Trading activities are based on technical analysis, market sentiment (asymmetric information, rumours, noise trading) and imitative behavoiur. This leads to unjustified biasness in decision making. To remove such subjectivity, this paper suggests a neural network model for the investors to decide whether buy or sell the shares. The model consists two wings - one, based on technical analysis and the other, on fundamental analysis. The integral part of this model is the existence of a hidden layer between the input layer and output layer. To remain away from the subjectivity, this model does not consider the behavioural factors in modeling.
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A Quantitative Neural Network Model (QNNM) for Stock Trading Decisions.pdf
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- Fully connected neural layer
- Piligrimus is a neural network indicator.
- "New Neural" is an Open Source neural network engine project for the MetaTrader 5 platform.
seekers_:
Trading activities are based on technical analysis, market sentiment (asymmetric information, rumours, noise trading) and imitative behavoiur. This leads to unjustified biasness in decision making. To remove such subjectivity, this paper suggests a neural network model for the investors to decide whether buy or sell the shares. The model consists two wings - one, based on technical analysis and the other, on fundamental analysis. The integral part of this model is the existence of a hidden layer between the input layer and output layer. To remain away from the subjectivity, this model does not consider the behavioural factors in modeling.
Good document. Thanks
Trading activities are based on technical analysis, market sentiment (asymmetric information, rumours, noise trading) and imitative behavoiur. This leads to unjustified biasness in decision making. To remove such subjectivity, this paper suggests a neural network model for the investors to decide whether buy or sell the shares. The model consists two wings - one, based on technical analysis and the other, on fundamental analysis. The integral part of this model is the existence of a hidden layer between the input layer and output layer. To remain away from the subjectivity, this model does not consider the behavioural factors in modeling.
Prediction of stock market returns is an important issue in finance. The aim of this paper is to investigate the profitability of using artificial neural networks (ANNs). In this study, the ANNs predictions are transformed into a simple trading strategy, whose profitability is evaluated against a simple buy-hold strategy. We adopt the neural network approach to analyze the Taiwan Weighted Index and the S&P 500 in the States. Consequently, we find that the trading rule based on ANNs generates higher returns than the buy-hold strategy.
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Recent years have witnessed the advancement of automated algorithmic trading
systems as institutional solutions in the form of autobots, black box
or expert advisors. However, little research has been done in this area
with sufficient evidence to show the efficiency of these systems. This
paper builds an automated trading system which implements an optimized genetic-algorithm neural-network
(GANN) model with cybernetic concepts and evaluates the success using a
modified value-at-risk (MVaR) framework. The cybernetic engine includes
a circular causal feedback control feature and a developed golden-ratio
estimator, which can be applied to any form of market data in the
development of risk-pricing models. The paper applies the Euro and Yen
forex rates as data inputs. It is shown that the technique is useful as a
trading and volatility
control system for institutions including central bank monetary policy
as a risk-minimizing strategy. Furthermore, the results are achieved
within a 30-second timeframe for an intra-week trading
strategy, offering relatively low latency performance. The results show
that risk exposures are reduced by four to five times with a maximum
possible success rate of 96%, providing evidence for further research
and development in this area.
Beating
the SP500 Index benchmark is a do-or-die among active portfolio
managers. We propose a new method to add a 2-layer augmentation to
relative strength and momentum based active portfolio management
methods; first layer is to add a filtering mechanism to add a momentum
filter in the recommendation engine and second is to include a multi
level- multi layer machine learning method to integrate an ensemble
model to decision making process. The ensemble model consists of
gradient boosted decision trees and neural network
models. Our initial results show that it is possible to beat the SP500
benchmark index by 600 basis points (in the calculations industry
standard trading costs are included) as it is demonstrated by comparing the overall performance of the proposed method.
According
to studies on the impossible trinity, under conditions of high
financial integration, the domestic interest rate is closely linked to
the foreign one if the possibility of maneuvering interest rates is
absent in this transaction. The Fisher effect is brought into this
escapade because interest rates generally trend positively with
inflation. Botswana has set her inflation target between 3-6% and this
study attempts to determine inflation spillover effects from the United
Kingdom, United States of America, Canada, Japan, China, Belgium,
France, Germany, South Africa, Nigeria, and Ghana using data from
1980-2012. Comparatively, the attempts made by previous studies to
examine spillovers generally lacked a long-run focus and channeled much
attention to periods of financial crisis. This study deviates from other
studies by using the Augmented Dickey Fuller (ADF) test to examine unit
roots for the countries under examination. The study further applies
the Johansen cointegration procedure, as well as the Granger causality
test. The results show that Botswana’s inflation dynamics trend
positively with all the countries under scrutiny except South Africa in a
long-run framework. However, the Granger causality test only proved
that Botswana’s inflation lead China’s inflation dynamics. In
conclusion, Botswana’s inflation is not driven by other countries’
inflation dynamics.
seekers_:
Any examples of using this?
Abstract—Developing a precise and accurate model of gold price is critical to manage assets because of its unique features. In this paper, artificial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANN model outperforms ARIMA model, in terms of different performance criteria during the training and validation phases.
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We
discuss risk, preference and valuation in classical economics, which led
academics to develop a theory of market prices, resulting in the
general equilibrium theories. However, in practice, the decision process
does not follow that theory since the qualitative aspect coming from
human decision making process is missing. Further, a large number of
studies in empirical finance showed that financial assets exhibit trends
or cycles, resulting in persistent inefficiencies in the market, that
can be exploited. The uneven assimilation of information emphasised the
multifractal nature of the capital markets, recognising complexity. New
theories to explain financial markets developed, among which is a
multitude of interacting agents forming a complex system characterised
by a high level of uncertainty. Recently, with the increased
availability of data, econophysics emerged as a mix of physical sciences
and economics to get the best of both world, in view of analysing more
deeply assets' predictability. For instance, data mining and machine
learning methodologies provide a range of general techniques for
classification, prediction, and optimisation of structured and
unstructured data. Using these techniques, one can describe financial
markets through degrees of freedom which may be both qualitative and
quantitative in nature. In this book we detail how the growing use of
quantitative methods changed finance and investment theory. The most
significant benefit being the power of automation, enforcing a
systematic investment approach and a structured and unified framework.
We present in a chronological order the necessary steps to identify
trading signals, build quantitative strategies, assess expected returns,
measure and score strategies, and allocate portfolios.
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What a Neural network is not ?
A neural network is not a magic system that takes inputs and find a way of making money by itself ! !
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