3,161 research outputs found
Machine Learning and Portfolio Optimization: an application to Italian FTSE-MIB Stocks
A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation.A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation
Hybrid deep neural networks for mining heterogeneous data
In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity.
The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and heterogeneous meta data are modeled. Detecting Copy Number Variations (CNVs) in genetic studies is used as a motivating example. A CNN-DNN blended neural network is proposed to authenticate CNV calls made by current state-of-art CNV detection algorithms. It utilizes hybrid deep neural networks to leverage both scatter plot image signal and heterogeneous numerical meta data for improving CNV calling and review efficiency.
The second part of this dissertation deals with data of various frequencies or scales in time series data analysis, the second kind of data heterogeneity. The stock return forecasting problem in the finance field is used as a motivating example. A hybrid framework of Long-Short Term Memory and Deep Neural Network (LSTM-DNN) is developed to enrich the time-series forecasting task with static fundamental information. The application of the proposed framework is not limited to the stock return forecasting problem, but any time-series based prediction tasks.
The third part of this dissertation makes an extension of LSTM-DNN framework to account for both temporal and spatial dependency among variables, common in many applications. For example, it is known that stock prices of relevant firms tend to fluctuate together. Such coherent price changes among relevant stocks are referred to a spatial dependency. In this part, Variational Auto Encoder (VAE) is first utilized to recover the latent graphical dependency structure among variables. Then a hybrid deep neural network of Graph Convolutional Network and Long-Short Term Memory network (GCN-LSTM) is developed to model both the graph structured spatial dependency and temporal dependency of variables at different scales.
Extensive experiments are conducted to demonstrate the effectiveness of the proposed neural networks with application to solve three representative real-world problems. Additionally, the proposed frameworks can also be applied to other areas filled with similar heterogeneous inputs
Machine Learning-Driven Decision Making based on Financial Time Series
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The dynamics of market efficiency: testing the adaptive market hypothesis in South Africa
A thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce,
Law and Management, University of the Witwatersrand in fulfilment of the requirements for
the degree of Doctor of Philosophy (Ph/D).
Johannesburg, South Africa
June 2016In recent years, the debate on market efficiency has shifted to providing alternate forms of the
hypothesis, some of which are testable and can be proven false. This thesis examines one
such alternative, the Adaptive Market Hypothesis (AMH), with a focus on providing a
framework for testing the dynamic (cyclical) notion of market efficiency using South African
equity data (44 shares and six indices) over the period 1997 to 2014. By application of this
framework, stylised facts emerged. First, the examination of market efficiency is dependent
on the frequency of data. If one were to only use a single frequency of data, one might obtain
conflicting conclusions. Second, by binning data into smaller sub-samples, one can obtain a
pattern of whether the equity market is efficient or not. In other words, one might get a
conclusion of, say, randomess, over the entire sample period of daily data, but there may be
pockets of non-randomness with the daily data. Third, by running a variety of tests, one
provides robustness to the results. This is a somewhat debateable issue as one could either run
a variety of tests (each being an improvement over the other) or argue the theoretical merits
of each test befoe selecting the more appropriate one. Fourth, analysis according to industries
also adds to the result of efficiency, if markets have high concentration sectors (such as the
JSE), one might be tempted to conclude that the entire JSE exhibits, say, randomness, where
it could be driven by the resources sector as opposed to any other sector. Last, the use of
neural networks as approximators is of benefit when examining data with less than ideal
sample sizes. Examining five frequencies of data, 86% of the shares and indices exhibited a
random walk under daily data, 78% under weekly data, 56% under monthly data, 22% under
quarterly data and 24% under semi-annual data. The results over the entire sample period and
non-overlapping sub-samples showed that this model's accuracy varied over time. Coupled
with the results of the trading strategies, one can conclude that the nature of market efficiency
in South Africa can be seen as time dependent, in line with the implication of the AMH.MT201
Deep learning with long short-term memory for stock market predictions and portfolio optimization
ope
An Automated System for Stock Market Trading Based on Logical Clustering
In this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analysis ratios, more precisely market valuation ratios, as clustering variables to differentiate between undervaluated and overvaluated stocks. To structure investment portfolio, the proposed system uses special weighting formulas which automatically diversify investment funds. Finally, a simple trading simulation engine is developed to test our system on real market data. The proposed system was tested on Belgrade Stock Exchange historical data and was able to achieve a high rate of return and to outperform the BelexLine market index as a benchmark variable. The paper has also provided in-depth analysis of the system’s investment decision making process which reveals some exciting insights
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
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Financial predictions using intelligent systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract
information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure,
recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis
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