This article was first published on the Harvest Exchange by Valens Research
Valens Research would like to share with you the Litman Letter for November 2016, which discusses how the surprise US election results earlier this month could spell continued upside potential for the market, even after the post-election run.
Specifically, a combination of an “all red” House and Senate and a Donald Trump presidency spells a lot of reasons for optimism for the market. Everything from infrastructure to regulation to, most importantly but least spoken about so far for valuation purposes, taxes are areas where an all Republican Washington could legislate stiff tailwinds for the factors that drive basic DCF models, and therefore the overall market.
There is still substantial uncertainty about what will actually happen with many policy decisions. Congress could resist deficit spending to finance infrastructure, tough talk on trade could stifle some highly geographically diversified sectors, or Trump’s actions could create some geopolitical instability, all of which could change the story down the road. However, assuming many of the key topics that are being discussed for his first 100 days come to fruition, US corporations and US investors are likely to benefit, especially relative to the policies that were expected for 2017 and beyond before the polls closed on November 8th.
In the file linked below, we discuss how the policies being proposed by the incoming administration could impact all three key drivers to a DCF model of the US market as a whole: profitability (ROA), growth (Asset Growth) and the cost of capital. And most importantly — it discusses how this spells a recipe for market upside and a “buy-the-dips” mindset.
We hope you find the article interesting.
Valens’ equity and credit research relies on a bedrock of deep fundamental forensic analysis with a coverage of over 4,000 equities and all major corporate credit. That means “cross-capital” equities and credit research are not siloed, but instead are fundamentally aggregated and compared with orthogonal data points.
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