Content uploaded by Poomjai Nacaskul
Author content
All content in this area was uploaded by Poomjai Nacaskul on Jan 02, 2021
Content may be subject to copyright.
ALGORITHMIC TRADING
– Lecture
Poomjai Nacaskul, PhD
2018.12.08
Algorithmic Trading …
… is NOT finding Alpha!
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Algorithmic Trading … in Context
•In which world do Alpha, Beta, CAPM live/operate?
i. Static/Stationary
ii. Multi-Variate Normal Returns
iii.Equilibrium at Risk-Return Market Efficiency
•What exactly do these people do?
i. Portfolio Managers: focus on allocating across vast multiplicity of asset
ii. investment Advisors: focus on matching risk-return characteristics w/ clients
preference
iii.Proprietary Traders: focus on timing individual tradable variables
iv.Arbitrage Traders: focus on exploiting failed parities
•What’s an Algorithm?
i. Prescribed Set of Actions?
ii. Prescribed Set of Rules?
iii.Prescribed Set of Machine Learning Protocol executing Actions generating Rules?
{ดร.พูมใจ นาคสกุล}{๒๗ พฤศจิกายน ๒๕๖๑}
Algorithmic Trading – a 'genealogy'
Algorithm +
Broker Screen
Sweep +
Market Portfolio
Rebalancing +
Straight Through
Processing +
Optimal Trading
Strategy +
Trading =
Arbitrage Rule
Base =
Tracking Error
Minimisation =
Active
Proprietary
Discretionary =
Market Volatility
Timing =
Algorithmic
Trading
Auto Arbitrage
Trader
Passive Index
Fund
Automated
Trading Platform
Quantitative
Model Trader
{ดร.พูมใจ นาคสกุล}{๑ ธันวาคม ๒๕๖๑}
Algorithmic Trading – a 'genealogy'
Algorithm +
Broker Screen
Sweep +
Market Portfolio
Rebalancing +
Straight Through
Processing +
Optimal Trading
Strategy +
Trading =
Arbitrage Rule
Base =
Tracking Error
Minimisation =
Active
Proprietary
Discretionary =
Market Volatility
Timing =
Algorithmic
Trading
Auto Arbitrage
Trader
Passive Index
Fund
Automated
Trading Platform
Quantitative
Model Trader
{ดร.พูมใจ นาคสกุล}{๑ ธันวาคม ๒๕๖๑}
What is … an Algorithm?
•algorithm (n.)
1690s, "Arabic system of computation," from French algorithme, refashioned
(under mistaken connection with Greek arithmos "number") from Old
French algorisme "the Arabic numeral system" (13c.), from Medieval
Latin algorismus, a mangled transliteration of Arabic al-Khwarizmi "native of
Khwarazm" (modern Khiva in Uzbekistan), surname of the mathematician
whose works introduced sophisticated mathematics to the West
(see algebra). The earlier form in Middle English wasalgorism (early 13c.), from
Old French. Meaning broadened to any method of computation; from mid-
20c. especially with reference to computing.
https://www.etymonline.com/word/algorithm
https://www.youtube.com/watch?v=Ar7CNsJUm58
{ดร.พูมใจ นาคสกุล}{๑ ธันวาคม ๒๕๖๑}
What is … Trading?
•trade (v.)
1540s, "to tread a path," from trade (n.). Meaning "to occupy oneself (in something)" is
recorded from c. 1600. Meaning "to barter" is by 1793. The U.S. sports team sense of "to
exchange one player for another" is attested from 1899. Related: Traded;trading.
To trade down is attested from 1942; trade up from 1959. Trade places "exchange
situations" is from 1917. Trading post is recorded from 1796.Trading stamp, given by
merchants and exchangeable for goods, is from 1897.
•trade (n.)
late 14c., "path, track, course of action," introduced by the Hanse merchants, from
Middle Dutch or Middle Low German trade "track, course" (probably originally of a
ship), cognate with Old Englishtredan (see tread (v.)).
Sense of "one's habitual business" (1540s) developed from the notion of "way, course,
manner of life" (mid-15c.); sense of "buying and selling, exchange of commodities" is
from 1550s. Meaning "act of trading" is from 1829. Trade-name is from 1821; trade-
route is from 1873; trade-war is from 1899.Trade union is attested from 1831. Trade
wind (1640s) has nothing to do with commerce, but preserves the obsolete sense of "in
a habitual or regular course." https://www.etymonline.com/word/trade
{ดร.พูมใจ นาคสกุล}{๓๐ พฤศจิกายน ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 16
“Financial model-trading is an area of application which involves using what is
known as a technical trading model to actively and speculatively trade a
financial instrument, such as a foreign exchange rate in the spot currency
market, over a specified trading period, in the hope of realising cumulative net
positive gain resulting from the differences between the market prices paid when
buying certain amounts of the financial instrument and the prices received when
selling. The two prominent problems in financial model-trading which we
address are:
(1) engineering an optimisable, i.e. profit maximising, financial time-series
trading model, and
(2) managing a portfolio of financial model-trading activities through the use of
assigned notional variables, i.e. portfolio weightings. ”
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Algorithmic Trading (Trading Decision Engine)
•Financial Time Series
i. TIME SERIES: realization of physical-time-indexed STOCHASTIC PROCESS, {Xt, t≥ 0}
ii. FINANCIAL: in the sense that there is a real-time actionable FINANCIAL MARKET
•Trading Set-Up
i. ACCOUNT: the sink/resource
(to borrow from THERMODYNAMIC PHYSICS/ENGINEERING)
ii. TRANSACTION: the action-decision set
(i.e. under REINFORCEMENT LEARNING paradigm)
iii.RISK-RETURN: the multi-criteria objective function to optimise
(OPERATIONS RESEARCH)
•Problem Formulation
i. MAXIMISATION: the sink/resource
(to borrow from THERMODYNAMIC PHYSICS/ENGINEERING)
ii. CONSTRAINT: w.r.t. transaction direction/frequency/notional,
w.r.t. trading account liquidity, etc.
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Algorithmic (Algorithmic Quantitative Model)
Trading
•Formulation One – Forecast Then Trade
i. Focus: on minimizing time-series forecast errors
ii. Trading strategy 'obvious': buy if price forecasted to go up, sell if price
forecasted to go down
iii.Additional constraint: transaction turnover rate (times fixed per-transaction
transaction cost)
iv.From a machine learning perspective: is a SUPERVISED LEARNING problem
•Formulation Two – Optimise Trading Rule
i. Focus: on optimally parameterizing signal trading rule
ii. Trading strategy: is the solution (not derived from it)
iii.Additional constraint: embed as part of optimality definition
iv.From a machine learning perspective: is a REINFORCEMENT LEARNING problem
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Before we go on any further … (1)
•Machine Learning, Artificial Intelligence & Neural Networks
i. MACHINE LEARNING:
all about getting machine to learn (robustly) a model of reality;
may or may not involve the use of ARTIFICIAL NEURAL NETWORKS
ii. ARTIFICIAL INTELLIGENCE:
all about getting machine to emulate (convincingly) human intelligence,
from superficially, i.e. 'ChatBot',
to creepily, i.e. 'SkyNet' (The Terminator, 1984), 'Ava’ (Ex Machina, 2014),
almost always having to involve the use of ARTIFICIAL NEURAL NETWORKS
•Machine Learning Paradigm
i. SUPERVISED LEARNING:
given dataset {xi, yi}, i= 1, … , n, and mapping f, find model parameter s.t. f(; x)
ii. UNSUPERVISED LEARNING:
given {xi, yi}, i= 1, … , n, find patterns, features, clusters, etc. of interest
iii. REINFORCEMENT LEARNING:
given environment and performance function, fperformance({environment, state, action}),
find decision engine optimises actions {actiont} generated by fdecision(environmentt, statet)
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Before we go on any further … (2)
•Big Data vs. Data Science vs. Data Analytics
i. BIG DATA describes the phenomenon
ii. DATA SCIENCE describes the discipline
iii.DATA ANALYTICS describes the regime
•Computational Intelligence
i. MACHINE LEARNING, especially involving ARTIFICIAL NEURAL NETWORKS
ii. GENETIC/EVOLUTIONARY ALGORITHM/OPTIMISATION, of which there are 3-4
prominent schools:
GENETIC ALGORITHM,
GENETIC PROGRAMMING,
EVOLUTIONARY ALGORITHM,
PARTICLE SWARM OPTIMISATION
iii.CYBERNETICS/MAN-MACHINE INTERFACE, e.g. FUZZY SETS/SYSTEMS
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 25-6
“ Genetic Algorithms (GA) are attributed to [Holland 1975], as updated in [Holland
1992a], which appeared alongside a ‘layman’ briefing [Holland 1992b]. Another ‘wide-
audience’ article of note is [Wayner 1991]. Beside Holland’s own work, [Davis 1987;
Goldberg 1989; Davis 1991; Michalewicz 1996] are often cited as standard GA
references. Holland’s GA is often one’s first tasting of EO methodology taught at the
university level [Beasley et al. 1993a; Beasley et al. 1993b; Jones 1995].”
“ GAs are highly suitable for solving combinatorial optimisation problems. The genetic
representation of a solution structure and the essentially combinatorial nature of the
genetic crossover operation means GAs are ideal search engines for covering a
combinatorial space and uncovering good solutions embedded therein, although large,
NP-hard combinatorial problems do benefit from additional heuristics, for example, a
‘divide and conquer’ strategy [Valenzuela & Jones 1994]. Moreover, as a genetic string
can also decode onto a set of real-valued and/or symbolic variables, GAs are also
capable of handling parametric optimisation problems as well. ”
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 27-8
“Genetic Programming (GP) is attributed to [Koza 1992]. GP has been promoted in
[Koza & Rice 1992], and various developments and applications can be found in [Kinnear Jr.
1994]. Algorithmically, a GP population evolves in much the same way as a GA. Such is one
reason why GP can be viewed as an off-shoot of GAs. The fundamental difference lies is not in
the simulated evolutionary process, but in the solutions themselves. Here, we note the difference
in terms of solution functionality. A GA solution stores parameters, while a GP solution is a
function, in particular, a ‘mini’ computer program with the mapping functionality of a parse tree.
”
“ GP is very popular among technical traders, especially those in commodities trading.
Commodities traders are well known for their pursuits of arbitrary rules, involving ‘model’ input
variables such as the difference between the day’s close and the previous day’s high, the day of
the month, and the lunar cycle! A GP is able to evolve a model-trading rule involving predicate
expression such as “If <a five-day moving average of the daily close is greater than the seven-
day moving average of the daily high> AND <it is the first half of the lunar calendar>, then <buy
at the first dip below the opening quote>”. Examples of a GP approach to evolving an optimal
financial model-trading rule include [Edmonds & Kershaw 1994; Ruggiero Jr. 1994; Levitt
1995; Ruggiero Jr. 1995; Ruggiero Jr. 1996].”
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Financial Trading Model as an Action-Decision Generator,
and as a Multi-Criteria Performance System, ibid. – page 46-53
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 47
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 47
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 56
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 48
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 50
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 51
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 51-2
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 52
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Moving Average Signal Trader, Optimisation Problem
Formulation, and Binary Encoding the Parametric Solution
Space, ibid. – page 54-62
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading –
page 54
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 54
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 56
{ดร.พูมใจ นาคสกุล}{๗ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading –
page 56
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading –
page 57-8
0612 18 24 30 36 42 48 54 60
4
8
12
16
20
24
28
32
36
40
-140
-120
-100
-80
-60
-40
-20
0
20
40
ACG(%)
short-term lag
lag-spread
ACG of Mom2LinMVA Trading Models: DEM/JPY Data
DEM/JPY: 12- vs. 28-Lagged Linear MVAs
71
71.5
72
72.5
73
73.5
74
74.5
75
200
214
228
242
256
270
284
298
312
326
340
354
368
382
396
410
424
438
452
466
480
494
508
522
536
550
564
578
592
time
bid price, linear MVAs
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Nacaskul, Poomjai (1998), Evo. Optm. & Fin. Model Trading
– page 58
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Binary Encoding Solution Search Space, ibid. – page 62
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Algorithmic Trading …
… and NOW a Demo!
{ดร.พูมใจ นาคสกุล}{๘ ธันวาคม ๒๕๖๑}
Poomjai Nacaskul’s Glossary – p.1/6
ARTIFICIAL INTELLIGENCE (AI) – the notion that human-like intelligence, behaviours and or responses to stimuli
indicative of human intelligence, can be replicated in an artificial apparatus, especially in the form of
programmable digital computer. However, AI is also associated with pre-1980s’ symbolic/semantics-
based EXPERT SYSTEM approach (think of computerised hierarchy of if-then rules used by a medical
doctor to diagnose diseases), hence to be differentiated from ANN-type approach that is inspired by
the computational architecture and signal processing dynamics of biological brains that gained
dominance in 1980’s, the beginning of an era known as “AI Winter”.
BUSINESS INTELLIGENCE (BI) – despite the fact that BI often uses AI, the term intelligence in BI is used in a very
different sense to how it appears in AI. In fact, BI arose as a business-world analog of military
intelligence. As such BI has more to do with the quality of information and the ability to translate
possession of said information immediately into tangible, actionable insights, rather than the ability to
learn from data, form internal model (representation of reality), make associations, infer causalities,
recognise patterns, produce predictions, and/or suggest (approximately optimal) courses of actions,
which are the primary aims of AI research. However, viewed from a software/system engineering
perspective, BI represents an encapsulation/user-interface wrapping of ENTERPRISE ANALYTICS of all kinds,
AI-based or otherwise.
Poomjai Nacaskul
2016.09.06
Poomjai Nacaskul’s Glossary – p.2/6
COMPUTATIONAL INTELLIGENCE (CI) – closer to AI in the use of the term intelligence. Here intelligence is
proxied by the ability to solve problems—finding an answer, finding the best or optimal answer from a
space of all possible answers, finding a means to ensure trajectorial stability vis-à-vis a dynamical
system, and so on. Three strains of research gained prominence: optimisation by means of computer
simulation algorithms inspired by and analogous to the process of genetic-biological evolution by
natural selection (i.e. GENETIC ALGORITHM, GENETIC PROGRAMMING, EVOLUTIONARY ALGORITHM), learning by
means of computational paradigm inspired by and analogous to the human brain (i.e. ARTIFICIAL NEURAL
NETWORK), and mapping between human concepts and machine representation (i.e. FUZZY SETS &
SYSTEMS, HUMAN COMPUTER INTERACTION/INTERFACE).
ENTERPRISE INTELLIGENCE (EI) – used interchangeably with BUSINESS INTELLIGENCE.
MACHINE INTELLIGENCE (MI) – combining the notion that a digital computer can process/possess
knowledge, a la ARTIFICIAL INTELLIGENCE, with the notion that digital devices can be endowed with
sensory organs, thereby able to gather/generate intelligent information about their surroundings and
operating conditions, a la INTERNET OF THINGS. This term is also used to refer to ARTIFICIAL INTELLIGENCE but
without the association with the pre-1980s’ symbolic/semantics-based EXPERT SYSTEM approach, which
itself gets rebranded as GOOD OLD-FASHIONED ARTIFICIAL INTELLIGENCE (GOFAI).
Poomjai Nacaskul
2016.09.06
Poomjai Nacaskul’s Glossary – p.3/6
MACHINE LEARNING – can be thought of as a generalisation of the ARTIFICIAL NEURAL NETWORK paradigm,
incorporating a multitude of learning modalities, knowledge representations, and symbolic/semantic
schemas.
DEEP LEARNING – can be thought of as a modern rebranding of a particular ARTIFICIAL NEURAL NETWORK
architecture dominant in/since 1980’s, the MULTI-LAYER PERCEPTRON (MLP). However, DEEP LEARNING also
distinguishes itself from its 1980’s root by virtue of (i) computational power hitherto unavailable—MLP
with more than a handful of layers of perceptrons used to get bogged down and easily overwhelmed
by large data set, (ii) algorithmic tweaks that prevent connection weights in ARTIFICIAL NEURAL NETWORK
architecture with many interconnected and/or self-connected layers of perceptrons from
degenerating (going to zero or infinity) during training, and (iii) recent breakthrough in
adapting/engineering ARTIFICIAL NEURAL NETWORK architecture for image recognition tasks, notably the
CONVOLUTIONAL NEURAL NETWORK (CNN) and LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK (LSTM-
RNN).
QUANTITATIVE MODELS (QM) – the all-encompassing term, covering everything from a basic linear regression
equation, rooting back a few centuries, to the latest DEEP LEARNING innovations of recent months. It is
based on the most undemanding pretext that any entities or phenomena under study can be
indirectly studied/analysed, hence modelled, on basis of their quantitative attributes. As “it’s always
cheaper to play with the toy than to muck about with the real thing”, modelling entities and
phenomena quantitatively saves one from making real mistakes.
Poomjai Nacaskul
2016.09.06
Poomjai Nacaskul’s Glossary – p.4/6
QUANTITATIVE ANALYTICS – the engineering and productionisation of QUANTITATIVE ANALYSIS into readily
deployable tools. The term also refers to a set of (system) statistics themselves, which are produced,
presumably on a regular basis, for the purpose of performance evaluation, fault diagnostics, and
managerial review.
QUANTITATIVE ANALYST – a job description, highlighting the fact that it is the ability to create and work with
QUANTITATIVE ANALYTICS, rather than possession of domain knowledge, that is the critical success factor in
practical applications. A QUANTITATIVE ANALYST may find him/herself in an insurance industry one day,
defense industry the next, and so on. There is a sort of universality with which problems in insurance and
defense industries alike, that can be mapped onto the same/similar mathematical structure, hence
analysed using the same/similar QUANTITATIVE MODELS.
ENTERPRISE ANALYTICS (EA) – refers to the comprehensive set of QUANTITATIVE ANALYTICS used in the strategic,
tactical, as well as routine management of an enterprise, especially large, for-profit corporations, but
also increasingly relevant vis-à-vis small, NETWORK ECONOMY-oriented social enterprises.
Poomjai Nacaskul
2016.09.06
Poomjai Nacaskul’s Glossary – p.5/6
BIG DATA – the term characterising three emergent data trends: (i) sheer volume, (ii) unprecedented
velocity with which bits and pieces of data get generated, pass through digital channels, and arrive at
depositories, and (iii) the variety of format, especially unstructured data streams comprising images,
sound files, and telemetric data logs. BIG DATA also refers to the premise, or rather the promise, that
hidden amongst the volume of high-velocity, multi-variety data lie patterns and nuances that powerful
enough DATA MINING and MACHINE LEARNING algorithms can cipher and distill into useful insights, thereby
uncovering a new set of information that would otherwise not have been captured by traditional
statistics and econometric methods.
DATA MINING – refers to the notion that data is a raw material within which precious, useful bits of
information lie hidden, and also to the process of distilling a vast amount of data for such precious,
useful bits of information, in direct analogy to the process of distilling a vast amount of ores for precious
gems.
DATA SCIENCE – the recognition of availability and arrival of BIG DATA, together with the maturity and utility
of our ability to engineer powerful QUANTITATIVE MODELS to analyse them. Moreover, there is an emergent
area of scientific investigation that study to very dynamics of data objects (be it data bases, network
connectivities, texts and contextual data objects, etc.), how they get generated, evolve,
travel/propagate, and meet their ultimate fate (do bits of data die, or do they live forever?).
Poomjai Nacaskul
2016.09.06
Poomjai Nacaskul’s Glossary – p.6/6
DATA SCIENTIST – either someone who is dedicated to the career of researching DATA SCIENCE proper, or
more commonly a (presumably very good) QUANTITATIVE ANALYST who heads a top-notch QUANTITATIVE
ANALYTICS team.
INTERNET OF THINGS (IOT) – the enhancement of mechanical-electrical devices with ability to gather,
process, generate, store, and report digitally-formatted data and communicate them (nearly) live via
the internet to the intended (sometimes unintended) users.
NETWORK ECONOMY – firstly refers to the extent to which modern economy is driven by and dependent
upon the internet, but latterly refers to the phenomenon by which—as a direct and indirect result of
internet becoming ubiquitous in our daily lives—people, city inhabitants, households, economic
production/consumption units, and so on, begin to interact and relate to one another based on
technology-enabled network topology rather than on the basis of physical proximities, geographic
localities, or even familial ties.
DIGITAL ECONOMY – overlaps with the notion of NETWORK ECONOMY, with perhaps stronger emphasis on the
prevalence of digital devices as key aspects of human productivity, digital communications as key
modes of human connectivity, and digital contents surpassing all other form of archives in human
history.
Poomjai Nacaskul
2016.09.06
bio.
Poomjai Nacaskul received his Bachelor of Arts degree in Physics and Economics, a double major, from WESTERN RESERVE
COLLEGE, CASE WESTERN RESERVE UNIVERSITY (CWRU) in 1992, a Master of Science degree in Operations Research, with Finance
Minor, from CWRU’s WEATHERHEAD SCHOOL OF MANAGEMENT in 1993, and a Doctor of Philosophy in Computational Intelligence
and Operational Research from IMPERIAL COLLEGE LONDON in 1998. Whilst at Imperial, Poomjai also became Quantitative
Analyst with the Quantitative Research and Trading unit of CHEMICAL BANK (UK), where he developed object-oriented
evolutionary optimisation programme for the ‘prop desk’ to explore intraday foreign-exchange trading.
Upon graduation from Imperial College in 1998, Poomjai immediately began his career as an Investment Officer with the
BANK OF THAILAND (BOT)’s INTERNATIONAL RESERVES MANAGEMENT team, later on establishing the bank’s first QUANTITATIVE MODELS
AND FINANCIAL ENGINEERING team, eventually graduating to the Principal Researcher position with BOT’s MONETARY POLICY
GROUP. At BOT, Poomjai researched and developed in cutting-edge areas, from Relative Numeraire Risk in Multi-Currency
Portfolio to Copula Modelling in Pricing Credit Derivatives, and Eigenvector Centrality Analysis of Systemically Important
Financial Institutions.
Poomjai joined SIAM COMMERCIAL BANK (SCB) as Head of the Credit Risk Analytics Department in 2013 and subsequently
created the bank’s first QUANTITATIVE MODELS AND ENTERPRISE ANALYTICS unit, which itself was merged into the bank’s BUSINESS
INTELLIGENCE/DATA ANALYTICS function in 2017, and became one of the bank’s first dedicated Lead/Principal Data Scientist.
At SCB, Poomjai’s involvements reached beyond credit risk, ranging from Predictive Analytics (hourly branch-level
customer queue prediction) to Financial Engineering (validation of Vanna-Volga method for pricing FX exotic derivatives),
and Network Flow Modelling (intra/extra-SCB flows of wholesale liquidity funding).
Poomjai is now a private consultant engaged to UOB ASSET MANAGEMENT (THAILAND). He also teaches Data Analytics and
Machine Learning at CHULALONGKORN UNIVERSITY’s SCHOOL OF INTEGRATED INNOVATION (SCII) and SASIN INSTITUTE OF MANAGEMENT.
Apart from risk management, quantitative finance, machine learning, and data science, Poomjai has keen interest in
Sufficiency Economy Philosophy and Sustainable Development initiatives, as well as AI in medicine/computational biology.
Poomjai Nacaskul
2020.09.30
pub.
•2019 Talk: 'Fuzzy Multi-Criteria Portfolio Optimisation w/ Python
… and Maybe a Lil' Bit of Mathematica'
•2019 Lecture: 'Machine Learning Fundamentals'
•2019 Lecture: 'Algorithmic Trading Lectures #2, #1'
•2018 Talk: 'Quantum Computing as Disruptive Technology'
•2018 Talk: 'Graph-Theoretic Computation in Python'
•2017 Paper: 'Financial Risk Management and Sustainability The
Sufficiency Economy Philosophy Nexus'
•2016 Publication: 'Survey of credit risk models in relation to capital
adequacy framework for financial institutions'
•2013 Paper: 'Sufficiency Economy Philosophy: Conceptual
Background & Introduction'
•2013 Paper: 'Sustainable Development Calling for Policy Analytics
and (Wittgensteinian Turn Towards) Economics of Moderation'
•2012 Paper (w/ Kritchaya Janjaroen & Suparit Suwanik): 'Economic
Rationales for Central Banking: Historical Evolution, Policy Space,
Institutional Integrity, and Paradigm Challenges'
•2011 Chapter: 'Relative Numeraire Risk and the Currency Allocation
of Sovereign Portfolios'
•2011 Paper (w/ Worawut Sabborriboon): 'Systemic Risk -- Identification,
Assessment and Monitoring based on Eigenvector Centrality Analysis of Thai
Interbank Connectivity Matrices'
•2010 Paper: 'Toward a Framework for Macroprudential Regulation and Supervision
of Systemically Important Financial Institutions (SIFI)'
•2010 Lecture: 'Financial Modelling with Copula Functions'
•2010 Paper: 'The Global Financial (nee US Subprime Mortgage) Crisis - 12
Contemplations from 3 Perspectives'
•2010 Paper: 'Systemic Import Analysis (SIA) – Application of Entropic Eigenvector
Centrality (EEC) Criterion for a Priori Ranking of Financial Institutions in Terms of
Regulatory-Supervisory Concern, with Demonstrations on Stylised Small Network
Topologies and Connectivity Weights'
•2009 Paper: 'International Reserves Management and Currency Allocation: A New
Optimisation Framework Based on a Measure of Relative Numeraire Risk (RNR)'
•2009 Paper (w/ Worawut Sabborriboon): 'Gaussian Slug - Simple Nonlinearity
Enhancement to the 1-Factor and Gaussian Copula Models in Finance, with
Parametric Estimation and Goodness-of-Fit Tests on US and Thai Equity Data'
•2001: Paper 'Toward a Framework of Economic Thoughts Based on Sufficiency
Economy'
•1998 Thesis: 'Evolutionary Optimisation and Financial Model-Trading'
Poomjai Nacaskul
2019.10.03
contact
•iPhone +66(0)90-980-2170
•email Poomjai.Nacaskul@gmail.com; Poomjai.N@chula.ac.th
•ResearchGate www.researchgate.net/profile/Poomjai_Nacaskul3
•Linkedin www.linkedin.com/in/poomjai-nacaskul-phd-dic-cfa-46b994142
•SSRN http://ssrn.com/author=363974
Poomjai Nacaskul
2020.10.01