866 research outputs found
Mimicking the probability distribution of a two-dimensional Grover walk with a single-qubit coin
Multi-dimensional quantum walks usually require large coin spaces. Here we
show that the non-localized case of the spatial density probability of the
two-dimensional Grover walk can be obtained using only a two-dimensional coin
space and a quantum walk in alternate directions. We present a formal proof of
this correspondence and analyze the behavior of the coin-position entanglement
as well as the x-y spatial entanglement in our scheme with respect to the
Grover one. We show that our experimentally simpler scheme allows to entangle
the two orthogonal directions of the walk more efficiently.Comment: 5 pages, 2 figures, RevTeX
Fractal Markets Hypothesis and the Global Financial Crisis: Scaling, Investment Horizons and Liquidity
We investigate whether fractal markets hypothesis and its focus on liquidity
and invest- ment horizons give reasonable predictions about dynamics of the
financial markets during the turbulences such as the Global Financial Crisis of
late 2000s. Compared to the mainstream efficient markets hypothesis, fractal
markets hypothesis considers financial markets as com- plex systems consisting
of many heterogenous agents, which are distinguishable mainly with respect to
their investment horizon. In the paper, several novel measures of trading
activity at different investment horizons are introduced through scaling of
variance of the underlying processes. On the three most liquid US indices -
DJI, NASDAQ and S&P500 - we show that predictions of fractal markets hypothesis
actually fit the observed behavior quite well.Comment: 11 pages, 3 figure
Fractal Profit Landscape of the Stock Market
We investigate the structure of the profit landscape obtained from the most
basic, fluctuation based, trading strategy applied for the daily stock price
data. The strategy is parameterized by only two variables, p and q. Stocks are
sold and bought if the log return is bigger than p and less than -q,
respectively. Repetition of this simple strategy for a long time gives the
profit defined in the underlying two-dimensional parameter space of p and q. It
is revealed that the local maxima in the profit landscape are spread in the
form of a fractal structure. The fractal structure implies that successful
strategies are not localized to any region of the profit landscape and are
neither spaced evenly throughout the profit landscape, which makes the
optimization notoriously hard and hypersensitive for partial or limited
information. The concrete implication of this property is demonstrated by
showing that optimization of one stock for future values or other stocks
renders worse profit than a strategy that ignores fluctuations, i.e., a
long-term buy-and-hold strategy.Comment: 12 pages, 4 figure
Predictability of large future changes in a competitive evolving population
The dynamical evolution of many economic, sociological, biological and
physical systems tends to be dominated by a relatively small number of
unexpected, large changes (`extreme events'). We study the large, internal
changes produced in a generic multi-agent population competing for a limited
resource, and find that the level of predictability actually increases prior to
a large change. These large changes hence arise as a predictable consequence of
information encoded in the system's global state.Comment: 10 pages, 3 figure
Evaluating Greek equity funds using data envelopment analysis
This study assesses the relative performance of Greek equity funds employing a non-parametric method, specifically Data Envelopment Analysis (DEA). Using an original sample of cost and operational attributes we explore the e¤ect of each variable on funds' operational efficiency for an oligopolistic and bank-dominated fund industry. Our results have significant implications for the investors' fund selection process since we are able to identify potential sources of inefficiencies for the funds. The most striking result is that the percentage of assets under management affects performance negatively, a conclusion which may be related to the structure of the domestic stock market. Furthermore, we provide evidence against the notion of funds' mean-variance efficiency
New procedures for testing whether stock price processes are martingales
We propose procedures for testing whether stock price processes are
martingales based on limit order type betting strategies. We first show that
the null hypothesis of martingale property of a stock price process can be
tested based on the capital process of a betting strategy. In particular with
high frequency Markov type strategies we find that martingale null hypotheses
are rejected for many stock price processes
Cross-Sectional Dispersion and Expected Returns
This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market,represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return, after accounting for its exposure to other systematic risk factors. Dispersion is associated with a significantly negative risk premium in the cross-section (-1.32% per annum) which is distinct from premia commanded by a set of alternative systematic factors. These results are robust to a wide set of stock characteristics, market conditions, and industry groupings
The Effects of Twitter Sentiment on Stock Price Returns
Social media are increasingly reflecting and influencing behavior of other
complex systems. In this paper we investigate the relations between a well-know
micro-blogging platform Twitter and financial markets. In particular, we
consider, in a period of 15 months, the Twitter volume and sentiment about the
30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We
find a relatively low Pearson correlation and Granger causality between the
corresponding time series over the entire time period. However, we find a
significant dependence between the Twitter sentiment and abnormal returns
during the peaks of Twitter volume. This is valid not only for the expected
Twitter volume peaks (e.g., quarterly announcements), but also for peaks
corresponding to less obvious events. We formalize the procedure by adapting
the well-known "event study" from economics and finance to the analysis of
Twitter data. The procedure allows to automatically identify events as Twitter
volume peaks, to compute the prevailing sentiment (positive or negative)
expressed in tweets at these peaks, and finally to apply the "event study"
methodology to relate them to stock returns. We show that sentiment polarity of
Twitter peaks implies the direction of cumulative abnormal returns. The amount
of cumulative abnormal returns is relatively low (about 1-2%), but the
dependence is statistically significant for several days after the events
Strategies used as spectroscopy of financial markets reveal new stylized facts
We propose a new set of stylized facts quantifying the structure of financial
markets. The key idea is to study the combined structure of both investment
strategies and prices in order to open a qualitatively new level of
understanding of financial and economic markets. We study the detailed order
flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This
enormous dataset allows us to compare (i) a closed national market (A-shares)
with an international market (B-shares), (ii) individuals and institutions and
(iii) real investors to random strategies with respect to timing that share
otherwise all other characteristics. We find that more trading results in
smaller net return due to trading frictions. We unveiled quantitative power
laws with non-trivial exponents, that quantify the deterioration of performance
with frequency and with holding period of the strategies used by investors.
Random strategies are found to perform much better than real ones, both for
winners and losers. Surprising large arbitrage opportunities exist, especially
when using zero-intelligence strategies. This is a diagnostic of possible
inefficiencies of these financial markets.Comment: 13 pages including 5 figures and 1 tabl
Improving trading saystems using the RSI financial indicator and neural networks.
Proceedings of: 11th International Workshop on Knowledge Management and Acquisition for
Smart Systems and Services (PKAW 2010), 20 August-3 September 2010, Daegu (Korea)Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.This work is supported by the Spanish Ministry of Industry,
Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148),
SONAR2 (TSI-020100-2008-665 and GO2 (TSI-020400-2009-127). Furthermore,
this work is supported by the General Council of Superior Technological Education of
Mexico (DGEST). Additionally, this work is sponsored by the National Council of
Science and Technology (CONACYT) and the Public Education Secretary (SEP)
through PROMEP.Publicad
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