Sunday, June 5, 2022

USDD misleading collateral

TRON is intentionally misleading investors into thinking that their algorithm stablecoin is more safe than UST.

As of 6 June 2022, there is 667,521,101 USDD in circulation which TRON claims is overcollateralized at 220%, meaning that there is USD 1,473 mil of collateral.
Source: https://tdr.org/#/

Let us take a look at the breakdown of this "reserve".

13,040.6 BTC (USD 406.7 mil)

240,000,000 USDT (USD 240mil)

1,906,000,176 TRX (USD 158.5 mil)

8,297,232,883 TRX (USD 667.5 mil) burnt to mint 667.5 mil of USDD

Basically, TRON is including the TRX burnt as collateral for USDD. This is equivalent to saying that on 7 May, UST was overcollateralized at 117% (3b of bitcoin and 18b of Luna). Look at how well 117% collateralized worked out. If you look at the Tron website, they classify themselves as the highest collateralized stablecoin against DAI, USDT, TUSD and USDC. The difference is that those stablecoins are backed by other assets that are not linked to the stablecoin itself.

In the event of a death spiral, TRX will not have any value. If we exclude TRX from the computation of reserves and assume that TRX hits zero value, USDD is only backed by USD 646.7mil (90% collateralized) and this assumes that the value of BTC does not fall.

Lastly, TRON released a statement that a minimum of 130% collateral ratio will be maintained. They did not state that the reserves will be in BTC or other stablecoins. They could remove BTC/USDT while increasing TRX holdings in their reserves and still meet their 130% collateral ratio. The only issue is that it will be 100% backed by TRX, the coin that will have no value if a death spiral occurs.
Source: https://www.ndtv.com/business/tron-modifies-usdd-stablecoin-to-avoid-terrausd-style-collapse-3040217


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Quant Finance Arxiv submissions 2022-05-30 - 2022-06-05

Quant Finance Arxiv submissions 2022-05-30 - 2022-06-05

This is your weekly snap of quant finance submissions to the Arxiv. Papers are sorted in reverse chronological order of the original publication date. i.e. the newest papers are at the top, revisions are lower down the list.

If any paper take your fancy, you're encouraged to submit a link post to the subreddit, and start the discussion in the comments. Or in this thread, what do I care I'm not your boss.

Adaptive Robust Online Portfolio Selection

Authors: Man Yiu Tsang, Tony Sit, Hoi Ying Wong

Categories: Optimization and Control, Portfolio Management

PDF: http://arxiv.org/pdf/2206.01064v1

Dates: originally published: 2022-06-02, updated: 2022-06-02

Summary: The online portfolio selection (OLPS) problem differs from classical portfolio model problems, as it involves making sequential investment decisions. Many OLPS strategies described in the literature capture market movement based on various beliefs and are shown to be profitable. In this paper, we propose a robust optimization (RO)-based strategy that takes transaction costs into account. Moreover, unlike existing studies that calibrate model parameters from benchmark data sets, we develop a novel adaptive scheme that decides the parameters sequentially. With a wide range of parameters as input, our scheme captures market uptrend and protects against market downtrend while controlling trading frequency to avoid excessive transaction costs. We numerically demonstrate the advantages of our adaptive scheme against several benchmarks under various settings. Our adaptive scheme may also be useful in general sequential decision-making problems. Finally, we compare the performance of our strategy with that of existing OLPS strategies using both benchmark and newly collected data sets. Our strategy outperforms these existing OLPS strategies in terms of cumulative returns and competitive Sharpe ratios across diversified data sets, demonstrating its adaptability-driven superiority.

The Evolution of Investor Activism in Japan

Authors: Ryo Sakai

Categories: General Finance

PDF: http://arxiv.org/pdf/2206.00640v1

Dates: originally published: 2022-06-01, updated: 2022-06-01

Summary: Activist investors have gradually become a catalyst for change in Japanese companies. This study examines the impact of activist board representation on firm performance in Japan. I focus on the only two Japanese companies with activist board representation: Kawasaki Kisen Kaisha, Ltd. ("Kawasaki") and Olympus Corporation ("Olympus"). Overall, I document significant benefits from the decision to engage with activists at these companies. The target companies experience greater short- and long-term abnormal stock returns following the activist engagement. Moreover, I show operational improvements as measured by return on assets and return on equity. Activist board members also associate with important changes in payout policy that help explain the positive stock returns. My findings support the notion that Japanese companies should consider engagements with activist investors to transform and improve their businesses. Such interactions can lead to innovative and forward-thinking policies that create value for Japanese businesses and their stakeholders.

RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring

Authors: Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang

Categories: Statistical Finance

PDF: http://arxiv.org/pdf/2206.00568v1

Dates: originally published: 2022-06-01, updated: 2022-06-01

Summary: In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejected samples, which leads to missing-not-at-random selection bias. Machine learning models trained on such biased data are inevitably unreliable. In this work, we find that the default/non-default classification task and the rejection/approval classification task are highly correlated, according to both real-world data study and theoretical analysis. Consequently, the learning of default/non-default can benefit from rejection/approval. Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL). Specifically, we propose a novel Reject-aware Multi-Task Network (RMT-Net), which learns the task weights that control the information sharing from the rejection/approval task to the default/non-default task by a gating network based on rejection probabilities. RMT-Net leverages the relation between the two tasks that the larger the rejection probability, the more the default/non-default task needs to learn from the rejection/approval task. Furthermore, we extend RMT-Net to RMT-Net++ for modeling scenarios with multiple rejection/approval strategies. Extensive experiments are conducted on several datasets, and strongly verifies the effectiveness of RMT-Net on both approved and rejected samples. In addition, RMT-Net++ further improves RMT-Net's performances.

Hedging option books using neural-SDE market models

Authors: Samuel N. Cohen, Christoph Reisinger, Sheng Wang

Categories: Risk Management, Statistical Finance, Computational Finance, Probability

PDF: http://arxiv.org/pdf/2205.15991v1

Dates: originally published: 2022-05-31, updated: 2022-05-31

Summary: We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options. In particular, we derive sensitivity-based and minimum-variance-based hedging strategies using these models and examine their performance when applied to various option portfolios using real-world data. Through backtesting analysis over typical and stressed market periods, we show that neural-SDE market models achieve lower hedging errors than Black--Scholes delta and delta-vega hedging consistently over time, and are less sensitive to the tenor choice of hedging instruments. In addition, hedging using market models leads to similar performance to hedging using Heston models, while the former tends to be more robust during stressed market periods.

Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models

Authors: Yang Shen, Bin Zou

Categories: Optimization and Control, Portfolio Management, Mathematical Finance

PDF: http://arxiv.org/pdf/2205.15905v1

Dates: originally published: 2022-05-31, updated: 2022-05-31

Summary: We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences. We obtain the precommitted optimal strategies to both problems in closed form and find that they coincide, without and with the presence of the conic constraint. This result generalizes the equivalence between MMV and MV preferences from non-constrained cases to a specific constrained case. A comparison analysis reveals that the orthogonality property under the conic convex set is a key to ensuring the equivalence result.

A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups

Authors: Kevin Kamm, Michelle Muniz

Categories: Risk Management

PDF: http://arxiv.org/pdf/2205.15699v1

Dates: originally published: 2022-05-31, updated: 2022-05-31

Summary: In this paper, we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose values are valid rating matrices, we noticed the geometric properties of stochastic matrices and its link to matrix Lie groups. We give a gentle introduction to this topic and demonstrate how It^o-SDEs in R will generate the desired model for rating transitions. To calibrate the rating model to historical data, we use a Deep-Neural-Network (DNN) called TimeGAN to learn the features of a time series of historical rating matrices. Then, we use this DNN to generate synthetic rating transition matrices. Afterwards, we fit the moments of the generated rating matrices and the rating process at specific time points, which results in a good fit. After calibration, we discuss the quality of the calibrated rating transition process by examining some properties that a time series of rating matrices should satisfy, and we will see that this geometric approach works very well.

Exact solution to two-body financial dealer model: revisited from the viewpoint of kinetic theory

Authors: Kiyoshi Kanazawa, Hideki Takayasu, Misako Takayasu

Categories: Trading and Market Microstructure, Physics and Society, Statistical Mechanics

PDF: http://arxiv.org/pdf/2205.15558v1

Dates: originally published: 2022-05-31, updated: 2022-05-31

Summary: The two-body stochastic dealer model is revisited to provide an exact solution to the average order-book profile using the kinetic approach. The dealer model is a microscopic financial model where individual traders make decisions on limit-order prices stochastically and then reach agreements on transactions. In the literature, this model was solved for several cases: an exact solution for two-body traders $N=2$ and a mean-field solution for many traders $N\gg 1$. Remarkably, while kinetic theory plays a significant role in the mean-field analysis for $N\gg 1$, its role is still elusive for the case of $N=2$. In this paper, we revisit the two-body dealer model $N=2$ to clarify the utility of the kinetic theory. We first derive the exact master-Liouville equations for the two-body dealer model by several methods. We next illustrate the physical picture of the master-Liouville equation from the viewpoint of the probability currents. The master-Liouville equations are then solved exactly to derive the order-book profile and the average transaction interval. Furthermore, we introduce a generalised two-body dealer model by incorporating interaction between traders via the market midprice and exactly solve the model within the kinetic framework. We finally confirm our exact solution by numerical simulations. This work provides a systematic mathematical basis for the econophysics model by developing better mathematical intuition.

A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Authors: Yanzhao Zou, Dorien Herremans

Categories: Statistical Finance

PDF: http://arxiv.org/pdf/2206.00648v1

Dates: originally published: 2022-05-30, updated: 2022-05-30

Summary: Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over ahold' or moving average strategy.

Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength

Authors: Huifang Huang, Ting Gao, Yi Gui, Jin Guo, Peng Zhang

Categories: Portfolio Management, Mathematical Finance

PDF: http://arxiv.org/pdf/2205.15056v1

Dates: originally published: 2022-05-30, updated: 2022-05-30

Summary: Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems. Most of the current financial applications using RL algorithms are based on model-free method, which still faces stability and adaptivity challenges. As lots of cutting-edge model-based reinforcement learning (MBRL) algorithms mature in applications such as video games or robotics, we design a new approach that leverages resistance and support (RS) level as regularization terms for action in MBRL, to improve the algorithm's efficiency and stability. From the experiment results, we can see RS level, as a market timing technique, enhances the performance of pure MBRL models in terms of various measurements and obtains better profit gain with less riskiness. Besides, our proposed method even resists big drop (less maximum drawdown) during COVID-19 pandemic period when the financial market got unpredictable crisis. Explanations on why control of resistance and support level can boost MBRL is also investigated through numerical experiments, such as loss of actor-critic network and prediction error of the transition dynamical model. It shows that RS indicators indeed help the MBRL algorithms to converge faster at early stage and obtain smaller critic loss as training episodes increase.

Hull and White and Alòs type formulas for barrier options in stochastic volatility models with nonzero correlation

Authors: Frido Rolloos

Categories: Pricing of Securities

PDF: http://arxiv.org/pdf/2205.05489v3

Dates: originally published: 2022-04-30, updated: 2022-05-31

Summary: Two novel closed-form formulas for the price of barrier options in stochastic volatility models with zero interest rate and dividend yield but nonzero correlation between the asset and its instantaneous volatility are derived. The first is a Hull and White type formula, and the second is a decomposition formula similar in form to the Al`os decomposition for vanilla options. A model-free approximation is also given.

Deep calibration of the quadratic rough Heston model

Authors: Mathieu Rosenbaum, Jianfei Zhang

Categories: Risk Management, Computational Finance, Pricing of Securities, Mathematical Finance

PDF: http://arxiv.org/pdf/2107.01611v2

Dates: originally published: 2021-07-04, updated: 2022-05-30

Summary: The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm. We apply multi-factor approximation and use deep learning methods to build an efficient calibration procedure for this model. We show that the model is able to reproduce very well both SPX and VIX implied volatilities. We typically obtain VIX option prices within the bid-ask spread and an excellent fit of the SPX at-the-money skew. Moreover, we also explain how to use the trained neural networks for hedging with instantaneous computation of hedging quantities.

Reverse Sensitivity Analysis for Risk Modelling

Authors: Silvana M. Pesenti

Categories: Risk Management

PDF: http://arxiv.org/pdf/2107.01065v2

Dates: originally published: 2021-07-02, updated: 2022-05-31

Summary: We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output's distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output's distribution and satisfies the stress. We further derive the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples and which is implemented in the R package SWIM on CRAN. The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models.

Efficient approximations for utility-based pricing

Authors: Laurence Carassus, Massinissa Ferhoune

Categories: Computational Finance, Pricing of Securities

PDF: http://arxiv.org/pdf/2105.08804v2

Dates: originally published: 2021-05-18, updated: 2022-05-30

Summary: In a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. However, this price is not closed and requires numerical methods such as Monte Carlo or polynomial approximations to evaluate it. We show that these methods can be inaccurate and propose a deterministic decomposition of the reservation price using the Lambert function. This decomposition allows us to perform an improved Monte Carlo method called LMC and to give deterministic approximations of the reservation price and of the optimal strategies based on the Lambert function. We also give an answer to the problem of selecting a hedging asset that minimizes the reservation price and also the cash invested. Our theoretical results are illustrated by numerical simulations.

Mortgage Contracts and Underwater Default

Authors: Yerkin Kitapbayev, Scott Robertson

Categories: Risk Management, Pricing of Securities

PDF: http://arxiv.org/pdf/2005.03554v4

Dates: originally published: 2020-05-07, updated: 2022-05-31

Summary: We analyze recently proposed mortgage contracts that aim to eliminate selective borrower default when the loan balance exceeds the house price (the ``underwater'' effect). We show contracts that automatically reduce the outstanding balance in the event of house price decline remove the default incentive, but may induce prepayment in low price states. However, low state prepayments vanish if the benefit from home ownership is sufficiently high. We also show that capital gain sharing features, such as prepayment penalties in high house price states, are ineffective as they virtually eliminate prepayment. For observed foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed-rate contracts at mortgage rate spreads between 20-50 basis points. We obtain these results for perpetual versions of the contracts using American options pricing methodology, in a continuous-time model with diffusive home prices. The contracts' values and optimal decision rules are associated with free boundary problems, which admit semi-explicit solutions.

How brokers can optimally plot against traders

Authors: Manuel Lafond

Categories: Trading and Market Microstructure

PDF: http://arxiv.org/pdf/1605.04949v2

Dates: originally published: 2016-04-08, updated: 2022-06-02

Summary: Traders buy and sell financial instruments in hopes of making profit, and brokers are responsible for the transaction. There are several hypotheses and conspiracy theories arguing that in some situations, brokers want their traders to lose money. For instance, a broker may want to protect the positions of a privileged customer. Another example is that some brokers take positions opposite to their traders', in which case they make money whenever their traders lose money. These are reasons for which brokers might manipulate prices in order to maximize the losses of their traders. In this paper, our goal is to perform this shady task optimally -- or at least to check whether this can actually be done algorithmically. Assuming total control over the price of an asset (ignoring the usual aspects of finance such as market conditions, external influence or stochasticity), we show how in quadratic time, given a set of trades specified by a stop-loss and a take-profit price, a broker can find a maximum loss price movement. We also look at an online trade model where broker and trader exchange turns, each trying to make a profit. We show in which condition either side can make a profit, and that the best option for the trader is to never trade.

** Original source code: https://github.com/machalejm/arxiv_scraper **


Basecamp mass resignation - 1 year reflection and riff

After the 1 year anniversary of Basecamp's mass employee resignation, I've had some thoughts about the event and just wanted to riff:

For those that aren't aware, on April 26th, 2021, two days after writing a public love letter to buying a sauna "sight-unseen", Jason Fried made an announcement to the masses about major changes to the company direction.

What he seemed to want to impart to us (and also his employees as this was the first they would hear about it as well) was that difficult conversations were becoming distracting at work, and they wanted to make it a friendly space for everyone.

The subtext however was not subtle: The employees were operating in a manner that took some small amount of control from the owners, and that had to end.

And when I say the subtext was not subtle, well we can just quote directly:

"No more committees... Back to basics, back to individual responsibility, back to work."

That's right. No organizing, no fraternizing, do your work and only that.

"No more lingering on past decisions... It's time to get back to making calls, explaining why once, and moving on."

Who makes a call? Jason and DHH. Who explains it once? Jason and DHH. Don't like it or want to discuss alternatives? Tough, move on.

"[W]hen we need advice or counsel we'll ask individuals... rather than a pre-defined group at large."

Jason and DHH don't want your input. They don't need it. They don't care about it.

And lots more of the same.

Nearly all of their changes are about having full control, and being completely unwilling to share decision responsibility with those who actually create alongside them.

The spin on all of this was to make a more peaceful working environment, but that's definitely not what happened - certainly not in the immediate aftermath.

DHH got so much flack on Twitter, that he tried to post through it (just posting one unrelated topic after another) until finally he just turned off replies so that he could turn a blind eye to it all.

Employees started leaking past conversations and how Jason and DHH actually treated the company, and cracks in the ethos of the "it doesn't have to be crazy at work" guys started seeming a little larger than any had suspected.

Because this was about absolute control, and not really about their claims of creating a peaceful workspace, they ignored all negative feedback. So this wasn't a "how can we make things better" thought process. It was a "how can we take back all decision making".

---

A year on, and DHH still hasn't opened up his Twitter replies. A strong indication that there is still a lot of badwill that he is unwilling to deal with. I've been keeping up to date on the types of posts and comments DHH has made since then, and it's fascinating to see the sheer amount of spin he has tried to use to justify their decision. It's never addressed directly, but it's so painfully obvious.

A recent post made on May 5th, 2022 titled "Bring your work self to work" is just a screed on why it's actually good to be a drone at work, why it's good to be a cog, why you shouldn't deal with life at all when you're at work, you just need to work.

And that's not an exaggeration. Here's some direct quotes:

"Holding some of yourself back means having something in reserve."

Don't let the world effect your work. Don't let a harsh reality of life be something that Jason or DHH have to deal with in any way.

"All this used to be obvious. Self evident. Encoded in our work-place uniforms, even... I think we lost something value when we collectively gave it up."

The fact that people are complex individuals and not easily swapped out pieces of machinery is apparently a lost value?

"Work isn't owed all of you. Don't offer it."

This last line, by far, is doing so much doublespeak it might as well say "freedom through work" and just be done with it.

So while it's true work isn't owed all of you, DHH is co-opting worker's resistance language to promote the very opposite of employee empowerment. It's truly insidious.

Couple all this with DHH's radical shift toward bitcoin advocacy and right-wing talking points and I think what we see is a man who was always pulling the wool over people's eyes. Jason and DHH captured an initial audience with words on cooperative trust and work openness that they didn't ever actually believe. But now, they're free of the burden of that dishonesty, and can be their true selves. Ironically, even at work.

---

TLDR: The changes at Basecamp were never about making a better work environment, it was about taking absolute control.


AMA LIFESTORY NFT

microphone2fire REMINDER, MEGA AMA (ask me anything) LIVE, IN LESS THAN TWO DAYS, JUNE 6th!

This is a reminder that our MEGA AMA will be held in less than two days, Monday, June 6th.

This will be the third AMA LIVE we are holding, the two past were amazing moments spent with the community.

BUT THIS WILL BE LIKE NEVER BEFOREfire

J-L Verhelst, blockchain advisor, "BITCOIN; The Blockchain and beyond" Author, trainer and speaker & Merouane Saadi, "Next Broadcast" Co-Founder, Crypto Advisor will efficiently keep us informed of the latest news and updates about LifeStory, and prepare you for the mint.

Huge live rewards will be distributed, we will announce incredible news and all your questions will be answered.

You can’t miss Our MEGA AMA LIVE, Monday, June 6th fire

stopwatchTimezones :

New York (EST): 10.30 AM London (GMT): 3.30 PM Paris (GMT+1): 4.30 PM Moscow (GMT+3): 6.30 PM Dubai (UTC+4): 7.30 PM Beijing (GMT+8): 11.30 AM Sydney (GMT+11): 2.30 AM

Mark your calendar and set your reminders for you to not miss out our unforgettable live session. We are waiting for you, your questions, your excitement and your hype!

  • Do not forget to ask your questions in advance by sending them in #❔┃mega-ama-questions .Our team will keep them and answer them in our Mega AMA Live session.*

Secure your spot by following the planned event, register by clicking on the external link, and get notified when it starts.

Follow the event to get notified: https://discord.gg/m4gZDe9H?event=982067462306672670

Register to attend the LIVE: https://event.webinarjam.com/channel/LIFESTORYAMA3 Share this link with your friends.

We will randomly offer 10 Star List Spots, to those who register to the AMA Live before!

PS: To get the reward you need to attend the MEGA AMA Live until the end. We want to reward the most passionate about our project!

PS2: As the mint date for Galaxy List is approaching ( June 8th),

WE HAVE 2 MEGA BIG SURPRISES TO ANNOUNCE!

Please keep an eye on the announcements section, because on the next two days we will announce 2 surprises concerning the mint that you will love crazily.

Stay connected & spread the news lifer!

Life Story, your story in a space of infinite possibilities


It's still 2014 in crypto payments, and buying a burrito is now a taxable event

Chipotle takes crypto now! https://www.coindesk.com/business/2022/06/02/chipotle-now-accepting-cryptocurrency-payments-at-us-locations/

Well, no, of course it doesn’t. Chipotle is using a platform called Flexa. You put your cryptos into Spedn (a registered typo-mark, in the proper dot-com manner), your Flexa wallet, then you use the Spedn app to generate a “flexcode” barcode, which Chipotle then scans in some manner, and they hand you a burrito! Flexa sells the crypto and sends Chipotle dollars.

If you put your cryptos into Flexa, you can’t ever take them out again. This is for (checks Crypto Excuse Calendar) anti-money-laundering. They’re sure they’ll work this out within the next (rolls dice) several months. This includes payments in Gemini USD, which is apparently the most trusted form of crypto for payments - though not so trusted that they can risk giving you back your own money. https://help.flexa.network/en/articles/3044427-can-i-withdraw-my-cryptocurrency-balance-from-spedn

Don’t put too much into your Flexa wallet - you can only spend $750 from Spedn in any given week. No reason is given, but I’m sure someone will follow up with explanations why this is absolutely what the consumer wants and is actually good news for Bitcoin. https://help.flexa.network/en/articles/3142914-how-much-can-i-spend-using-flexa

How are your deposits insured? They’re covered by Gemini’s commercial insurance on their great big pile of cryptos. Gemini will absolutely claim any loss was your fault for not being good enough at being your own bank. https://help.flexa.network/en/articles/3142920-how-are-my-spedn-wallet-deposits-insured https://www.gemini.com/legal/user-agreement#section-digital-asset-insurance

Also, every burrito purchase triggers a taxable event - just what every consumer expects from using a payment system to buy a burrito. Flexa won’t be filing any paperwork, but you can have fun figuring out what to do with a CSV dump, or use one of their affiliate partners. Visa are quaking in their boots. https://help.flexa.network/en/articles/3740783-are-flexa-payments-taxable-events

In conclusion, cryptocurrency payment systems have not advanced one dot since 2014, and may in fact have gone backwards.

What times we live in, where this sentence exists: buying a burrito is a taxable event.