drug potency, solubility, selectivity, toxicity and  other constraints all at the same time. Tweaking  for one variable can impact another, making drug  discovery kind of like solving a giant Rubik’s Cube.  The average cost to bring a new drug to market is  approximately $3 billion ($US) today compared to  approximately $1 billion ($US) at the turn of the century. 

While AI-driven drug discovery has been talked  about for 30 years, innovation in the field will likely  accelerate over the next decade, encouraged by two  key drivers. The first driver has been the emergence  of powerful and cost-effective GPUs and scalable  cloud infrastructure, which is needed to process the  billions of calculations needed to model potential  drugs in a virtual environment. The second driver has  been the emergence of more accurate application  layers, which can model how potential drugs may  interact in the human body.  

Put together, AI-driven drug discovery has the  potential to shave 2–3 out of the 10 years needed  on average to bring a new drug to market, cut the  number of drugs that need to be tested in the lab  by stress-testing them in a simulated environment  ahead of time, and increase the number of drugs that  ultimately succeed. 

For pharmaceutical and biotechnology companies,  this can ultimately shave hundreds of millions  off the cost of discovering a new drug, while  also accelerating revenue growth by driving new  discoveries. This is also a big win for society as  accelerated drug discovery holds the promise of  developing new cures for previously unmet needs,  ranging from cancer to Alzheimer’s.

What does this mean for Investors?/h2

AI is here to stay. While the buzz may be starting to die down, and it  is possible that those who overestimated how quickly AI would be  implemented may start to feel disappointment in the coming months,  we believe that AI innovation will continue to see growth and contribute  to corporate success in the coming years. We will continue to monitor  the ways in which this theme will impact portfolios. 

As always, we use a combination of proprietary models and a long history of investment experience to help  us assess fundamentals and navigate towards quality. AI is not alone and improving itself in a vacuum;  its possibilities are being ever further expanded by a robust technology system enabled by enormous  contributions from the fields of semiconductors, telecommunications and machine vision, among others. We continue to believe companies who are able to innovate and move with, rather than against, technological  change will continue to outperform companies who remain static and stuck in the past. 

Asset Class Assumptions/h2

To close out the quarter, we were modest overweight to fixed income, modest underweight to equities, a neutral allocation to alternatives and a neutral allocation to cash and cash equivalents. The TD Wealth Asset  Allocation Committee meets monthly and will make necessary strategic adjustments to asset class views as  the environment unfolds 

Written By: TD Asset Management