Quantifying News For Automated Trading - Methodology and ProfitabilityQuantInsti
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
News is the prime factor which affects prices of financial assets, everything else is secondary. However, owing to the huge volume of news information continuously released by modern electronic communication, it becomes increasingly difficult to process all the information in a timely manner.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/quantifying-news-for-automated-trading-methodology-and-profitability/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Trading plan is very important for you to be successful in forex trading.forex trading plan in pdf file. In this ebook will be cover on your plan to be successful forex trader, your trading goal, money management,your strategy and how you going to do your trading.
Quantifying News For Automated Trading - Methodology and ProfitabilityQuantInsti
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
News is the prime factor which affects prices of financial assets, everything else is secondary. However, owing to the huge volume of news information continuously released by modern electronic communication, it becomes increasingly difficult to process all the information in a timely manner.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/quantifying-news-for-automated-trading-methodology-and-profitability/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Trading plan is very important for you to be successful in forex trading.forex trading plan in pdf file. In this ebook will be cover on your plan to be successful forex trader, your trading goal, money management,your strategy and how you going to do your trading.
Conference on Option Trading Techniques - Option Trading StrategiesQuantInsti
This presentation was delivered by QuantInsti founders Rajib Ranjan Borah & Nitesh Khandelwal at a conference on 'Options Trading Techniques' organized in Bangkok on 6-October-2014. This event was organized by 'Stock Exchange of Thailand', ' Thailand Futures Exchange', 'FlexTrade' and supported by 'QuantInsti'.
The presentation looks at various categories of strategies that could be traded using options - for e.g. usage of option derivatives as a methodology to express viewpoint on volatility, correlation between index components, etc, etc.
This presentation was a part of a series of presentations delivered by Rajib Ranjan Borah and Nitesh Khandelwal to a gathering of around 150 Thai traders. The rest of the presentations in the conference included the following topics:
i) Option Derivative Fundamentals
ii) Option Trading Strategies
iii) Managing Option Portfolios - lower and higher order derivatives
iv) Global Option Trading Landscapes
option market trading strategy
share market trading with options
buying and selling of options based upon strategy
derivative market trading strategy
how to trade in option market with different strategy
learn option market trading strategy with reference from NSE
making profit by trading in share market with different method and strategy
Detailed presentation on how price is determined, factors effecting price.
The price determination under following markets,
1). Perfect Competition
2). Monopoly
3). Duopoly
4). Oligopoly
have been described in detail.
Price Determination Under Short & Long Period, Cournot Model & Stackelberg Model are also discussed.
A Quantitative Case Study on the Impact of Transaction Cost in High-Frequency...Cognizant
High-frequency trading (HFT) aims to achieve a small positive alpha on every trade, so transaction costs determine whether the algorithm is profitable. We offer a case study demonstrating the relationship between alphas, transaction costs, and profitability.
Conference on Option Trading Techniques - Option Trading StrategiesQuantInsti
This presentation was delivered by QuantInsti founders Rajib Ranjan Borah & Nitesh Khandelwal at a conference on 'Options Trading Techniques' organized in Bangkok on 6-October-2014. This event was organized by 'Stock Exchange of Thailand', ' Thailand Futures Exchange', 'FlexTrade' and supported by 'QuantInsti'.
The presentation looks at various categories of strategies that could be traded using options - for e.g. usage of option derivatives as a methodology to express viewpoint on volatility, correlation between index components, etc, etc.
This presentation was a part of a series of presentations delivered by Rajib Ranjan Borah and Nitesh Khandelwal to a gathering of around 150 Thai traders. The rest of the presentations in the conference included the following topics:
i) Option Derivative Fundamentals
ii) Option Trading Strategies
iii) Managing Option Portfolios - lower and higher order derivatives
iv) Global Option Trading Landscapes
option market trading strategy
share market trading with options
buying and selling of options based upon strategy
derivative market trading strategy
how to trade in option market with different strategy
learn option market trading strategy with reference from NSE
making profit by trading in share market with different method and strategy
Detailed presentation on how price is determined, factors effecting price.
The price determination under following markets,
1). Perfect Competition
2). Monopoly
3). Duopoly
4). Oligopoly
have been described in detail.
Price Determination Under Short & Long Period, Cournot Model & Stackelberg Model are also discussed.
A Quantitative Case Study on the Impact of Transaction Cost in High-Frequency...Cognizant
High-frequency trading (HFT) aims to achieve a small positive alpha on every trade, so transaction costs determine whether the algorithm is profitable. We offer a case study demonstrating the relationship between alphas, transaction costs, and profitability.
The Predictor is designed for application in the banks, investment companies, stock markets, companies with operations in the stock markets and securities markets.
Based on innovative mathematical models of multifractal and wavelet analysis, this tool is carrying out continuous scanning and processing of time series derived from the financial markets and produces signals that precede a sharp change (20%) of the securities prices or indexes exchange rate and warn about approaching of the crisis.
4 things maybe you don't know about nasdaq-100 (posted 23th June 2017)Pietro Di Leo
This is a presentation of a work abount the American Index that I made at the end of June.
I hope it's useful for someone, even there are questions, criticism or advises don't hesitate to contact me, thank you.
Check it out and have a good read!
In the aftermath of the financial meltdown, the virtue of competitive markets is being questioned. It is little appreciated, however, that the economic concept of "competitive markets" is abstract—just as a "vacuum" is an idealized environment for physicists—and that the theory rarely spells out how to implement markets in practical terms. Peter Bossaerts, professor of finance at the California Institute of Technology, explains what economists mean by the term "competitive markets," what the markets are theoretically supposed to do, and where they fail. He’ll also evaluate whether or not eBay, the New York Stock Exchange, the real estate market, the Over-the-Counter credit derivative markets, and other institutions are really instances of "competitive markets." Audience members participate in hands-on demos of market behavior.
From Backtesting to Live Trading by Vesna Straser at QuantCon 2016Quantopian
Dr. Vesna Straser will discuss the differences in expected slippage between live trading, simulation trading and backtesting. Typically in backtesting signal generation and order fill assumptions are simplified to obtain strategy performance data faster. For example, many commercial back testing software providers will work with sampled data such as minute open or close price points and assume that the signal is triggered at the close of one bar and filled at the close price of the next bar, per the assumed slippage model. Simulation trading, however, will typically run on tick trading data (live or replayed) potentially resulting in quite different dynamics versus back testing. Orders are filled per fill assumptions that may vary significantly between different providers. In live trading, orders are triggered and executed immediately under real market conditions and order type. Depending on the trading strategy, live trading results can differ dramatically from back-testing and/or simulation trading. Vesna will outline the issues, analytics to track, factors to consider and how to account for them to achieve “realistic” back-testing results.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Lateral Ventricles.pdf very easy good diagrams comprehensive
Econophysics II: Detailed Look at Stock Markets and Trading - Thomas Guhr
1. FAKULT ¨AT F ¨UR PHYSIK
Econophysics II:
Detailed Look at Stock Markets and Trading
Thomas Guhr
Let’s Face Complexity, Como, 2017
Como, September 2017
2. Outline
• stock markets and trading in reality
• two extreme model scenarios: Efficient Market Hypothesis,
Zero Intelligence Trading
• large scale data analysis reveals non–Markovian features
• artifical stock market to encircle mechanisms
• trading strategies and temporal correlations
Como, September 2017
4. Clearing House and Orders
trading via clearing house, buy and sell offers/orders (bids and
asks)
limit order: bid or ask for a specific volume at a specific price
within a certain time window,
best ask a(t), best bid b(t), always a(t) ≥ b(t)
if equal −→ trade, price S(t) = a(t) = b(t) immediately thereafter
market order: buy or sell immediately what is offered,
S(t) = b(t) or S(t) = a(t)
Como, September 2017
5. Order Book
make public to provide all traders with same information
limit orders appear in the order book, market orders do not
Como, September 2017
6. Midpoint, Bid–ask Spread, Trade Sign
in between trades, there is no price !
bid–ask spread s(t) = b(t) − a(t) < 0
the higher the trading frequency, the smaller is s(t)
midpoint m(t) =
a(t) + b(t)
2
immediately after a trade, define trade sign
ϑ(t) =
+1 if S(t) is higher than the last m(t)
−1 if S(t) is lower than the last m(t)
positive, if trade was triggered by a market order to buy
negative, if trade was triggered by a market order to sell
Como, September 2017
7. Traders and Liquidity
market is liquid, if there are always enough shares at a
“reasonable” price to ensure that every planned trade can be
carried out and if the trading happens continuously
small bid–ask spread s(t) = b(t) − a(t) is indicator
limit orders make a market liquid ←→ liquidity providers
market orders absorb liquidity ←→ liquidity takers (“informed”)
liquidity providers and takers are not static populations, these
rôles change constantly
Como, September 2017
9. First Extreme Model Scenario — EMH
Efficient Market Hypothesis
Traders always act fully rationally. Market price results from
consensus between the traders about the “fair” price. It always
exists and reflects quantifiable economic value of asset.
Deviation of market price from “fair” price −→ arbitrage −→
disappears. Consensus comes about, because group of traders
processed all available information. Price can only change if new
information arrives. The new information is totally random.
Como, September 2017
10. Second Extreme Model Scenario — ZIT
Zero Intelligence Trading
Individual trader is irrational and acts fully randomly. The other
traders do not know that and interpret the buy and sell decisions
made by others as potentially information driven. Price change is
not attributed to new information, it automatically follows from the
fact that trading takes place −→ demand and supply. There is no
fair price, midpoint m(t) moves as well. Traders immediately
accept the new midpoint as the new reference point about which
they send out their random buy and sell orders.
Como, September 2017
11. Where is the Truth ?
both scenarios lead to a Markovian random walk model for price !
both partly compatible with reality, but there are objections:
EMH: “fair” price deeply obscure −→ what is then rational ? —
high volatilities incompatible with rational pricing — time scales of
trading not consistent with those of information flow
ZIT: irrationality not realistic either, traders use information
truth is somewhere in between −→ need detailed data analysis
Como, September 2017
13. Data Analysis
Bouchaud, Gefen, Potters and Wyart, Quantitative Finance 4
(2004) 176
• fully electronically traded French stocks 2001–2002
• intraday
• high frequency, up to 10000 trades/day
• volumes between a few and 80000 shares
• trade time instead of real time
Como, September 2017
14. Volatility and Diffusion
substract drift from S(t) −→ detrended price Z(t)
diffusion function D(τ) = (Z(t + τ) − Z(t))2
largely constant
volatility function
D(τ)/τ
for France–Telecom
diffusive motion !
Como, September 2017
15. Average Response to Trading
response function R(τ) = (Z(t + τ) − Z(t)) ϑ(t)
average impact of trading at t on subsequent price changes
non–zero empirical
result proves
non–Markovian
behavior !
Como, September 2017
16. Distribution of Sign Supplemented Price Changes
sign supplemented price changes u(t, τ) = (Z(t + τ) − Z(t)) ϑ(t)
response R(τ) = u(t, τ) , diffusion function D(τ) = u2
(t, τ)
distribution p(u(t, τ))
for τ = 128
moment R(128) > 0
small arbitrage
truly informed traders
Como, September 2017
17. Power Law Autocorrelations in Trade Signs
trade sign autocorrelation Θ(τ) = ϑ(t + τ)ϑ(t) − ϑ(t) 2
power law
Θ(τ) ∼
1
τγ
with γ < 0
non–Markovian,
outrules ZIT idea !
Como, September 2017
19. Non–Markovian Model
Z(t) =
t
t′=1
G0(t − t′
)ϑ(t′
) ln V (t′
) +
t
t′=1
ε(t′
)
first term non–Markovian, second Markovian
ansatz for bare impact function G0(τ) ∼
1
(1 + τ/τ0)β
−→ D(τ) ∼ τ2−2β−γ
, critical exponent βc =
1 − γ
2
β = βc diffusive, β > βc sub–diffusive, β < βc super–diffusive
possible to reproduce empirical R(τ) for β ≈ βc and τ0 ≈ 20
Bouchaud, Gefen, Potters, Wyart (2004)
Como, September 2017
20. Liquidity Takers versus Liquidity Providers
Reality is non–Markovian, interpreted as a competition: Consider
trader who is “informed” that price of a company will go up. He
wants to buy shares, likely by market orders −→ liquidity taker.
Not be wise to place big offer, because this would alert liquidity
providers who emit the limit orders to sell (“knows something”).
They would place their limit orders at higher price. Liquidity taker
is aware −→ divides his market order into smaller chunks which
he places one after the other −→ introduces temporal
autocorrelations Θ(τ). Liquidity providers want to mean revert
price −→ R(τ) → 0 for large τ. They do that slowly, because they
do not know whether liquidity taker’s information becomes true
−→ maximum of R(τ). −→ Persistence: liquidity providers do not
sufficiently mean revert the price −→ super–diffusive.
Antipersistence: they mean revert too strongly −→ sub–diffusive.
−→ Subtle balance between sub– and super–diffusive −→
effectively diffusive. Compares to balancing a stick on the palm.
Como, September 2017
22. Agent Based Modeling
financial markets are complex systems: many degrees of
freedom, non–linear effects, basic processes and time evolution
governed by probabilistic rules, not by deterministic equations
top–down approach: schematic models, stochastic processes
−→ successes and limitations
bottom–up approach: artificial stock market on computer with
virtual traders −→ agent based modeling
• set up system microscopically and let evolve
• price dynamics and all other macroscopic observables result
• identify crucial mechanisms by encircling them
various examples in biology, social sciences, economics,
one of the first is Conway’s Game of Life (1970)
Como, September 2017
23. Wigner’s Caveat
“It is nice to know that the computer
understands the problem. But I would
like to understand it too.”
Eugene Paul Wigner, 1902–1995
Como, September 2017
24. Impact of Trading Strategies
introduce different types of traders traders −→ top–down element
in an otherwise bottom–up approach, not adaptive, simple
• ZeroIntelligenceTrader
• RandomTrader
• EagerTrader
• LiquidityProvider
• RandomInformedTrader
• SerialTrader
• ExpectingTrader
let evolve and see what happens !
Berseus, Schäfer, Guhr (2007)
Como, September 2017
25. Zero Intelligence Trading
population of 300 ZeroIntelligenceTraders
return distribution after 10000 trades
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
non–Gaussian,
heavy tails !
Como, September 2017
26. Heavy Tails and Order Book
population of 300 traders, distributions of price differences
EagerTraders and three versions of RandomTraders
non–Gaussian
when order book
becomes
more important
-2 0 2
0
0.1
0.2
0.3
0.4
-2 -1 0 1 2
0
0.1
0.2
0.3
0.4
0.5
0.6
-1 -0.5 0 0.5 1
0
0.5
1
1.5
-0.5 0 0.5
0
1
2
3
4
5
6
Como, September 2017
27. Mixed Populations — Volatility Function
LiquidityProvider and three versions of RandomInformedTraders
10
0
10
1
10
2
10
3
10
4
0
0.02
0.04
0.06
0.08
0.1
0.12
l (trade time)
standarddeviationalconstantD(l)
λ = 0.1
λ = 0.01
λ = 0.001
largely diffusive
depends sensitively on likeliness to emit market orders
Como, September 2017
28. Mixed Populations — Trade Sign Autocorrelations
0 5 10 15 20 25 30
0
0.2
0.4
0.6
0.8
1
l (trade time)
C
0
(l)=<ε
n+l
ε
n
>
200 400 600 800 1000 1200 1400
-0.05
0
0.05
0.1
l (trade time)
C
0
(l)=<ε
n+l
ε
n
>
LiquidityProviders,
SerialTraders
0 5 10 15 20 25 30
0
0.2
0.4
0.6
0.8
1
l (trade time)
C
0
(l)=<ε
n+l
ε
n
>
200 400 600 800 1000 1200 1400
-0.05
0
0.05
0.1
l (trade time)
C
0
(l)=<ε
n+l
ε
n
>
LiquidityProviders,
ExpectingTraders
ExpectingTrader waits for gap between m(t) and “fair” price,
Serial Trader does not −→ trade sign autocorrelations
Como, September 2017
29. More Advanced Program — Heavy Tails
keep the setting simple, but optimize algorithms and
speed up numerics
one–minute return distributions
µlt = 1200s µlt = 300s µlt = 120s
chosen average lifetime of orders µlt competes with the
average waiting time µwt
Schmitt, Schäfer, Münnix, Guhr (2012)
Como, September 2017
30. More Advanced Program — Realistic Scenario
measure for tails: kurtosis excess volatility distribution
model versus empirical data
µlt = 120s
no strategies, but still realistic results !
Schmitt, Schäfer, Münnix, Guhr (2012)
Como, September 2017
31. Summary and Conclusions
• stock markets and trading in reality
• two extreme model scenarios: Efficient Market Hypothesis,
Zero Intelligence Trading
• large scale data analysis reveals non–Markovian features
• schematic top–down stochastic model
• artifical stock market as bottom–down approach
• heavy tails are order book effect
• trading strategies sensitively determine temporal correlations
Como, September 2017