113 research outputs found
Dynamic portfolio optimization with inverse covariance clustering
Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in the maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity
The rapid progress of Artificial Intelligence research came with the
development of increasingly complex deep learning models, leading to growing
challenges in terms of computational complexity, energy efficiency and
interpretability. In this study, we apply advanced network-based information
filtering techniques to design a novel deep neural network unit characterized
by a sparse higher-order graphical architecture built over the homological
structure of underlying data. We demonstrate its effectiveness in two
application domains which are traditionally challenging for deep learning:
tabular data and time series regression problems. Results demonstrate the
advantages of this novel design which can tie or overcome the results of
state-of-the-art machine learning and deep learning models using only a
fraction of parameters
Topological Portfolio Selection and Optimization
Modern portfolio optimization is centered around creating a low-risk
portfolio with extensive asset diversification. Following the seminal work of
Markowitz, optimal asset allocation can be computed using a constrained
optimization model based on empirical covariance. However, covariance is
typically estimated from historical lookback observations, and it is prone to
noise and may inadequately represent future market behavior. As a remedy,
information filtering networks from network science can be used to mitigate the
noise in empirical covariance estimation, and therefore, can bring added value
to the portfolio construction process. In this paper, we propose the use of the
Statistically Robust Information Filtering Network (SR-IFN) which leverages the
bootstrapping techniques to eliminate unnecessary edges during the network
formation and enhances the network's noise reduction capability further. We
apply SR-IFN to index component stock pools in the US, UK, and China to assess
its effectiveness. The SR-IFN network is partially disconnected with isolated
nodes representing lesser-correlated assets, facilitating the selection of
peripheral, diversified and higher-performing portfolios. Further optimization
of performance can be achieved by inversely proportioning asset weights to
their centrality based on the resultant network
Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing
The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), raises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM) on a very recent dataset covering BTC options on the popular trading platform Deribit. Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we apply this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion and put the procedure to action on a new dataset covering previously unexplored DA dynamics
A novel radar signal recognition method based on a deep restricted Boltzmann machine
Radar signal recognition is of great importance in the field of electronic intelligence reconnaissance. To deal with the problem of parameter complexity and agility of multi-function radars in radar signal recognition, a new model called radar signal recognition based on the deep restricted Boltzmann machine (RSRDRBM) is proposed to extract the feature parameters and recognize the radar emitter. This model is composed of multiple restricted Boltzmann machines. A bottom-up hierarchical unsupervised learning is used to obtain the initial parameters, and then the traditional back propagation (BP) algorithm is conducted to fine-tune the network parameters. Softmax algorithm is used to classify the results at last. Simulation and comparison experiments show that the proposed method has the ability of extracting the parameter features and recognizing the radar emitters, and it is characterized with strong robustness as well as highly correct recognition rate
Homological Convolutional Neural Networks
Deep learning methods have demonstrated outstanding performances on
classification and regression tasks on homogeneous data types (e.g., image,
audio, and text data). However, tabular data still pose a challenge, with
classic machine learning approaches being often computationally cheaper and
equally effective than increasingly complex deep learning architectures. The
challenge arises from the fact that, in tabular data, the correlation among
features is weaker than the one from spatial or semantic relationships in
images or natural language, and the dependency structures need to be modeled
without any prior information. In this work, we propose a novel deep learning
architecture that exploits the data structural organization through
topologically constrained network representations to gain relational
information from sparse tabular inputs. The resulting model leverages the power
of convolution and is centered on a limited number of concepts from network
topology to guarantee: (i) a data-centric and deterministic building pipeline;
(ii) a high level of interpretability over the inference process; and (iii) an
adequate room for scalability. We test our model on 18 benchmark datasets
against 5 classic machine learning and 3 deep learning models, demonstrating
that our approach reaches state-of-the-art performances on these challenging
datasets. The code to reproduce all our experiments is provided at
https://github.com/FinancialComputingUCL/HomologicalCNN.Comment: 26 pages, 5 figures, 11 tables, 1 equation, 1 algorith
Deep Reinforcement Learning for Gas Trading
Deep Reinforcement Learning (Deep RL) has been explored for a number of
applications in finance and stock trading. In this paper, we present a
practical implementation of Deep RL for trading natural gas futures contracts.
The Sharpe Ratio obtained exceeds benchmarks given by trend following and mean
reversion strategies as well as results reported in literature. Moreover, we
propose a simple but effective ensemble learning scheme for trading, which
significantly improves performance through enhanced model stability and
robustness as well as lower turnover and hence lower transaction cost. We
discuss the resulting Deep RL strategy in terms of model explainability,
trading frequency and risk measures
Deep Reinforcement Learning for Power Trading
The Dutch power market includes a day-ahead market and an auction-like
intraday balancing market. The varying supply and demand of power and its
uncertainty induces an imbalance, which causes differing power prices in these
two markets and creates an opportunity for arbitrage. In this paper, we present
collaborative dual-agent reinforcement learning (RL) for bi-level simulation
and optimization of European power arbitrage trading. Moreover, we propose two
novel practical implementations specifically addressing the electricity power
market. Leveraging the concept of imitation learning, the RL agent's reward is
reformed by taking into account prior domain knowledge results in better
convergence during training and, moreover, improves and generalizes
performance. In addition, tranching of orders improves the bidding success rate
and significantly raises the P&L. We show that each method contributes
significantly to the overall performance uplifting, and the integrated
methodology achieves about three-fold improvement in cumulative P&L over the
original agent, as well as outperforms the highest benchmark policy by around
50% while exhibits efficient computational performance
Design of Moderator of a Compact Accelerator-driven Neutron Source for Coded Source Imaging
AbstractCoded source imaging (CSI) is a possible method to solve the contradiction between neutron flux and L/D ratio. Peking University neutron imaging facility (PKUNIFTY) is a RFQ accelerator based facility. The CSI experiments were carried out on PKUNFTY to test the benefits that this technique might bring. The CSI technique gets more restricts on the moderator, especially the neutron distribution in the inner collimator, where the coded mask sampling the source. The effect caused by the non-uniformity of neutron distribution on the mask plane was investigated. The slope type non-uniformity should less than 20% to keep the artifact in the reconstructed image insignificant. The PKUNIFTY moderator was modified according to the above limit. The preliminary experiments shown the moderator design for coded source imaging is acceptable
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