In this briefing:
- A Simple Model that Could Take OECD US GDP Forecasts to Predict the US Dow Jones Index Level?
- New US Sanctions Against Venezuela: Impact on the Oil Sector and Prices
- Major Highlights of SK Telecom’s 4Q18 Earnings Conference Call
- China Housing: Cities Without Tiers – An Alternative Data Cut With Illuminating Patterns
Following on from last months publication that gave a brief introduction into the viability of constructing a model using machine learning techniques that can predict the direction of daily moves in the European high yield index using data from the previous trading day.
In this months note, we have taken a step back and constructed a simple model using traditional statistical techniques that use GDP forecasts to predict moves in the Dow Jones ETF’s.
Over the coming months, we will endeavor to show how data science can be used as an integral part of the investment management process and highlight its advantage over basic statistical modeling techniques traditionally used in finance.
With further modeling and analysis to confirm this basic investigation, investors may be able to use this information in their asset allocation decisions and profit from equity market beta-selection.
US sanctions against Venezuela’s central bank and PDVSA, announced on Monday (January 28), have sent refiners on the US Gulf Coast scrambling for replacement supplies of heavy crude. Though they do not cover the business of non-US entities with PDVSA, the move has put Venezuelan crude importers in China and India on notice.
For US refiners, the three main alternative suppliers of heavy, sour crude — Canada, Mexico and Saudi Arabia — are either constrained in their ability to step up supply or are deliberately reducing shipments.
Venezuela’s upstream oil sector has been limping for a long time now. But the sanctions against PDVSA may deal it a death blow. The crude market is keeping a wary eye on the situation but appears unwilling to price in the worst-case scenario for the time being, as it remains fixated on the global economic prospects and concerns over oil demand growth.
We look at the fallout of the latest move by Washington on the primary entities doing oil business with Venezuela: refiners in the US, China and India (the main markets for Venezuelan crude) and Russian giants Rosneft and Lukoil.
We also discuss the likelihood and impact of Venezuelan crude production grinding down from the current 1 million b/d to zero.
- SK Telecom (017670 KS) reported disappointing 4Q18 earnings results. SK Telecom’s revenue of 4,351.7 billion won was 0.2% lower than consensus and its operating profit of 225 billion won was 23% lower than the consensus in 4Q18. Despite the disappointing 4Q18 results (especially due to lower operating income and lack of flow through of SK Hynix dividends to SKT), we remain positive on SK Telecom.
- We believe that SK Telecom’s 10,000 won DPS in 2018 is a disappointment. However, we believe the stage has been set for higher DPS policy, linking SK Hynix’s dividends to SK Telecom and as mentioned in the conference call numerous times, this is likely to be announced in the AGM in March. In terms of amount, we believe 13,000 won to 15,000 won appears to be reasonable in 2019.
- The company’s comment about its sales and profits improving starting in 2H 2019 is consistent with its previous statement in the third quarter conference call. However, the company’s statement about its revenue target of more than 1 trillion won growth YoY in 2019 is new and positive. In 2018, SK Telecom generated consolidated sales of 16.9 trillion won, down 3.7% YoY. If the company is able to generate revenue of 17.9 trillion won in 2019, this would represent a growth of 5.9% YoY. The current consensus estimate of the company’s sales is 17.47 trillion won in 2019. Thus, the company has basically guided the 2019 sales target by 2.5% higher than the current consensus estimate.
Beyond the aggregate figures, most investors interested in China housing markets would look at the breakdown by Tier. We introduce a few alternative ways to cut the house price growth data, illustrating some consistent patterns that could be more meaningful for any investment thesis than the simple by-tier.