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ASPECTS OF FINANCIAL SYSTEM Flashcards

1
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What were the largest 5 investment banks in the US in the time of crisis?

A

Bear Stearns, Lehman Brothers, Merrill Lynch, Goldman Sachs, and Morgan Stanley

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2
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

In broad terms, how was the government regulation of finance firms before the crisis?

A

A well-supervised financial system could have been more resilient to such event, but the impact on real economy was much larger than necessary.

The largest firms were permitted to have insufficient capital and liquidity relative to the risks they took.
Oversight of the capital adequacy of the largest investment banks by the Securities and Exchange Commission (SEC) was particularly lax. AIG was not effectively supervised as well.

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3
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

Who are market makers?

A

A dealer in securities or other assets who undertakes to buy or sell at specified prices at all times.
E.g. sometimes if you want to sell a security, no one is willing to buy it from you (sad), thus, a market maker will buy it, and sell it sometime later, so everyone’s happy. He gets bid-ask rate as profit.

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4
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What are repos?

A

Short-term borrowing for dealers in government securities. One sells government securities to someone else, usually on an overnight basis, and buys them back the following day at a slightly higher price.

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5
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What are tri-party repos?

A

Banks deal with repos, so that two parties do not have to deal with it themselves. Cash investors held their collateral securities (overnight) at tri-party banks.

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6
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What is a credit crunch?

A

Decrease in lending by financial institutions. Usually an outcome of flight to safety, when the banks cannot pay back the money or issue new loans.

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7
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

2 things that triggered the financial crisis.

A

Over-leveraged homeowners,

severe downturn in US housing markets.

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8
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What are the key sources of why crisis happened? (4)(fragility)

A
  1. Weakly supervised balance sheets of largest banks
  2. The run-prone designs
  3. Weak regulation of the markets for securities and OTC derivatives.
  4. Reliance of regulators on the market discipline.
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9
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

Why was supervision of the banks so bad? (5)

A
  1. SEC put it as their mission to protect customers of financial firms rather than financial stability, they devoted very few resources for supervising (only 4 staff members for big firms). Investment firms actually knew this and chose SEC instead of FED.
  2. High difficulty level of assessing risk, derivatives and flight-proneness (everyone too dumb dumb to understand what the heck was happening)
  3. Everyone assigned low probabilities to disasters happening
  4. Reliance on market discipline
  5. Historical emphasis on decentralized banking system.
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10
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

Describe credit provision in the US.

A

Credit provision in the US is more dependent on capital markets (where savings and investments are moved between suppliers of capital and those who are in need of capital) rather than how it’s usually done.

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11
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

Why were repos a problem?

A

Capital markets in the US rely heavily on largest dealers who used short-term financing A LOT (mainly through repos). Intra-day financing (when investment bank repos expired, they repaid cash, and were in need for financing until new repos, which was provided by tri-party agent banks) created systemic risks. (2.8$ trillion were issued).

When there was a risk of solvency, cash investors could decide to not renew daily financing (not do repos anymore), thus, the other party (the one selling collateral) needs to sell it really quickly - at fire-sale prices. Also, banks would sell the collateral, as the dealer (investment bank) is not paying back cash, and the cash investors who had bought collateral would sell it too. All this is done at fire -sale (very low) prices.

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12
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What is a SIV?

A

Special Investment Vehicles. They attempt to profit from the spread between short-term debt and long-term investments by issuing commercial paper of varying maturities.

Particularly prone to runs, could have caused a complete meltdown of securities financing market. It happened in 2008, only FED and US Treasury invoked emergency lending and saved economy from an even worse state.

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13
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What regulations have been implemented since the crisis? (7)

A
  1. Elimination of intra-day credit provision by tri-party agent banks.
  2. Securities inventories are smaller - reduced need for financing
  3. Declining presumption of “too big to fail” has led dealer financing costs to increase, incentive to hold giant inventories is reduced.
  4. Tighter regulation of money funds - Reduced dependence of dealers on flight-prone financing from money market mutual funds.
  5. Bank capital requirements apply to all large dealers
  6. The two investment banks that survived, took banking charters and are regulated as banks (under FED).
  7. New bank liquidity coverage regulations introduced.
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14
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

Why were OTC derivatives dangerous?

A

No regulations, very complex trading, no observable risk exposures. When derivatives runs happened, they drain liquidity and eliminate hedges needed by the dealer, increase in concern about creditworthiness of investment banks.

AIG had sudden heavy margin calls on credit-default-swap protection that it had provided to major dealers.
The dependence of these dealers on AIG’s performance on these credit default swaps was an important factor in the decision by the Fed and then the Treasury to rescue AIG.

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15
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What is a margin call?

A

Margin call occurs when the value of an investor’s margin account (that is, one that contains securities bought with borrowed money) falls below the broker’s required amount. A margin call is the broker’s demand that an investor deposit additional money or securities so that the account is brought up to the minimum value, known as the maintenance margin.

A margin call is usually an indicator that one or more of the securities held in the margin account has decreased in value. When a margin call occurs, the investor must choose to either deposit more money in the account or sell some of the assets held in their account.

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16
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What regulations have been set for OTC derivatives since the crisis?

A
  1. Increased use of CENTRAL CLEARING (clearinghouses enter a derivatives trade as the buyer to the original seller, and as the seller to the original buyer).
    - -> original counterparties become insulated from each other’s default risk
  • -> improves the transparency of derivatives positions
  • -> enforces uniform collateral practices that are more easily supervised by regulators, all swap transactions must be reported publicly
  1. New REGULATORY CAPITAL REQUIREMENTS (amount of capital a bank or other financial institution has to have as required by its financial regulator)
  2. COMPRESSION TRADING —> fintech approach that helps to eliminate redundant sequences of derivative positions (way to reduce the number of outstanding contracts but keep the same economic exposure)
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17
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

Why is “too big to fail” bad?

A

People assumed that:
1. Banks can be relied upon to provide rigorous risk control.

In reality, banks risk management were topped by going after profits.

  1. Markets will always self-correct.
    People relied on market discipline - excessive risk taking will be limited by cost of debt financing risk of losses at insolvency. BUT, there was no plan for resolving insolvency systemically important financial firms without triggering or deepening a crisis.
    People started saying that largest firms are too big to fail, government would save them - moral hazard was created.

The incentive to borrow caused by being too big
to fail and the lack of methods for safely resolving
an insolvency of any of these firms, combined
with the forbearance of regulators, created an
increasingly toxic brew of systemic risk.

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18
Q

Prone to Fail: The Pre-Crisis Financial System
Duffie, D. (2019)

What are the unresolved problems of the financial crisis?

A
  1. There is still no known operational planning for US government failure resolution of derivatives clearinghouses
  2. Regulations have forced the majority of derivatives risk into these clearinghouses, which are the new “too big to fail” financial firms
  3. A threat that fading memories of the costs of the last crisis will lower the resolve and vigilance of legislatures and financial regulators to monitor changes in practice and to take steps to control socially excessive risk-taking
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19
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe Moore’s Law in financial markets

A

From 1929 to 2009 the total market capitalisation of the US stock market has been doubling every decade

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20
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe Murphy’s Law

A

“whatever can go wrong will go wrong” (faster and with worse consequences when computers are involved)

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21
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is algorithmic trading?

A

The use of mathematical models, computers, and telecommunications networks to automate the buying and selling of financial securities

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22
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What are (3) benefits of Algorithmic trading?

A
  • lowering costs / scalability
  • reducing human error

• increasing productivity

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23
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What major developments in the financial industry have facilitated growth of algorithmic trading? (5)

A
  1. Quantitative models in finance
  2. The emergence and rapid increase of index funds
  3. Arbitrage trading activities
  4. The push for lower costs of intermediation and execution
  5. The increase of high-frequency trading
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24
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

How has quantitative finance contributed to rise of algorithmic trading? (what are the breaktrough models?)

A
  1. Portfolio Optimization Theory (Markowitz)
  2. CAPM
  3. Statistical and computational achievements
  4. BMS
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25
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is two fund separation theorem?

A

A risk-less bond and a mutual fund—the tangency portfolio— are the only investments needed to satisfy the demands of all mean–variance portfolio optimisers. (think CML)

Once a portfolio has been established, the algorithmic trading strategy—the number of shares of each security to be bought or sold—is given by the difference between the optimal weights and the current weights.
(think CML but done by computers)

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26
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

How has concept of passive investing changed?

A

Now, investment is called “passive” if it does not require any discretionary human intervention— it is based on a welldefined and transparent algorithm. Today, a passive investor may be an active trader to minimise transaction costs, manage risks, participate in new investment opportunities or respond more quickly to changing objectives and market conditions.

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27
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is the “recipe” for an index fund?

A
  1. define a collection of securities by some set of easily observable attributes
  2. construct a portfolio of such securities weighted by their market capitalisation
  3. add and subtract securities from this collection from time to time to ensure that the portfolio continues to accurately reflect the desired attributes
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28
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Why it was usual to fix the set of securities and value-weight them (index funds)?

A
  1. reduce the amount of trading needed to replicate the index in a cash portfolio;
  2. a value-weighted portfolio need never be rebalanced since the weights automatically adjust proportionally as market valuations fluctuate

—> most investors and managers equated “passive” investing with low-cost, static, value-weighted portfolios

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29
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What are new investment products that are now passive investing despite active nature of their trading due to automation?

A
  1. TARGET-DATE FUNDS (class of mutual funds that rebalances asset class weights over time so that it begins heavier to stocks when you are younger and heavier to bonds as you age)
  2. ETFs
  3. STRATEGY INDEXES such as 130/30 (short selling 30% of stocks in portfolio)
  4. CURRENCY CARRY-TRADE ( a high-yielding currency funds the trade with a low-yielding currency)
  5. HEDGE - FUND REPLICATION
  6. TREND - FOLLOWING FUTURES
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30
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

How has arbitrage trading contributed to the rise of algorithmic trading?

A

Algorithmic traders try to profit from deviations in the law of one price (the most popular method). However, in practice, it is rarely the case that prices differ for seemingly identical cash flows, and risk-less profit does not exist.

However, if the statistical properties of the arbitrage portfolios can be quantified and managed, the risk/reward profiles of these strategies might be very attractive to investors with the appropriate tolerance for risk:
Examples:
Development of “statistical arbitrage strategies” in the 1980s - large portfolios of equities were constructed to maximise expected returns while minimising volatility.

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31
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe statistical arbitrage strategies.
Why can they profit during market downturns?

A

Large portfolios of equities constructed to maximise expected returns while minimising volatility.

  • The risks embedded in statistical arbitrage strategies are different from market risk. Arbitrage portfolios are long and short, and hence they can be profitable during market downturns.
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32
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What 2 important roles do arbitrage strategies play in the financial system? Describe how.

A

LIQUIDITY PROVISIONS: arbitrageurs increase the amount of trading activity, ensuring greater liquidity (investors can now buy or sell securities more quickly, in larger quantities, and with lower price impact).

PRICE DISCOVERY: because arbitrage trading exploits temporary mispricings, it tends to improve the informational efficiency of market prices.
• However, if arbitrageurs become too dominant in any given market, they can create systemic instabilities —> “Quant Meltdown”

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33
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe the quant meltdown.

A

In 2007 during 2 days some of the most
successful hedge funds suffered record losses. These losses were highest among quantitatively managed equity market-neutral or “statistical arbitrage” hedge funds.

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34
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe Market Making

A

An intermediary participates in buying and selling securities to smooth out temporary imbalances in supply and demand because buyers and sellers do not always arrive at the same time.

35
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Why is algorithmic trading central to the automation of large buy and sell orders of publicly traded securities?

A

Executing a large order in a single transaction is typically more costly than breaking up the order into a sequence of smaller orders.

Automation of the trading process means that the rewards from market making activities accrue not necessarily to those who register with the exchanges
as their designated market makers, but to those with the best connectivity, best algorithms, and best access to customer order flow. (nu pagaidām ir liels wtf)

36
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe the steps to become a market maker

A
  1. Register as a designated MM —> satisfy certain net capital requirements
  2. Provide continuous two-sided quotes during trading hours (bid-ask)
  3. Demand compensation (the bid-ask spread) to protect yourself against possible losses
37
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is high frequency trading?

A

A form of automated trading that takes advantage of innovations in computing and telecommunication to consummate millions upon millions of trades per day.

High-frequency trading is estimated to account for 40 to 60
percent of all trading activity across the universe of financial markets (2012).

38
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Do high frequency traders engage in provision of liquidity in markets?

A

In contrast to a number of public claims, high-frequency traders do not engage in the provision of liquidity like traditional market makers. IN FACT, THOSE THAT DO NOT provide liquidity ARE THE MOST PROFITABLE and their profits increase with the degree of “aggressive,” liquidity-taking activity.

39
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe Unwind Hypthosis (with respect to the quant meltdown)

A

To unwind a position is to close it out.
A large equity market - neutral portfolio had to raise cash and unwind, leading to big price impacts and other similar portfolios now had to do the same to lessen losses -> the deadly loop of coordinated forced liquidations leading to the deterioration of collateral value.

Bottom line: the scaling up and down of portfolios can affect many other portfolios and investors.
Algorithmic trading greatly magnifies the impact of these consequences

40
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is a market-neutral strategy?

A

Investors seek to profit from both increasing and decreasing prices in market, while attempting to completely avoid some specific form of market risk.

41
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe the Flash Crash of 2010

A

In the course of 33 minutes, US financial markets experienced one of the most turbulent periods in their
history. The Dow Jones Industrial Average declined enormously and stock prices of some of the world’s largest companies traded at weird prices ( Accenture traded at a penny a share, while Apple traded at $100,000 per share)

Why did it happen?

  1. Algorithm caused a rapid sale of 75000 E-mini S&P 500 stock index future contracts (worth about 4.1$ billion)
  2. High frequency traders reached critical inventory levels after 10 minutes and began unwinding when liquidity was an issue
  3. Cross-market arbitrage trading algorithms rapidly sped up price declines in the E-mini futures market to the markets for stock index ETFs

BOTTOM LINE: A liquidity event in the futures market triggered by an automated selling program cascaded into a systemic event for the entire US financial market system.

42
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe Pricing Initial Public Offerings in the Digital Age - Facebook

A

Interest in Facebook shares was too high —> NASDAQ system was trying to (re)calculate the price while
new offers and cancellations kept coming in, which created an infinite loop that required human

intervention
• a 30-minute delay in Facebook’s IPO
• errors persisted for hours
• heavy losses to traders, some didn’t get any shares, others overbought (around $100m)

43
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe Pricing Initial Public Offerings in the Digital Age - BATS

A

• BATS operated as the third-largest stock exchange in the United States and was among the most
technologically advanced firms

  • BATS decided to list its IPO on its own stock exchange;
  • When BATS launched its own IPO its price plunged to less than a tenth of a penny in a second and a half due to a software bug affecting stocks with ticker symbols from A to BFZZZ, creating an infinite loop that made these symbols inaccessible on the BATS system
  • BATS was forced to suspend trading in its own stock, and cancelled its IPO
44
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

Describe what happened to Knight Capital Group

A

Knight Capital Group sent out incorrect orders into the market because of a technology issue with newly-installed software.
These orders and the unintended trades resulted in a rapid accumulation of positions “unrestricted by volume caps”—> significant price swings in 150 stocks.

The company had to liquidate its positions, resulting in a $457.6 million loss, wiped out capital, stock lost 70% of value, were forced to seek rescuers.

45
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is spoofing?

A

Intentionally manipulating prices by placing an order to buy or sell a security and then canceling it shortly thereafter, at which point the spoofer completes a trade in the opposite direction of the canceled order.

YOU ARE ORDERING TO BUY STOCK, CANCEL SHORTLY AFTER, THEN SELL STOCK (you have pushed the price up, now it is profitable to sell)

Happens over fractions of seconds

46
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is layering?

A

Placing a sequence of limit orders at successively increasing or decreasing prices to give the
appearance of a change in demand and artificially increase or decrease the price that unsuspecting

investors are willing to pay; after a trade is completed at the manipulated price, the layered limit orders
are canceled.

YOU PLACE A LOT OF LIMIT ORDER TO BUY STOCKS, ARTIFICIALLY INCREASE PRICE, SELL SHARE AT THIS HIGH PRICE, CANCEL LIMIT ORDERS.

Happens over fractions of seconds

47
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What is the regulatory struggle of algorithmic trading?

A

The software and hardware that control financial markets have become so complex that no individual or group of individuals is capable of conceptualising all possible interactions that could occur among various components of the financial system.

Financial regulatory framework has become obsolete in the face of rapid technological advances that drastically reduced costs to intermediation, but have not correspondingly increased or distributed the benefits of greater immediacy

—> the tradeoff between

1) the costs to different types of intermediaries for maintaining a continuous presence in a market and
2) the benefits to different types of market participants for being able to execute trades as “immediately” as possible

48
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What are the proposed policies in regulating Algorithmic Trading? Describe

A

1.DO NOTHING: would allow intermediaries to find more ways to reduce the costs of being continuously present in the market, leading to an even greater supply of immediacy and more efficient trading, but is unlikely to address investors’ concerns about fair and orderly markets
2. BAN: will lead to fairness, but also reduce liquidity, efficiency, and capital formation
3. CHANGE THE RULES OF WHO CAN BE A DESIGNATED INTERMEDIARY: may also
lead to more fair and orderly markets since such designations will prevent them from withdrawing from the market when their services are needed most. However, such redesignation would also increase the cost to intermediaries of being present in the
market due to higher capital requirements, additional compliance costs for each designated market, and greater legal costs by virtue of being a regulated entity.
4. FORCE TRADING TO OCCUR AT FIXED DISCRETE INTERVALS OF TIME: leads to reduced immediacy. Need more analysis to evaluate the effects connected with demand for immediacy
5. TOBIN TAX on all financial transactions: will reduce trading activity, liquidity and make hedging more costly and remove HFT

49
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What are the proposed policies in regulating Algorithmic Trading? Describe.

A

1.DO NOTHING: would allow intermediaries to find more ways to reduce the costs of being continuously present in the market, leading to an even greater supply of immediacy and more efficient trading, but is unlikely to address investors’ concerns about fair and
orderly markets

  1. BAN: will lead to fairness, but also reduce liquidity, efficiency, and capital formation
  2. CHANGE THE RULES OF WHO CAN BE A DESIGNATED INTERMEDIARY: may also
    lead to more fair and orderly markets since such designations will prevent them from withdrawing from the market when their services are needed most. However, such redesignation would also increase the cost to intermediaries of being present in the
    market due to higher capital requirements, additional compliance costs for each designated market, and greater legal costs by virtue
    of being a regulated entity.
  3. FORCE TRADING TO OCCUR AT FIXED DISCRETE INTERVALS OF TIME: leads to reduced immediacy. Need more analysis to evaluate the effects connected with demand for immediacy
  4. TOBIN TAX on all financial transactions: will reduce trading activity, liquidity and make hedging more costly and remove HFT
50
Q

Moore’s Law vs. Murphy’s Law: Algorithmic Trading and Its Discontents
Kirilenko, Andrew (2013)

What financial regulation design principles do authors suggest about algorithmic trading?

A
  1. SYSTEMS: should approach financial markets as complex systems composed of multiple software applications, hardware devices and human
    personnel
  2. SAFEGUARDS: both human and machine safeguards are necessary to ensure the safe functioning of the system
  3. TRANSPARENCY: should aim to make the design of financial products more transparent and accessible to regular automated audits
  4. PLATFORM: 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
51
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What is the main point Zingales is trying to get across?

A

Interaction of concentrated corporate power
and politics is a threat to the functioning of the free market economy and to the economic prosperity it can generate, and a threat to democracy as well.

52
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Describe the current situation of large firms

A

10 companies are among the largest 30 entities in the world in a list of corporate and government revenues (2015). All of them have higher annual revenues than government of Switzerland, Norway, Russia.

Large corporations have large security forces, public relations offices, more lawyers than the US Justice Department, enough money to capture majority of elected representatives.

COMPANIES ARE OFTEN MORE POWERFUL THAN GOVERNMENTS lacking only direct power to wage war and legal power to detain people.

53
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Describe theory of the firm. Is it true?

A

Firm is simply a nexus (collection) of contracts with no objectives separate from contracting parties.

This is NOT reality of giant global corporations. The largest modern firms have massive economic (and political) power in the hands of a few people, who are not really accountable to anyone.

54
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Describe the situation of East India Company

A

Formed for the exploitation of trade.
A 15-year monopoly right that lasted 233 years.

A harsh reminder of how dangerous the mixing of economic and political power can be.

As British Parliament tried to introduce competition for the East India Company, company’s stockholders bought shares of a rival and forced to merge, becoming monopoly again.
When monopoly expired, they would pay bribes to extend it.
The company prohibited local traders and dealers from
“hoarding” rice —> 10 million people died of starvation when drought struck.
Company lost monopoly of trade with India, it promoted export of opium to China, started two opium wars.

55
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Describe the Gilded Age (19th century) in the context of corporations

A

Incorporation (the legal process used to form a corporate entity or company) became a right of citizens.

The rise in economies of scale during Second Industrial Revolution contributed to ensuring market power to firms.

In a winner-take-all economy, entrepreneurs lobby and corrupt, not only to seize a crucial first-mover advantage, but also to preserve their power over time as they fear political expropriation (the action by the state of taking property from its owner for public use or benefit).

56
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Describe The Power of Competition and Takeovers (20th century)

A
  1. Competitive selection process eliminates a lot of managerial discretion by the pressure of the corporate control market. A publicly traded firm that is not performing good, is an arbitrage opportunity - someone can buy it, fix it, resell or operate, make money.
  2. Moving attention more away from the “power” aspect towards a more technological one. Neoclassical economics argued, in a world with perfect competition and no transactions costs, firms are nothing
    more than isoquant maps. However, it turns out that even in a perfectly competitive environment, corporations are powerless only if there is perfect contractibility (an agreement enforceable by law)
57
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What is the Incomplete Contract Paradigm?

A

Most contracts are incomplete - they will not fully specify the division of surplus in every possible way. The theory of incomplete contracts creates scope for lobbying, rent seeking, and power grabbing.

58
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What factors affect incompleteness contract, which creates room for bargaining?

A
  1. Which party has the ownership
  2. Availability of alternatives
  3. Institutional environment

All these factors determine the allocation of authority or power.

59
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What is the “Medici vicious circle”?

A

MONEY USED TO GAIN POLITICAL POWER
->POWER USED TO MAKE MONEY -> MONEY USED TO GAIN POLITICAL POWER ->ETC.

Most firms are actively engaged in protecting their source of competitive advantage through a mixture of innovation, lobbying, or both.

—> when there is more INNOVATION -> NOthing to WORRY about
—> when LOBBYING is dominant, things get
PROBLEMATIC

60
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What has happened with US industries in the last two decades? (think: size, public listings)

A

In the last two decades, more than 75% of US industries experienced an increase in concentration levels

The size of the average publicly listed company in the United States tripled in market capitalisation.

61
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Name two trends to why US companies have become so large in size.

A
  1. the reduction in the rate of birth of new firms

2. very much merger activity (you merge, you get big)

62
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What are (3) explanations for US firms to have become so large?

A
  1. NETWORK EXTERNALITIES: situations in which an increase in usage leads to a direct increase in value for other users (Internet had no value initially but when a lot of people started using it, it is super nice)
  2. WINNER-TAKE-ALL INDUSTRIES: the increase of information-intensive goods that have high fixed and low- marginal costs (Walmart)
  3. REDUCED ANTITRUST ENFORCEMENT:
    antitrust laws - a collection of federal and state government laws that regulates the conduct and organization of business corporations, generally to promote competition for the benefit of consumers.
    When this is reduced, monopolies can arise more.
63
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Name and describe (3)
the power of corporations to shape the rules of
the game has become stronger for three main reasons:

A
  1. Increased SIZE and MARKET SHARE —> lower competition; corporations are more powerful.
  2. The size and complexity of REGULATION has INCREASED, which makes it easier to tilt the playing field in the firm’s advantage for personal reasons of involvement.
  3. END OF ANTIBUSINESS IDEOLOGY that previously prevailed among Democrats. This has reduced the costs of being perceived as too friendly to the interests of big business for both parties
64
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Describe ideal state between power of state and power of firms (goldilocks)

A
  1. If the state is too weak to enforce property rights, then firms will either enforce them by themselves or collapse.
  2. If a state is too strong, rather than enforcing property rights it will be tempted to take property from firms
  3. When firms are too weak they risk being expropriated (they will need to give their property to government either directly or by paying large returns on investments)
  4. When firms are too strong they may shape the definition of property rights and its enforcement in their own interest and not in the interest of the public.
65
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What is goldilocks principle?

A

The Goldilocks principle states that something must fall within certain margins, as opposed to reaching extremes. (no pasakas “Zeltmatīte un 3 lāči”

66
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What are the mechanisms (4) in the formation of a democratic consensus?

A
  1. The world of the MEDIA, can be influenced by the political power (through censorship, ownership,
    subsidies, and leaks) and by the economic power (through advertising, direct ownership, financing, and

access to information). SHOULD NOT BE AFFECTED BY ANY.
2. ELECTORAL PROCESS shaped both by the electoral law and by the rules for campaign financing. A mixture of limitations on private donations, matched by public financing, is an attempt to find a
balance
3. IDEOLOGY: in some countries, political legitimisation is linked to a formal election process; in other
countries, governments formed in different ways are nonetheless regarded as legitimate. Ideology is also
based on perceptions of the relative benefits of being dominated by economic interests
4. JUDICIARY POWERS: differ in their degree of independence from the political and the
economic powers and in their prevalent ideology

67
Q

Towards a Political Theory of the Firm.
Zingales (2017)

Economic characteristics: what does company’s ability to obtain what it wants from the political system depend on (4)?

A
  1. ability to make credible LONG-TERM PROMISES (for example, future employment opportunities for
    politicians and regulators), which is highly dependent upon a company’s long-term survival probability
  2. the GRIP a company has ON the market for SPECIFIC HUMAN CAPITAL (how many potential
    employers of nuclear engineers are there?)
  3. company’s ability to wrap its self-interest in a bigger, noble IDEA (for example, Fannie Mae and the goal
    that every American should be able to borrow to purchase a house)
  4. the CONTROL that a company has THROUGH its IMAGE in society by way of employment, data ownership, media ownership, advertising, research funding, and other methods

A firm’s size and the level of concentration within a market affect positively all the crucial factors that determine a firm’s ability to influence the political system.

68
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What political tools may be put to use to limit corporate risks to politics? (6)

A
  1. increases in TRANSPARENCY of corporate activities;
  2. improvements in CORPORATE DEMOCRACY;
  3. better rules against revolving doors and more attention to the risk of capture of scientists and economists by corporate interests (?????)
  4. more AGGRESSIVE USE of the ANTITRUST authority;
  5. attention to the functioning and the INDEPENDENCE of the MEDIA market.

BROADER PUBLIC AWARENESS

69
Q

Towards a Political Theory of the Firm.
Zingales (2017)

What non-market factors Medici vicious circle depends upon? (5)

A
  1. the main source of political power
  2. the conditions of the media market
  3. the independence of judiciary power
  4. the campaign financing laws
  5. the dominant ideology
70
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

Describe the basics of Bitcoin, how does it work, the blockchain, the motivation behind it.

A
  • Bitcoin (BTC) is a digital cash ecosystem with three main components: users, miners and the blockchain.
  • User has a wallet, which consists of a Bitcoin address and a password; there are no limitations for the number of wallets per user; no personal information needed
  • When a user sends bitcoins, she uses the private key and a Bitcoin address to specify the recipient. She also sets a voluntary transaction fee that will be paid once the transaction is processed and stored in the blockchain.
  • Bitcoin’s blockchain does not contain bitcoins nor does it store the balance of each user, it is simply a ledger that records all transactions that have ever been made with bitcoins, and takes the form of a sequence of blocks of validated transactions. It is publicly available.
71
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

What are BTC miners, what do they do?

A
  • Transactions are processed in blocks by miners who will record them into the blockchain; in addition to transaction fees, miners collect block rewards in BTC
  • There are many miners in the network, each with a copy of the blockchain
  • The perspective of a hefty reward from the creation of a block triggers a competition between miners: all miners seek to be the one adding the next block. Consensus will thus be facilitated if with very high probability, for any sufficiently large time window, only one miner wins this competition.
  • In order to solve the block, miners solve an unnecessarily complex cryptographic puzzle; processing transactions with intensive computations is called proof-of-work (PoW)
  • Once a miner has found a solution that gives an acceptable output, she will broadcast the new block to the network, expecting the other miners to add it to the blockchain.
72
Q

“The microeconomics of cryptocurrencies”
(Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

What is a fork, how does it happen (accidentally) and what is the suggested solution for it?

A
  • Because there are multiple miners working on the same problem (solving the block), there is a possibility that two different miners find and broadcast, at approximately the same time, a correct solution
  • As a result, we obtain a fork, i.e., two competing branches (versions) of the blockchain; this happens frequently on Bitcoin
  • Solution proposed by Satoshi Nakomoto (creator of Bitcoin) is the longest chain rule (LCR) – miners should choose the longest branch
  • This works because the pace at which blocks are added to the blockchain is stochastic → sooner or later one of the versions will be longer than the other versions
73
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

How can forks be an attack to the ecosystem? What is the reasoning behind them?

A
  • An attacker privately operates their own blockchain for a period of time as a sole miner, and if the private blockchain grows quicker, then at one point that will become an accepted branch under LCR.
  • Motivation - “double spending”: pay in BTC, receive a good (outside the blockchain) and then fork the blockchain by proposing another branch that does not contain the payment, and also empty the wallet so that the original transaction can no longer be processed (at the end, attacker has both goods and money → money printing).
  • This would ruin trust in Bitcoin (and any other blockchain-based solution)
  • Nakamoto says this is only possible if attacker controls 50%+ of total computing power of the network (hash power) because only then would the attacker’s private blockchain grow quicker
  • In reality it’s possible also with a smaller fraction of hash power, but it is guaranteed to succeed with 50%+
74
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

What 3 properties define a permissionless network?

A
  • Anonymity: if any two miners can change their identities they inherit the selection probability of one another
  • Robustness to Sybil Attacks: a miner cannot split its performance into two or more entities and pose as a new entrant to increase his selection probability
  • Robust to Merging: miners cannot increase their selection probability by merging
75
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

What alternative to PoW is proposed?

A

PoW consumes a lot of energy and work takes the form of an unnecessarily difficult computational contest.

Alternative - proof-of-stake, where entry for miners is not real computational power, but stake in the network (tokens), which signals a miner’s intent to operate honestly. These tokens are frozen as a “deposit”.

  • Does not solve consensus problems (“nothing-at-stake”)
  • No design choices that will lead to greater sustainability than under PoW
76
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

What drives the demand side of BTC? What have been the stages of BTC? Who are the users?

A
  • Lack of trust in banks or government does not appear to be a primary driver of cryptocurrencies adoption
  • Existence of alternative methods of payments like credit cards and PayPal has a negative effect of Bitcoin usage and weak banking system has a positive effect
  • Bitcoin is likely to have gone through 3 phases of use:
  1. Early stage (up to the beginning of 2012), dominated by miners
  2. “Illegal” stage (2012-2013), dominated by black market and gambling
  3. “Business” (since early 2014) or “legitimate” stage, dominated by cryptocurrency exchanges (mostly financial speculation)
  • ~1/4 of bitcoin users are involved in illegal activity, this represents 46% of bitcoin transactions or $76bn/year; US and European illegal drug market is only slightly larger
  • 5/500 largest online retailers accepted bitcoin in 2017, only 3/500 in 2018 → people prefer other payment methods for “legal” goods because of price volatility and processing speed
77
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

Who are BTCs competitors? What is BTCs relationship with them?

A
  • BTCs market share has dropped to less than half; other popular cryptocurrencies were Ethereum (20%) and Ripple (10%)
  • When bitcoin’s price rises (falls,) the price of other cryptocurrencies rises (falls) more and bitcoin’s dominance declines (increases.) Thus, in some sense, bitcoin is the safe asset in the cryptocurrency ecosystem.
  • There has been an emergence of rather small cryptocurrencies (market cap between $1m and $100m): 30 in 2014, 700 in 2020 → potential for “pump-and-dump” market manipulation
  • Pump-and-dump consists of buying suddenly large quantities so as to create a price increase momentum, and then sell it back once it has attracted sufficient traction
  • Evidence of 3,000+ pump-and-dump schemes over just 6 months period in 2018!
  • We are observing increased competition among cryptocurrency exchanges, and these institutions are far from being well-governed (e.g., they exaggerate reported trading volumes)
78
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

How does BTCs price react to its mentions in social media/ the news?

A
  • Mentions of Bitcoin in social networks are related to bitcoin’s price, but correlation does not mean causation
  • Sentiment of “silent majority” (less active social media contributors) is a better predictor of price than sentiment of “vocal minority” → surprising, but perhaps sentiments of the silent majority tend to be more concise, relevant, and less noisy
  • Bitcoin price is better predicted by innovation and technological progress than “buzz”. How? Relatively small share of population actually participates in the market, and those would be on average more technologically inclined people
  • They might also choose to discuss on forums, not Twitter tho
79
Q

“The microeconomics of cryptocurrencies” (Halaburda, Hanna, Guillaume Haeringer, Joshua Gans, and Neil Gandal (2021))

For economists, what 3 purposes does a currency serve? How does this correlate with BTC?

A

For economists a currency serves 3 purposes:
• It serves a medium of exchange
• It serves as a unit of account
• It serves a store of value (volatility of bitcoin price makes it a bad store of value → speculative asset)

80
Q

Blockchain Analysis of the Bitcoin Market (Makarov, Igor, and Antoinette Schoar, 2022)

What is the split of transaction volume by network participant types?

A

90% of volume is a by-product of Bitcoin protocol design and preference of participants for anonymity (e.g., sending bitcoins to themselves)

From the real volume, 75% is linked to exchanges or exchange-like entities (trading and speculation), 3% – to illegal activities

81
Q

Blockchain Analysis of the Bitcoin Market (Makarov, Igor, and Antoinette Schoar, 2022)

Why did the findings on the proportion of illegal activities with Bitcoin differ substantially from different pieces of research?

A

3% in this paper much less than 46% we saw in Foley et al. (2019) (Tālis’) paper. Why?
◦ Foley et al. look at non-exchange volume only
◦ Foley et al. do not filter out spurious volume

82
Q

Blockchain Analysis of the Bitcoin Market (Makarov, Igor, and Antoinette Schoar, 2022)

Why does imposing KYC norms on exchanges not work entirely?

A

One could still transfer funds from KYC exchanges to non-KYC exchanges with servers in countries that tolerate non-KYC.

e.g. HydraMarket (one of the largest DarkNet sites). Direct interaction with KYC exchange Coinbase is 196+126 BTC, but indirect – 530k+218k BTC!
The majority of flows to Hydra from Coinbase is through temporary wallets typically created to hide the origin

83
Q

Blockchain Analysis of the Bitcoin Market (Makarov, Igor, and Antoinette Schoar, 2022)

How concentrated is mining capacity entity- and region-wise? What does that imply about the network?

A

Concentration shows the safety of the network.

Mining needs a lot of hashing power-> strong incentive to pool resources and then split rewards.

-> Very high concentration: top 10% miners control 90% of hashing power, top 0.1% – 50%. Top 0.1% is just 50 miners!

Concentration is counter-cyclical: smaller miners join when bitcoin price goes up, leave when it goes down (think when 51% attacks are more likely)

Between 60% to 80% of power is in China

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