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We had a great conversation spanning many topics, including:

- Potential applications of data science in financial services.
- The current state of data science in financial services in both the U.S. and China.
- His experience recruiting, training, and managing data science teams in both the U.S. and China.

Here are some highlights from our conversation:

There's a customer acquisition piece and then there's a customer retention piece. For customer acquisition, we can see that new technologies can really add value by looking at all sorts of data sources that can help a financial service company identify who they want to target to provide those services. So, it's a great place where data science can help find the product market fit, not just at one instance like identifying who you want to target, but also in a continuous form where you can evolve a product and then continuously find the audience that would best fit the product and continue to analyze the audience so you can design the next generation product. ... Once you have a specific cohort of users who you want to target, there's a need to be able to precisely convert them, which means understanding the stage of the customer's thought process and understanding how to form the narrative to convince the user or the customer that a particular piece of technology or particular piece of service is the current service they need.

... On the customer serving or retention side, for financial services we commonly talk about building hundred-year businesses, right? They have to be profitable businesses, and for financial service to be profitable, there are operational considerationsâquantifying risk requires a lot of data science; preventing fraud is really important, and there is garnering the long-term trust with the customer so they stay with you, which means having the work ethic to be able to take care of customer's data and able to serve the customer better with automated services whenever and wherever the customer is. It's all those opportunities where I see we can help serve the customer by having the right services presented to them and being able to serve them in the long term.

A few important areas in the financial space in China include mobile payments, wealth management, lending, and insuranceâbasically, the major areas for the financial industry.

For these areas, China may be a forerunner in using internet technologies, especially mobile internet technologies for FinTech, and I think the wave started way back in the 2012/2013 time frame. If you look at mobile payments, like Alipay and WeChat, those have hundreds of millions of active users. The latest data from Alipay is about 608 million users, and these are monthly active users we're talking about. This is about two times the U.S. population actively using Alipay on a monthly basis, which is a crazy number if you consider all the data that can generate and all the things you can see people buying to be able to understand how to serve the users better.

If you look at WeChat, they're boasting 1 billion users, monthly active users, early this year. Those are the huge players, and with that amount of traffic, they are able to generate a lot of interest for the lower-frequency services like wealth management and lending, as well as insurance.

- Kai-Fu Lee outlines the factors that enabled China's rapid ascension in AI
- Gary Kazantsev on how âData science makes an impact on Wall Streetâ
- Juan Huerta on âUpcoming challenges and opportunities for data technologies in consumer financeâ
- Geoffrey Bradway on âProgramming collective intelligence for financial tradingâ
- Jason Dai on why âCompanies in China are moving quickly to embrace AI technologiesâ
- Haoyuan Li on why âIn the age of AI, fundamental value resides in dataâ

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Their swap contracts

At BitMEX they call the basis the Funding Rate, and it is applied every 8 hours. Below are the annualized funding rates, by month, for the BTC and ETH swaps.

I asked people in Reddit their best explanation, and the answers were related to sentiment or simply a market imbalance. Yet there's a futures contract for the ETHBTC contract, and the basis there is quite small, about 5% annualized, which is inconsistent with this explanation. Further, ETH and BTC are highly correlated, about 90% using 2019 data, so it is implausible to think these two assets would have such radically different risk premia.

The trick is to adjust the funding rate for the BTC~ETH covariance in order to capture the convexity of USD returns. With this adjustment, the swap funding rates are about the same. This reminds me of the convexity adjustment in eurodollar futures is needed to compare eurodollar rates to interest rate swap rates due to the fact that eurodollar futures are settled daily while an interest rate swap is settled only at expiration. The convexity adjustment became well-publicized in the mid-90s, but for a good 5 years traders were oblivious, and while there was sufficient liquidity they did not make this convexity adjustment, generating arbitrage profits for a couple of savvy banks (you needed direct access to libor rates, so as an individual you could not arb this). In contrast, here it seems markets have always priced for this while it is still not widely understood or documented.

Let us ignore the funding rate and look just at the profit generated by the asset price for a BTCUSD swap. At BitMEX, the long profit is generated in BTC as follows for an entry price at time t, and exit at t+1:

- BTCswap Profit (in BTC)=Notional*[1/BTC(t) - 1/BTC(t+1)]

- BTCswap profit (in USD)=$1000*[BTC(t+1)/BTC(t)-1]

- BTCswap profit (in BTC)=$1000*[BTC(t+1)/BTC(t)-1]/BTC(t+1)

- BTCswap profit (in BTC)=$1000*[1/BTC(t)-1/BTC(t+1)]

In contrast, the BitMEX ETH swap uses a different formula that BitMEX CEO Arthur Hayes notes is "attractive to speculators who wish to have exposure to a foreign asset, but without the corresponding exchange risk." This makes little sense because most investors care about their return in fiat, not BTC, and it is inconsistent with BitMEX's BTC swap, which generates a linear return in fiat currency. Most importantly, if you are trading on an asset's USD price the notional should be in USD; if the asset price is in BTC the notional should be in BTC. In this contract, the BitMEX ETH notional is basically in BTC but the asset price is in USD. The ETH perpetual swap is based on a profound misunderstanding of how a Quanto works.

The BitMEX ETH swap is a position defined by the number of contracts times a multiplier (1E-6). This is then multiplied by the difference in ETH's USD price to generate the position payoff. Abstracting from the funding rate, the payoff function for the ETH perpetual swap is:

- ETHswap payoff (in BTC)=0.000001*#Contracts*[ETH(t+1) - ETH(t)]

To see what this implies, it is best to simply take the payoff function and turn it into USD by multiplying it by the USD BTC price at completion:

- ETHswap payoff (in USD)=0.000001*#Contracts*[ETH(t+1) - ETH(t)]*BTC(t+1)

Given the ETH and BTC returns are about 90% correlated, this then generates the opposite return pattern to the BTC swap, where the USD return is convex, while the BTC return is linear. For example, if the ETH price rises 10% the BTC price will also probably rise, increasing returns for up movements. The same process works on downward movements to dampen losses. The result is the opposite of the BTC swap, a convex payoff in USD and a linear payoff in BTC.

The USD return can be derived by looking at the initial USD investment, which is the following at time t:

The USD return can be derived by looking at the initial USD investment, which is the following at time t:

- ETHswap USD investment=BTC(t)*0.000001*#Contracts*ETH(t)

As return=payoff/investment, the USD return on this ETH position is thus

- ETHswap USD % return=BTC(t+1)/BTC(t)*[ETH(t+1) /ETH(t)-1]

or

- ETHswap USD return=[1+ret(B)]*ret(E)

where ret(B) and ret(E) are the net returns for bitcoin and ether. The expected value of this is

- E{ETHswap USD return}=ret(E)+covariance(ret(E),ret(B))

as the covariance(a,b)=correl(a,b)*[stdev(a)*stdev(b)], this covariance term generates a boost to the USD payoff due to the significant ETH~BTC correlation. You can see why it is needed by looking at the convexity in the USD returns, an example of Jensen's inequality. Since June 2017, the volatility for BTC and ETH has been 90% and 118% respectively, with a correlation of 60%, generating a covariance adjustment term annualizes out to be about 64%. This is the return premium of the ETH swap compared to the ETH USD return over that period: 51% vs. 115% (all data reflecting arithmetically annualized returns). Thus if the average ETH return is boosted by 60% via the covariance adjustment, the 45% funding rate implies a comparable -15% funding rate for ETH, just as in the BTC. You can download an Excel sheet showing the math and data here.

A 15% return premium for going long at BitMEX is high, but not insanely so, and not without risk. The rate bounces around, and BitMEX could be hacked or shut down by regulators at any time, so I think the 15% funding credit longs get is fairly priced.

There are two qualifications. First, it neglects the fact you need to buy bitcoin to enter into them, and so you are essentially long the notional amount of bitcoin as well. Secondly, given a total return is essentially a geometric return, while an arithmetic return maintains a constant USD notional each period, one has to rebalance their position each period to generate the arithmetic return, so investors would need to adopt such an approach to generate the arithmetic return (see here for difference).

**Conclusion**

The BitMEX formula is poorly conceived: the swap has a BTC notional and references a USD price creating a convex USD payoff. Investors are forced to not only take a position on the future move in ETH, but also the ETH~BTC covariance. I understand why it is so popular, as it is very difficult to put on such a position hassle-free anywhere else, yet BitMEX should offer something with a linear fiat payoff like their BTC swap, as I think this would be even more popular. This could be done by having the BTC notional contract trade on the BTCETH price; alternatively, they could add a BTC return adjustment to make the USD return linear. Either approach would remove the importance of estimating the covariance term, which is a distraction for most investors, and also one that few really understand.

A 15% return premium for going long at BitMEX is high, but not insanely so, and not without risk. The rate bounces around, and BitMEX could be hacked or shut down by regulators at any time, so I think the 15% funding credit longs get is fairly priced.

There are two qualifications. First, it neglects the fact you need to buy bitcoin to enter into them, and so you are essentially long the notional amount of bitcoin as well. Secondly, given a total return is essentially a geometric return, while an arithmetic return maintains a constant USD notional each period, one has to rebalance their position each period to generate the arithmetic return, so investors would need to adopt such an approach to generate the arithmetic return (see here for difference).

The BitMEX formula is poorly conceived: the swap has a BTC notional and references a USD price creating a convex USD payoff. Investors are forced to not only take a position on the future move in ETH, but also the ETH~BTC covariance. I understand why it is so popular, as it is very difficult to put on such a position hassle-free anywhere else, yet BitMEX should offer something with a linear fiat payoff like their BTC swap, as I think this would be even more popular. This could be done by having the BTC notional contract trade on the BTCETH price; alternatively, they could add a BTC return adjustment to make the USD return linear. Either approach would remove the importance of estimating the covariance term, which is a distraction for most investors, and also one that few really understand.

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However, much of SBHâs rural sales reps will be on the losing side of the new tax law. A typical SBH traveling sales rep might earn $55,000 per year, but will incur $20,000 in unreimbursed travel expenses. Thus, this rep really only earns $35,000 per year.

Under the old tax code, the typical rural SBH sales rep could deduct the $20,000 of unreimbursed travel expenses (stuff like 25,000 miles on their personal car and hotel and meals away from home) as a Miscellaneous Itemided Deduction via Form 2106 for Unreimbursed Employee Expenses. So, while the SBH rep that really only netted $35,000 from their job, only paid taxes on the $35,000.

However, the new tax law completely eliminated all Miscellaneous Itenized Deductions. Now the SBH rep has to pay income taxes like they earned $55,000 even though they spent $20,000 to earn that paycheck. This will result in roughly $4,000-$6,000 in higher taxes for the SBH traveling sales rep.

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Just got back from the FTF Sec Ops Conference in Boston.

read more...]]>

The copies of the NAG Library documentation on our website have already been updated, for example Fortran Library Documentation and C Library Documentation.

However, if you have a locally downloaded copy of the Fortran or C Library Documentation (NAG Toolbox

html/styles/nagmathml.js

and change line 4 to refer to a new location for MathJax.

The existing line is:

var nagmathjax= ((window.location.protocol=="https:") ? "https" : "http" ) + "://cdn.mathjax.org/mathjax/latest/";

The URL recommended by the MathJax consortium for users who wish to use a freely available web hosted library is:

var nagmathjax= ((window.location.protocol=="https:") ? "https" : "http" ) + "://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/";

As this change has already been applied to the copies on our website you may alternatively just download this and copy it into your local documentation.

We apologise for this inconvenience, but the underlying changes to the MathJax server arrangements are not in our control.]]>