Bookflare net big data and machine learning in quantitative investment

Table of Contents Cover CHAPTER 1: Do Algorithms Dream About Artificial Alphas? 1.1 INTRODUCTION 1.2 REPLICATION OR REINVENTION 1.3 REINVENTION WITH MACHINE LEARNING 1.4 A MATTER OF TRUST 1.5 ECONOMIC EXISTENTIALISM: A GRAND DESIGN OR AN ACCIDENT? 1.6 WHAT IS THIS SYSTEM ANYWAY? 1.7 DYNAMIC FORECASTING AND NEW METHODOLOGIES 1.8 FUNDAMENTAL FACTORS, FORECASTING AND MACHINE LEARNING 1.9 CONCLUSION: LOOKING FOR NAILS NOTES CHAPTER 2: Taming Big Data 2.1 INTRODUCTION: ALTERNATIVE DATA – AN OVERVIEW 2.2 DRIVERS OF ADOPTION 2.3 ALTERNATIVE DATA TYPES, FORMATS AND UNIVERSE 2.4 HOW TO KNOW WHAT ALTERNATIVE DATA IS USEFUL (AND WHAT ISN'T) 2.5 HOW MUCH DOES ALTERNATIVE DATA COST? 2.6 CASE STUDIES 2.7 THE BIGGEST ALTERNATIVE DATA TRENDS 2.8 CONCLUSION REFERENCE NOTES CHAPTER 3: State of Machine Learning Applications in Investment Management 3.1 INTRODUCTION 3.2 DATA, DATA, DATA EVERYWHERE 3.3 SPECTRUM OF ARTIFICIAL INTELLIGENCE APPLICATIONS 3.4 INTERCONNECTEDNESS OF INDUSTRIES AND ENABLERS OF ARTIFICIAL INTELLIGENCE 3.5 SCENARIOS FOR INDUSTRY DEVELOPMENTS 3.6 FOR THE FUTURE 3.7 CONCLUSION REFERENCES NOTES CHAPTER 4: Implementing Alternative Data in an Investment Process 4.1 INTRODUCTION 4.2 THE QUAKE: MOTIVATING THE SEARCH FOR ALTERNATIVE DATA 4.3 TAKING ADVANTAGE OF THE ALTERNATIVE DATA EXPLOSION 4.4 SELECTING A DATA SOURCE FOR EVALUATION 4.5 TECHNIQUES FOR EVALUATION 4.6 ALTERNATIVE DATA FOR FUNDAMENTAL MANAGERS 4.7 SOME EXAMPLES 4.8 CONCLUSIONS REFERENCES CHAPTER 5: Using Alternative and Big Data to Trade Macro Assets 5.1 INTRODUCTION 5.2 UNDERSTANDING GENERAL CONCEPTS WITHIN BIG DATA AND ALTERNATIVE DATA 5.3 TRADITIONAL MODEL BUILDING APPROACHES AND MACHINE LEARNING 5.4 BIG DATA AND ALTERNATIVE DATA: BROAD BASED USAGE IN MACRO BASED TRADING 5.5 CASE STUDIES: DIGGING DEEPER INTO MACRO TRADING WITH BIG DATA AND ALTERNATIVE DATA 5.6 CONCLUSION REFERENCES CHAPTER 6: Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales 6.1 INTRODUCTION 6.2 QUANDL'S EMAIL RECEIPTS DATABASE 6.3 THE CHALLENGES OF WORKING WITH BIG DATA 6.4 PREDICTING COMPANY SALES 6.5 REAL TIME PREDICTIONS 6.6 A CASE STUDY: http://amazon.com SALES REFERENCES NOTES CHAPTER 7: Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework 7.1 INTRODUCTION 7.2 A PRIMER ON BOOSTED TREES 7.3 DATA AND PROTOCOL 7.4 BUILDING THE MODEL 7.5 RESULTS AND DISCUSSION 7.6 CONCLUSION REFERENCES NOTES CHAPTER 8: A Social Media Analysis of Corporate Culture 8.1 INTRODUCTION 8.2 LITERATURE REVIEW 8.3 DATA AND SAMPLE CONSTRUCTION 8.4 INFERRING CORPORATE CULTURE 8.5 EMPIRICAL RESULTS 8.6 CONCLUSION REFERENCES CHAPTER 9: Machine Learning and Event Detection for Trading Energy Futures 9.1 INTRODUCTION 9.2 DATA DESCRIPTION 9.3 MODEL FRAMEWORK 9.4 PERFORMANCE 9.5 CONCLUSION REFERENCES NOTES CHAPTER 10: Natural Language Processing of Financial News 10.1 INTRODUCTION 10.2 SOURCES OF NEWS DATA 10.3 PRACTICAL APPLICATIONS 10.4 NATURAL LANGUAGE PROCESSING 10.5 DATA AND METHODOLOGY 10.6 CONCLUSION REFERENCES CHAPTER 11: Support Vector Machine Based Global Tactical Asset Allocation 11.1 INTRODUCTION 11.2 FIFTY YEARS OF GLOBAL TACTICAL ASSET ALLOCATION 11.3 SUPPORT VECTOR MACHINE IN THE ECONOMIC LITERATURE 11.4 A SVR BASED GTAA 11.5 CONCLUSION REFERENCES CHAPTER 12: Reinforcement Learning in Finance 12.1 INTRODUCTION 12.2 MARKOV DECISION PROCESSES: A GENERAL FRAMEWORK FOR DECISION MAKING 12.3 RATIONALITY AND DECISION MAKING UNDER UNCERTAINTY 12.4 MEAN VARIANCE EQUIVALENCE 12.5 REWARDS 12.6 PORTFOLIO VALUE VERSUS WEALTH 12.7 A DETAILED EXAMPLE 12.8 CONCLUSIONS AND FURTHER WORK REFERENCES CHAPTER 13: Deep Learning in Finance: Prediction of Stock Returns with Long Short Term Memory Networks 13.1 INTRODUCTION 13.2 RELATED WORK 13.3 TIME SERIES ANALYSIS IN FINANCE 13.4 DEEP LEARNING 13.5 RECURRENT NEURAL NETWORKS 13.6 LONG SHORT TERM MEMORY NETWORKS 13.7 FINANCIAL MODEL 13.8 CONCLUSIONS Appendix A REFERENCES Biography CHAPTER CHAPTER CHAPTER CHAPTER CHAPTER CHAPTER CHAPTER CHAPTER CHAPTER CHAPTER 10 CHAPTER 11 CHAPTER 12 CHAPTER 13 End User License Agreement List of Tables Chapter Table 2.2 Key criteria for assessing alternative data usefulness Chapter Table 4.1 Average annualized return of dollar neutral, equally weighted portf Table 4.2 Do complaints count predicts returns? Table 4.3 The average exposure to common risk factors by quintile Table 4.4 Regression approach to explain the cross section of return volatili Table 4.5 Complaints factor: significant at the 3% or better level every year Chapter Table 7.1 Summary and examples of features per family type Table 7.2 Analytics Chapter Table 8.1 Descriptive statistics on the user profiles of Glassdoor.com Table 8.2 Summary statistics of Glassdoor.com dataset Table 8.3 Regression of reviewers' overall star ratings Table 8.4 Topic clusters inferred by the topic model Table 8.5 Illustrative examples of reviewer comments Table 8.6 Descriptive statistics of firm characteristics Table 8.7 Regression of company characteristics for performance orientated fi Table 8.8 Regression of performance orientated firms and firm value Table 8.9 Regression of performance orientated firms and earnings surprises Chapter Table 9.1 Performance statistics Table 9.2 Summary statistics for RavenPack Analytics Table 9.3 In sample performance statistics Table 9.4 Out of sample performance statistics Table 9.5 Out of sample performance statistics Table 9.6 Performance statistics Chapter 10 Table 10.1 Fivefold cross validated predictive performance results for the Ne Chapter 11 Table 11.1 Universe traded Chapter 13 Table 13.1 Experiment 1: comparison of performance measured as the HR for LST Table 13.2 Experiment (main experiment) Table 13.3 Experiment (baseline experiment) Table 13.4 Experiment (stocks used for this portfolio) Table 13.5 Experiment (results in different market regimes) Table 13.A.1 Periods for training set, test set and live dataset in experimen List of Illustrations Chapter Figure 2.1 The law of diffusion of innovation Figure 2.2 Spending on alternative data Figure 2.3 Alternative dataset types Figure 2.4 Breakdown of alternative data sources used by the buy side Figure 2.5 Breakdown of dataset's annual price Figure 2.6 Neudata's rating for medical record dataset Figure 2.7 Neudata's rating for Indian power generation dataset Figure 2.8 Neudata's rating for US earnings performance forecast Figure 2.9 Neudata's rating for China manufacturing dataset Figure 2.10 Neudata's rating for short positions dataset Figure 2.11 Carillion's average net debt Figure 2.12 Neudata's rating for short positions dataset Figure 2.13 Neudata's rating for invoice dataset Figure 2.14 Neudata's rating for salary benchmarking dataset Figure 2.15 Ratio of CEO total compensation vs employee average, 2017 Figure 2.16 Neudata's rating for corporate governance dataset Chapter Figure 3.1 AI in finance classification Figure 3.2 Deep Learning Framework Example CHAPTER Michael Kollo is Deputy Global Head of Research at Rosenberg Equities and is focused on applications of machine learning and big data, factor research and quantitative strategy for equity portfolios Prior to joining Rosenberg Equities, Michael was Head of Risk for Renaissance Asset Management, in charge of dedicated emerging market equity strategies Before Renaissance, Michael held senior research and portfolio management positions at Fidelity and BlackRock Michael's experience spans factor investing from risk modelling to signal generation, portfolio management and product design Michael obtained his PhD in Finance from the London School of Economics and holds bachelor's and master's degrees from the University of New South Wales in Australia He lectures at Imperial College and is an active mentor for FinTech firms in London CHAPTER Rado Lipuš, CFA, is the founder and CEO of Neudata, an alternative data intelligence provider Prior to founding Neudata, Rado's professional experience spanned 20 years of FinTech leadership, sales management and data innovation for the buy side He spent several years in quantitative portfolio construction and risk management at MSCI (Barra) and S&P Capital IQ and raised funds for CITE Investments Rado worked latterly as Managing Director at PerTrac in London, a leading FinTech and data analytics solutions provider to hedge fund allocators and institutional investors in EMEA and Asia He also has experience with financial data firms such as eVestment, 2iQ Research, I/B/E/S and TIM Group An acknowledged expert on alternative data, Rado is regularly invited to speak at conferences and industry events Rado received his Master of Business Administration from the University of Graz, Austria, and is a CFA charter holder Daryl Smith, CFA, is Head of Research at Neudata He and his team are responsible for researching and discovering alternative datasets for a wide range of asset managers worldwide Prior to Neudata, Daryl worked as an equity research analyst at boutique investment firm Liberum Capital across a number of sectors, including agriculture, chemicals and diversified financials Prior to Liberum, he worked at Goldman Sachs as an equity derivatives analyst and regulatory reporting strategist Daryl holds a master's degree in mechanical engineering from the University of Bath and is a CFA charter holder CHAPTER Ekaterina Sirotyuk is a Portfolio Manager, Investment Solutions and Products at Credit Suisse and the lead author of ‘Technology enabled investing’, a department piece on applications of AI/big data in investment management Prior to joining Credit Suisse in 2014, Ekaterina was a manager at a German investment company, responsible for sourcing and evaluating energy related investments as well as deal structuring Before that she was an associate at Bank of America Merrill Lynch in London in the Fixed Income, Currencies and Commodities department, doing cross asset class structuring for European pensions and insurers Ekaterina started her career as an investment analyst at UBS Alternative and Quantitative Investments, based in New York and Zurich She received her BSc in Economics and Management (first class honours) from the University of London (lead college – London School of Economics) and her MBA from INSEAD, where she also did her doctorate coursework in finance In addition, Ekaterina has been a leader at the Swiss Finance + Technology Association CHAPTER Vinesh Jha is CEO and founder of ExtractAlpha, established in 2013 in Hong Kong with the mission of bringing analytical rigour to the analysis and marketing of new datasets for the capital markets From 1999 to 2005, Vinesh was Director of Quantitative Research at StarMine in San Francisco, where he developed industry leading metrics of sell side analyst performance as well as successful commercial alpha signals and products based on analyst, fundamental and other data sources Subsequently he developed systematic trading strategies for proprietary trading desks at Merrill Lynch and Morgan Stanley in New York Most recently he was Executive Director at PDT Partners, a spinoff of Morgan Stanley's premiere quant prop trading group, where in addition to research he applied his experience in the communication of complex quantitative concepts to investor relations Vinesh holds an undergraduate degree from the University of Chicago and a graduate degree from the University of Cambridge, both in mathematics CHAPTER Saeed Amen is the founder of Cuemacro Over the past decade, Saeed has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura Independently, he is also a systematic FX trader, running a proprietary trading book trading liquid G10 FX since 2013 He is the author of Trading Thalesians: What the Ancient World Can Teach Us About Trading Today (Palgrave Macmillan, 2014) Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading Saeed's clients have included major quant funds and data companies such as RavenPack and TIM Group He is also a co founder of the Thalesians Saeed holds an MSc in Mathematics and Computer Science from Imperial College London Iain J Clark is managing director and founder of Efficient Frontier Consulting Ltd, an independent quant consultancy that provides consultancy and training services to banks, hedge funds, exchanges and other participants in the financial services sector He specializes in FX, FX/IR and commodities, and is an industry expert in volatility modelling and the application of numerical methods to finance Iain has 14 years' finance experience, including being Head of FX and Commodities Quantitative Analysis at Standard Bank and Head of FX Quantitative Analysis at UniCredit and Dresdner Kleinwort; he has also worked at Lehman Brothers, BNP Paribas and JP Morgan He is the author of Foreign Exchange Option Pricing: A Practitioner's Guide (Wiley, 2011) and Commodity Option Pricing: A Practitioner's Guide (Wiley, 2014) Iain is a hands on quant technologist as well as an expert quant modeller and strategy consultant, having considerable practical expertise in languages such as C++ (multithreading, Boost, STL), C#, Java, Matlab, Python and R CHAPTER Giuliano De Rossi heads the European Quantitative Research team at Macquarie, based in London He joined from PIMCO where he was an analyst in the Credit and Equity Analytics and Asset Allocation teams Prior to that he worked for six years in the quant research team at UBS He has a PhD in economics from Cambridge University and worked for three years as a college lecturer in economics at Cambridge before joining the finance industry on a full time basis Giuliano's master's degree is from the London School of Economics; his first degree is from Bocconi University in Milan He has worked on a wide range of topics, including pairs trading, low volatility, the tracking error of global ETFs, cross asset strategies, downside risk and text mining His academic research has been published in the Journal of Econometrics and the Journal of Empirical Finance Jakub Kolodziej joined the European Quantitative Research team in London in 2014, prior to which he worked as an investment analyst at a quantitative hedge fund He holds a master's degree in Finance and Private Equity from the London School of Economics and a bachelor's degree in Finance and Accounting from Warsaw School of Economics Gurvinder Brar is Global Head of Quantitative Research group at Macquarie The Global Quantitative Research group comprises 13 analysts, with teams operating in all the major equity market regions They aim to produce cutting edge, topical and actionable research focusing on alpha, risk and portfolio construction issues and are keen to form deep partnerships with clients The regional teams work closely, aiming to build a common global knowledge base of techniques, backed up with specific local expertise where required In addition, the group undertakes custom projects for clients which assist with all aspects of the investment processes CHAPTER Tony Guida is a senior quantitative portfolio manager, managing multi factor equity portfolios for the asset manager of a UK pension fund in London Prior to that Tony was Senior Research Consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta, advising asset owners on how to construct and allocate to risk premia Before joining EDHEC Tony worked for eight years at UNIGESTION as a senior research analyst Tony was a member of the Research and Investment Committee for Minimum Variance Strategies and he was leading the factor investing research group for institutional clients Tony is the editor and co author of Big Data and Machine Learning In Quantitative Investment (Wiley, 2018) He holds bachelor's and master's degrees in econometry and finance from the University of Savoy in France Tony is a speaker on modern approaches for quantitative investment and has held several workshops on ‘Machine learning applied for quants’ Guillaume Coqueret has been an Assistant Professor of Finance at the Montpellier Business School since 2015 He holds a PhD in Business Administration from ESSEC Business School Prior to his professorship at MBS, he was a senior quantitative research analyst at the EDHEC Risk Institute from 2013 to 2015 He holds two master's degrees in the field of quantitative finance His work has been published in such journals as Journal of Banking and Finance, Journal of Portfolio Management and Expert Systems with Applications CHAPTER Andy Moniz is the Global Markets Chief Data Scientist at Deutsche Bank Andy is an expert in natural language processing and was previously a quantitative portfolio manager at UBS, responsible for long short stock selection and macro strategies at UBS O'Connor and systematic environmental social and governance (ESG) strategies at UBS Asset Management using accounting signals with unstructured data Prior to UBS, Andy was a senior quantitative portfolio manager at APG Asset Management, where he was responsible for factor premia, text mining and ESG stock selection strategies Andy began his career in 2000 as a macroeconomist at the Bank of England Between 2003 and 2011 he worked in quantitative equities for various investment banks Andy holds a BA and MA in Economics from the University of Cambridge, an MSc in Statistics from the University of London, and a PhD in Information Retrieval and Natural Language Processing from Erasmus University, The Netherlands CHAPTER Peter Hafez is the head of data science at RavenPack Since joining RavenPack in 2008, he's been a pioneer in the field of applied news analytics, bringing alternative data insights to the world's top banks and hedge funds Peter has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston and Saxo Bank He holds a master's degree in Quantitative Finance from Sir John Cass Business School along with an undergraduate degree in Economics from Copenhagen University Peter is a recognized speaker at quant finance conferences on alternative data and AI, and has given lectures at some of the world's top academic institutions, including London Business School, Courant Institute of Mathematics at NYU and Imperial College London Francesco Lautizi is Senior Data Scientist at RavenPack, where he researches how big data and news analytics are reshaping financial markets and provides insights on how these new sources of information can be used by financial institutions for portfolio and risk management purposes He holds a PhD in Economics and Finance from University of Rome Tor Vergata, where he studied how estimation error impacts the performance on large scale portfolios He has been a visiting student at EIEF and has a Master of Science in Finance from University of Rome Tor Vergata CHAPTER 10 M Berkan Sesen, PhD, vice president, is a quantitative researcher and portfolio manager in a major US asset manager Prior to this, he worked as a quantitative analyst at Citigroup, supervising a small team with the mandate to build/maintain statistical models to assist algorithmic trading and electronic market making He also co led the global data analytics working group within the quantitative analysis department in Citigroup Berkan holds a doctorate in artificial intelligence from the University of Oxford and specializes in machine learning and statistics He also holds an MSc with Distinction in Biomedical Engineering from the University of Oxford Yazann Romahi, PhD, CFA, managing director, is CIO at a major US asset manager focused on developing the firm's factor based franchise across both alternative beta and strategic beta Prior to that he was Head of Research and Quantitative Strategies, responsible for the quantitative models that help establish the broad asset allocation reflected across multi asset solutions portfolios globally Yazann has worked as a research analyst at the Centre for Financial Research at the University of Cambridge and has undertaken consulting assignments for a number of financial institutions, including Pioneer Asset Management, PricewaterhouseCoopers and HSBC Yazann holds a PhD in Computational Finance/Artificial Intelligence from the University of Cambridge and is a CFA charter holder Victor Li, PhD, CFA, executive director, is Head of Equity and Alternative Beta Research and a portfolio manager at a major US asset manager Victor's primary focus includes management of the research agenda, as well as model development and portfolio management for the quantitative beta suite of products Victor holds a PhD in Communications and Signal Processing from Imperial College London, where he was also employed as a full time research assistant Victor obtained an MSc with Distinction in Communications Engineering from the University of Manchester and is a CFA charter holder CHAPTER 11 Joel Guglietta is Macro Quantitative Portfolio Manager of Graticule Asset Management in Hong Kong, managing a multi assets hedge funds using machine learning algorithms Prior to that Joel was a macro quantitative strategist and portfolio manager for hedge funds and investment banks in Asia and Australia (Brevan Howard, BTIM, HSBC) for more than 12 years His expertise is in quantitative models for asset allocation, portfolio construction and management using a wide range of techniques, including machine learning techniques and genetic algorithms Joel is currently a PhD candidate at GREQAM (research unit jointly managed by CNRS, EHESS and Ecole Centrale) He has been a speaker at many deep learning and machine learning events in Asia CHAPTER 12 Gordon Ritter completed his PhD in Mathematical Physics at Harvard University in 2007, where his published work ranged across the fields of quantum computation, quantum field theory, differential geometry and abstract algebra Prior to Harvard he earned his bachelor's degree with honours in mathematics from the University of Chicago Gordon is a senior portfolio manager at GSA Capital and leader of a team trading a range of systematic absolute return strategies across geographies and asset classes GSA Capital has won the Equity Market Neutral & Quantitative Strategies category at the EuroHedge Awards four times, with numerous other awards including in the long term performance category Prior to joining GSA, Gordon was a vice president of Highbridge Capital and a core member of the firm's statistical arbitrage group, which although operating with fewer than 20 people, was responsible for billions in profit and trillions of dollars of trades across equities, futures and options with low correlation to traditional asset classes Concurrently with his positions in industry, Gordon teaches courses including portfolio management, econometrics, continuous time finance and market microstructure in the Department of Statistics at Rutgers University, and also in the MFE programmes at Baruch College (CUNY) and New York University (both ranked in the top five MFE programmes) Gordon has published original work in top practitioner journals including Risk and academic journals including European Journal of Operational Research He is a sought after speaker at major industry conferences CHAPTER 13 Miquel Noguer Alonso is a financial markets practitioner with more than 20 years of experience in asset management He is currently Head of Development at Global AI (big data artificial intelligence in finance company) and Head of Innovation and Technology at IEF He worked for UBS AG (Switzerland) as Executive Director He has been a member of the European Investment Committee for the past 10 years He worked as a chief investment officer and CIO for Andbank from 2000 to 2006 He started his career at KPMG Miquel is Adjunct Professor at Columbia University, teaching asset allocation, big data in finance and FinTech He is also Professor at ESADE, teaching hedge funds, big data in finance and FinTech He taught the first FinTech and big data course at London Business School in 2017 Miquel received an MBA and a degree in Business Administration and Economics at ESADE in 1993 In 2010 he earned a PhD in Quantitative Finance with a Summa Cum Laude distinction (UNED – Madrid, Spain) He completed a postdoc at Columbia Business School in 2012 He collaborated with the mathematics department of Fribourg University, Switzerland, during his PhD He also holds the Certified European Financial Analyst (CEFA) 2000 distinction His academic collaborations include a visiting scholarship in the Finance and Economics Department at Columbia University in 2013, in the mathematics department at Fribourg University in 2010, and presentations at Indiana University, ESADE and CAIA, plus several industry seminars including the Quant Summit USA 2017 and 2010 Gilberto Batres Estrada is a senior data scientist at Webstep in Stockholm, Sweden, where he works as a consultant developing machine learning and deep learning algorithms for Webstep's clients He develops algorithms in the areas of computer vision, object detection, natural language processing and finance, serving clients in the financial industry, telecoms, transportation and more Prior to this Gilberto worked developing trading algorithms for Assa Bay Capital in Gothenburg, Sweden He has more than nine years of experience in IT working for a semi government organization in Sweden Gilberto holds both an MSc in Theoretical Physics from Stockholm university and an MSc in Engineering from KTH Royal Institute of Technology in Stockholm, with a specialization in applied mathematics and statistics Aymeric Moulin is a graduate student at Columbia University in the IEOR department where he is majoring in Operations Research He studied theoretical mathematics and physics in classes préparatoires in France and completed a Bachelor of Science at CentraleSupélec engineering school, from which he will soon receive a master's degree He has spent the past few years focusing on deep learning and reinforcement learning applications to financial markets He is currently an intern at JP Morgan in Global Equities WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley's ebook EULA ... CONCEPTS WITHIN BIG DATA AND ALTERNATIVE DATA 5.3 TRADITIONAL MODEL BUILDING APPROACHES AND MACHINE LEARNING 5.4 BIG DATA AND ALTERNATIVE DATA: BROAD BASED USAGE IN MACRO BASED TRADING 5.5 CASE... engineering, valuation and financial instrument analysis, as well as much more For a list of available titles, visit our website at www.WileyFinance.com Big Data and Machine Learning in Quantitative. .. rare and pose especially interesting areas for empirical and theoretical work 1.3 REINVENTION WITH MACHINE LEARNING Reinvention with machine learning poses a similar opportunity for us to reinvent