586 research outputs found
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
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
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
High frequency statistical arbitrage via the optimal thermal causal path
We consider the problem of identifying similarities and causality relationships in a given set of financial time series data streams. We develop further the “Optimal Thermal Causal Path” method, which is a non-parametric method proposed by Sornette et al. The method considers the mismatch between a given pair of time series in order to identify the expected minimum energy path lead-lag structure between the pair. Traders may find this a useful tool for directional trading, to spot arbitrage opportunities. We add a curvature energy term to the method and we propose an approximation technique to reduce the computational time. We apply the method and approximation technique on various market sectors of NYSE data and extract the highly correlated pairs of time series. We show how traders could exploit arbitrage opportunities by using the method
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The diminished effect of index rebalances
The author revisits the strategy of trading S&P 500 index re-compositions under the pre- and post-crisis financial environments, proving that the return structure has
significantly changed. The results show for the first time, that there are currently no tradable abnormal returns between announcement and event dates in the post-crisis
sample period, indicating smoother rebalancing mechanisms by bank’s client facing desks and better services for passive end-investors. The newly added firms inflate the
S&P 500 index by less than 10 basis points per year. The results could be attributed to improved execution algorithms used by the banks, and potentially to the new
regulatory reforms in the sector, which prevents financial institutions from taking large trading positions with their balance sheets
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