­­FIN310 Class Web Page, Fall ' 20

Instructor: Maggie Foley

Jacksonville University

 

The Syllabus    

Term project (due with final)

 

Weekly SCHEDULE, LINKS, FILES and Questions

Chapter

Coverage, HW, Supplements

-        Required

References

 

Chapter 1, 2

Live Stream web link

 

 Tuesday  -  Group 1 in classroom                                        Thursday   -    Group 2 in classroom

8/18 – syllabus, market watch game                          

8/20 – chapter 1, flash crash, high frequency trading

8/25 – High frequency trade

8/27 – flash crash, chapter 2: what is money

9/1 – M1, M2, fractional banking system I

9/3  fractional banking system II, bitcoin (Thanks, Chris)

9/8 – order types

9/10 – IPO, SEO

9/15 – Time value of money

9/17 – First Mid Term exam, online, 11am-3pm, answers posted on bb

9/22 – bond concept, risk, finra.com for bond info

9/24 – high yield bond discussion

9/29 – credit rating, z score

10/1 – z score, market news regarding bond downgrading

10/6 – yield curve, inverted yield curve, flattened yield curve

10/8 – Diversification, S&P500 index: weights, losing and winning stocks

10/13 – Mutual fund, Risk tolerance test, investment strategy

10/15 – QQQ vs SP500

10/20 – stock market, behavior finance part i

10/22 – behavior finance part ii

10/27

10/29

11/3

11/5

11/10

11/12

11/17

11/19

11/24

11/26

Final Week

 

 

 

 

Marketwatch Stock Trading Game (Pass code: havefun)

Use the information and directions below to join the game.

1.     URL for your game: 
https://www.marketwatch.com/game/jufin310-20fall

2.   Password for this private game: havefun

3.     Click on the 'Join Now' button to get started.

4.     If you are an existing MarketWatch member, login. If you are a new user, follow the link for a Free account - it's easy!

5.     Follow the instructions and start trading!

 

Discussion:  How to pick stocks (finviz.com)

Daily earning announcement: http://www.zacks.com/earnings/earnings-calendar

IPO schedule:  http://www.marketwatch.com/tools/ipo-calendar

 

 

Chapter 1 Introduction 

Introduction to Capital Markets - ION Open Courseware (Video)

 

image002.jpg

 

Note:

Flow of funds describes the financial assets flowing from various sectors through financial intermediaries for the purpose of buying physical or financial assets.

*** Household, non-financial business, and our government

 

Financial institutions facilitate exchanges of funds and financial products.

*** Building blocks of a financial system. Passing and transforming funds and risks during transactions.

*** Buy and sell, receive and deliver, and create and underwrite financial products.

*** The transferring of funds and risk is thus created. Capital utilization for individual and for the whole economy is thus enhanced.

 

For class discussion:

1.     What is the business model of each player in the above graph?

2.     Which player is the most important one in the financial market?

3.     Can anyone of them be removed from the market?

 

 

Chapter 1 

 

 

ppt

 

1.       What are the six parts of the financial markets

Money:

·         To pay for purchases and store wealth (fiat money, fiat currency)

 

What is Bitcoin for BEGINNERS in 7-Min. & Bitcoin Explained | What is Cryptocurrency Explained 2019

 

Financial Instruments:

·         To transfer resources from savers to investors and to transfer risk to those best equipped to bear it.  

 

Where do student loans go? (video)

An Introduction to Securitized Products: Asset-Backed Securities (ABS) (video)

 

 

Financial Markets:

·         Buy and sell financial instruments

·         Channel funds from savers to investors, thereby promoting economic efficiency

·         Affect personal wealth and behavior of business firms. Example?

 

Financial Institutions.

·         Provide access to financial markets, collect information & provide services

·         Financial Intermediary: Helps get funds from savers to investors

 

Central Banks

·         Monitor financial Institutions and stabilize the economy

 

Regulatory Agencies

·         To provide oversight for financial system.

The role of financial regulation (Video) -  Do you agree with her?

 

2.      What are the five core principals of finance

  • Time has value
  • Risk requires compensation
  • Information is the basis for decisions
  • Markets determine prices  and allocation resources
  • Stability improves welfare

 

 

3.      What is stock?

 

4.      Why do we need stock exchanges?

·         Transparency

·         Anonymous

·         Guarantee and settlement

·         Regulated

 

5.      What is high frequency trading? pros and cons

 

Ppt

 

Videos

High Frequency Trading (video)

 How high frequency trading works (video)

 

Strategies And Secrets Of High Frequency Trading (HFT) Firms

 

By PRABLEEN BAJPAI

Updated Sep 21, 2014

 

Secrecy, Strategy and Speed are the terms that best define high frequency trading (HFT) firms and indeed, the financial industry at large as it exists today.

 

 

HFT firms are secretive about their ways of operating and keys to success. The important people associated with HFT have shunned limelight and preferred to be lesser known, though that's changing now.

 

 

The firms in the HFT business operate through multiple strategies to trade and make money. The strategies include different forms of arbitrage – index arbitrage, volatility arbitrage, statistical arbitrage and merger arbitrage along with global macro, long/short equity, passive market making, and so on.

 

 

HFT rely on the ultra fast speed of computer software, data access (NASDAQ TotalView-ITCH, NYSE OpenBook, etc) to important resources and connectivity with minimal latency (delay).

 

Let’s explore some more about the types of HFT firms, their strategies to make money, major players and more.

 

HFT firms generally use private money, private technology and a number of private strategies to generate profits. The high frequency trading firms can be divided broadly into three types.

 

The most common and biggest form of HFT firm is the independent proprietary firm. Proprietary trading (or "prop trading") is executed with the firm’s own money and not that of clients. LIkewise, the profits are for the firm and not for external clients.

Some HTF firms are a subsidiary part of a broker-dealer firm. Many of the regular broker-dealer firms have a sub section known as proprietary trading desks, where HFT is done. This section is separated from the business the firm does for its regular, external customers.

Lastly, the HFT firms also operate as hedge funds. Their main focus is to profit from the inefficiencies in pricing across securities and other asset categories using arbitrage.

 

Prior to the Volcker Rule, many investment banks had segments dedicated to HFT. Post-Volcker, no commercial banks can have proprietary trading desks or any such hedge fund investments. Though all major banks have shut down their HFT shops, a few of these banks are still facing allegations about possible HFT-related malfeasance conducted in the past.

 

 

How Do They Make Money?

 

There are many strategies employed by the propriety traders to make money for their firms; some are quite commonplace, some are more controversial.

 

These firms trade from both sides i.e. they place orders to buy as well as sell using limit orders that are above the current market place (in the case of selling) and slightly below the current market price (in the case of buying). The difference between the two is the profit they pocket. Thus these firms indulge in “market making” only to make profits from the difference between the bid-ask spread. These transactions are carried out by high speed computers using algorithms.

 

Another source of income for HFT firms is that they get paid for providing liquidity by the Electronic Communications Networks (ECNs) and some exchanges. HFT firms play the role of market makers by creating bid-ask spreads, churning mostly low priced, high volume stocks (typical favorites for HFT) many times in a single day. These firms hedge the risk by squaring off the trade and creating a new one.

 

Another way these firms make money is by looking for price discrepancies between securities on different exchanges or asset classes. This strategy is called statistical arbitrage, wherein a proprietary trader is on the lookout for temporary inconsistencies in prices across different exchanges. With the help of ultra fast transactions, they capitalize on these minor fluctuations which many don’t even get to notice.

 

HFT firms also make money by indulging in momentum ignition. The firm might aim to cause a spike in the price of a stock by using a series of trades with the motive of attracting other algorithm traders to also trade that stock. The instigator of the whole process knows that after the somewhat “artificially created” rapid price movement, the price reverts to normal and thus the trader profits by taking a position early on and eventually trading out before it fizzles out. 

 

The Players

 

The HFT world has players ranging from small firms to medium sized companies and big players. A few names from the industry (in no particular order) are Automated Trading Desk (ATD), Chopper Trading, DRW Holdings LLC, Tradebot Systems Inc., KCG Holdings Inc. (merger of GETCO and Knight Capital), Susquehanna International Group LLP (SIG), Virtu Financial, Allston Trading LLC, Geneva Trading, Hudson River Trading (HRT), Jump Trading, Five Rings Capital LLC, Jane Street, etc.

 

Risks

 

The firms engaged in HFT often face risks related to software anomaly, dynamic market conditions, as well as regulations and compliance. One of the glaring instances was a fiasco that took place on August 1, 2012 which brought Knight Capital Group close to bankruptcy--It lost $400 million in less than an hour after markets opened that day. The “trading glitch,” caused by an algorithm malfunction, led to erratic trade and bad orders across 150 different stocks. The company was eventually bailed out. These companies have to work on their risk management since they are expected to ensure a lot of regulatory compliance as well as tackle operational and technological challenges.

 

The Bottom Line

 

The firms operating in the HFT industry have earned a bad name for themselves because of their secretive ways of doing things. However, these firms are slowly shedding this image and coming out in the open. The high frequency trading has spread in all prominent markets and is a big part of it. According to sources, these firms make up just about 2% of the trading firms in the U.S. but account for around 70% of the trading volume. The HFT firms have many challenges ahead, as time and again their strategies have been questioned and there are many proposals which could impact their business going forward.

 

 

 

6.      What is flash crash? (refer to the two articles on the right)

Flash crash

From Wikipedia, the free encyclopedia

flash crash is a very rapid, deep, and volatile fall in security prices occurring within an extremely short time period. A flash crash frequently stems from trades executed by black-box trading, combined with high-frequency trading, whose speed and interconnectedness can result in the loss and recovery of billions of dollars in a matter of minutes and seconds.

Occurrences

The Flash Crash

This type of event occurred on May 6, 2010. A $4.1 billion trade on the New York Stock Exchange (NYSE) resulted in a loss to the Dow Jones Industrial Average of over 1,000 points and then a rise to approximately previous value, all over about fifteen minutes. The mechanism causing the event has been heavily researched and is in dispute. On April 21, 2015, the U.S. Department of Justice laid "22 criminal counts, including fraud and market manipulation" against Navinder Singh Sarao, a trader. Among the charges included was the use of spoofing algorithms.

2017 Ethereum Flash Crash

On June 22, 2017, the price of Ethereum, the second-largest digital cryptocurrency, dropped from more than $300 to as low as $0.10 in minutes at GDAX exchange. Suspected for market manipulation or an account takeover at first, later investigation by GDAX claimed no indication of wrongdoing. The crash was triggered by a multimillion-dollar selling order which brought the price down, from $317.81 to $224.48, and caused the following flood of 800 stop-loss and margin funding liquidation orders, crashing the market.

British pound flash crash

On October 7, 2016, there was a flash crash in the value of sterling, which dropped more than 6% in two minutes against the US dollar. It was the pound's lowest level against the dollar since May 1985. The pound recovered much of its value in the next few minutes, but ended down on the day's trading, most likely due to market concerns about the impact of a "hard Brexit"—a more complete break with the European Union following Britain's 'Leave' referendum vote in June. It was initially speculated that the flash crash may have been due to a fat-finger trader error or an algorithm reacting to negative news articles about the British Government's European policy.

FLASH CRASH! Dow Jones drops 560 points in 4 Minutes! May 6th 2010 (video)

Flash Crash 2010: Trader Relives Nightmare Three Years Later (video)

Flash Crash: Can Only One Trader Be Responsible? (video)

What Is High-Frequency Trading? Finance, Algorithms, Software, Strategies, Firms (2014) (Video, optional)

THE HUMMINGBIRD PROJECT Clips + Trailer (2019) (video)

 

  

Flash Crash 2010 - VPRO documentary – 2011 (video, optional)

 

For discussion:

·        Next time, when a flash crash happens, can you think of a strategy to make money from this incident? Why or why not?

·        After the flash crash, the price will recover almost completely. So why the market is afraid of it. It is not a big deal, right?

 

 

 

Homework of the 1st week (due with the first mid-term exam):

1.     What is high frequency trading (HFT)? How does it work? 

2.     Do you anticipate rapid growth of HFT in US?  Shall the SEC ban HFT?

3.     What is spoofing? Why is it harmful to the market?

4.     What is flash crash? How does it make investors so worried? How can HFT trigger flash crash?

5.     After the flash crash, the price will recover almost completely. So why the market is afraid of it?

 

 

Tuesday-group 1

Thursday-group 2

Samuel

Baker

1

Kelly

Brassington

n/a

William

Burckley

1

Tyler

Cahill

1

Reed

Davis

1

Joshua

Hancock

1

Centraya

Kenny

1

Julia

Kolderman

1

 

Jack

Madren

2

Jennifer

Madrid

2

Sean

Martin

2

Maxwell

Moore

2

Devina

Petrone

2

Christopher

Raudez

2

Jacob

Sims

2

Nicholas

Zipperer

2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The World of High-Frequency Algorithmic Trading

 

In the last decade, algorithmic trading (AT) and high-frequency trading (HFT) have come to dominate the trading world, particularly HFT. During 2009-2010, more than 60% of U.S. trading was attributed to HFT, though that percentage has declined in the last few years.1

 

 

Heres a look into the world of algorithmic and high-frequency trading: how they're related, their benefits and challenges, their main users and their current and future state.

 

 

High-Frequency Trading HFT Structure

First, note that HFT is a subset of algorithmic trading and, in turn, HFT includes Ultra HFT trading. Algorithms essentially work as middlemen between buyers and sellers, with HFT and Ultra HFT being a way for traders to capitalize on infinitesimal price discrepancies that might exist only for a minuscule period.

 

 

Computer-assisted rule-based algorithmic trading uses dedicated programs that make automated trading decisions to place orders. AT splits large-sized orders and places these split orders at different times and even manages trade orders after their submission.

 

Large sized-orders, usually made by pension funds or insurance companies, can have a severe impact on stock price levels. AT aims to reduce that price impact by splitting large orders into many small-sized orders, thereby offering traders some price advantage.

 

The algorithms also dynamically control the schedule of sending orders to the market. These algorithms read real-time high-speed data feeds, detect trading signals, identify appropriate price levels and then place trade orders once they identify a suitable opportunity. They can also detect arbitrage opportunities and can place trades based on trend following, news events, and even speculation.

 

 

High-frequency trading is an extension of algorithmic trading. It manages small-sized trade orders to be sent to the market at high speeds, often in milliseconds or microsecondsa millisecond is a thousandth of a second and a microsecond is a thousandth of a millisecond.

 

 

These orders are managed by high-speed algorithms which replicate the role of a market maker. HFT algorithms typically involve two-sided order placements (buy-low and sell-high) in an attempt to benefit from bid-ask spreads. HFT algorithms also try to sense any pending large-size orders by sending multiple small-sized orders and analyzing the patterns and time taken in trade execution. If they sense an opportunity, HFT algorithms then try to capitalize on large pending orders by adjusting prices to fill them and make profits.

 

 

Also, Ultra HFT is a further specialized stream of HFT. By paying an additional exchange fee, trading firms get access to see pending orders a split-second before the rest of the market does.

 

Profit Potential from HFT

Exploiting market conditions that can't be detected by the human eye, HFT algorithms bank on finding profit potential in the ultra-short time duration. One example is arbitrage between futures and ETFs on the same underlying index.

 

 

Automated Trading

In the U.S. markets, the SEC authorized automated electronic exchanges in 1998. Roughly a year later, HFT began, with trade execution time, at that time, being a few seconds. By 2010, this had been reduced to millisecondssee the speech by the Bank of England's Andrew Haldane's "Patience and finance"and today, one-hundredth of a microsecond is enough time for most HFT trade decisions and executions. Given ever-increasing computing power, working at nanosecond and picosecond frequencies may be achievable via HFT in the relatively near future.

 

Bloomberg reports that while in 2010, HFT "accounted for more than 60% of all U.S. equity volume, that proved to be a high-water mark. By 2013, that percentage had fallen to roughly 50%. Bloomberg further noted that where, in 2009, "high-frequency traders moved about 3.25 billion shares a day. In 2012, it was 1.6 billion a day and average profits have fallen from about a tenth of a penny per share to a twentieth of a penny.

 

HFT Participants

HFT trading ideally needs to have the lowest possible data latency (time-delays) and the maximum possible automation level. So participants prefer to trade in markets with high levels of automation and integration capabilities in their trading platforms. These include NASDAQ, NYSE, Direct Edge, and BATS.

 

HFT is dominated by proprietary trading firms and spans across multiple securities, including equities, derivatives, index funds, and ETFs, currencies and fixed income instruments. A 2011 Deutsche Bank report found that of then-current HFT participants, proprietary trading firms made up 48%, proprietary trading desks of multi-service broker-dealers were 46% and hedge funds about 6%. Major names in the space include proprietary trading firms like KWG Holdings (formed of the merger between Getco and Knight Capital) and the trading desks of large institutional firms like Citigroup (C), JP Morgan (JPM) and Goldman Sachs (GS).

 

HFT Infrastructure Needs

For high-frequency trading, participants need the following infrastructure in place:

 

·         High-speed computers, which need regular and costly hardware upgrades;

·         Co-location. That is, a typically high-cost facility that places your trading computers as close as possible to the exchange servers, to further reduce time delays;

·         Real-time data feeds, which are required to avoid even a microsecond's delay that may impact profits; and

·         Computer algorithms, which are the heart of AT and HFT.

 

Benefits of HFT

HFT is beneficial to traders, but does it help the overall market? Some overall market benefits that HFT supporters cite include:

 

·         Bid-ask spreads have reduced significantly due to HFT trading, which makes markets more efficient. Empirical evidence includes that after Canadian authorities in April 2012 imposed fees that discouraged HFT, studies suggested that the bid-ask spread rose by 9%," possibly due to declining HFT trades.7

·         HFT creates high liquidity and thus eases the effects of market fragmentation.

·         HFT assists in the price discovery and price formation process, as it is based on a large number of orders

 

Challenges Of HFT

Opponents of HFT argue that algorithms can be programmed to send hundreds of fake orders and cancel them in the next second. Such spoofing momentarily creates a false spike in demand/supply leading to price anomalies, which can be exploited by HFT traders to their advantage. In 2013, the SEC introduced the Market Information Data Analytics System (MIDAS), which screens multiple markets for data at millisecond frequencies to try and catch fraudulent activities like spoofing."

 

Other obstacles to HFT's growth are its high costs of entry, which include:

 

·         Algorithms development

·         Setting up high-speed trade execution platforms for timely trade execution

·         Building infrastructure that requires frequent high-cost upgrades

·         Subscription charges towards data feed

 

The HFT marketplace also has gotten crowded, with participants trying to get an edge over their competitors by constantly improving algorithms and adding to infrastructure. Due to this "arms race," it's getting more difficult for traders to capitalize on price anomalies, even if they have the best computers and top-end networks.

 

And the prospect of costly glitches is also scaring away potential participants. Some examples include the Flash Crash" of May 6, 2010, where HFT-triggered sell orders led to an impulsive drop of 600 points in the DJIA index.9 Then there's the case of Knight Capital, the then-king of HFT on NYSE. It installed new software on Aug 1, 2012, and accidentally bought and sold $7 billion worth of NYSE stocks at unfavorable prices.10 Knight was forced to settle its positions, costing it $440 million in one day and eroding 40% of the firms value. Acquired by another HFT firm, Getco, to form KCG Holdings, the merged entity still continues to struggle.

 

So, some major bottlenecks for HFT's future growth are its declining profit potential, high operational costs, the prospect of stricter regulations and the fact that there is no room for error, as losses can quickly run in the millions.

 

 

The Bottom Line

The growth of computer speed and algorithm development has created seemingly limitless possibilities in trading. But, AT and HFT are classic examples of rapid developments that, for years, outpaced regulatory regimes and allowed massive advantages to a relative handful of trading firms. While HFT may offer reduced opportunities in the future for traders in established markets like the U.S., some emerging markets could still be quite favorable for high-stakes HFT ventures.

 

 

 

 

 

 

 

 

Goldman Sachs says computerized trading may make next 'flash crash' worse

·         Goldman Sachs is worried the increasing dominance of computerized trading may cause more volatility during market downturns.

·         The firm says high-frequency trading machines may "withdraw liquidity" at the worst possible moment in the next financial crisis.

 | 

CNBC.com

 

Goldman Sachs is cautioning its clients that computerized trading may exacerbate the volatility of the next big market sell-off.

"One theory that has been proposed for why market fragility could be higher today is that because HFTs [high-frequency trading] supply liquidity without taking into account fundamental information, they are forced to withdraw liquidity during periods of market stress to avoid being adversely selected," Charles Himmelberg, co-head of global markets research at Goldman, said in a report Tuesday. "In our view, this at least raises the risk that as machines have replaced people, and speed has replaced capital, the inability of the market's liquidity providers to process complex information may lead to surprisingly large drops in liquidity when the next crisis hits."

Himmelberg noted the higher level of computerized trading has not been truly "stress tested" during the bull market since the financial crisis. He said the increasing incidents of volatility in various markets such as the VIX spike on Feb. 5, the 10-year Treasury bond on Oct. 15, 2014, and the British pound on Oct. 6, 2016, may be precursors of a bigger one to come.

"The rising frequency of 'flash crashes' across many major markets may be an important early warning sign that something is not quite right with the current state of trading liquidity," he said. "These warning signs plus the rapid growth of high-frequency trading (HFT) and its near-total dominance in many of the largest and most widely traded markets prompt us to more carefully consider the possibility (not necessarily the probability) that the long expansion accompanied by relatively low market volatility may have helped disguise an under-appreciated rise in 'market fragility.'"

The strategist said computerized trading is generally not backed by large levels of capital, which could drive the "collapse" of liquidity if the machines suffer any big losses during a significant market downturn.

"Future flash crashes may not end well," he warned. "The quality of trading liquidity for even the biggest, most heavily-traded markets should not be taken for granted."

 With reporting by CNBC's Michael Bloom.

 

 

The stock market halted trading Monday—here’s why younger investors shouldn’t panic

https://www.cnbc.com/2020/03/09/the-stock-market-halted-trading-younger-investors-shouldnt-panic.html

Published Mon, Mar 9 202011:30 AM EDTUpdated Tue, Mar 10 20209:25 AM EDT

Megan Leonhardt@MEGAN_LEONHARDT

 

The stock market opened on a rough note this week as fears that the coronavirus will continue to have widespread economic impact drove down stock prices. On Monday morning, the S&P 500 fell more than 7% at the open, triggering circuit breakers that led the New York Stock Exchange to halt all market trading for 15 minutes.

The plunge, which occurred just after the market opened, triggered what’s called a ‘circuit breaker’ that immediately halted trading. Basically, this is a fail-safe that’s built into the system to allow for a short cool down period.

“The market circuit breakers are designed to slow trading down for a few minutes, give investors the ability to understand what’s happening in the market, consume the information and make decisions based on market conditions,” New York Stock Exchange President Stacey Cunningham told CNBC’s Bob Pisani. “This is operating as it’s supposed to.”

The current system of circuit breakers has never been tripped. A revamped system was put in place in February 2013 after the last set failed to prevent the May 2010 flash crash.

 

During regular trading hours, a circuit breaker can be triggered in a few situations:

1.         If the S&P 500 drops 7%, then trading will pause for 15 minutes.

2.         If the S&P 500 declines 13% on or before 3:25 p.m. ET, then trading will be paused again for 15 minutes. If the drop occurs after 3:25 p.m., then there’s no halt.

3.         If the S&P 500 falls 20%, then trading will be suspended for the rest of the day.

 

Trading started back up at 9:49 a.m. ET and the S&P 500 continued to slide. Meanwhile, the Dow Jones Industrial Average, which tracks 30 stocks, fell 2,000 points, or 7.3%, at one point during morning trading. The Nasdaq, which features some of the market’s biggest technology names as well as an assortment of other companies, fell 6.9% during the same period. 

“The bull market’s 11-year birthday is today, but investors are not in a celebratory mood,” says Greg McBride, chartered financial analyst and chief financial analyst at Bankrate.com.

 

What it means for you

Over 66% of millennials have investments of some type. About a third of millennials invested in a taxable brokerage account in 2018, while another third invested in a retirement account, according to a study of over 1,800 millennials (ages 23 to 38) sponsored by the CFA Institute and the FINRA Investor Education Foundation.

If you’re part of that group, the roller coaster markets do have an impact on your investments, including your 401(k). But before you panic, keep in mind that market downturns are fairly common. Market pullbacks with declines of less than 20% have occurred over 100 times since 1946, according to investment firm Guggenheim Funds.

“Investing should never be about a moment in time; it should always be about a process over time,” Liz Ann Sonders, chief investment strategist at Charles Schwab, tells CNBC Make It.

That’s a nice way of saying: Don’t time the market. Most millennials (ages 24 to 39) have a long time horizon for their investments. Since there are likely decades before you retire, even if a recession hits tomorrow or next year, there’s plenty of time for your investments to bounce back. Recessions and market downturns are part of a normal, healthy market cycle.

2:19

NYSE President Stacey Cunningham explains why stock trading was halted for 15 minutes

The best course of action right now is to keep investing and making regular contributions to your 401(k). This routine influx of money into your investment accounts is a strategy that experts call dollar-cost averaging. It’s great for long-term investors because it takes emotion out of the equation and keeps you from selling out during market lows and buying in at market highs.

A 401(k) is actually a good place to invest amid market volatility, Sonders says. Typically, they’re structured in a way so that you’re buying on a regimented basis and many have the option to invest in target date funds, which have an automatic rebalancing process.

“As the uncertainty persists, the market frenzy will continue, perhaps for weeks, perhaps for months,” McBride says. “But long-term investors must think in terms of years or decades.”

Finally, just take a deep breath. Many millennials have strong “muscle memory” from their own involvement, or their parents’ experiences, with the market during the last financial crisis, Sonders says. Yet the reality is that that market event was not the rule; it was more on the exceptional end of the spectrum.

“Markets fall sharply, but can also rebound quickly,” McBride says. “No one knows when that comes and you don’t want to be sitting on the sidelines when that happens.”

 

The Work-From-Home Trader Who Shook Global Markets (Bloomberg) (optional)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 2 What is Money

 

Ppt

 

Part I What is Money?  

 

·         There is no single "correct" measure of the money supply: instead, there are several measures, classified along a spectrum or continuum between narrow and broad monetary aggregates.

•         Narrow measures include only the most liquid assets, the ones most easily used to spend (currency, checkable deposits). Broader measures add less liquid types of assets (certificates of deposit, etc.)

 

Type of money

M0

MB

M1

M2

M3

Notes and coins in circulation (outside Federal Reserve Banks and the vaults of depository institutions) (currency) 

Notes and coins in bank vaults (Vault cash)

Federal Reserve Bank credit (required reserves and excess reserves not physically present in banks)

Traveler’s checks of non-bank issuers

Demand deposits

Other checkable deposits (OCDs)

Savings deposits

Time deposits less than $100,000 and money market deposit accounts for individuals

Large time deposits, institutional money market funds, short-term repurchase and other larger liquid assets

All money market funds

·         M0: In some countries, such as the United Kingdom, M0 includes bank reserves, so M0 is referred to as the monetary base, or narrow money.

·         MB: is referred to as the monetary base or total currency.  This is the base from which other forms of money (like checking deposits, listed below) are created and is traditionally the most liquid measure of the money supply.

·         M1: Bank reserves are not included in M1. (M1 and Components @ Fed St. Louise website)

·         M2: Represents M1 and "close substitutes" for M1. M2 is a broader classification of money than M1. M2 is a key economic indicator used to forecast inflation. (M2 and components @ Fed St. Louise website)

·         M3: M2 plus large and long-term deposits. Since 2006, M3 is no longer published by the US central bank. However, there are still estimates produced by various private institutions. (M3 and components at Fed St. Louise website)

·         

 FYI: Fed balance sheet

 

Lets watch this money supply video: Khan academy money supply M0, M1, M2 (video)

 

Draw Me The Economy: Money Supply (video)

 

 

For discussion:

·         What could happen if we increase money supply?

·          What about reduce money supply?

·         What are the possible ways to reduce money supply?

·         Among M0, M1, M2, M3, which one is the correct measure of money?

·         Why M2 is >> M0?

·         Why does M2 increase much faster than M1? Does it has any impact on you?

 

For more information, please visit http://www.data360.org/report_slides.aspx?Print_Group_Id=168

 

https://fred.stlouisfed.org/categories/24

 

M0, M1, and M2 Over Time

The top three graphs show M0, M1, and M2 money supply indicators over the past 40 odd years. The bottom three graphs show M0, M1, and M2 money supply indicators from June 2010. We see that the money supply has increased steadily over the years. In particular, the increase in money supply has been greatest in the recession years. This correlates to attempts made by the government to stimulate the economy and follow an expansionary monetary policy.

 

     image010.jpgimage009.jpg

 

 

For discussion:

·         Among M0, M1, and M2, which one is used as a measure for money supply in US?

·         Why is M2 multiple times of Mo?

·         What are the expected consequences resulted from a big increase in money supply?

·         Do you think that US$ will devalue in the near future?

·         What do you suggest in terms of investment? Bitcoin? Commodity? Stock? Bond? Why?

 

 

 

Summary:

Money Supply M2 in the United States increased to 14872.10 USD Billion in July from 14755.10 USD Billion in June of 2019. oney Supply M2 in the United States averaged 4121.70 USD Billion from 1959 until 2019, reaching an all time high of 14872.10 USD Billion in July of 2019 and a record low of 286.60 USD Billion in January of 1959.

 

From https://tradingeconomics.com/united-states/money-supply-m2

 

image007.jpg

Actual

Previous

Highest

Lowest

Dates

Unit

Frequency

18326.80

18357.30

18357.30

286.60

1959 - 2020

USD Billion

Monthly

Current Prices, SA

 

United States Money

Last

Previous

Highest

Lowest

Unit

Interest Rate

0.25

0.25

20.00

0.25

percent

[+]

Interbank Rate

0.25

0.25

10.63

0.22

percent

[+]

Money Supply M0

4700401.00

5001978.00

5149527.00

48362.00

USD Million

[+]

Money Supply M1

5328.60

5249.90

5328.60

138.90

USD Billion

[+]

Money Supply M2

18326.80

18357.30

18357.30

286.60

USD Billion

[+]

 Central Bank Balance Sheet

6911119.00

6902265.00

7113208.00

672444.00

USD Million

[+]

Banks Balance Sheet

20039200.00

19971600.00

20342894.00

697581.70

USD Million

[+]

Foreign Exchange Reserves

133890.00

132239.00

153075.00

12128.00

USD Million

[+]

Loans to Private Sector

2822.46

2900.06

3030.13

13.65

USD Billion

[+]

Foreign Bond Investment

28900.00

-27719.00

118012.00

-299126.00

USD Million

[+]

Private Debt to GDP

220.20

216.20

224.50

162.90

percent

[+]

Repo Rate

0.11

0.12

6.94

-0.01

[+]

 

 

 

M2 of other countries

 

Country

Last

Previous

Range

Argentina

2043886.20

Jun/19

1949229

2123331 : 712

ARS Million

Brazil

2882184.74

Jul/19

2878391

2882185 : 0.01

BRL Million

Canada

1737081.00

Jul/19

1721566

1737081 : 25523

CAD Million

China

193550.00

Aug/19

191941

193550 : 5840

CNY Billion

Euro Area

12130349.00

Jul/19

12056341

12130349 : 1070365

EUR Million

France

2279384.00

Jul/19

2256216

2279384 : 263286

EUR Million

Germany

3111.10

Jul/19

3100

3111 : 34.4

EUR Billion

India

36941.69

Aug/19

37154

37404 : 1127

INR Billion

Indonesia

5937500.00

Jul/19

5918515

5937500 : 5156

IDR Billion

Italy

1595700.00

Jun/19

1576757

1595700 : 132635

EUR Million

Japan

1030974.80

Jul/19

1029615

1030975 : 8404

JPY Billion

Mexico

8967387851.00

Jul/19

8938303088

10147459591 : 15370409

MXN Thousand

Netherlands

874900.00

Jul/19

871121

877560 : 102486

EUR Million

Russia

47351.00

Jul/19

47348

47351 : 1090

RUB Billion

Saudi Arabia

1693334.00

Jul/19

1693532

1693532 : 169395

SAR Million

Singapore

620413.00

Jul/19

620328

622406 : 6182

SGD Million

South Africa

2965855.00

Jul/19

2905091

2965855 : 2887

ZAR Million

South Korea

2790153.00

Jun/19

2778413

2790153 : 591

KRW Billion

Spain

1226327.00

Jul/19

1246563

1246563 : 294870

EUR Million

Switzerland

1028773.00

Jul/19

1026714

1030195 : 198227

CHF Million

Turkey

2266821545.10

Aug/19

2178802052

2266821545 : 236620702

TRY Thousand

United Kingdom

2441750.00

Jul/19

2437574

2441750 : 167427

GBP Million

United States

14872.10

Jul/19

14755

14872 : 287

USD Billion

 

For discussion:

Money supplies have increased rapidly in every country. Why? Does it make any sense? What are be the possible consequences, in your opinion?

 

From https://www.federalreserve.gov/releases/h6/current/default.htm

Table 1

Money Stock Measures. Billions of dollars.

Date

Seasonally adjusted

Not seasonally adjusted

M1 1

M2 2

M1 1

M2 2

Aug. 2018

3,686.4

14,197.0

3,686.2

14,171.6

Sept. 2018

3,704.0

14,228.5

3,671.6

14,207.2

Oct. 2018

3,719.1

14,235.4

3,718.5

14,209.7

Nov. 2018

3,698.1

14,245.4

3,676.6

14,263.2

Dec. 2018

3,746.4

14,351.7

3,796.8

14,456.5

Jan. 2019

3,740.4

14,434.6

3,745.4

14,433.6

Feb. 2019

3,759.6

14,464.3

3,702.0

14,411.4

Mar. 2019

3,729.8

14,511.8

3,753.8

14,582.9

Apr. 2019

3,780.9

14,558.7

3,819.8

14,634.3

May 2019

3,792.4

14,654.3

3,787.7

14,585.2

June 2019

3,832.8

14,782.6

3,828.1

14,745.4

July 2019

3,858.1

14,862.1

3,860.7

14,824.9

Aug. 2019

3,853.2

14,933.3

3,847.1

14,906.1

Sept. 2019

3,903.0

15,022.9

3,874.3

14,997.0

Oct. 2019

3,922.8

15,149.8

3,921.6

15,123.8

Nov. 2019

3,947.4

15,251.2

3,922.2

15,270.4

Dec. 2019

3,976.9

15,307.1

4,041.2

15,422.8

Jan. 2020

3,975.0

15,402.1

3,980.0

15,405.2

Feb. 2020

4,003.0

15,446.9

3,939.6

15,392.7

Mar. 2020

4,256.4

15,989.9

4,287.4

16,066.4

Apr. 2020

4,797.2

17,020.8

4,847.6

17,113.6

May 2020

5,031.6

17,870.4

5,012.9

17,779.0

June 2020

5,209.6

18,166.7

5,212.4

18,118.7

July 2020

5,329.1

18,327.0

5,330.5

18,278.8

Make Full Screen

Percent change at seasonally adjusted annual rates

M1

M2

3 Months from Apr. 2020 TO July 2020

44.4

30.7

6 Months from Jan. 2020 TO July 2020

68.1

38.0

12 Months from July 2019 TO July 2020

38.1

23.3

 

Table 2

Money Stock Measures. Billions of dollars.

Period ending

Seasonally adjusted

Not seasonally adjusted

M1

M2

M1

M2

13-week
average

4-week
average

week
average

13-week
average

4-week
average

week
average

13-week
average

4-week
average

week
average

13-week
average

4-week
average

week
average

May 18, 2020

4,547.4

4,989.5

5,032.7

16,619.1

17,683.9

17,909.6

4,551.9

4,972.4

4,948.1

16,651.5

17,599.6

17,821.9

May 25, 2020

4,631.7

5,032.2

5,066.0

16,811.8

17,816.9

17,938.6

4,643.0

4,977.9

5,130.8

16,840.6

17,715.0

17,797.0

June 1, 2020

4,707.6

5,039.8

5,013.2

17,001.1

17,910.3

17,973.4

4,733.2

5,032.1

5,252.6

17,027.2

17,809.4

17,928.9

June 8, 2020

4,786.9

5,051.2

5,092.8

17,193.0

17,971.1

18,062.8

4,811.4

5,072.4

4,957.9

17,212.1

17,897.3

18,041.3

June 15, 2020

4,868.4

5,089.2

5,184.9

17,378.7

18,028.5

18,139.0

4,886.5

5,093.9

5,034.2

17,388.7

17,974.2

18,129.5

June 22, 2020

4,943.5

5,144.9

5,288.5

17,539.1

18,103.2

18,237.4

4,956.8

5,133.7

5,290.1

17,535.9

18,055.5

18,122.3

June 29, 2020

5,003.5

5,214.5

5,291.9

17,669.9

18,168.2

18,233.5

5,015.8

5,205.8

5,541.1

17,654.8

18,117.3

18,175.9

July 6, 2020

5,050.6

5,241.6

5,200.9

17,803.0

18,233.9

18,325.7

5,062.3

5,269.6

5,213.0

17,775.7

18,196.6

18,358.5

July 13, 2020

5,094.4

5,266.5

5,284.7

17,931.6

18,296.4

18,388.9

5,100.4

5,290.2

5,116.7

17,885.8

18,256.2

18,368.1

July 20, 2020

5,132.9

5,281.0

5,346.4

18,027.2

18,316.6

18,318.3

5,133.9

5,296.5

5,315.1

17,968.6

18,293.7

18,272.2

July 27, 2020

5,168.7

5,298.2

5,360.9

18,094.8

18,329.5

18,285.2

5,164.3

5,287.2

5,504.0

18,030.9

18,286.1

18,145.5

Aug. 3, 2020

5,207.6

5,370.1

5,488.4

18,145.4

18,312.7

18,258.4

5,209.3