A quick take on a long-running theme in markets, especially fixed-income.
Investors are reaching for a toolkit of exchange traded funds, mutual funds and credit derivatives to make up for a dearth of liquidity in parts of the financial system, according to market participants and research from Barclays.
Many have turned to ETFs, mutual funds and certain derivatives to make up for a lack of liquidity. ETFs use a network of banks and trading firms to give investors cheap and instant exposure to a wide variety of assets.
The trend is particularly pronounced in the fixed income market, where new rules aimed at increasing bank capital and reducing the risk of a run in the “repo market” — Ground Zero for the financial crisis — are said to have most hurt ease of trading.
Risks squeezed out of banks pop up elsewhere
… Financial companies have the option of using data-guzzling technologies that make the observation of shopping habits look downright primitive. A plethora of information gathered from social media, digital data brokers and online trails can be used to mathematically determine the creditworthiness of individuals, or to market products specifically targeted to them.
The degree to which such algorithms are utilised by mainstream banks and credit card companies is unclear, as are their inputs, calculations and the resulting scores. While many types of data-driven algorithms have been criticised for opacity and intrusiveness, the use of digital scorecards in finance raises additional issues of fairness. Using such information to make predictions about borrowers can, critics say, become self-fulfilling, hardening the lines between the wealthy and poor by denying credit to those who are already associated with not having access to it.
“You can get in a death spiral simply by making one wrong move, when algorithms amplify a bad data point and cause cascading effects,” says Frank Pasquale, a professor of law at University of Maryland and author of a book on algorithms called The Black Box Society.
I’ve said before that I am incredibly proud of this Financial Times piece exploring the impact of big data on finance and equality. Researching this kind of topic is challenging because details on the use of big data remain murky – even more so when it comes to banks and financial companies. For that reason, much of the discussion remains theoretical, although it’s hard not to believe that this is the direction we are heading when you read that Google – a company notorious for using big data to personalise ads and search engine results in the name of advertising dollars- is now trialling money transfers. The British bank Barclays has reportedly also begun selling aggregated customer data to third-party companies.
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It’s been ages since the Federal Reserve last raised interest rates. Who remembers how that works? (Certainly not me, I was a baby reporter covering airlines back then) Here are some excerpts:
At least at the junior end, Wall Street is now peppered with traders and investors who possess no first-hand professional experience of an interest rate rise. Even for finance veterans, the habit of measuring one’s profit and loss on a day-to-day basis leads to notoriously goldfish-like memory spans and it has been six years since rates were last above the zero bound.
Years of ultra-low interest rates have become integrated into the very fabric of markets. Asset managers live and die by their interest rate bets. Hundreds of billions of dollars have poured into riskier asset classes as investors seek out higher returns with borrowed money, or leverage, used to amplify profits.
Markets display total recoil on Fed interest rate rises
Chances are, when you think of the repo market you think of banks and broker-dealers and the craziness that went down in 2008. This column, based on an amazing research paper by Zoltan Pozsar, suggests that’s a mistake.
(Bonus: It calls out Pimco on window-dressing its balance sheet)
Here’s an excerpt:
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