Bloomberg report on Japanese investors, facing ongoing negative rates domestically, are buying dollars and risk assets
- “The presence of the Japanese as the main carry trade driver seems to be growing as they must turn to overseas investments”
- In April, Japan’s money managers bought the most U.S. corporate debt in eight years and the second-highest amount of equities in five years
- “Japanese investors use yen to fund purchases of Treasuries or U.S. corporate bonds, for instance, to seek credit spreads and these flows are continuing,” said Koichi Sugisaki, a strategist at Morgan Stanley MUFG Securities Co. in Tokyo.
Fitch Ratings has revised the Outlook on India’s Long-Term Foreign-Currency Issuer Default Rating (IDR) to Negative from Stable and affirmed the rating at ‘BBB-‘.
KEY RATING DRIVERS
The revision of the Outlook to Negative on India’s Long-Term IDRs reflects the following key rating drivers:
The coronavirus pandemic has significantly weakened India’s growth outlook for this year and exposed the challenges associated with a high public-debt burden. Fitch expects economic activity to contract by 5% in the fiscal year ending March 2021 (FY21) from the strict lockdown measures imposed since 25 March 2020, before rebounding by 9.5% in FY22. The rebound will mainly be driven by a low-base effect. Our forecasts are subject to considerable risks due to the continued acceleration in the number of new COVID-19 cases as the lockdown is eased gradually. It remains to be seen whether India can return to sustained growth rates of 6% to 7% as we previously estimated, depending on the lasting impact of the pandemic, particularly in the financial sector.
The humanitarian and health needs have been pressing, but the government has shown expenditure restraint so far, due to the already high public-debt burden going into the crisis, with additional relief spending representing only about 1% of GDP by our estimates. Most elements of an announced package totalling 10% of GDP are non-fiscal in nature. Some further fiscal spending of up to 1 percentage point of GDP may still be announced in the next few months, which was indicated by a recent announcement of additional borrowing for FY21 of 2% of GDP, although we do not expect a steep rise in spending. Continue reading »
Roach is a former Morgan Stanley Asia chairman and is now a senior fellow at Yale University.
- “The U.S. economy has been afflicted with some significant macro imbalances for a long time, namely a very low domestic savings rate and a chronic current account deficit”
- “The dollar is going to fall very, very sharply.”
- “These problems are going from bad to worse as we blow out the fiscal deficit in the years ahead”
On a broad sense most commonly used algorithmic strategies are Momentum strategies, as the names indicate the algorithm start execution based on a given spike or given moment. The algorithm basically detects the moment (e.g spike) and executed by and sell order as to how it has been programmed.
One another popular strategy is Mean-Reversion algorithmic strategy. This algorithm assumes that prices usually deviate back to its average.
A more sophisticated type of algo trading is a market-making strategy, these algorithms are known as liquidity providers. Market Making strategies aim to supply buy and sell orders in order to fill the order book and make a certain instrument in a market more liquid. Market Making strategies are designed to capture the spread between buying and selling price and ultimately decrease the spread.
Another advanced and complex algorithmic strategy is Arbitrage algorithms. These algorithms are designed to detect mispricing and spread inefficiencies among different markets. Basically, Arbitrage algorithms find the different prices among two different markets and buy or sell orders to take advantage of the price difference.
Among big investment banks and hedge funds trading with high frequency is also a popular practice. A great deal of all trades executed globally is done with high-frequency trading. The main aim of high-frequency trading is to perform trades based on market behaviors as fast and as scalable as possible. Though, high-frequency trading requires solid and somewhat expensive infrastructure. Firms that would like to perform trading with high frequency need to collocate their servers that run the algorithm near the market they are executing to minimize the latency as much as possible.
Adaptive Implementation Shortfall algorithm designed for reduction of market impact during executing large orders. It allows keeping trading plans with automatic reactions to price liquidity.
Basket Orders is a strategy designed to automated parallel trading of many assets, balancing their share in the portfolio’s value.
Bollinger bands strategy is a trading algorithm that computes three bands – lower, middle and upper. When the middle band crosses one of the other from the proper side then some order is made.
- The tension of riding a profit or loss may be quite intense for some investors. By liquidating too early, they are relieved of the tension, and, therefore, the mere termination of this situation will have the same result as a positive experience. They will be more likely to behave the same way in future trades.
- Those who ride losses to unacceptably large amounts also tend to experience the positive effects of relief. Again the relief can serve to reward an otherwise inappropriate act. It’s like the man who, when asked why he kept banging his head against the wall, replied, “because it feels so good when I stop.”
- A speculator should make it a rule each time he closes a successful deal to take 50% of his profits and lock this sum up in a safe deposit box. The only money that is ever taken out of Wall Street by speculators is the money they draw out of their accounts after closing a successful deal.
- There is no better time than after a large “win” on a stock. Cash is your secret bullet in the chamber, keep a cash reserve.
- The single largest regret I have ever had in my financial life was not paying enough attention to this rule.
Forex futures positioning data for the week ending May 26, 2020
- EUR long 75K vs 72K long last week. Longs increased by 3K
- GBP short 22K vs 19K short last week. Shorts increased by 3K
- JPY long 35K vs 28K long last week. Longs increased by 7K
- CHF long 9K vs 9K long last week. Unchanged
- AUD short 40k vs 39K short last week. Shorts increased by 1K
- NZD short 15K vs 16K short last week. Shorts trimmed by 1K
- CAD short 34k vs 35K short last week. Shorts trimmed by 1K
Highlights for the week:
- EUR remains the largest speculative position at 75 him K followed by the AUD at 40K. The traders are long EUR (short USDs) and short AUD (long USDs). The JPY is the next largest position at 35K. The speculative position is long JPY (short USD).
- There are three currencies that are long vs the USD (EUR, JPY an CHF) and 4 currencies that are short vs the USD (GBP, AUD, NZD and CAD).
It’s almost June
Why Traders Naturally Cannot Follow Their Trading Plan
- The brain automatically engages “distinct mechanisms” to handle these two scenarios differently: (i) risky situation where the probabilities are known, and (ii) ambiguous situation with incomplete information where historical probabilities provide only a clue. For the latter, there will be a “uncertainty circuit” that will raise a red flag to say “more information needed”.
- This results in traders trying to do exactly what they planned while their brain fights them to find more information or to scramble in the face of a clear, but maybe only subconsciously perceived, threat.
- Just because you decided on taking a long or short trading position, your “brain on uncertainty” doesn’t change how it goes about making judgment calls in uncertain circumstances. The basic process steps through the context-belief-perception cycle because it can’t help it.
- Uncertainty means — at least to part of your neural and white matter networks — that a black bear, ready to eat all your apples (and you with them) could be just around the corner. The more uncertainty, the more you can realize how much you are relying on contextual clues in order to make sense of the situation.