Big Data, Machine Learning, and QuantumComputing at the Riksbank

Isaiah Hull

Sveriges Riksbank

October 20, 2019

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Big Data

Traditional Big Data

I Trade credit.

I Jacobson and von Schedvin (2015); Amberg et al. (2016);Amberg, Jacobson, von Schedvin (2018).

I Household finance.

I Jansson et al. (2012); Jansson, Haliassos, and Karabulut(2012); Jansson (2017); and Jansson, Haliassos, andKarabulut (2017).

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Big Data

Nontraditional Big Data: Housing

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Big DataNontraditional Big Data: Grocery Prices

Figure: Figure taken from Hull, Lof, and Tibblin (2017).

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Big Data

Nontraditional Big Data: Grocery Prices

Figure: Figure taken from Hull, Lof, and Tibblin (2017).

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Big Data

Nontraditional Big Data

I Housing transactions

I Rental contracts

I Satellite images

I Financial filings

I Local public services

I Online loan funding

I Fruit and vegetable prices

I Foreign travel packageprices

I Central bank speeches

I Newspaper archive

I EPU index

I Fraud complaints

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Big Data

Nontraditional Big Data: Central Bank Communication

Figure: Dynamic plot of Bank of England net sentiment.

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Big DataNontraditional Big Data: Central Bank Communication

au

in

it

jp

ez

es

be

at

lu

cz

ca

cnfr

de

pt

mx

kr

gb

us

noidse

ch

Figure: Figure taken from Armelius, Bertsch, Hull, and Zhang (2018)

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Big DataNontraditional Big Data: Housing Transactions

Figure: Daily approximation of monthly apartment price index.

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Big DataNontraditional Big Data: Housing Transactions

1973 1978 1983 1988 1993 1998 2003 2008 2013−3.5

−3.0

−2.5

−2.0

−1.5

−1.0

−0.5

0.0

JPUSGBSE

Figure: Impact of manufacturing’s decline on house price volatility(Hull, Olovsson, Walentin, and Westermark, 2017)

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Big Data

Nontraditional Big Data: Online Lending

-0.25

0.00

0.25

-200 0 200Hours Since Liftoff

Rol

ling

Mea

n of

Coe

ffici

ents

Figure: Figure taken from Bertsch, Hull, Zhang (2016).

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Machine Learning

Machine Learning

I Tax capitalizationmeasurement

I Fraud classification

I Real estate bubbleprediction

I Central bankcommunication

I Corporate defaultprediction

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Machine Learning

Machine Learning: Debiased Machine Learning

Figure: Figure taken from Grodecka and Hull (2019).

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Machine LearningMachine Learning: Transfer Learning

.........

... ...

...

...

Agree Disagree

you underestimated inflation

t+1 t+2t

0 0 0 0 21 257 351... ...

... ... ...

ht+2t+1h ht+2 ht+2

...

...

...

Figure: Figure taken from Apel, Blix Grimaldi, and Hull (2019).

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Quantum Computing

Diamanti, Hull, Sattath, Wendin (2019)

I Quantum supremacy achievable within a few years(Wendin, 2017).

I Speed-ups will enable advancement of econometrics andcomputational economics.

I Attacks will render classically-secured financial systemvulnerable.

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Quantum Computing

Diamanti, Hull, Sattath, Wendin (2019)

I MathematicalPreliminaries

I Notational Preliminaries

I Quantum Circuits

I Quantum States

I Quantum Dynamics

I Quantum Algorithms

I True Random NumberGeneration

I Quantum Money

I Pseudocode

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Quantum Computing

True Random Number Generation

Hq0 : |0

c00 : 0

Figure: Figure taken from Diamanti, Hull, Sattath, Wendin (2019).

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Quantum Computing

Diamanti, Hull, Sattath, Wendin (2019)

I Working paper available 2019:Q4.

Email: isaiah.hull@riksbank.se

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