Market Trader Insurance Quotes

In other words, money isn’t a material reality – it is a psychological construct. It works by converting matter into mind. But why does it succeed? Why should anyone be willing to exchange a fertile rice paddy for a handful of useless cowry shells? Why are you willing to flip hamburgers, sell health insurance or babysit three obnoxious brats when all you get for your exertions is a few pieces of coloured paper? People are willing to do such things when they trust the figments of their collective imagination. Trust is the raw material from which all types of money are minted. When a wealthy farmer sold his possessions for a sack of cowry shells and travelled with them to another province, he trusted that upon reaching his destination other people would be willing to sell him rice, houses and fields in exchange for the shells. Money is accordingly a system of mutual trust, and not just any system of mutual trust: money is the most universal and most efficient system of mutual trust ever devised. What created this trust was a very complex and long-term network of political, social and economic relations. Why do I believe in the cowry shell or gold coin or dollar bill? Because my neighbours believe in them. And my neighbours believe in them because I believe in them. And we all believe in them because our king believes in them and demands them in taxes, and because our priest believes in them and demands them in tithes. Take a dollar bill and look at it carefully. You will see that it is simply a colourful piece of paper with the signature of the US secretary of the treasury on one side, and the slogan ‘In God We Trust’ on the other. We accept the dollar in payment, because we trust in God and the US secretary of the treasury. The crucial role of trust explains why our financial systems are so tightly bound up with our political, social and ideological systems, why financial crises are often triggered by political developments, and why the stock market can rise or fall depending on the way traders feel on a particular morning.

If Jim was back at the imaginary dinner party, trying to explain what he did for a living, he'd have tried to keep it simple: clearing involved everything that took place between the moment someone started at trade — buying or selling a stock, for instance — and the moment that trade was settled — meaning the stock had officially and legally changed hands. Most people who used online brokerages thought of that transaction as happening instantly; you wanted 10 shares of GME, you hit a button and bought 10 shares of GME, and suddenly 10 shares of GME were in your account. But that's not actually what happened. You hit the Buy button, and Robinhood might find you your shares immediately and put them into your account; but the actual trade took two days to complete, known, for that reason, in financial parlance as 'T+2 clearing.' By this point in the dinner conversation, Jim would have fully expected the other diners' eyes to glaze over; but he would only be just beginning. Once the trade was initiated — once you hit that Buy button on your phone — it was Jim's job to handle everything that happened in that in-between world. First, he had to facilitate finding the opposite partner for the trade — which was where payment for order flow came in, as Robinhood bundled its trades and 'sold' them to a market maker like Citadel. And next, it was the clearing brokerage's job to make sure that transaction was safe and secure. In practice, the way this worked was by 10:00 a.m. each market day, Robinhood had to insure its trade, by making a cash deposit to a federally regulated clearinghouse — something called the Depository Trust & Clearing Corporation, or DTCC. That deposit was based on the volume, type, risk profile, and value of the equities being traded. The riskier the equities — the more likely something might go wrong between the buy and the sell — the higher that deposit might be. Of course, most all of this took place via computers — in 2021, and especially at a place like Robinhood, it was an almost entirely automated system; when customers bought and sold stocks, Jim's computers gave him a recommendation of the sort of deposits he could expect to need to make based on the requirements set down by the SEC and the banking regulators — all simple and tidy, and at the push of a button.

By now, though, it had been a steep learning curve, he was fairly well versed on the basics of how clearing worked: When a customer bought shares in a stock on Robinhood — say, GameStop — at a specific price, the order was first sent to Robinhood's in-house clearing brokerage, who in turn bundled the trade to a market maker for execution. The trade was then brought to a clearinghouse, who oversaw the trade all the way to the settlement. During this time period, the trade itself needed to be 'insured' against anything that might go wrong, such as some sort of systemic collapse or a default by either party — although in reality, in regulated markets, this seemed extremely unlikely. While the customer's money was temporarily put aside, essentially in an untouchable safe, for the two days it took for the clearing agency to verify that both parties were able to provide what they had agreed upon — the brokerage house, Robinhood — had to insure the deal with a deposit; money of its own, separate from the money that the customer had provided, that could be used to guarantee the value of the trade. In financial parlance, this 'collateral' was known as VAR — or value at risk. For a single trade of a simple asset, it would have been relatively easy to know how much the brokerage would need to deposit to insure the situation; the risk of something going wrong would be small, and the total value would be simple to calculate. If GME was trading at $400 a share and a customer wanted ten shares, there was $4000 at risk, plus or minus some nominal amount due to minute vagaries in market fluctuations during the two-day period before settlement. In such a simple situation, Robinhood might be asked to put up $4000 and change — in addition to the $4000 of the customer's buy order, which remained locked in the safe. The deposit requirement calculation grew more complicated as layers were added onto the trading situation. A single trade had low inherent risk; multiplied to millions of trades, the risk profile began to change. The more volatile the stock — in price and/or volume — the riskier a buy or sell became. Of course, the NSCC did not make these calculations by hand; they used sophisticated algorithms to digest the numerous inputs coming in from the trade — type of equity, volume, current volatility, where it fit into a brokerage's portfolio as a whole — and spit out a 'recommendation' of what sort of deposit would protect the trade. And this process was entirely automated; the brokerage house would continually run its trading activity through the federal clearing system and would receive its updated deposit requirements as often as every fifteen minutes while the market was open. Premarket during a trading week, that number would come in at 5:11 a.m. East Coast time, usually right as Jim, in Orlando, was finishing his morning coffee. Robinhood would then have until 10:00 a.m. to satisfy the deposit requirement for the upcoming day of trading — or risk being in default, which could lead to an immediate shutdown of all operations. Usually, the deposit requirement was tied closely to the actual dollars being 'spent' on the trades; a near equal number of buys and sells in a brokerage house's trading profile lowered its overall risk, and though volatility was common, especially in the past half-decade, even a two-day settlement period came with an acceptable level of confidence that nobody would fail to deliver on their trades.

In a now famous experiment they found that the majority of people, whether predictors or nonpredictors, will judge a deadly flood (causing thousands of deaths) caused by a California earthquake to be more likely than a fatal flood (causing thousands of deaths) occurring somewhere in North America (which happens to include California). As a derivatives trader I noticed that people do not like to insure against something abstract; the risk that merits their attention is always something vivid.

Social money and payments: iZettle, Payatrader, mPowa, SumUp, payleven, Inuit GoPayment, Square •   Social lending and saving: Zopa, RateSetter, smava, Prosper, Lending Club, Cashare •   Social insurance: Friendsurance •   Social investing and trading: StockTwits, eToro, Myfxbook, Fxstat, MetaTrader Trade Signals, Collective2, Tradeo, ZuluTrade, Nutmeg •   Social trade financing: MarketInvoice, Platform Black, the Receivables Exchange, Urica •   Payday Lending: Wonga, Cash America, Advance America •   Goal setting and gamification: SmartyPig, Moven, Simple •   Crowdfunding: Funding Circle, Kickstarter, Indiegogo, crowdrise, Razoo

A lot of applications of alternative data are being found today in insurance and credit markets (see e.g. Turner, 2008; Turner, 2011; Financial Times, 2017).

As a derivatives trader I noticed that people do not like to insure against something abstract; the risk that merits their attention is always something vivid.