23,726 research outputs found
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization
The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle
Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage
trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a
Neural Network fitness function for financial forecasting purposes. This is done by
benchmarking the ARBF-PSO results with those of three different Neural Networks
architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model
(ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy.
More specifically, the trading and statistical performance of all models is investigated in a
forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time
series over the period January 1999 to March 2011 using the last two years for out-of-sample
testing
Deterministic Factors of Stock Networks based on Cross-correlation in Financial Market
The stock market has been known to form homogeneous stock groups with a
higher correlation among different stocks according to common economic factors
that influence individual stocks. We investigate the role of common economic
factors in the market in the formation of stock networks, using the arbitrage
pricing model reflecting essential properties of common economic factors. We
find that the degree of consistency between real and model stock networks
increases as additional common economic factors are incorporated into our
model. Furthermore, we find that individual stocks with a large number of links
to other stocks in a network are more highly correlated with common economic
factors than those with a small number of links. This suggests that common
economic factors in the stock market can be understood in terms of
deterministic factors.Comment: 4 pages, 1 figur
Price Variations in a Stock Market With Many Agents
Large variations in stock prices happen with sufficient frequency to raise
doubts about existing models, which all fail to account for non-Gaussian
statistics. We construct simple models of a stock market, and argue that the
large variations may be due to a crowd effect, where agents imitate each
other's behavior. The variations over different time scales can be related to
each other in a systematic way, similar to the Levy stable distribution
proposed by Mandelbrot to describe real market indices. In the simplest, least
realistic case, exact results for the statistics of the variations are derived
by mapping onto a model of diffusing and annihilating particles, which has been
solved by quantum field theory methods. When the agents imitate each other and
respond to recent market volatility, different scaling behavior is obtained. In
this case the statistics of price variations is consistent with empirical
observations. The interplay between ``rational'' traders whose behavior is
derived from fundamental analysis of the stock, including dividends, and
``noise traders'', whose behavior is governed solely by studying the market
dynamics, is investigated. When the relative number of rational traders is
small, ``bubbles'' often occur, where the market price moves outside the range
justified by fundamental market analysis. When the number of rational traders
is larger, the market price is generally locked within the price range they
define.Comment: 39 pages (Latex) + 20 Figures and missing Figure 1 (sorry), submitted
to J. Math. Eco
Practical Deep Reinforcement Learning Approach for Stock Trading
Stock trading strategy plays a crucial role in investment companies. However,
it is challenging to obtain optimal strategy in the complex and dynamic stock
market. We explore the potential of deep reinforcement learning to optimize
stock trading strategy and thus maximize investment return. 30 stocks are
selected as our trading stocks and their daily prices are used as the training
and trading market environment. We train a deep reinforcement learning agent
and obtain an adaptive trading strategy. The agent's performance is evaluated
and compared with Dow Jones Industrial Average and the traditional min-variance
portfolio allocation strategy. The proposed deep reinforcement learning
approach is shown to outperform the two baselines in terms of both the Sharpe
ratio and cumulative returns
Community detection for correlation matrices
A challenging problem in the study of complex systems is that of resolving,
without prior information, the emergent, mesoscopic organization determined by
groups of units whose dynamical activity is more strongly correlated internally
than with the rest of the system. The existing techniques to filter
correlations are not explicitly oriented towards identifying such modules and
can suffer from an unavoidable information loss. A promising alternative is
that of employing community detection techniques developed in network theory.
Unfortunately, this approach has focused predominantly on replacing network
data with correlation matrices, a procedure that tends to be intrinsically
biased due to its inconsistency with the null hypotheses underlying the
existing algorithms. Here we introduce, via a consistent redefinition of null
models based on random matrix theory, the appropriate correlation-based
counterparts of the most popular community detection techniques. Our methods
can filter out both unit-specific noise and system-wide dependencies, and the
resulting communities are internally correlated and mutually anti-correlated.
We also implement multiresolution and multifrequency approaches revealing
hierarchically nested sub-communities with `hard' cores and `soft' peripheries.
We apply our techniques to several financial time series and identify
mesoscopic groups of stocks which are irreducible to a standard, sectorial
taxonomy, detect `soft stocks' that alternate between communities, and discuss
implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR
Steering Capital: Optimizing Financial Support for Innovation in Public Education
Examines efforts to align capital to education innovation and calls for clarity and agreement on problems, goals, and metrics; an effective R&D system; an evidence-based culture of continuous improvement; and transparent, comparable, and useful data
Criteria for Assessing Sustainable Development: Theoretical Issues and Empirical Evidence for the Case of Greece
We formulate two kinds of sustainability criteria by using feedback and arbitrary rules for selecting policy variables in non optimizing economies. We show that when policy variables are selected arbitrarily their accounting prices could determine sustainability in addition to the accounting prices of the economy’s assets. We use our theoretical framework to obtain estimates of sustainability conditions in real economies. Thus, the paper’s contribution consists in developing a systematic theoretical framework for determining value functions, accounting prices and sustainability criteria, under fairly general non-optimizing behavioral rules, and then showing that this framework can be used in applied work to estimate sustainability conditions. Based on our theoretical model, we examined the case of the Greek economy. When there is no binding environmental policy then migration rate, growth of capital per worker and exogenous technical change are strong positive factors for sustainability. When we introduce potential environmental damages due to sulphur dioxide (SO2) emissions, our findings indicate that these damages affect negatively the sustainability criterion.Sustainability criteria, Non-declining social welfare, Accounting prices, Non optimizing economy, Feedback rule, Arbitrary rule
- …