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Table of Contents
by Steven Krawciw, Irene Aldridge
Real-Time Risk
Cover
Title Page
Copyright
Dedication
Acknowledgments
Chapter 1: Silicon Valley Is Coming!
Everyone Is into Fintech
The Millennials Are Coming
Social Media
Mobile
Cheaper and Faster Technology
Cloud Computing
Blockchain
Fast Analytics
In the End, It's All About Real‐Time Data Analytics
End of Chapter Questions
Chapter 2: This Ain't Your Grandma's Data
Data
The Risk of Data
Technology
Blockchain
What Elements Are Common to All Blockchains?
Conclusions
End of Chapter Questions
Chapter 3: Dark Pools, Exchanges, and Market Structure
The New Market Hours
Where Do My Orders Go?
Executing Large Orders
Transaction Costs and Transparency
Conclusions
End of Chapter Questions
Chapter 4: Who Is Front‐Running You?
Spoofing, Flaky Liquidity, and HFT
Order‐Based Negotiations
Conclusions
End of Chapter Questions
Chapter 5: High‐Frequency Trading in Your Backyard
Implications of Aggressive HFT
Aggressive High‐Frequency Trading in Equities
Aggressive HFT in US Treasuries
Aggressive HFT in Commodities
Aggressive HFT in Foreign Exchange
Conclusions
End of Chapter Questions
Chapter 6: Flash Crashes
What Happens During Flash Crashes?
Detecting Flash‐Crash Prone Market Conditions
Are HFTs Responsible for Flash Crashes?
Conclusions
End of Chapter Questions
Chapter 7: The Analysis of News
The Delivery of News
Preannouncement Risk
Data, Methodology, and Hypotheses
Conclusions
End of Chapter Questions
Chapter 8: Social Media and the Internet of Things
Social Media and News
The Internet of Things
Conclusions
End of Chapter Questions
Chapter 9: Market Volatility in the Age of Fintech
Too Much Data, Too Little Time—Welcome, Predictive Analytics
Want to Lessen Volatility of Financial Markets? Express Your Thoughts Online!
Market Microstructure Is the New Factor in Portfolio Optimization
Yes, You Can Predict T + 1 Volatility
Market Microstructure as a Factor? You Bet
Case Study: Improving Execution in Currencies
For Longer‐Term Investors, Incorporate Microstructure into the Rebalancing Decision
Conclusions
End of Chapter Questions
Chapter 10: Why Venture Capitalists Are Betting on Fintech to Manage Risks
Opportunities for Disruption Are Present, and They May Not Be What They Seem
Data and Analytics in Fintech
Fintech as an Asset Class
Where Do You Find Fintech?
Fintech Success Factors
The Investment Case for Fintech
How Do Fintech Firms Make Money?
Fintech and Regulation
Conclusions
End of Chapter Questions
Authors' Biographies
Index
End User License Agreement
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Prev
Previous Chapter
Title Page
Next
Next Chapter
Copyright
Table of Contents
Cover
Title Page
Copyright
Dedication
Acknowledgments
Chapter 1: Silicon Valley Is Coming!
Everyone Is into Fintech
The Millennials Are Coming
Social Media
Mobile
Cheaper and Faster Technology
Cloud Computing
Blockchain
Fast Analytics
In the End, It's All About Real‐Time Data Analytics
End of Chapter Questions
Chapter 2: This Ain't Your Grandma's Data
Data
The Risk of Data
Technology
Blockchain
What Elements Are Common to All Blockchains?
Conclusions
End of Chapter Questions
Chapter 3: Dark Pools, Exchanges, and Market Structure
The New Market Hours
Where Do My Orders Go?
Executing Large Orders
Transaction Costs and Transparency
Conclusions
End of Chapter Questions
Chapter 4: Who Is Front‐Running You?
Spoofing, Flaky Liquidity, and HFT
Order‐Based Negotiations
Conclusions
End of Chapter Questions
Chapter 5: High‐Frequency Trading in Your Backyard
Implications of Aggressive HFT
Aggressive High‐Frequency Trading in Equities
Aggressive HFT in US Treasuries
Aggressive HFT in Commodities
Aggressive HFT in Foreign Exchange
Conclusions
End of Chapter Questions
Chapter 6: Flash Crashes
What Happens During Flash Crashes?
Detecting Flash‐Crash Prone Market Conditions
Are HFTs Responsible for Flash Crashes?
Conclusions
End of Chapter Questions
Chapter 7: The Analysis of News
The Delivery of News
Preannouncement Risk
Data, Methodology, and Hypotheses
Conclusions
End of Chapter Questions
Chapter 8: Social Media and the Internet of Things
Social Media and News
The Internet of Things
Conclusions
End of Chapter Questions
Chapter 9: Market Volatility in the Age of Fintech
Too Much Data, Too Little Time—Welcome, Predictive Analytics
Want to Lessen Volatility of Financial Markets? Express Your Thoughts Online!
Market Microstructure Is the New Factor in Portfolio Optimization
Yes, You Can Predict
T
+ 1 Volatility
Market Microstructure as a Factor? You Bet
Case Study: Improving Execution in Currencies
For Longer‐Term Investors, Incorporate Microstructure into the Rebalancing Decision
Conclusions
End of Chapter Questions
Chapter 10: Why Venture Capitalists Are Betting on Fintech to Manage Risks
Opportunities for Disruption Are Present, and They May Not Be What They Seem
Data and Analytics in Fintech
Fintech as an Asset Class
Where Do You Find Fintech?
Fintech Success Factors
The Investment Case for Fintech
How Do Fintech Firms Make Money?
Fintech and Regulation
Conclusions
End of Chapter Questions
Authors' Biographies
Index
End User License Agreement
List of Tables
Chapter 3: Dark Pools, Exchanges, and Market Structure
Table 3.1 List of National Securities Exchanges (Stock Exchanges) Registered with the U.S. Securities and Exchange Commission under Section 6 of the Securities Exchange Act of 1934, as of August 4, 2016
Table 3.2 Exchanges Registered by the SEC to Trade Equity Futures, as of August 4, 2016
Table 3.3 Dark Pools Trading Equities in the United States, Tier 1, 1st Quarter, 2016, Tier 1 Stocks, Ordered by Total Share Volume
Chapter 4: Who Is Front‐Running You?
Table 4.1 A Sample from the Level III Data (Processed and Formatted) for GOOG on October 8, 2015
Table 4.2 Distribution of Order Sizes in Shares Recorded for GOOG on October 8, 2015
Table 4.3 Distribution of Difference, in Milliseconds, between Sequential Order Updates for All Order Records for GOOG on October 8, 2015
Table 4.4 Size and Shelf Life of Orders Canceled in Full with a Single Cancellation for GOOG on October 8, 2015
Table 4.5 Distribution of Times (in milliseconds) between Subsequent Order Revisions for GOOG on October 8, 2015
Table 4.6 Distribution of Duration (in milliseconds) of Limit Orders Canceled with an Order Message Immediately following the Order Placement Message
Chapter 5: High‐Frequency Trading in Your Backyard
Table 5.1 Average Aggressive HFT Participation in Selected Commodities and Equities on August 31, 2015
Table 5.2 Employment Figures as Reported by Bloomberg
Chapter 7: The Analysis of News
Table 7.1 Correlation of realized values of Construction Spending Index (“Construction”) and ISM Manufacturing Index (“Manufacturing”) Less Prior Month Values and Less Forecasted Values
Chapter 9: Market Volatility in the Age of Fintech
Table 9.1 AbleMarkets Flash Crash Index, Predictability of T+1 Downward Volatility
Chapter 10: Why Venture Capitalists Are Betting on Fintech to Manage Risks
Table 10.1 Raymond James Estimates of Enterprise Value Premia over Revenues for Fintech Businesses (USD in millions)
List of Illustrations
Chapter 1: Silicon Valley Is Coming!
Figure 1.1 Global fintech investment
Figure 1.2 Zopa originations by month
Chapter 2: This Ain't Your Grandma's Data
Figure 2.1 Breaking a row‐oriented database into columns
Figure 2.2 Volume of computer manufacturing in US billions by geography
Figure 2.3 Evolution of technology and computing power over the past century
Figure 2.4 Simultaneous input of broken down information packers into the world's network systems
Chapter 3: Dark Pools, Exchanges, and Market Structure
Figure 3.1 Sample limit order book
Figure 3.2 How NBBO execution works
Chapter 4: Who Is Front‐Running You?
Figure 4.1 Stages of order identification
Figure 4.2 Aggressive HFT's orders impact bid‐ask spreads
Figure 4.3 Illustration of a passive HFT order placement
Figure 4.4 Buy‐side available liquidity exceeds sell‐side liquidity
Figure 4.5 Example of impact of flickering quotes
Figure 4.6 Limit order book in the dark pools and phishing
Figure 4.7 Histogram of number of order messages per each added limit order
Chapter 5: High‐Frequency Trading in Your Backyard
Figure 5.1 Stylized representation of market making in a limit order book of a given financial instrument
Figure 5.2 The consequences of adverse selection for market makers
Figure 5.3 One‐minute performance of aggressive HFTs identified by AbleMarkets.com Aggressive HFT Index
Figure 5.4 Stylized liquidity taking (panel a) and making (panel b)
Figure 5.5 S&P 500 ETF (NYSE: SPY) on October 2, 2015. A sudden drop in price circa 8:30 AM coincided with smaller‐than‐expected job gain figures.
Figure 5.6 Proportion of aggressive HFT buyers and sellers in the S&P500 ETF (NYSE: SPY) on October 2, 2015. Shown: 10‐minute moving averages of aggressive HFT buyer and seller participation
Figure 5.7 Average participation of aggressive HFT buyers and sellers, as percentage by volume traded, among all the Dow Jones Industrial stocks on October 2, 2015
Figure 5.8 Aggressive HFT buyers and sellers in American Express (NYSE:AXP) on October 2, 2015
Figure 5.9 Evolution of aggressive HFT participation in the US Treasuries as a percentage of volume traded, measured by the AbleMarkets Aggressive HFT Index (HFTIndex.com)
Figure 5.10 Daily average aggressive HFT on crude oil and corresponding price and implied vol on crude oil
Figure 5.11 Daily average aggressive HFT on crude oil and implied vol on crude oil
Figure 5.12 Aggressive HFT participation as a percentage of volume traded in foreign exchange (daily averages)
Chapter 6: Flash Crashes
Figure 6.1 The number of flash crashes in the Dow Jones Industrial Average index per year. Flash crashes are defined as the intraday percentage loss in the DJIA index from market open to the daily low that exceeds –0.5 percent, –1 percent, and –2 percent, respectively.
Figure 6.2 The number of flash crashes in IBM per year, defined as a percentage loss in the IBM stock from market open to the daily low
Figure 6.3 Net Share Issuance of ETFs, billions of dollars, 2002–2014
Figure 6.4 Total net assets of ETFs concentrated in large‐cap domestic stocks, billions of dollars, December 2014
Figure 6.5 Average monthly ETF turnover on Deutsche Borse Xetra
Figure 6.6 Number of flash crashes per year in the S&P 500 ETF (NYSE:SPY) and the annual trading volume in the S&P 500 ETF. The number of flash crashes appears to be exactly tracking the volume in the S&P 500 ETF.
Figure 6.7 Number of flash crashes in the S&P 500 index (not ETF) and the respective annual share volume in the stocks comprising the S&P 500. The S&P 500 trading volume appears to lag the number of flash crashes—increase following an increase in flash crashes.
Figure 6.8 250‐day rolling correlation of the intraday downward volatility (low/open –1) and daily volume of the S&P 500 ETF (NYSE:SPY)
Figure 6.9 Timeline of cross‐asset institutional activity on the day of the flash crash of October 15, 2014, as estimated by AbleMarkets
Figure 6.10 Number of single‐stock crashes (when daily low fell below the daily open over 0.5 percent) among the 30 constituents of the Dow Jones Industrial Average
Figure 6.11 An illustration of positive, negative, non‐positive, and non‐negative runs
Figure 6.12 Empirical conditional probabilities of observing a longer run given the present length of a run
Figure 6.13 Conditional probabilities of continuing in a run measured on one‐second data on May 6, 2010. Identical conditional probabilities are observed for positive and negative runs at one‐second frequencies.
Figure 6.14 Average empirical economic gain and loss observed in positive and negative runs
Figure 6.15 Conditional probability of observing
N
lags in a run of non‐negative returns, given the run has lasted
N
– 1 lags
Figure 6.16 Conditional probability of observing
N
lags in a run of non‐positive returns, given the run has lasted
N
– 1 lags
Figure 6.17 The average economic value of a non‐negative run corresponding to Figure 6.15
Figure 6.18 The average economic value of a non‐positive run corresponding to Figure 6.16
Figure 6.19 The difference between the maximum length of a positive run and the maximum length of a negative run observed on a given day
Chapter 7: The Analysis of News
Figure 7.1 Aggressive HFT (the difference of aggressive HFT sellers and aggressive HFT buyers), as a percentage of 10‐minute volume
Figure 7.2 Institutional investor participation in Wal‐Mart (WMT) trading on October 14, 2015, as a percentage of daily volume
Figure 7.3 Institutional investor participation in Wal‐Mart (WMT) trading as a percentage of 30‐minute volume
Figure 7.4 Instantaneous price adjustment in response to positive publicly released news, according to the efficient markets hypothesis
Figure 7.5 Instantaneous price adjustment in response to negative news, according to the efficient markets hypothesis
Figure 7.6 Actual price adjustment in response to positive publicly released news, according to behavioral studies
Figure 7.7 Actual price adjustment in response to negative news, according to behavioral studies
Figure 7.8 Realized average price changes for the Russell 3000 stocks in response to (1) higher‐than‐previous values of the ISM Manufacturing Index (Realized vs Prior Avg Cum +), (2) lower‐than‐previous values of the ISM Manufacturing Index (Avg Cum −), and (3) all announcements (AVG)
Figure 7.9 Cumulative price change of Agilent (NYSE:A) surrounding the 10:00 AM ISM Manufacturing Index announcement recorded in BATS‐Z on July 1, 2015
Figure 7.10 Participation of aggressive HFT by volume in Agilent (NYSE:A) on July 1, 2015, before and after the ISM Manufacturing Index and Construction Spending figures announcements at 10:00 AM
Figure 7.11 Average cumulative price change for all the Russell 3000 stocks surrounding the ISM Manufacturing and Construction Spending announcements at 10:00 AM on July 1, 2015
Figure 7.12 Average cumulative price change and price change volatility across all the Russell 3000 stocks surrounding Construction Spending announcement at 10:00 AM on July 1, 2015
Figure 7.13 Participation of aggressive HFT averaged across all Russell 3000 stocks around 10:00 AM news on July 1, 2015
Figure 7.14 Standard deviation of average Russell 3000 cumulative price responses surrounding ISM Manufacturing Index announcements. Shown price volatility is measured for cases where the realized news was higher than the prior month's news, lower than the prior month's news and across all the cases.
Figure 7.15 The
t
‐ratios of the cumulative price responses of the Russell 3000 stocks around the ISM Manufacturing Index announcements
Figure 7.16 Average price response of the Russell 3000 stocks to the changes in Construction Spending relative to the prior month's announcements. Many times, the Construction Spending figures remained unchanged relative to their prior values.
Figure 7.17 Average price response across the Russell 3000 stocks in response to (1) realized ISM Manufacturing Index spending exceeding consensus forecast (Avg Cum+), (2) realized ISM Manufacturing Index falling below the consensus forecast for that day (Avg Cum−), and in response to all cases. Data covers January 2013 to October 2015
Figure 7.18
t
‐ratios of price response of the Russell 3000 stocks to the ISM Manufacturing Index announcements from January 2013 through October 2015 whenever the realized Manufacturing Index exceeded the forecast (t avg Cum+), underachieved the forecast (t avg Cum−), and all cases (t avg)
Figure 7.19 Cumulative price response of Russell 3000 stocks to the Construction Spending announcement when the realized construction spending exceeds the forecasted value (Avg Cum+), and falls short of the forecasted value (Avg Cum−)
Figure 7.20 Statistical significance of cumulative price responses of Russell 3000 stocks measured around Construction Spending announcements when realized Construction Spending figures exceed forecasted values (t avg Cum +), fall short of the forecasted values (t avg Cum−), and all cases
Figure 7.21 Behavior of aggressive HFT
buyers
around the ISM Manufacturing Index Announcements in instances when the realized news was higher (Avg Cum+) and lower (Avg Cum−) than the previous month's value
Figure 7.22 Behavior of aggressive HFT
sellers
around the ISM Manufacturing Index announcements in instances when the realized news was higher (Avg Cum+) and lower (Avg Cum−) than the previous month's value
Figure 7.23 The difference between aggressive HFT buyer participation when the realized Construction Spending Index exceeds the forecast and that when the realized value falls short of the forecast
Chapter 8: Social Media and the Internet of Things
Figure 8.1 AAPL in social media leads AAPL closing prices.
Figure 8.2 Normalized social media conversations, as measured by AbleMarkets Social Media Quotient (left axis) vs. same‐day intraday range volatility for VMware (ticker VMW)
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