Intelligent Finance Global Monitoring and Observatory

- A New Perspective for Global Macro beyond Big Data

Heping Pan*

proposed by Pan (2003)[1] and elaborated by Pan, Sornette



Abstract²This paper presents a new perspective of

intelligent finance towards Intelligent Finance Global and Kortaneke (2006) [2]. At the beginning, intelligent finance
Monitory and Observatory as a theoretical and technical was recognized as a new direction of modern financial theory
framework for quantitative investment and trading in global emerging from the confluence of several distinct disciplines
financial markets and for global risk management. It is in financial market analysis, investing and trading, removing
recognized that intelligent finance is entering into a new and any historical or artificial barrier between them. It is
fourth generation with global monitoring after its preceding conceived as the science, technology and art of the
and still ongoing overlapping three generations: quantitative comprehensive, predictive, dynamic and strategic analysis of
mechanical trading strategies, intelligent portfolio trading, global financial markets, towards a unification and
and big data-driven portfolio trading. The complex ecological integration of academic finance and professional finance. As
system of global financial markets is seen as a circular a comprehensive approach, it is a quest for absolute positive
layered structure consisting of, from inner to exterior layers, and non-trivial returns in investing and trading by exploiting
arbitrage pricing, stochastic dynamic processes of financial complete information about financial markets from all
market prices, general equilibrium of global economies, and general perspectives, drawing ideas, theories, models and
game equilibrium of world financial powers. For the long-run techniques from many related academic disciplines, such as
survival and robust sustainability of a global hedge fund, macroeconomics, microeconomics, economic finance,
Intelligent Finance Global Monitoring is a must both as a financial mathematics, econophysics, behavioral finance and
business model and as an operating system framework of computational finance, and from professional schools of
global trading and global risk monitoring, and it should be thought, including value investing, growth investing,
structured with four subsystems: 1) quantitative investment macrowave investing, trend following, fundamental analysis,
global trading, 2) global financial risk monitoring, 3) global technical analysis, mind analysis, active speculation, etc. In
economic situation monitoring, 4) global environmental risk terms of risk management, intelligent finance is expected to
monitoring with remote sensing finance. minimize the very last risk²the incompleteness of an
Keywords: intelligent finance, global monitoring, global investing or trading method or system. The theoretical
trading, global risk management, global macro hedge funds, framework of intelligent finance consists of four major
global economic situation monitoring, global environmental components: financial information fusion, multilevel
risk monitoring, remote sensing finance. stochastic dynamic process models of market prices, active
portfolio and total risk management, and financial strategic
I. INTRODUCTION
analysis. Today, 15 years later since the inception, the
Today, both the academic finance communities and the essential concept, the objectives of study, the four basic
professional finance sectors have witnessed the essential rise principles of intelligent finance still remain valid and
of Intelligent Finance as the latest and seemingly lasting new adequate.
form of finance in the age of intelligent economy and The understanding and vision under the umbrella of
intelligent society. intelligent finance was formulated in a basic theory of
There are three distinct perspectives of and to intelligent intelligent finance by Pan (2011) [3], which provides a new
finance: First, intelligent finance as a new science of modern paradigm of financial investment. The main observed fact is
financial theory; Second, intelligent finance as a new that financial markets are always in a state of swing between
technology of financial infrastructure and enabling efficient and inefficient modes on multiple levels of time
applications; Third, intelligent finance as a new form of scale; it is possible to go beyond the efficient market theory to
financial businesses and development strategies. study the dynamic evolving process of markets between
equilibrium and far-from-equilibrium states; there are robust
Intelligent finance as a new science of modern financial
dynamic patterns ± high-dimensional invariance in this
theory was formally started in academic finance since
evolving process, which may be exploitable via intelligent
2003-2004. The very concept of intelligent finance was first
trading systems. On the foundation of the four principles -
comprehensive, predictive, dynamic and strategic, the basic
*Research supported by the National Social Science Fund of China with theory takes the information sources into the loop as the
Grant 17BGL231 ³Intelligent Portfolio Theory and Investing Strategies´. starting points for all the market analysis, introducing the
Professor Heping Pan is Founding Director of Chinese Cooperative scale space of time into the pricing process analysis in order
Innovation Center for Intelligent Finance, Institute for Advanced Studies and
to detect and capture trends, cycles and seasonality on
School of Business, Chengdu University; Intelligent Finance Research
Center, Chongqing Institute of Finance (parallel first); Cooperative Research multiple intrinsic levels of time scale which are then used as
Center for Remote Sensing Finance, Wuhan University; and Swingtum the dynamic basis for constructing and managing portfolios.
Prediction, Australia (phone: +86-13896169035, e-mail: panhp@swingtum.com).

l-))) 
In stock markets, the theory exhibits itself in the form of an expand the breadth and depth of financial services, so that the
Intelligent Dynamic Portfolio Theory, which integrates whole society can obtain equal, efficient and professional
predictive modeling of a bull-bear market cycle, sector financial services, and realize the intelligentization[12],
rotation, and portfolio optimization with a reactive trend individualization and customization of financial services.
following trading strategy. This theory was further developed Although intelligent finance covers all the main areas of
into a general Intelligent Portfolio Theory and a Strength financial activities and services, quantitative investment and
Investing Methodology by Pan (2017-2019) [4] with a few trading in the global financial markets remains the most
innovative techniques [5-7] important core of everything, simply because in the long run,
Intelligent finance as a new technology of financial the profit or loss in all other major areas such as banking and
engineering has been developed and practiced by Wall Street insurance still depend on the global trading and global market
professionals since 1990¶s proliferation of the Internet trading. risk management.
The technology is exemplified by the success of a few This paper elaborates on the comprehensive principle of
renowned quantitative hedge funds such as Renaissance intelligent finance, expanding the usual scope of quantitative
Technologies and Bridgewater Associates (Dalio 2018) [8] as investment and trading in a few selected financial markets to
well as many CTA¶s running trend following trading systems. global trading and global risk management from global
This new technology of financial engineering is called financial market risks through global economic risks to global
P-quant in contrast from the Q-quant, where P refers to the environmental risks. Thus, Intelligent Finance Global
historical probability estimated from the data while Q refers Monitoring (IFGM) as a new concept, a new theoretical
to the theoretical probability according to certain financial construct and a methodology emerges. The complete system
mathematical models. Today, a plethora of new enabling of IFGM may be called the Intelligent Finance Global
technologies have emerged to a great extent, including Observatory (IFGO).
information and communication technology (ICT) such as 5G, The rest of the paper is organized as follows: Section II
Internet, artificial intelligence (AI), big data, block chains, presents the background and the current situations from
cloud computing, cyber-physical systems, and Internet of where IFGM emerges as a natural consequence of intelligent
Things (IoT). These new technologies empower intelligent finance evolution; Section III defines the objectives and a
finance lifting to a very new level of intelligent capabilities. general structure of IFGM; Section IV provides a structure of
Intelligent finance originated from quantitative investment the core of IFGM ± Quantitative Investment Global Trading
and trading and further empowered with AI, big data, block (QIGT); Sections V-VII then describe the general structure
chains and cloud computing has been generalized to Fintech and functionalities of the three layers of global risk
(Financial Technologies), which has transformed virtually all management from financial markets through global
the aspects of financial business. Fintech is a new financial economies to global environments.
industry that applies technology to improve financial
II. THE TIMES OF INTELLIGENT FINANCE GLOBAL MONITORING
activities [9, 10]. It is an umbrella covering the new applications,
processes, products, or business models in the financial Finance is an existence both as a sector of economy acting
services industry, composed of one or more complementary for allocation of assets and liabilities over space and time
financial services and provided as an end-to-end process via under conditions of risk or uncertainty and as a self-driving
the Internet. Key areas of Fintech includes Internet banking system of money chasing profit. Modern financial theory is
with automated biometric recognition, credit risk also an existence both as a part of economics and as a new
management, intelligent trading, automated insurance, discipline of monetary science. The theory of finance has
intelligent customer service, intelligent operational risk evolved from economic finance through quantitative finance,
management, etc. The use of smart phones for mobile to intelligent finance.
banking, investing services and cryptocurrency are examples Economic finance, also called financial economics, is a
of fintech running on cloud computing infrastructure aiming branch of economics studying the interrelation of
to make financial services more accessible to the general financial variables, such as prices, interest rates and shares,
public. Block chains revolutionizes the information security as opposed to goods and services. It concentrates on
and financial data integrity and quality. The coupling of big influences of real economic variables on financial ones, in
data and AI unleashes the power of knowledge and contrast to pure finance. It focuses on managing risk in the
case-based reasoning and data mining algorithms, so not only context of the financial markets, and the resultant economic
the problem-solving capabilities are lifted to an and financial models.
unprecedented level, but also financial services can be Quantitative finance goes beyond the economic principles
personalized via intelligent expert advisors (software agents). of economic finance and establishes itself with more
In China, 20. June 2017ˈBaidu and the Agriculture Bank of mathematical modeling of financial markets, an area called
China launched a campaign of strategic cooperation where Li mathematical finance. And it moves further into financial
Yanhong, President of Baidu, has reinvented the concept of engineering for the design and application of financial
intelligent finance as the comprehensive integration of AI and products with derivative pricing. There are two separate
finance [11]. With high and new technologies such as AI, big branches of quantitative finance that require advanced
data, cloud computing and block chains as the core elements, quantitative techniques: derivatives pricing, and risk and
it can empower financial institutions in an all-round way, portfolio management. One of the main differences is that
improve the service efficiency of financial institutions, they use different probabilities, namely the risk-neutral


probability (or arbitrage-pricing probability), denoted by ³Q´, theory[14], and quantum theory[15]. Ray (2010) [16] provides a
and the actual (or actuarial) probability, denoted by ³P´. Their comprehensive treatment on extreme risk management.
practioners are often distinguished between ³Q-quant´ versus Looking back through the 2 to 3 decades of quantitative
³P-quant´. However, their methodologies are all based on investment and trading, it can be seen that intelligent finance
mathematical modeling with statistical estimation of financial with its core focus on quantitative investment and trading has
markets. experienced four technical stages of development, which are
Intelligent finance, as theoretically proposed and called here the 4 Levels of Intelligent Finance, from low to
envisioned by Pan ˄2003-2019˅is a breakthrough out of the high:
risk-neutral assumptions and the limitations of mathematical Level 1: Quantative Mechanical Trading Strategies:
and statistical modeling into a new horizon of computational Apart from statistical arbitrage of 2 to 3 markets, generally a
intelligence modeling of financial markets as complex trading strategy is designed to trade a single target market
systems and market participants as intelligent agents. Today, with a rule-based system where the rules for entry and exit are
the advent of big data, more powerful AI, cloud computing mechanical using technical indicators, not intelligent with
and block chains is transforming the infrastructure of adaptability and learnability. Trend following is the most
financial markets and driving the ever faster evolution of popular type of mechanical trading strategies.
financial market ecologies. Level 2: Intelligent Portfolio Trading Systems: Given a
In a broader perspective, it appears that the evolution of the single market, multiple trading strategies can be applied, so
mankind ± the homo sapiens ± is becoming ever faster they can form a portfolio of strategies. A portfolio of multiple
towards a new unprecedented intelligent era with intelligent assets can then be transformed to an intelligent portfolio (Pan
finance driving intelligent economies and intelligent societies 2016-2019) consisting of an asset portfolio and the allocation
worldwide. It can be viewed as the complexity of the universe of each asset consisting of a strategy portfolio. Computational
has ever increasing from the Creation through inorganic intelligence can be endowed to the asset portfolio, the
matters, to organic substances, to plants, to animals, to strategy portfolios and the trading strategies themselves.
humans, to homo sapiens. In the new intelligent era, humans Level 3: Big-Data Intelligent Portfolio Trading Systems:
and robots must co-exist. Intelligent trading systems, such as Financial big data expands the regular data source ± market
the Expert Advisors (EA) in forex trading or Robo-Advisors price and volume time series - used for financial price time
in stock investing, are software robots operating in the cyber series modeling for prediction and trading by reaching out for
space. Today and in the future, participants in the financial corporate seasonal financial statements, corporate
markets worldwide can be either human traders or trading announcements and self-protraits (enterprise holograms),
robots, or hybrid cooperative systems of both types. monthly or seasonal economic indicators, market calendar,
Since China became a member of WTO in December 2001, fiscal policies, government meetings, etc. Big data can be
the economic globalization has since turned to a nearly used through data mining and predictive analysis to generate
complete reality in parallel with political and cultural useful market intelligence such as market sentiment, industry
globalizations. Economic globalization drives the increasing prosperity, government attitude and political preferences, as
economic integration and interdependence of national, well as other strategic information. Market intelligence
regional, and local economies across the world through an extracted from financial big data can be used to enable more
intensification of cross-border movement of goods, services, powerful intelligent portfolio trading systems.
technologies and capital. Financial globalization is the fastest Level 4: Big-Monitoring Intelligent Portfolio Trading
and most liquid part of economic globalization, which is Systems: Sofar the big data are passive, unorganized, and
based on the global financial system - the worldwide unreliable, so a large portion of big data analytic technology
framework of legal agreements, institutions, and both formal has to be designed with cost to extract useful information
and informal economic actors that together facilitate from big data. Still, there is a limit of usefulness. We believe
international flows of financial capital for purposes that the availability of ICT, Internet, IoT, AI and business
of investment and trade financing. As a consequence of intelligence technologies makes it possible to develop active
financial globalization, the financial markets of the developed global monitoring technologies that are capable of running
and emerging developing economies have become more and global observation systems, which we call big monitoring.
more connected, and their prices have become more and more The totality of such global observation systems for
correlated. Volatility and sentiment from world major monitoring global financial markets, global economies and
markets propagate or spillover to the ones of other economies. global environments is termed here the Intelligent Finance
The ecology of the global financial markets has become far Global Observatory (IFGO).
more complex than any single human trader or any trading III. A STRUCTURE OF INTELLIGENT FINANCE GLOBAL
fund is capable to comprehend and cope with. Therefore, it is MONITORING AND OBSERVATORY
a realistic assumption and recognition that every trader (a
human, a robot, or a hybrid team) is bounded in rationality, The ecology of the global financial markets (Fig.1) is made
information conditions and capabilities of information up of four market forces, each driving a type of market
processing, decision making and trade control (IDC). activity dynamics:
Along with the increasing complexity of the world (1) Arbitrage pricing
financial markets, new perspectives have emerged from the (2) Financial equilibrium (price fluctuation dynamics)
complexity science as started by W. Brian Arthur[13], gauge (3) Economic equilibrium


(4) Game equilibrium bounded in rationality and limited in information conditions
and intelligence capabilities. The layer of Global Financial
Game
Equilibrium
Risk Monitoring (GFRM) expands the traditional market risk
management to covering the financial market risk over the
Economic globe through US, China, G8 and G20. The layer of Global
Equilibrium Economic Situation Monitoring (GESM) delves deeper into
Financial the grand unification of global macro-economic modeling,
Equilibrium surveillance and simulation, so global economic situations
can be constantly monitored and assessed, providing more
reliable fundamental and economic as well as political
Arbitrage background information and knowledge behind financial
Pricing market phenomenon. On the outmost layer, as if looking from
the perspective of aliens, the global financial markets and
economies of the humanity are parts of the geographical
Fig.1 Ecology of Global Financial Markets sphere of the planet Earth. It is reasonable to imagine what
At the centrospheres of the global financial market ecology can be observed from aerospace remote sensing technologies
is the arbitrage pricing being conducted by high-frequency and networks. This is a new area of emerging
trading machines of big international hedge funds and interdisciplinary research direction which is just recently
investment banks. Apart from arbitrage pricing, financial called ³remote sensing finance´ by the author (October 14, 2018,
market prices still fluctuate with intraday and daily trends, Wuhan University).
breakouts, swings, jumps, waves, cycles, etc, showing certain In the rest of the paper, attention is focused on the
seemingly profitable opportunities with uncertainties or risks. centrospheres of global trading and the layer of global
These can be classified into the layer of financial equilibrium. financial risk monitoring.
Behind financial market dynamics lie the economic
IV. QUANTITATIVE INVESTMENT GLOBAL TRADING (QIGT)
equilibrium, most of it actuates the financial equilibrium, of
course, not always or everywhere. At the outermost layer, A global trading system of intelligent finance is to be made up
international financial powers such as hedge funds, of functional components such as:
investment banks, multinational companies, or world super (1) A base of quantitative trading strategies, which
power governments or political groups, are always there includes the five basic types mainly: statistical arbitrage,
playing their games in global financial markets, with strategic trend following, swing trading, mean reversion, and grid
intents and mobilizing their money power. Any trading plan trading.
intended by an outer layer should not violate the immediacy (2) Single-market multi-strategy portfolio trading, which
of the inner layers. diversifies the capital allocation for a given single market
The Intelligent Finance Global Monitoring (IFGM) and onto a portfolio of multiple trading strategies.
Observatory (IFGO) should have a functional structure (3) Multi-market single-strategy portfolio trading, which
consisting of 5 cyclic layers, from inner to exterior (Fig.2): applies a given single trading strategy onto each market of
(1) Quantitative Investment Global Trading (QIGT) a multi-market portfolio. This is the paradigm of the
(2) Global Financial Risk Monitoring (GFRM) classical portfolio theory since Markowitz (1952).
(3) Global Economic Situation Monitoring (GESM) (4) Multi-market multi-strategy portfolio trading, which
(4) Global Environmental Risk Monitoring (GERM) constructs a multi-market multi-strategy portfolio in
(5) Global Macro Supercomputing Simulation (GMSS) which the capital allocation for each market is constructed
in a portfolio of trading strategies. This is the general form
Global Future Simulation of an intelligent portfolio.
Environmental
Risk Monitoring
(5) Multi-factor models, which are constructed to drive
either a single-market multi-strategy portfolio, or a
Global Economic
Situation multi-market single-strategy portfolio, or a general
Monitoring
intelligent portfolio.
Global Financial
Risk Monitoring (6) Big data intelligence, which is used to predict market
price trends and volatilities, to assess market sentiment or
industry prosperity, to discover hidden factors, to draw
Intelligent market intelligence, etc. Such intelligence provides more
Finance
Global
trading opportunities or forewarns unseen risks.
Trading (7) Market calendar, which lists the regular market
activities scheduled to happen in a natural calendar order.
Fig.2 Structure of Intelligent Finance Global Monitoring Each activity will become a market event once happened.
Global trading (QIGT) is the centrospheres of the whole (8) Market surprises, which are not regular market events,
system (IFGM), focusing on a small number of financial generally considered impossible to predict or anticipate.
markets dynamically selected from the whole universe of Such market surprises often impact global financial
global trading markets. However, any trading system is market through cascading effects.


(9) Remote sensing intelligence, which are generated by directed asymmetric influences among these markets. The
remote sensing systems, often observable from aerospace, conditional probability distribution for a node (market) given
bearing economic or financial sensitivity or information its influencing nodes (markets) may be represented by using a
value. probability ensemble of neural networks (PENN) (Pan 2005).
These functional components correspond to nine types of Nevertheless, both the structure of a SBIN and the PENN as
global trading systems, from simple to complex. conditional probability distribution have to be updated
regularly or dynamically.
V. GLOBAL FINANCIAL RISK MONITORING (GFRM)
For the stock market of any given economy, such as USA
Financial risk is any of various types of risk associated with or China, the credit risk of any given stock (listed company)
financing, financial trading, and financial operations. can be dynamically assessed using Internet big-data
According to Basel Accord, three major types of financial technologies. Yuan et al (2019) [17], established a dynamical
risk for a financial institution are credit risk, market risk, and mechanism for SMEs evolution through a hologram approach
operational risk. Other types of risk include asset-backed risk, in the Chinese context. The so-called hologram refers to a
foreign investment risk, foreign exchange risk, liquidity risk, new tool for big-data fusion of structured and non-structured
reputational risk, legal risk, IT risk, and model risk, etc. All data provided by a network of business behavior for a given
these types of risk in the global financial systems are targeted enterprise to deal with complexity of network structures in
by the Global Financial Risk Monitoring (GRFM). However, finance associated with systemic risk in banking ecosystems.
still, the main targets are global financial market risk and Risk Tree of Stock Market:
global credit risk, with liquidity risk included. Assuming the credit risk of a stock can be dynamically
Global Financial Market Risks: assessed by the ongoing big-data technologies of this kind or
Specifically, the global financial market risk monitoring another, the risk structure of the whole stock market can then
typically covers the four major types of financial markets: be described in a structure which we call here the Risk Tree of
(1) Securities (assets) Stock Market (RTSM). For a concrete definition of RTSM,
(2) Bonds (liabilities) we need to introduce a Standard Classification Tree of
(3) Commodities (futures) Industries (SCTI). Let E denote the entire economy, Di the
(4) Currencies (forex)
Globally, the order of priority for monitoring different i-th big industry, M ij the j-th medium industry in Di , Sijk
countries or economies should correspond to the order of the k-th small industry in M ij , X ijkl the l-th enterprise in Sijk .
politico-economic power, vitality and influence. A sensible
ordering of three groups is: If we only talk about the stock market, E refers to the market
(1) G1 = US index, and X ijkl to a stock (listed company). With this
(2) G8= G1 + (UK, Germany, France, Italy, Japan, Canada, notation, we have a SCTI structured as
Russia)
(3) G20 = G8 + (China, Argentina, Australia, Brazil, India, ­° E [ D1, D2 , , DN ], Di [ M i1, M i 2 , , M iNi ] ½°
Indonesia, Korea, Mexico, Saudi Arabia, South Africa, ® ¾ (1)
Turkey, Singapore) ¯° M ij [Sij1, Sij 2 , , SijNij ], Sijk [ X ijk 1, X ijk 2 , , X ijkNijk ] ¿°
Accordingly to the four market types and G1 through G10 where the indexes are calculated using appropriate weights
to G20 countries, four types of global portfolio can be w ¶s proportional to stock capitalization or otherwise
constructed: Ni
­ N
½
(1) Global Equity Portfolio consisting of G20 stock indices ° E ¦w D i i Di ¦w M ij ij °
(2) Global Bond Portfolio consisting of G20 treasury ° i 1 j i1 ° (2)
® N ij N ijl ¾
bonds ° M °
(3) Global Commodity Futures Portfolio of G8 major ° ij ¦w ijk Sijk Sijk ¦w ijkl RX ijkl
°
¯ k ij1 l ijk 1 ¿
international commodity futures markets: Gold, Silver,
Oil, Copper, Steel, Aluminium, Crude Rubber, Soy, Let R( X ) denote the risk of X . Assume the risk of any
Corn, Wheat, Coffee, Cotton, etc. stock R( X ijkl ) is assessed dynamically by a big-data
(4) Global Currency Portfolio of G8 countries and technology, the Risk Tree of Stock Market (RTSM) can be
European Union: USD, EUR, GBP, AUD, NZD, JPY, computably structured as
CAD , CHF. Ni
­ N
½
The trends, cycles, seasonality, volatility, liquidity, ° R( E ) ¦ w R( D )i i R( Di ) ¦ w R( M )
° ij ij
financial bubbles and anti-bubbles, jumps or gaps, and other ° i 1 j i1 ° (3)
characteristic dynamics for each market in each of these four ® Nij Nijl ¾
° R( M ) wijk R( Sijk ) R( Sijk ) ¦ wijkl R( X ijkl ) °
types of portfolios as well as the connections, co-movements,
° ij ¦ °
correlations and mutual influences of these markets fall into ¯ k ij1 l ijk 1 ¿
the target domain of the monitoring. The risk tree RTSM for the stock market is useful for
Theoretically, it is possible and sensible to construct super mapping the aggregation or distribution of the credit risks of
Bayesian influence networks (SBIN) whose nodes being the the listed companies in the space of industrial classification.
financial markets of selected economies, and whose links are The stocks can be ordered according to their risk ratings.


Risky stocks can be quickly filtered out as the first step in REFERENCES
stock selection. [1] Heping Pan (2003): A Unified Science of Intelligent Finance ± Part 1.
VI. GLOBAL ECONOMIC SITUATION MONITORING (GESM) Journal of ATAA (Australian Technical Analysts Association),
2003(7/8):11-21; Part 2. Journal of ATAA, 2003(9/10):14-19. Also
Value Tree of Stock Market: appeared as: A Joint Review of Technical and Quantitative Analysis of
Let V ( X ) denote the value of X . Assuming there is a Financial Markets towards A Unified Science of Intelligent Finance.
valuation methodology and technology that can compute the Proc. 2003 Hawaii International Conference on Statistics and Related
net value or investment value of a stock (listed company), Fields, Hawaii, USA.
such as the value investing[18], and Buffettology[19] , or EVA [2] Heping Pan, D. Sornette, K. Kortanek (2006): Intelligent Finance ± An
(economic value added) used by the Chinese government for Emerging Direction. Quantitative Finance, Vol. 6, No. 4, August 2006,
steering state-owned enterprises. Corresponding to the 273-277. Also appeared in Chinese as: Intelligent Finance ± An
industrial classification tree (SCTI) of (1) and the risk tree of Introduction. China Journal of Finance, Vol. 3, No. 2, 182-203.
(3), the Value Tree of Stock Market (VTSM) can be [3] Heping Pan (2011): A Basic Theory of Intelligent Finance. New
structured with valuation Mathematics and Natural Computation, 7(2): 197-227.
Ni
­ N
½ [4] Heping Pan (2017-2019): Intelligent Portfolio Theory and Strength
° V (E) ¦ wV
i ( Di ) V ( Di ) ¦ wijV ( M ij ) ° Investing in the Confluence of Business & Market Cycles and Sector &
° i 1 j i1 ° (4) Location Rotations. C.F. Lee (Editor), Handbook of Financial
® Nij Nijl ¾
° V (M ) Econometrics, Mathematics, Statistics, and Technology, World
° ij ¦ wijkV (Sijk ) V ( Sijk ) l¦ijk1 wijklV ( X ijkl ) °° Scientific, 2019. Presented at 25th Annual Conference on Pacific Basin
¯ k ij1 ¿ Finance, Economics, Accounting, and Management (PBFEAM)
Once this tree is dynamically updated, the industries ± big, ³)LQDQFLDO 0DUNHW DQG 3ROLF\ & Financial and Economic Policy for
medium, small ± can be ordered, so the industries with better ASEAN CRXQWULHV´ 22-24 November 2017, National University of
value can be selected as candidates for investment allocation. Singapore (NUS) and Rutgers University, USA.
In the same way, stocks with better value can be selected too. [5] Heping Pan (2012): Yin-Yang Volatility in Scale Space of Price-Time ±
The value trees and risk trees of two different economies, A Core Structure of Financial Market Risk. China Finance Review
such as USA and China, can be comparatively examined for International, CFRI 2.4, 377-405.
finding relationships and influences between them. [6] Pan H.P., I. Haidar, S. Kulkarni (2009): Daily Prediction of Short-Term
More generally, macro-economic indicators of G8 to G20 Trends of Crude Oil Prices using Neural Networks exploiting
Multimarket Dynamics. Frontiers of Computer Science in China, 3(2):
countries can be comparatively monitored towards mapping 177-191.
out the long-run trends of growth, business cycles and market [7] Pan H.P. & C.Z. Zhang (2018): FEPA: An Adaptive Integrated
cycles with sector rotation. Prediction Model of Financial Time Series, Chinese Journal of
Most generally, the quantitative national macro-economic Management Science, 2018 Vol. 26(6): 26-38.
models of G8 to G20 countries can be further developed and [8] Dalio R. (2018): The Principles. Bridgewater Associates.
dynamically updated. These national models may be linked [9] Schueffel P. (2017). Taming the Beast: A Scientific Definition of
and integrated into a grand unification model ± the Global Fintech, Journal of Innovation Management. 4 (4): 32±54.
Macro-Economic Model (GMEM) for modeling, simulation [10] Chishti S. and Barberis J. (2016): The FINTECH Book: The Financial
and prediction. Technology Handbook for Investors, Entrepreneurs and Visionaries.
Wiley.
VII. GLOBAL ENVIRONMENTAL RISK MONITORING (GERM)
[11] China News Network (2017): Li Yanhong: AI Fintech driving the age
The geographical sphere of the planet earth constantly of intelligent finance to come. 20. June 2017,
brew environmental risks to which the economies and even http://finance.ifeng.com/a/20170620/15470541_0.shtml
the mankind are exposed. This kind of risks often surprise and [12] Agrawal A., J. Gans, & A. Goldfarb (2018): Prediction Machines ± the
impact financial markets worldwide. A seemingly far-fetched Simple Economics of Artificial Intelligence. Harvard Business Review
event was that an earthquake amid the Japanese sea on March Press.
11, 2011, caused tsunami hitting the nuclear power plant on [13] Arthur W.B. (2014): Complexity and the Economy. Oxford University
the Japanese shore, which then led to the abnormal surge of Press.
salt-related stock prices 2 days later.
[14] Ilinski K. (2001): Physics of Finance ± Gauge Modelling in
Along with the ever-faster pace of ICT and aerospace
Non-equilibrium Pricing. Wiley.
technologies, earth-observation satellites are forming remote
[15] Orrell D. (2018): Quantum Economics ± the New Science of Money.
sensing networks monitoring the earth surface and
Icon Books.
geographical sphere with ever completer coverage in terms of
spatial resolution, time frequency and electromagnetic [16] Ray C. (2010): Extreme Risk Management. McGraw-Hill.
spectrum. Virtually every type of economic activities that are [17] Yuan G.X., H. Wang, T. Zeng, T. Wu, S. Gao, & H. Ma (2019): The
Dynamical Mechanism for SMEs Evolution under the Hologram
observable from aerospace remote sensors may fall into the Approach. SSRN Working Paper #3325013, available at SSRN:
category of sensible targets to be monitored. Naturally, those https://ssrn.com/abstract=325013.
observables bearing a high possibility of direct impacting [18] Graham B. and D. Dodd (1934): Security Analysis: Principles and
tradable financial markets should be closely investigated in Technique, New York and London: McGraw-Hill.
the new field of remote sensing finance. [19] Buffett M., D. Clark (1999): Buffettology: The Previously Unexplained
Techniques That Have Made Warren Buffett The Worlds Most Famous
Investor, Scribner.