1) The document presents a Stochastic Trading Behavior Model (STBM) using long short-term memory to predict the next actions of high-frequency market making traders on the Tokyo Stock Exchange. 2) The STBM was trained on order book data from 2015 and tested on data from August 2015. It was able to successfully predict frequent and periodic trading actions in some cases. 3) The STBM outperformed baseline models and demonstrated the benefits of a stochastic approach over deterministic models for predicting uncertain trader behavior.

1. STBM: Stochas-c Trading Behavior
Model for Financial Markets Based
on Long Short-Term Memory
★Masanori HIRANO, Hiroyasu MATSUSHIMA,
Kiyoshi IZUMI, Hiroki SAKAJI
Izumi Lab.
School of Engineering, The University of Tokyo
hirano@g.ecc.u-tokyo.ac.jp
hKps://mhirano.jp/

2. Increasing Uncertainty in Financial Market
• The 2007-2008 financial crisis
• Flash crashes
• happened in stock market, currency, etc.…
• The causes: Auto trading, Auto news analysis…
• DJIA on May 6, 2010 <-- One big sell order
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3. Ar#ficial Financial Market
• SimulaTons on computer using virtual markets
• We can test hypotheTcal situaTons!
• Promising approach for financial market analysis
• But…
• Models are human-made
• Humans can overpass key features for model
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Are the simula-ons realis-c?
=> Model based on data is needed

4. The Outline of This Study
• Purpose: Automa.cally building a model imita.ng traders
based on data
• Only focus on HFT-MM ß Specific trader & strategy
• Target: Tokyo Stock Exchange
• We analyzed a special data
provided by JPX
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Tokyo Stock Exchange
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5. What’s the HFT-MM?
• High-Frequency-Trader Market-Making strategy
• Market-making strategy:
• (Basically) order near the best price
• Get profit by the spread (1001-999=2)
• Do repeatedly
• Risk-hedge by high-frequency-trade:
• Always have price move risk (Price move >> spread)
• Do acSon faster & hedge risk by seUng off their inventory
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Sell Buy
1200 1002
300 1001
1000
999 1200
998 1000
997 2100
Sell Buy
1200 1002
300 1001
100 1000
100 999 1200
998 1000
Sell
Buy

6. Data Extrac)on
We need HFT-MM ordering data…
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7. Data
• “Order-book reproducTon data”
provided by Japan Exchange Group (JPX)
• Containing masked trader informaTon
<- Called “Virtual Server (VS)”
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Time Ticker Kind Buy/sell VS Price
11:11:50.702813 A Limit Order sell VS1 2570
11:11:50.703600 B Executed buy VS4 Market Order
11:11:50.704001 A Cancel sell VS1 2570
Sample
Some columns are not shown such as volume

8. Indices for clustering (extracting HFT-MM)
• The logarithm of ac.on per .cker
ActionsPerTicker =
newOrders + changeOrders + (cancelOrders)
(numTicker)
ActionsPerTickerLOG = ln ActionsPerTicker
• Inventory Ra.o
InventoryRatioABS
= 𝑀𝑒𝑑𝑖𝑎𝑛!"#$%&
soldVolume !"#$%& − boughtVolume !"#$%&
soldVolume !"#$%& + boughtVolume !"#$%&
• Executed order ra.o
• Cancel order ra.o
• Market order ra.on
• The logarithm of .cker per VS
TickerPerVSLOG = ln
(numTicker)
(numVS)
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Many order
Low inventory
Many VS usage
Low executed ratio
High cancel raTo
Low market order ratio

9. Data outline
• Jan. 2015 – mid-Sep. 2015: All 178 business days
• All: 2654 Traders
• Only HFT: 181 Traders <= based on ActionsPerTicker ≥ 100
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10. Hierarchical Clustering [Uno et al. 18]
• NormalizaTons for each indices & clustering
• Euclidean distance
• Ward’s method
• 10 clusters
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HFT-MM cluster based on indices

11. Data split
• We got ordering data of HFT-MM
• 2015/01-07 => model training & evaluaTon of HFT-MM
• 2015/08 => tesTng of model
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12. HFT-MM Machine Learned Model
• Using machine learning for data, we build a model
• Model predict the next acTon of traders
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LSTM
Cell
LSTM
Cell
LSTM
Cell
LSTM
Cell
LSTM
Cell
Market State
(Order book)
10:37
Trader’s State
(Ordering State)
Trader’s Actions
in the next 1 min.
(Prediction)
10:38 10:39 10:40 10:41
Dense
/MLP
Dense
/MLP
Dense
/MLP
Dense
/MLP
1 min. 1 min. 1 min. 1 min.
input &
embedded
input & embed.
pred. pred. pred. pred.
Time
input &
embedded
input &
embedded
input &
embedded
input &
embedded
input & embed. input & embed. input & embed.
input &
embed.
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13. Input: Market State
• Current market order volume in each minutes
: 20 classes (probabiliTes)
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8 or more
Best ask
Best bid
8 or more
1 tick below
2 ticks below
3 ticks below
4 ticks below
5 ticks below
6 ticks below
7 ticks below
BidAsk
7 ticks above
6 ticks above
5 ticks above
4 ticks above
3 ticks above
2 ticks above
1 tick above
+ Best Ask Price
+ Best Bid Price
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14. Input: Trader State
• Current order volumes in each minutes
: 18 classes (probabiliTes)
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©M.HIRANO & Izumi Lab.
8 or more
Best ask
Best bid
8 or more
1 tick below
2 ticks below
3 ticks below
4 ticks below
5 ticks below
6 ticks below
7 ticks below
BidAsk
7 ticks above
6 ticks above
5 ticks above
4 ticks above
3 ticks above
2 ticks above
1 tick above
+ Best Ask Price
+ Best Bid Price
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15. Predic#on
• PredicTons of traders’ acTon in next 1 min.: 21 paterns
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8 or more
Best ask
Best bid
8 or more
1 tick below
2 ticks below
3 ticks below
4 ticks below
5 ticks below
6 ticks below
7 ticks below
Above Best Bid
BidAsk
7 ticks above
6 ticks above
5 ticks above
4 ticks above
3 ticks above
2 ticks above
1 tick above
Below Best Ask
+ Do Nothing
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16. Evalua#on & comparison
• Evaluation
• MSE of 21 patterns of actions
• Comparison
• By-chance model
• The probability of all actions are 1/21
• Ideal deterministic model
• Ordinally deterministic HFT-MM model like [Avellaneda 2002]
• But, we assumed the prediction performance is the best
= The model knows the real actions
• Always make two actions – one sell and one buy
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17. Results (Examples of Predic#on)
• Case 1
• Success in predicSon of frequent acSons
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2 4 6 8 10
0.00
0.02
0.04
0.06
0.08
P edic i n
G nd h
Ac i e
Pb.
12 14 16 18 20
0.00
0.02
0.04
0.06
0.08
P edic i n
G nd h
Ac i e
Pb.
Bid acLons Ask actions
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18. Results (Examples of Predic#on)
• Case 2
• Success in predicSon of frequent acSons & periodical acSons
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Bid acLons Ask acLons
2 4 6 8 10
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
P edic i n
G nd h
Ac i e
Pb.
12 14 16 18 20
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
P edic i n
G nd h
Ac i e
Pb.
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19. Results (Examples of Predic#on)
• Case 3
• Fail to predict
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Bid acLons Ask acLons
2 4 6 8 10
0.00
0.10
0.20
0.30
0.40
P edic i
G d h
Ac i e
Pb.
12 14 16 18 20
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
P ed c
G d h
Ac i e
Pb.
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20. Results & Discussion
• Our STBM outperforms others
• The benefits of stochasTc model
• Applicable to mulSple acSons in one minutes
• In future work, we have to make model how many orders will be placed
• Good fit for simulaSons
• SimulaLons should not be determinisLc
• QuanLficaLon of uncertainty
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21. Conclusion
• We proproposed STBM: StochasTc Trading Behavior Model
for Financial Markets
• Our model outperformed by-chance model and
determinisTc model: The benefits of stochasTc model is also
confirmed.
• Future work:
• ApplicaSon to other types of traders
• Apply to agent-based simulaSons
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