Guiding seminar 3 (2020) Flashcards
Bitcoin: Economics, Technology, and Governance
What are the Bitcoin intermediaries?
▪ Currency exchanges: designated institutions that convert traditional
currencies into bitcoins. CEs are in the purview of many regulators and are
often the targets of perpetrators
▪ Digital wallets: applications for managing a user’s account in a more
convenient way (less space, more mobility, better interface), but prone to
hackers
▪ Mixers: sending orders from multiple accounts to one and then from that
account to destination accounts. All transaction traces are lost in this way
(actually, not really..)
▪ Mining pools: miners can join their (computational) efforts and split the
rewards
Bitcoin: Economics, Technology, and Governance
What are the uses of Bitcoin?
▪ Illicit activities (e.g. drugs trading through the Silk Road). Gambling
services. Evading international capital controls
▪ Consumer payments: very low cost for retailers compared to credit card
charges, mixed effects for consumers (no rebated or bonuses; currency
exchange charges; block chain processing time and storage burdens)
▪ Buy-and-hold (for price appreciation)
▪ Possible future: general purpose payments, mainstream store of value,
and enabling technology (international remittances, transfers of digital
property, and other services besides payments)
Bitcoin: Economics, Technology, and Governance
What are the risks associated with Bitcoin?
▪ Market risk: the value of bitcoins is highly volatile (fluctuations in the
exchange rate between bitcoin and other currencies)
▪ Liquidity risk: (the shallow market problem) – a person seeking to trade a
large amount of bitcoins typically cannot do so quickly without affecting
the market price
▪ Counterparty risk: currency exchanges may cease operations (45%)
without reimbursing their consumers (46%), while digital wallet services
are lucrative targets for cybercriminals
▪ Transaction risk: i) no built-in mechanism to cancel a transaction if bitcoins
are sent due to error or fraud (irreversible transactions); ii) possibility to
cancel the payment or double-spend during the 10-minute interval of
block processing (miner collusion); iii) blacklisting stolen bitcoin transfers
losses to those who accepted them and gives abuse power to list managers
▪ Operational risk: operator errors, security flaws, and malware (miner “51
percent attack”, denial-of-service attack – swamping a target firm with
messages and requests so that it becomes unusable or very slow)
▪ Privacy-related risks: transactions could be linked back to people who
made them (real names are often revealed at currency exchanges or in
purchase details)
▪ Legal and regulatory risks: a law-abiding user could lose funds in an
exchange frozen or seized due to criminal activity
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What is Moore’s and Murphy’s law?
▪ Moore’s law in financial markets: from 1929 to 2009 the total market capitalization of the US stock market has been doubling every decade
▪ Murphy’s law: “whatever can go wrong will go wrong” (faster and with worse consequences when computers are involved)
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What is algorithmic trading? What are the benefits of AT?
Algorithmic trading (AT): the use of mathematical models, computers, and
telecommunications networks to automate the buying and selling of financial
securities (benefits: cost savings, operational efficiency, and scalability)
▪ Cheaper, enhances operational efficiency and promotes economies of scale, but
creates tighter interconnections in the financial system
▪ Regulation is outdated and its philosophy is ripe for an overhaul
▪ AT is not bad per se, but it needs to be appropriately monitored to benefit the
society at large
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What encouraged the rise of AT?
▪ The financial industry is becoming more complex over time (not less) – marginal product of more sophisticated financial technology is increasing
▪ A number of breakthroughs in quantitative modelling of financial markets: Black, Cox, Fama, Lintner, Markowitz, Merton, Miller, Modigliani, Ross, Samuelson,
Scholes, Sharpe, and others
▪ Parallel breakthroughs in computer technology – Moore’s law in technology. Storage and processing speeds have changed the way financial technology
operates
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What 5 developments fueled the popularity of AT?
1) Quantitative models in finance:
▪ Markowitz portfolio theory. The CAPM. Factor models for return estimation. The BMS options pricing model. Dynamically complete markets
2) The emergence of index funds:
▪ “passive” investing: value-weighted portfolio need not be adjusted as it automatically does so as Mcap changes; Samsonite’s pension fund: rebalancing
weights to keep the $1 investment in each stock
3) Arbitrage trading (incl. statistical arbitrage)- one human cannot monitor all possible possibilities, AT can.
▪ Even if most arbitrage opportunities are not riskless, if you can manage and quantify their risks, they become an attractive investment opportunity. Statistical
arbitrage: large arbitrage portfolios are formed to maximize expected returns while minimizing volatility
4) Automated order execution and market making:
▪ Slicing and dicing orders to execute them in the optimal (cost-efficient) manner
without moving the price too much (despite a downward-sloping demand curve)
▪ Market-making: continuously quoting prices and standing ready to buy and sell
securities at those prices so as to gain on bid–ask spreads. AT allows for more
sophisticated dynamic risk management (specify how an bid/ask should be
adjusted following bid/ask orders)
5) High-frequency trading (HFT):
▪ Trade at incredibly high frequencies and short time intervals. HFTs’ impact on
liquidity and market quality remains ambiguous: more trading can promote
liquidity, but HFTs are often accused of predatory trading that consummates
liquidity
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What are some examples of AT failure?
- Facebook IPO glitch–> too high demand, the AT had to recompute price with every new order–> created a 30 min delay
- The perfect financial storm- in the source of 33 minutes, prices os most actively traded companies crashed and recovered (Apple traded only $100,000 per share).
- BATS IPO- software bug made symbols of stock from A to BFZZ inaccessible –> IPO was canceled
- Knight Capital Group- because of a technology issue sent out erroneous orders into the market–> the company had to liquidate the position–> huge losses
- High-frequency manipulation- spoofing and layering- placing orders to create a false impression of demand/supply and later trade in the opposite direction.
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What is spoofing? Layering?
Spoofing- placing an order in a certain direction (buy/sell) to create a false impression of demand/supply for a particular security and to later trade in the
opposite direction of the initial order, profiting from the imbalance
Layering- the same but or a series of orders at different prices (?)
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What are the regulation proposals for AT?
–> Do nothing: will result in cost reductions for intermediaries, but will not address
the issue of fair and orderly markets
–> Ban AT altogether: will yield a more “fair” and orderly market, but also reduce
liquidity, efficiency, and capital formation
–> Change the definition and requirements of market makers to include HFT: will
lead to a more fair and orderly market, but will also increase the costs for
intermediaries
–> Fix time intervals between trades (market continuity): leads to reduced
immediacy. Need more analysis to evaluate the effects connected with demand for
immediacy
–> Introduce a “Tobin tax” on all transactions: will reduce trading activity, liquidity
and make hedging more costly (think option dynamic replication) and remove HFT
Moore’s Law versus Murphy’s Law: Algorithmic
Trading and Its Discontents
What are financial regulations 2.0 for the AT?
- -> Systems-engineered: should approach financial markets as complex systems composed of multiple software applications, hardware devices and human personnel
- -> Safeguards-heavy: both human and machine safeguards are necessary to ensure the safe functioning of the system
- -> Transparency-rich: should aim to make the design of financial products more transparent and accessible to regular automated audits
- -> Platform-neutral: should be designed to encourage innovation in technology and finance and should be neutral with respect to the specifics of how core computing technologies work
The Economic Consequences of Legal Origins
What is a legal origin? What are the possible reasons for divergance?
Legal origin (LO) is a style of social control of economic (and not only) life (English common law vs. French civil law) ▪ English law puts a greater emphasis on judicial independence and the enforcement of property rights, in part through the active use of case law (precedents) ▪ French law sets maintaining social order as its top priority and attempts to write laws and statutes that require strict enforcement rather than interpretation
Possible historical reasons for the divergence:
▪ Common law – developed because the side of lawyers and property owners wanted to limit the
crown’s ability to interfere in the markets;
▪ Civil law – rediscovered in the Middle Ages and adopted by the Catholic church. Policyimplementing, state-desired allocations. Written during the 19th century French Revolution in
Napoleon’s codes to deprive judges, who were on the losing side with royalty, of law-making
power (i.e. create a legislation that can foresee all future circumstances)
The Economic Consequences of Legal Origins
Compare the investor/ creditor protection between the common and civil law? Regulations? Judicial institutions?
LO → investor/creditor protection → economic/financial development
▪ Common law is associated with significantly better protection of creditors and outside investors,
both through rules and enforcement
LO → regulation → economic/financial development
▪ Common law is associated with less intervention in the markets/societal organization
LO → judicial institutions → contract enforcement/property rights
▪ Common law is associated with more judicial independence
Common law appears to be associated with better rules and in turn stronger financial development that leads to better economic outcomes (e.g. [arguably] faster economic growth). (Arguably because there are other confounding variables, such as human
capital)
The Economic Consequences of Legal Origins
Why does common law lead to better economic outcomes on average?
▪ Common law is more respective of private property and contracts than civil law
▪ Common law puts more emphasis on unconditioned private contracting rather than
centrally-directed regulation (“dispute-resolving” vs. “policy-implementing”)
▪ Common law features a more adaptive framework and evolves over time thanks to
greater judicial independence
The Economic Consequences of Legal Origins
When does common law works worse for the economy?
▪ When there’s disorder, civil law solutions will cope better (the example of Nigeria)
▪ Other country-specific variables have to be considered in evaluating the suitability of a legal
system
▪ All countries mix both systems and can implement solutions from both (neither of the two
systems is perfect)
