In today’s briefing:
- EQD | Introducing the Market Reversal Matrix (MRM): Predict Trend Reversal with Bayesian Models
- HDFC – Amalgamated, We List Its Own Key Synergies, Which Appear Tremendous
- Bank of Chongqing – One Of Worst NIM Contraction, But With High Loan Growth
- Morning Views Asia: MGM China Holdings, Sino-Ocean Service, Vedanta Resources
EQD | Introducing the Market Reversal Matrix (MRM): Predict Trend Reversal with Bayesian Models
- A market-timing device that classifies historical trend patterns/reversals in “buckets”, to generate a “matrix” showing the market’s average trend reversal “behavior” at the end of each trend “type”.
- A purely statistical approach: using Bayesian Models to determine the probability of DAILY or WEEKLY trend reversal at precise LONG/SHORT support prices at fixed points in time.
- The analogy with the game of Blackjack: knowing the probability of a trend reversal can be 30% or 90% (not always =50%) can be a significant edge for the investor.
HDFC – Amalgamated, We List Its Own Key Synergies, Which Appear Tremendous
- News out this morning that HDFC Bank and HDFC merger became effective 1 July
- This occurred after the Boards approved the merger scheme on 30 June
- Funding cost benefits, operating cost synergies, product cross-sale: major benefits
Bank of Chongqing – One Of Worst NIM Contraction, But With High Loan Growth
- Combination of high NIM contraction with high loan growth is not a good combination
- BOC has seen its NIM down 14% in the past year but against 12% loan growth
- PBOC pressure to cut rates and for banks to support ailing debtors, is a risk
Morning Views Asia: MGM China Holdings, Sino-Ocean Service, Vedanta Resources
Lucror Analytics Morning Views comprise our fundamental credit analysis, opinions and trade recommendations on high yield issuers in the region, based on key company-specific developments in the past 24 hours. Our Morning Views include a section with a brief market commentary, key market indicators and a macroeconomic and corporate event calendar.